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• What Is an Observational Study? | Guide & Examples

What Is an Observational Study? | Guide & Examples

Published on March 31, 2022 by Tegan George . Revised on June 22, 2023.

An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects, and no control and treatment groups .

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Observational studies are generally used in hard science, medical, and social science fields. This is often due to ethical or practical concerns that prevent the researcher from conducting a traditional experiment . However, the lack of control and treatment groups means that forming inferences is difficult, and there is a risk of confounding variables and observer bias impacting your analysis.

Table of contents

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs. experiment, other interesting articles, frequently asked questions.

There are many types of observation, and it can be challenging to tell the difference between them. Here are some of the most common types to help you choose the best one for your observational study.

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There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies .

Cohort studies

Cohort studies are more longitudinal in nature, as they follow a group of participants over a period of time. Members of the cohort are selected because of a shared characteristic, such as smoking, and they are often observed over a period of years.

Case–control studies

Case–control studies bring together two groups, a case study group and a control group . The case study group has a particular attribute while the control group does not. The two groups are then compared, to see if the case group exhibits a particular characteristic more than the control group.

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyze a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analyzing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for practical or ethical reasons , or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

• Are you determining what you want to observe beforehand, or going in open-minded?
• Is there another research method that would make sense in tandem with an observational study?
• If yes, make sure you conduct a covert observation.
• If not, think about whether observing from afar or actively participating in your observation is a better fit.
• How can you preempt confounding variables that could impact your analysis?
• You could observe the children playing at the playground in a naturalistic observation.
• You could spend a month at a day care in your town conducting participant observation, immersing yourself in the day-to-day life of the children.
• You could conduct covert observation behind a wall or glass, where the children can’t see you.

Overall, it is crucial to stay organized. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

• Plan ahead: If you’re interested in day cares, you’ll need to call a few in your area to plan a visit. They may not all allow observation, or consent from parents may be needed, so give yourself enough time to set everything up.
• Determine your note-taking method: Observational studies often rely on note-taking because other methods, like video or audio recording, run the risk of changing participant behavior.
• Get informed consent from your participants (or their parents) if you want to record:  Ultimately, even though it may make your analysis easier, the challenges posed by recording participants often make pen-and-paper a better choice.

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or “lurking” variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyze your data

After you finish your observation, immediately record your initial thoughts and impressions, as well as follow-up questions or any issues you perceived during the observation. If you audio- or video-recorded your observations, you can transcribe them.

Your analysis can take an inductive  or deductive approach :

• If you conducted your observations in a more open-ended way, an inductive approach allows your data to determine your themes.
• If you had specific hypotheses prior to conducting your observations, a deductive approach analyzes whether your data confirm those themes or ideas you had previously.

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis .

Step 6: Discuss avenues for future research

Observational studies are generally exploratory in nature, and they often aren’t strong enough to yield standalone conclusions due to their very high susceptibility to observer bias and confounding variables. For this reason, observational studies can only show association, not causation .

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.

• Observational studies can provide information about difficult-to-analyze topics in a low-cost, efficient manner.
• They allow you to study subjects that cannot be randomized safely, efficiently, or ethically .
• They are often quite straightforward to conduct, since you just observe participant behavior as it happens or utilize preexisting data.
• They’re often invaluable in informing later, larger-scale clinical trials or experimental designs.

Disadvantages

• Observational studies struggle to stand on their own as a reliable research method. There is a high risk of observer bias and undetected confounding variables or omitted variables .
• They lack conclusive results, typically are not externally valid or generalizable, and can usually only form a basis for further research.
• They cannot make statements about the safety or efficacy of the intervention or treatment they study, only observe reactions to it. Therefore, they offer less satisfying results than other methods.

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The key difference between observational studies and experiments is that a properly conducted observational study will never attempt to influence responses, while experimental designs by definition have some sort of treatment condition applied to a portion of participants.

However, there may be times when it’s impossible, dangerous, or impractical to influence the behavior of your participants. This can be the case in medical studies, where it is unethical or cruel to withhold potentially life-saving intervention, or in longitudinal analyses where you don’t have the ability to follow your group over the course of their lifetime.

An observational study may be the right fit for your research if random assignment of participants to control and treatment groups is impossible or highly difficult. However, the issues observational studies raise in terms of validity , confounding variables, and conclusiveness can mean that an experiment is more reliable.

If you’re able to randomize your participants safely and your research question is definitely causal in nature, consider using an experiment.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

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Open Access

Peer-reviewed

Research Article

A 10-year observational study on the trends and determinants of smoking status

Roles Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of medicine, internal medicine, Lausanne University Hospital, Lausanne, Switzerland

Affiliation Department of Ambulatory Care and Community Medicine, Policlinique Médicale Universitaire, Lausanne, Switzerland

• Daryoush Samim, 
• Marie Méan, 
• Carole Clair, 
• Pedro Marques-Vidal

• Published: July 6, 2018
• https://doi.org/10.1371/journal.pone.0200010
• Reader Comments

Introduction

Most studies on motivation and intention to quit smoking have been conducted among adolescents and young adults but little is known regarding middle-aged subjects. We aimed to assess the trends and determinants of smoking status in a population-based cohort.

Observational, prospective study with a first mean follow-up at 5.6 years and a second at 10.9 years. Data from 3999 participants (49.2% women, aged 35–75 years) living in Lausanne (Switzerland).

Baseline prevalence of never, former and current smokers was 41.3, 34.3 and 24.3%, respectively. During the study period, more than 90% of never and former and almost 60% of current smokers at baseline retained their status after 10.9 years. Among 973 current smokers, 216 (22.2%) had quit for at least 5 years. Multivariable analysis showed increasing age to be positively associated with quitting (p-value for trend <0.001). Among 1373 former smokers, 149 (10.9%) had relapsed; increasing age (p-value for trend <0.001) was negatively associated and family history of lung disease was positively associated with relapse [OR and 95% CI: 1.53 (1.06–2.21)]. Among 1653 never smokers, 128 (7.7%) initiated smoking; Male gender [1.46 (1.01–2.12)] and living in coupled relationship [0.66 (0.45–0.97)] were associated with smoking initiation.

Most middle-aged never and former smokers did not change their status with time, while 22.2% of current smokers sustained quitting. This is encouraging and could be improved with adequate supportive methods. In comparison to available data, this study confirms the difficult task of identifying subjects at risk of a negative behavioral change.

Citation: Samim D, Méan M, Clair C, Marques-Vidal P (2018) A 10-year observational study on the trends and determinants of smoking status. PLoS ONE 13(7): e0200010. https://doi.org/10.1371/journal.pone.0200010

Editor: Raymond Niaura, Legacy, Schroeder Institute for Tobacco Research and Policy Studies, UNITED STATES

Received: March 28, 2018; Accepted: June 18, 2018; Published: July 6, 2018

Copyright: © 2018 Samim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Relevant data are included in the paper and its Supporting Information files, however some data have not been included due to privacy stipulations included in the participant consent forms approved by the Ethics Committee of the University of Lausanne, which afterwards became the Ethics Commission of Canton Vaud ( www.cer-vd.ch ). Data inquiries may be directed to the following Ethics Commission e-mail address: ( [email protected] ). Pr Pedro Marques-Vidal ( [email protected] ) may also be contacted about data.

Funding: The CoLaus study was and is supported by research grants from GlaxoSmithKline, the Faculty of Biology and Medicine of the University of Lausanne, and the Swiss National Science Foundation (grants 3200B0-105993, 3200B0-118308, 33CSCO-122661, 33CS30-139468 and 33CS30-148401). The funding sources had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. CC is supported by a grant from the Swiss National Science Foundation (PZ00P3_154732 Ambizione Grant).

Competing interests: The CoLaus study was and is supported by research grants from GlaxoSmithKline, the Faculty of Biology and Medicine of the University of Lausanne, and the Swiss National Science Foundation. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Cigarette smoking is the most important modifiable risk factor for premature death in the world, causing more than 6.4 million deaths yearly [ 1 ] and representing 5.7% of global health expenditure [ 2 ]. Prevalence of smoking varies between 8.7% to more than 35% in European countries with differing trends [ 3 ]. In Switzerland, smoking prevalence declined from 33% in 1997 to 28% in 2007, due to an increase in funding for tobacco control [ 4 ]. National data showed that, in 2015, 25% of Swiss adults smoked and that this prevalence tended to stabilize [ 5 ], but a recent study suggested that this prevalence might be underestimated [ 6 ].

A European study conducted in 2010 showed that older age, being divorced, having friends/family or parents who smoke were all significantly associated with ever smoking [ 7 ]. The influence of peers was also reported in Italy [ 8 ] and in a larger European study [ 9 ]. Interestingly, the last study found no association between design and marketing features of tobacco products and early initiation of regular smoking [ 9 ]. We have previously shown that over two third of Swiss smokers want to quit, but that only a small part wishes to do so in the short term [ 10 ]. Further, a study using nationally representative Swiss data showed an educational and income gradient in successful quitting and abstinence duration in favor of the people with higher socio-economic status [ 11 ].

Importantly, in these last studies, other sociodemographic covariates (place of birth and age of youngest child for example), clinical covariates, lifestyle, psychiatric disorders and use of smoking cessation aids use were not analysed. Also, most studies assessing the determinants of smoking initiation and cessation were either cross-section or conducted among adolescents and young adults [ 12 ], and information is lacking regarding middle-aged subjects using prospective data. Therefore, we aimed to assess the trends and determinants of smoking status in a middle aged, population-based cohort followed for a 10.9-year period.

Population and methods

Study design.

The CoLaus Study ( www.colaus.ch ) aims to assess the prevalence of cardiovascular risk factors and to identify new molecular determinants of these risk factors in participants aged 35–75 years living in the city of Lausanne (Switzerland). The sampling procedure of the CoLaus Study has been described previously [ 13 ]. Briefly, the source population was defined as all subjects aged between 35 and 75 years of the population register of the city. All subjects living in Lausanne for more than 90 days have their names included in the register, which also includes information on age and sex. A simple, non-stratified random sample of 19,830 subjects (corresponding to 35% of the source population) was drawn and the selected subjects were invited by letter. If no answer was obtained, a second letter was sent, and if no answer was obtained, the subjects were contacted by phone. The baseline study began in June 2003 and ended in May 2006. The first follow-up was performed between April 2009 and September 2012, 5.6 years on average after baseline; The second follow-up was performed between May 2014 and July 2017, 10.9 years on average after baseline. All study periods included an interview, a physical exam and blood analysis.

Smoking status

Smoking status was assessed at baseline, first and second follow-ups using self-reported data. Smoking status was defined as never, former (irrespective of the time since quitting) and current.

When a participant reported being a never smoker at one follow-up and had reported being current or former in the previous examination, the status was corrected to “former” (n = 37, 0.9%).

Participants who shifted their condition at first and/or second follow-up were secondly categorized into three subgroups: initiators if they were never smokers before and reported starting smoking; relapsers if they reported restarting smoking; quitters if they were current smokers at baseline if they reported quitting smoking at FU1 and FU2 (i.e. category CFF).

Socio-demographic and clinical covariates were collected by self-filled questionnaires, interview and/or physical examination. Educational level was categorized as low (obligatory school or apprenticeship), medium (high school), or high (university degree). Marital status was categorized into living as a couple or alone. Nationality was defined as Swiss born and other. Personal and family history of cardiovascular or pulmonary events were considered as present if the participant responded positively to the questions “have you or a member of your family (parents, siblings or children) been told that you had a cardiovascular disease (angina, myocardial infarction, stroke)” and “have you or a member of your family (parents, siblings or children) been told that you had lung disease (asthma, emphysema or chronic bronchitis)”

We defined participants as physically active if they exercised at least 20 minutes of leisure time physical activity per week [ 14 , 15 ]. Alcohol consumption was self-reported and expressed in standard units consumed per week [ 16 ] or as alcohol drinkers (yes/no).

Personal history of anxiety and depression was collected by questionnaire (self-reported). In a subgroup of participants (n = 3719) aged between 35 and 66 years, current and previous occurrence of anxiety, depression and substance abuse were evaluated by a structured interview (PsyCoLaus study). Details of the methodology and characteristics of the PsyCoLaus sample have been previously described [ 17 ]. Briefly, the psychiatric baseline evaluations included the French version of the semi-structured Diagnostic Interview for Genetic Studies (DIGS) [ 18 , 19 ]. In this study, we grouped all types of anxiety disorder and all types of depression. Substance abuse (ever or current) was defined as taking either THC, cocaine, amphetamines, solvents or opiates.

For participants with children (n = 3888), age of the youngest child was collected and categorized into <5 and ≥5 years. Our hypothesis was that parents with younger children would quit smoking to protect their children against the effects of second hand smoking.

Participants reported which medicines (prescribed or obtained over the counter) they consumed. Intake of smoking cessation aids, bupropion, varenicline and nicotine replacement therapy was obtained for baseline, first and second follow-ups. Due to differences in coding between surveys, the intake of varenicline and nicotine had to be grouped in a single variable.

Body weight and height were measured with participants standing without shoes in light indoor attire. Body weight was measured in kilograms to the nearest 100 g using a Seca ® scale (Hamburg, Germany). Height was measured to the nearest 5 mm using a Seca ® (Hamburg, Germany) height gauge. Blood pressure (BP) was measured using an Omron ® HEM-907 automated oscillometric sphygmomanometer after at least a 10-minute rest in a seated position, and the average of the last two measurements were used. Overweight was defined as 25≤ body mass index (BMI)<30 kg/m 2 and obesity as BMI ≥30 kg/m 2 .

Blood samples were collected after an overnight fast and biological assays were performed by the Clinical Laboratory of the Lausanne university hospital on fresh blood samples within 2 hours of blood collection. All measurements were conducted in a Modular P apparatus (Roche Diagnostics, Switzerland). The following analytical procedures (with maximum inter and intra-batch CVs) were used: total cholesterol by CHOD-PAP (1.6%–1.7%); HDL-cholesterol by CHOD-PAP + PEG + cyclodextrin (3.6%–0.9%); triglycerides by GPO-PAP (2.9%–1.5%) and glucose by glucose dehydrogenase (2.1%–1.0%). LDL-cholesterol was calculated using the Friedewald formula.

Dyslipidemia was defined as an HDL-cholesterol <1 mmol/L in men and <1.29 mmol/L in women and/or LDL-cholesterol ≥4.1 mmol/L (≥2.6 mmol/L if personal history of CVD or diabetes) and/or triglyceride ≥2.2 mmol/L and/or presence of a hypolipidemic drug treatment. Hypertension was defined as a systolic BP ≥140 mm Hg and/or diastolic BP ≥90 mm Hg and/or presence of an antihypertensive drug treatment. Diabetes was defined as a fasting plasma glucose ≥7 mmol/L and/or presence of oral hypoglycaemic or insulin treatment.

Exclusion criteria

We excluded patients who a) missed smoking data at baseline, first or second follow-up; b) did not participate in the follow-up (first or second) and c) missed covariates at baseline.

Statistical analysis

Statistical analyses were performed using Stata version 15.0 for windows (Stata Corp, College Station, Texas, USA). Descriptive results were expressed as number of participants (percentage) for categorical variables and as average ± standard deviation or median and [interquartile range] for continuous variables. Bivariate analyses were performed using chi-square or Fisher’s exact test for qualitative variables and Student’s t-test, analysis of variance or Kruskall-Wallis test for quantitative variables.

Multivariable analysis was performed using stepwise logistic regression and the results were expressed as multivariable-adjusted odds ratio (OR) and 95% confidence interval (CI). Both forward and backward logistic regressions were performed to assess consistency of the results. All variables included in the bivariate analysis (i.e. sex, age, education, country of birth, marital status, BMI categories, personal and family history of cardiovascular and lung disease, hypertension, dyslipidaemia, diabetes, alcohol consumption, physical activity, anxiety and depression) were included in the stepwise process. Multivariable analysis were performed in two steps.

Firstly, we analysed factors associated with initiating, relapsing or quitting and maintaining quitting (i.e. category “CFF”) among never, former and current smokers, respectively. Second, sensitivity analyses were conducted as follows: 1) including age of the youngest child in the model and 2) using the psychological assessments performed in PsyCoLaus instead of the self-reported ones. The first sensitivity analysis was conducted because we hypothesized that parents with young children might change their smoking habits to prevent second-hand smoking. The second sensitivity analysis used objective, professionally diagnosed diseases instead of self-reported ones. Statistical significance was assessed for a two-sided test with p<0.05.

Ethical statement

The institutional Ethics Committee of the University of Lausanne (Canton de Vaud, Commission cantonale d'éthique de la recherche sur l'être Humain; Available from: http://www.cer-vd.ch/2014 ) approved the baseline CoLaus study (protocol reference 16/03, decisions of 13 th January and 10 th February 2003) and the approval was renewed for first (protocol reference 33/09, decision of 23 rd February 2009) and second (reference 26/14, decision of 11 th March 2014) follow-ups. The full decisions can be obtained from authors upon request. The study was performed in agreement with the Helsinki declaration and in accordance with the applicable Swiss legislation. All participants gave their signed informed consent before entering the study.

Characteristics of participants

Of the initial 6733 participants, 3999 (59.4%) were retained for analysis. The reasons for exclusion are indicated in Fig 1 and the characteristics of included and excluded participants are summarized in S1 Table . Included participants were younger, more often women, with higher education level, living in coupled circumstance and born in Switzerland. They reported less anxiety and depression, were more engaged in a physical activity, had a lower BMI, lower levels of hypertension, dyslipidemia and diabetes, and less personal history of cardiovascular disease (CVD). Included participants also were more often alcohol drinkers and had more often a family history of lung disease. Finally, included participants were more frequently former smokers and less frequently current smokers.

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https://doi.org/10.1371/journal.pone.0200010.g001

The baseline characteristics of the included participants according to smoking status are summarized in Table 1 .

https://doi.org/10.1371/journal.pone.0200010.t001

Trends in smoking categories

The different smoking categories at baseline, first and second follow-ups are summarized in Fig 2 . Most participants did not change their smoking status during follow-up. Among all participants, almost 40% of participants remained never smokers, 30% former smokers and 14% current smokers. Approximately 36% of current smokers at baseline quitted smoking at FU1 and/or FU2. During the study period, more than 90% of never, 90% of former and almost 60% of current smokers at baseline retained their status after 10 years ( Fig 2 ).

https://doi.org/10.1371/journal.pone.0200010.g002

Determinants of quitting smoking

Among the 973 current smokers, 216 (22.2%) quit and remained former smokers for at least 5 years. The bivariate analysis of the factors associated with quitting is summarized in Table 2 . Increasing age had a higher frequency of quitting. This was further confirmed in both forward and backward multivariable analysis. Relative to the age group [35–44], the OR and 95% CI were 0.76 (0.51–1.13), 1.52 (1.01–2.30) and 2.76 (1.50–5.09) for age groups [45–54], [55–64] and [65–75], respectively (p-value for trend <0.001) ( S2 Table ). The first sensitivity analysis confirmed the association between age and quitting (p-value for trend <0.001) and identified children aged <5 years [0.41 (0.21–0.80)] and dyslipidemia [0.59 (0.37–0.93)] as being negatively associated with quitting. The second sensitivity analysis showed that relative to university level, the OR and 95% CI were 0.61 (0.36–1.06), 0.52 (0.31–0.88) and 0.56 (0.30–1.07) for high school, apprenticeship and basic school, respectively (p-value for trend = 0.064) ( S2 Table ).

https://doi.org/10.1371/journal.pone.0200010.t002

Determinants of relapsing smoking

Among the 1373 former smokers, 149 (10.9%) relapsed. The bivariate analysis of the factors associated with smoking relapse is summarized in Table 2 . Increasing age had a lower frequency of relapse, and this was further confirmed in both forward and backward stepwise multivariable analysis: relative to the age group [35–44], the OR and 95% CI were 0.60 (0.40–0.90), 0.37 (0.23–0.59) and 0.20 (0.09–0.42) for age groups [45–54], [55–64] and [65–75], respectively (p-value for trend <0.001). The multivariable analysis also identified family history of lung disease as being positively associated with relapse: 1.53 (1.06–2.21) ( S2 Table ). The first sensitivity analysis confirmed age as being inversely associated with relapse in both the forward and the backward selection procedure (p-value for trend <0.001), while no effect was found for family history of lung disease. The second sensitivity analysis confirmed age (p-value for trend <0.001) and family history of lung disease [1.59 (1.03–2.46)] as being associated with relapse ( S2 Table ).

Determinants of smoking initiation

Among the 1653 never smokers, 128 (7.7%) initiated smoking. The bivariate analysis of the factors associated with smoking initiation is summarized in Table 2 . No clinical or lifestyle factor was associated with initiation; backward multivariable analysis identified male gender OR and 95% CI: 1.46 (1.01–2.12) and living in as a couple 0.66 (0.45–0.97) as being associated with smoking initiation, while no variable was found in the forward procedure ( S2 Table ). The first sensitivity analysis confirmed the association between gender and smoking initiation in both the forward and the backward selection procedure: 1.60 (1.02–2.53). The second sensitivity analysis showed substance abuse [3.70 (1.62–8.42)] and depression [0.60 (0.37–0.99)] to be associated with initiation, while the effect of male gender was no longer significant ( S2 Table ).

Few studies have assessed the determinants of smoking initiation and cessation among middle-aged subjects. Using 10-year prospective data, we show that, among people aged 35 to 75 years, the majority of never and former smokers retain their status, while four out of ten smokers will make at least one quit attempt and one fifth will quit and maintain this status for at least 5 years. Our results also show that quitting is positively associated with older age and negatively associated with having children aged <5 years or dyslipidaemia. Relapsing is positively associated with younger age and family history of lung disease, while initiation is positively associated with male gender, living alone and substance abuse, and negatively associated with depression.

Smoking prevalence

Prevalence of current smokers at baseline was 24.3%, a value comparable to Swiss national data in 2015 (25% of Swiss adults) but lower than pooled data from the 2001–2007 editions of the Swiss Tobacco Monitoring Survey (30.9%) [ 11 ]. A first explanation is that the former study included participants aged 14–65 years-old, thus over 10-years younger than our participants (35–75 years-old). A second explanation is the fact that our study was a prospective one, and it is well known that smokers tend to quit more frequently [ 20 ]; hence, it is likely that the prevalence of current smokers in our study is underestimated.

Approximately one in five current smokers sustained quitting, a value considerably higher than reported in an Italian study (6.9%) [ 21 ]. A possible explanation is that the latter study was partly based on data derived from periods during which funding for tobacco control and public health measures were limited. Another possible explanation is that the Italian study included participants aged 25–64 years-old, thus 10-years younger than our participants (35–75 years-old).

Indeed, increasing age was positively associated with quitting smoking. This finding is in agreement with findings from Marti who showed that older smokers are more likely to quit successfully compared with smokers aged 18–24 years [ 11 ]. It is also in agreement with the previous finding that increasing age is negatively associated with the risk of relapsing. A likely explanation is that former smokers quit because of current tobacco-related health conditions and because of the future harmful effect of smoking on health [ 22 ]. As older smokers have more tobacco-related health conditions and more comorbidities, they are motivated to quit more often.

Dyslipidaemia was negatively associated with quitting smoking. Our finding partly disagrees with a previous cohort study [ 23 ] which showed that newly diagnosed dyslipidaemia was positively associated with quitting. However, the positive association was only significant for men and no association was found for known dyslipidaemia. Furthermore, the latter study was based on subjects aged 40 years at baseline. A possible explanation is that a significant fraction of dyslipidaemic subjects are not aware of their status [ 24 , 25 ] and thus do not take preventive measures to reduce their CV risk factor levels.

Lower educational level tended to be negatively associated with quitting in the second sensitivity analysis. This is in agreement with a previous study conducted in Switzerland showing a positive educational and income gradient in successful cessation and abstinence duration for both genders [ 11 ]. The reasons for lower educated subjects to be less prone to quit smoking are varied [ 26 ]. Lower educated subjects have a lower health literacy, making them less receptive to preventive messages [ 27 ]. Lower health literacy is also associated with more positive smoking outcome expectancies (e.g., smoking facilitates social interactions, smoking reduces boredom or negative affect) and less negative smoking outcome expectancies (e.g., smoking is harmful to health) [ 26 ].

Having a child aged <5 years was negatively associated with quitting smoking. This unexpected finding is in disagreement with a previous cross-sectional study [ 28 ] which showed that parents with a child under age 3 years had higher odds of successfully quitting at 12 months. One possible explanation for our finding is that participants with children aged <5 years were younger (41.3±5.5 vs. 54.1±9.6 years, p<0.001), and that increasing age was positively associated with quitting smoking.

Determinants of smoking relapse

Approximately 11% of former smokers relapsed during follow-up. This value is lower than the relapse rate reported in a workplace study conducted in Switzerland (14.4% after intervention and 31.1% at one year) [ 29 ]. Still, as relapse rates are dependent of several factors such as smoking dependency or time after quitting, comparison with other studies is difficult, as those variables were not collected in our study.

Increasing age was negatively associated with smoking relapse, a finding in agreement with a previous study [ 30 ]. Our results suggest that as age increases, likelihood of relapsing decreases, possibly due to the occurrence of tobacco-(un)related diseases and the subsequent increased pressure from health professionals to stop smoking.

Family history of lung disease was positively associated with smoking relapse. To our knowledge, this is the first study assessing the association between personal or family history of lung disease and smoking relapse. Previous studies showed that having parents who smoke was significantly associated with smoking [ 7 – 9 ]. Thus, participants with family history of lung disease may be more prone to relapse because of a more unfavorable family environment regarding smoking.

In our study, 7.7% never smokers initiated smoking in this older group, a value considerably higher than reported in New Zealand (<1.0%) [ 31 ]. A possible explanation is the fact that Switzerland has only a partial smoking ban and further restrictions have been rejected by the population [ 32 ]. Switzerland is one of the few European countries which has signed but not ratified The Framework Convention on Tobacco Control [ 33 ]. There is therefore a permissive politic with relatively low prices for tobacco, few restrictions on marketing in particular indirect marketing and easy access to tobacco products. We postulate that the rather benevolent legislation regarding smoking in Switzerland might favour smoking initiation even among middle-aged subjects.

Male gender was positively associated with smoking initiation. This finding is in agreement with several cohort studies conducted among North American adults and adolescents [ 34 – 37 ], but not with a recent cohort study conducted among young Canadian adults [ 38 ]. Possible explanations include a lower health awareness among men and the fact that men still consider smoking as a masculine characteristic, although a better assessment of the rationale for middle-aged men to initiate smoking is needed.

Living as a couple was negatively associated with smoking initiation. This finding is in agreement with two cohort studies [ 34 , 39 ] and another Swiss [ 11 ] and European [ 7 ] cross-sectional studies. A possible explanation is the mutual psychological support and influence, which might prevent smoking initiation.

Substance abuse was strongly associated with smoking initiation. This finding is in agreement with a systematic review [ 37 ] which showed that the use of alcohol and illegal drugs was associated with smoking initiation. Similarly, a cohort study [ 34 ] among American adults confirmed that substance use disorder is associated with smoking initiation. Possible explanations include a genetic predisposition regarding joint consumption of tobacco and other substances, as well as environmental conditions favouring multisubstance use [ 40 ].

No association was found between self-reported depression and smoking initiation in the initial analyses, while on sensitivity analysis a negative association between having been ever depressed and smoking initiation was found. This latter finding disagrees with other studies, which have shown a positive association between depression and smoking initiation [ 34 , 41 – 43 ]. Several explanations can be put forward to explain these contrasting results. Firstly, most studies focused on adolescents [ 41 – 43 ], and the reasons for smoking initiation in adolescents might differ from those among middle-aged adults [ 44 ]. Secondly, depression was grouped with all axis I clinical disorders in one study [ 34 ] and based on self-reported symptoms in others [ 41 – 43 ], while in our sensitivity analysis it was objectively diagnosed using validated criteria. Indeed, a possible explanation for the negative association between depression and smoking initiation is that depressed subjects are advised by their doctors not to use smoking as a deterrent for their depressive symptoms. Still, the association between depression and smoking initiation needs further investigation.

Strengths and limitations

To our knowledge, this is the first study exploring demographic, clinical, psychiatric and lifestyle determinants of smoking changes in a population-based sample.

This study has also some limitations. Firstly, participants excluded from the analysis were more frequently smokers. Hence, it is likely that the smokers included in the analysis were more health conscious and thus more prone to quit; our quitting rates might be overestimated. Secondly, despite collecting smoking cessation aids (bupropion and varenicline/nicotine), we could not include them in our analysis for different reasons. Indeed, Bupropion was also prescribed as an antidepressant for never smokers and current smokers. Therefore, we could not analyse its association with quitting smoking. On the other hand, despite being the most commonly (16.2%) used smoking cessation aid in Switzerland in 2013(41), six participants only reported using nicotinic replacement. In spite of asking for prescribed or obtained over the counter medicines in our questionnaires, participants may have probably under-reported nicotinic replacement. Thirdly, it was not possible to assess duration of smoking/quitting, or the magnitude of smoking dependency at baseline. Hence, it was not possible to assess whether tobacco-related factors influenced smoking trajectories, although it has been shown that increased tobacco dependency negatively influences quitting [ 45 ]. Fourthly, smoking status was assessed during the baseline and follow-up visits, and we have no information regarding smoking status between visits. Hence, a participant might be considered as a quitter in both follow-ups while he/she actually relapsed and quit again between visits. Hence, our quitting rates might be overestimated. Still, our results show that one fifth of current smokers will quit over 10 years, which is encouraging and could be even improved if adequate supportive methods were provided.

In this population-based prospective study, most middle-aged never and former smokers do not change their status with time, while one fifth of current smokers will quit permanently. The determinants of change vary according to the smoking status. In comparison to available data, this study confirms the difficult task to identify subjects at risk of negative behaviour change.

Supporting information

S1 table. baseline socio-demographic and clinical characteristics of included and excluded participants..

https://doi.org/10.1371/journal.pone.0200010.s001

S2 Table. Multivariable analysis of the factors associated with initiation, relapse or quitting smoking according to selection procedure.

https://doi.org/10.1371/journal.pone.0200010.s002

Acknowledgments

The authors would like to express their gratitude to the participants in the Lausanne CoLaus study and to the investigators who have contributed to the recruitment.

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Classical RCTs are designed to maximise internal validity and to establish an unequivocal cause-and-effect relationship between an intervention and an outcome. Classical RCT populations represent only minority groups of real-life patients and are thus limited in their extent. Complementary data from studies designed to reflect more closely the nature of real-world patients and medicines usage are required to inform guidelines and extrapolate research findings across the broad heterogeneous patient populations encountered in everyday clinical practice.

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Clinical characteristics, treatment, and outcomes for elderly patients in a dedicated Covid-19 ward at a primary health care facility in western Norway: a retrospective observational study

• Bård Reiakvam Kittang 1 , 2 , 3 ,
• Ane Tveiten Øien 1 ,
• Einar Engtrø 1 ,
• Marian Skjellanger 1 &
• Kjell Krüger 1  

BMC Health Services Research volume  24 , Article number:  1098 ( 2024 ) Cite this article

Metrics details

The coronavirus pandemic has hit the oldest and frailest individuals hard, particularly patients and residents in nursing homes. In March 2020, we established a Covid-19 ward at a nursing home in Bergen, western Norway for elderly patients with Sars-CoV-2 infection and in the need of treatment and care in a primary health care facility. The aims of this study were to describe the organization of the ward, the clinical outcomes of infection, treatment, mortality rates in the population, the level of advanced care planning, and end-of-life care for those who died.

We present patient characteristics, outcomes, vaccination status, treatment, decisions regarding treatment intensity upon clinical deterioration, and mortality for the patients in the ward. Clinical factors possibly related to a fatal outcome were analysed with chi square test (categorical variables) or t-test (continuous variables).

257 patients were included from March 2020 to April 2022. Fifty-nine patients (23.0%) developed respiratory failure. Ten patients (3.9%) were admitted to hospital. Advance care planning was undertaken for 245 (95.3%) of the patients. 30-day mortality rate decreased from 42 to 4% during the study period. Of the 29 (11.3%) patients who died, all were well alleviated in the terminal phase, and 26 (89.7%) of them had a Clinical Frailty Scale (CFS) value ≥ 7. A high score for CFS, respiratory failure and respiratory co-infection were significantly associated with Covid-19 related death within 30 days.

Conclusions

Covid-19-related mortality markedly decreased during the study period, and a high score for CFS was related to a fatal outcome. Thorough planning of treatment intensity upon deterioration, low hospitalization rates, and good relief for those who died suggest that dedicated Covid-19 wards in nursing homes can provide good treatment for the patients and relieve other nursing homes and specialist health care services.

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Introduction

The burden of Covid-19 in nursing homes has been high throughout the pandemic, even after the introduction of Covid-19 vaccines [ 1 ]. Nursing home patients are susceptible to a severe course of respiratory infections and have a high prevalence of cognitive impairment [ 2 , 3 ]. Along with resident crowding and reduced staffing capacity at nursing homes with Covid-19 outbreaks, this can hinder the implementation of adequate infection control measures, facilitate transmission of SARS-CoV-2 and increase Covid-19 related morbidity and mortality in this frail population [ 4 , 5 , 6 ]. As a consequence, particularly in the early phase of the pandemic, large outbreaks of Covid-19 with substantial mortality rates were observed in nursing homes [ 7 , 8 ], and the nursing workload in both nursing homes and hospitals has been reported to be high [ 9 , 10 ]. As such, advance care planning is an important aspect of proper care for vulnerable nursing home patients [ 11 ], particularly during the coronavirus pandemic.

In the public and private nursing homes in the municipality of Bergen in western Norway, nurse and doctor staffing is highly variable, and we experienced severe outbreaks of Covid-19 in three of our institutions early in the first pandemic phase [ 12 ]. Therefore, we established a citywide Covid-19 ward at Fyllingsdalen Treatment Centre in March 2020, with the purpose of providing close follow-up for particularly challenging nursing home patients with Covid-19 and relieving other nursing homes and hospitals in our community.

The aims of this retrospective, observational study was to describe the organization of the ward, and explore the clinical course of infection, treatment, mortality rates at the ward through the study period, clinical factors possibly related to a poor outcome, the level of advanced care planning, and the end-of-life care for those who died.

Materials and methods

Organization of the ward and study population.

The municipality of Bergen has 23 public and 11 private nursing homes, with approximately 2000 long-term beds and 500 short-time beds. The Covid-19 ward accounted for 11 out of 96 short-term beds at Fyllingsdalen Treatment Centre (FTC). The admission criteria were not strict, but patients with a RT-PCR verified Sars-CoV-2 infection fulfilling one or more of the following criteria were prioritized for admittance: (i) long- or short-term residents at other nursing homes requiring increased staffing and/or increased medical support due to cognitive failure and/or a severe clinical course, (ii) patients stabilized at hospital, but still requiring institutional care, and (iii) old and frail individuals not able to cope at home/care home services due to covid-19. During the establishment period of the ward, information regarding admission criteria was conveyed to nursing homes in the municipality of Bergen and the two local hospitals: Haukeland University Hospital (HUH) and Haraldsplass Deaconess Hospital (HDH).

The medical activity at the ward was closely monitored and supervised by an infectious disease specialist. Dedicated nurses and doctors received special training in infection control measures, with a particular focus on proper use of personal protective equipment (PPE), including eye and face protection, gloves, and single use gowns, along with correct collection and handling of clinical samples and waste management. Since the number of patients admitted to the ward varied considerably during the study period, there was a high degree of flexibility of ward staffing. In time periods with particularly challenging patients and/or high turnover rates, nurse and doctor resources were allocated from other wards at FTC. Furthermore, the ward was prioritized regarding access PPE and O2-concentrators. Since many patients had dementia and a need to wander around, the ward consisted of separate rooms, a spacious hall, and a large living room. One or two nurses was attending the cohort area at regular shifts. The ward doctor had several rounds with patients visits each weekday and had daily contact with the supervising infectious disease specialist. During weekends the attending nurses consulted a nursing home doctor on call in the municipality of Bergen when needed.

Altogether, 261 patients with Covid-19 were treated in the ward. These patients were transferred either from HUH or HDH, other nursing homes, care home services or their private homes. The patients admitted from other nursing homes were from 21 different institutions in the municipality of Bergen, and these residents all had a severe disease course and/or dementia preventing proper isolation during infection. During their stay at the ward, the patients were systematically evaluated regarding frailty and treatment intensity upon clinical deterioration, including cardiopulmonary resuscitation status, potential indications for admission to hospital, systemic corticosteroid treatment upon Covid-19 related respiratory failure, and antibiotic treatment for respiratory co-infections. Furthermore, morphine and midazolam were ordinated as emergency medication for all patients upon admission, in case of the development of an acute respiratory crisis.

Diagnosis and clinical assessment

Patients were included from March 2020 to April 2022, when the ward was closed. All were diagnosed with Covid-19 using RT-PCR for SARS-CoV-2 from nasopharyngeal or throat swabs [ 13 ]. Clinical information was obtained from the nursing home’s semi-structured medical records system. Frailty assessment was conducted using the Clinical Frailty Scale (CFS), ranging from 1 (very fit) to 9 (terminally ill), classifying patients as non-frail (1–4), moderately frail (5–6), or severely frail (7–9) [ 14 ]. The assessment of the value for CSF was based on the degree of frailty at least two weeks prior to Covid-19, was conducted by the ward doctors based on information in medical records, from the patients themselves, and/or next of kin. Respiratory failure was defined as persistent hypoxia (oxygen saturation < 90% without supplementation of oxygen in lung-healthy individuals, or oxygen saturation < 85% without supplementation of oxygen in patients with chronic lung disease). Data on the quality of palliative care for those who died of Covid-19 was based on detailed documentation in the medical records system, from both nurses and doctors working in the unit, by using a structured journal scheme called “Livets Siste Dager” (“The Last Days of Life”), which is regularly used in Norwegian hospitals and primary care facilities, and developed and refined from Liverpool Care Pathway [ 15 ]. Covid-19 related death was defined as death within 30 days after symptom onset, or from the time of positive test for initially asymptomatic patients.

Statistical analysis

Categorical variables (gender, respiratory failure, and respiratory co-infection) were analysed by chi square test. Continuous variables (mean age and mean score for CFS) were analysed by t-test. A two-sided p-value < 0.05 was considered statistically significant. All analyses were performed by using JMP 16.2.0 (SAS Institute Inc.). Vaccination status, along with treatment with corticosteroids and anticoagulants were omitted from the statistical analysis of risk factors for death, as these prophylactic and treatment measures were not available during the first pandemic phase, when most of the deaths occurred.

We included 257 out of a total of 261 patients admitted to the ward. Four patients opted out. Eighty-five patients (33.1%) were transferred from hospital, while the remaining 172 (66.9%) patients were admitted from other nursing homes ( n  = 149, 58.0%), care home services ( n  = 10, 3.9%), or their own homes ( n  = 13, 5.1%). Ten patients (3.9%) were acutely admitted to a hospital from the ward, all with severe Covid-19 related respiratory failure or clinical signs of septicaemia caused by respiratory co- infection.

We observed a significant reduction in mortality throughout the study period; from 42% in spring 2020 to 4% from January to April 2022 (Fig.  1 ).

Covid-19 related mortality rates at Fyllingsdalen Treatment Centre during March 2020-April 2022

Covid-19 related 30-day mortality rates at the ward during the study period from March 2020 to April 2022. Mortality rates (%) are displayed on the y-axis, and different time periods on the x-axis. The data are separated into four time periods: i) The first pandemic phase, with presumably Wuhan – 1 as the dominating Sars-CoV-2 variant. ii), the pandemic phase with the introduction of anticoagulation therapy for most patients with covid-19, and corticosteroid therapy for selected patients with respiratory failure. In this period, there was presumably a steadily transition of the dominating variant of Sars-Cov-2 from Wuhan -1, to Alpha, and then to Beta. iii), the pandemic phase with Delta presumably as the dominating variant of Sars-Cov-2. iiii), The pandemic phase with Omicron presumably as the dominating variant of Sars-Cov-2

The total number of patients in the four groups, from i), ii), iii) and iiii), were 19, 28, 56 and 154, respectively

As shown in Table  1 , the mean age and score for CFS were 83.2 years and 6.3 respectively. Women accounted for 166 (64.6%) of the patients. Fifty-nine patients (23.0%) developed respiratory failure, of which 33 (55.9%) received treatment with corticosteroids. Advance care planning was performed for 245 (95.3%) patients, and all of 29 patients who died during the course of infection were well alleviated in the terminal phase. Of those who died, 26 (89.7%) had a CFS score > = 7.

As displayed in Table  2 , patients who suffered from Covid-19 related death had a higher mean CFS score ( p  < 0.001), more often respiratory failure ( p  < 0.001) and respiratory co-infection treated with antibacterial agents ( p  = 0.0340) than those who survived. One hundred and eighty-four (71.6%) of the patients were vaccinated against Covid-19 with one or more doses, whereas 33 (12.8%) and 182 (70.8%) of the patients received treatment with corticosteroids and anticoagulants, respectively.

To our knowledge, this is the first study describing clinical patient characteristics, structured advance care planning and quality of palliative care in a dedicated Covid-19 nursing home ward. During the study period, we observed a significant decrease in Covid-19 associated mortality rates, from 42% in the early pandemic phase to 4% in the Omicron era. Similar findings were evident from international studies [ 16 , 17 ] and probably related to the introduction of Covid-19 vaccines, the emergence of Sars-Cov-2 variants (particularly Omicron) with attenuated pathogenicity, and perhaps, the introduction of novel treatment (anticoagulants and corticosteroids) in our Covid-19 ward from the second pandemic phase. Up until Covid-19 vaccination was initiated at our nursing homes in January 2021, the 30-day mortality rate in our patient population was approximately 25%, comparable to those observed in other countries [ 7 , 18 ].

Coronavirus vaccination protects against severe outcomes of Covid-19, also among the elderly in and outside of nursing homes [ 19 , 20 ], and a high chronological age is associated with poor outcomes [ 21 ]. In our sample, the mean age was, as expected, high among both those who died, and those who survived the infection, and chronological age was not associated with fatal outcome. Furthermore, the vast majority of those who died had a CFS score that indicated severe frailty, and a high CFS score was significantly associated with death. Hence, in nursing homes, assessment of clinical frailty appears to be more important than chronological age when evaluating the risk for severe Covid-19 outcomes, in line with findings from studies on Covid-19 patients in hospital and Spanish nursing homes [ 22 , 23 , 24 ].

Treatment with systemic corticosteroids of hospitalized Covid-19 patients with respiratory failure has been shown to reduce mortality [ 25 ]. However, to our knowledge, no data on the effect of such treatment for nursing home patients with Covid-19 has been published. Given the relatively high mortality and incidence of severe respiratory failure among our patients in the early stages of the pandemic [ 12 ], along with the thrombogenic properties of SARS-CoV-2 and a high incidence of venous thrombotic events among nursing home residents in general [ 26 , 27 ], we chose to offer anticoagulants to a majority of the patients, along with corticosteroid treatment to more than half of those with Covid-19 related respiratory failure from September 2020 until April 2022.

Important aspects in the care for Covid-19 patients in nursing homes include a systematic assessment of proper treatment intensity for each patient, and providing good palliative care for those who die from the infection [ 28 , 29 ]. In our material, advance care planning was undertaken for more than 90% of the patients. Only 10 patients were admitted to hospital during the infection, and all 29 who died were well palliated in their final days of life according to structured information from their medical records. Hence, our findings suggest that dedicated Covid-19 wards in nursing homes offer good care for the patients and provide relief for other nursing homes and hospitals.

This study has several limitations. First, retrospective collection of clinical information and the inclusion of a relatively small number of patients from a rather limited geographical area, in a high-income country with a resourceful health care system [ 30 ], may have limited the transferability of our findings to other regions/countries. Second, we did not include data on comorbidities potentially contributing to a poor outcome. Nevertheless, the use of structured journal forms and CFS likely contributed to increasing the reliability and validity of our data. Furthermore, CFS score probably reflects the burden of comorbidities to a certain extent. Third, the good and flexible access to clinical personnel and PPE might have influenced both outcomes and quality of care in the Covid-19 ward compared to other nursing home wards. Another inherent limitation introducing biases in the interpretation of our data, was the development of Covid-19 vaccines, the steadily changing virus variants, and the changes in treatment options during the study period. As such, statistical analyses of independent risk factors for fatal outcome were not performed.

We found a significant reduction in Covid-19 related mortality during the study period. The hospital admission rate was low, palliative care in the final days of life was good for those who died, decisions regarding proper treatment intensity were available for the vast majority of the patients, and very few of them were acutely admitted to hospital. Through our experience from this Covid-19 ward, we have learnt that a systematic approach towards the patients, along with allocation of PPE and human resources might ease the burden of pandemics on both primary care institutions and hospital, and possibly lead to better patient care.

Hopefully, our approach towards care elderly patients during the Covid-19 pandemic, might inspire future health care plans for this frail population if a new pandemic were to occur in the years to come.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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We sincerely thank the participating patients, along with the staff at the Covid-19 ward in Fyllingsdalen Treatment Centre. We also thank Linda Lykken Eriksen and Geir Inge Nedrebø for distributing information letters to the patients living at home.

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BRK designed the study, collected and analyzed data and drafted and reviewed the original and revised manuscript. ATØ collected data and reviewed the manuscript, EE analyzed data and reviewed the manuscript, MS collected data and reviewed the manuscript, KK analyzed data and reviewed the manuscript.

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Kittang, B.R., Øien, A.T., Engtrø, E. et al. Clinical characteristics, treatment, and outcomes for elderly patients in a dedicated Covid-19 ward at a primary health care facility in western Norway: a retrospective observational study. BMC Health Serv Res 24 , 1098 (2024). https://doi.org/10.1186/s12913-024-11539-2

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Presenteeism and missed nursing care: a descriptive, correlational and observational study

• Ezgi Dirgar   ORCID: orcid.org/0000-0001-8214-7441 1 ,
• Soner Berşe   ORCID: orcid.org/0000-0001-9108-3216 2 ,
• Ayşe Şahin   ORCID: orcid.org/0000-0002-0112-2371 3 ,
• Betül Tosun   ORCID: orcid.org/0000-0002-4505-5887 4 &
• Juan Manuel Levya-Moral   ORCID: orcid.org/0000-0003-4241-4992 5  

BMC Nursing volume  23 , Article number:  652 ( 2024 ) Cite this article

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Missed nursing care poses a significant challenge for healthcare staff in terms of patient safety and care quality.

To evaluate presenteeism and missed care attitudes of nurses and to determine the correlation between presenteeism and missed care.

This descriptive, correlational, and observational study was conducted between February and August 2023. The Stanford Presenteeism Scale-Short Form and the MISSCARE Survey were used to collect the data among nurses at two public hospitals in a city in Turkey. The study was completed with 229 nurses representing 27.4% of the total number of nurses who met the inclusion criteria. The data was analyzed using a comprehensive analytical approach, including Cronbach’s alpha analysis, frequency and percentage distribution, the Shapiro–Wilk test of normality, correlation coefficient analysis, Pearson correlation coefficient, and the Bonferroni test.

The participants’ mean age was 30.22 ± 7.14 years, and 74.2% of them were female. 53.3% of the participants reported difficulty providing patient care due to material shortages, and 62.9% experienced challenges delivering care due to the intensity of paperwork in the clinic. Nurses who felt that paperwork intensity affected patient care and were not confident in their care provision had higher levels of presenteeism ( p  = 0.041) and a significantly higher frequency of missed care instances ( p  < 0.001).

Conclusions

Material shortages and high paperwork intensity are contributing factors to the difficulties experienced by nurses in their practice. These difficulties may lead to an increase in presenteeism and instances of missed nursing care. It is important to address these challenges to ensure adequate care provision and reduce the likelihood of presenteeism among nurses. The correlation between presenteeism and instances of missed nursing care highlights the impact of presenteeism behaviors on the quality of patient care.

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Introduction

Nursing is a fundamental element of healthcare organizations and has a pivotal impact on patient care quality. The health and motivation of nurses at work are essential for ensuring that patients receive safe and effective care [ 1 , 2 ]. The improvement of patient safety and the enhancement of patient outcomes represent a priority for healthcare practitioners, organizations, and governments across the globe. Despite their potential to enhance patient and cost outcomes, nurses may unwittingly impede care due to the prevalence of fatigue, burnout, and disillusionment [ 3 ].

The phenomenon of missed nursing care (MNC) represents a significant global health concern. It is typically defined as a form of malpractice, characterized as inadequate nursing practice that can occur at any stage of care due to inadequate provision of nursing care [ 4 , 5 ]. Factors such as extended work hours [ 6 ], increased workload, and limited nursing experience [ 7 , 8 ] have been identified as factors contributing to an elevated nurse-to-patient ratio [ 9 , 10 ]. The lack of adequate materials and equipment, along with nursing staff shortages, presents a significant challenge for nurses in determining the priority of patient care tasks and the ability to defer certain responsibilities. This, in turn, contributes to the problem of missed nursing care, as evidenced in the literature [ 10 , 11 , 12 , 13 , 14 ].

The phenomenon of presenteeism can be defined as the state in which employees feel compelled to attend work despite the presence of unsafe working conditions, fear of job loss, or an excessive workload. This can result in extended periods of work or the appearance of productivity despite the absence of actual productivity [ 15 , 16 ]. The concept of presenteeism is reflected in the healthcare service, with notable implications for the nursing profession. For instance, Shan et al. reported a prevalence of 94.25% among Chinese nurses [ 17 ].

Nursing is a profession that often involves substantial workloads, extended work hours, and challenging working conditions. Additionally, healthcare professionals may feel compelled to report to work despite being physically or mentally unwell [ 18 ]. Nurses may be reluctant to request leave because they are aware that their duties will be assumed by another colleague in their absence. In order to retain their duties and prevent the transfer of responsibilities to a substitute in their absence, some nurses refrain from requesting leave, even in instances where it is not strictly necessary for them to be at work. Furthermore, colleagues may be reluctant to approve frequent leave requests. Additionally, nurses who are paid hourly may be hesitant to request overtime due to concerns about potential salary deductions [ 19 , 20 ]. In some instances, nurse managers may unexpectedly require nurses to work additional shifts or terminate their shifts with minimal advance notice. This can have adverse effects on the affected nurses, such as missed nursing care, reduced job satisfaction, and a potential decline in the quality of care [ 21 , 22 ].

Presenteeism in nursing is a critical issue as it can reduce nurses’ ability to provide high-quality healthcare, which can put patients at risk [ 20 ]. Addressing presenteeism and its multiple antecedents may positively affect patient care and provider health and well-being [ 3 ]. However, to our knowledge, presenteeism and missed nursing care have not previously been examined together in the literature. This study aims to determine the correlation between presenteeism and missed nursing care among nurses in Turkey.

The study aimed to explore the correlation between presenteeism and missed nursing care among nurses in Turkey, shedding light on the impact of nurses working under suboptimal conditions on the quality of care provided in healthcare settings. Specifically, the study sought to answer the following questions:

To what extent does presenteeism among nurses correlate with an increase in missed nursing care in healthcare settings?

Which factors contribute most significantly to presenteeism and missed nursing care, particularly focusing on the roles of paperwork intensity and material shortages?

What strategic systemic changes can be implemented within healthcare environments to mitigate the effects of presenteeism on missed nursing care, aiming to improve both patient care quality and workplace satisfaction for nurses?

Study desing

This descriptive, correlational and observational research was conducted between February and August 2023 in two public hospitals located in the Southeastern Anatolia Region of Turkey. STROBE checklist were followed for reporting in the study.

Study sample

The study population comprised of 945 nurses who were employed in two public hospitals located in different cities in the Southeastern Anatolia Region of Turkey. Sample size calculation was performed using the G*Power software package. A sample size of 293 was determined as appropriate, considering 50% heterogeneity, a 5% margin of error, and a 95% confidence level. Since no previous study has investigated presenteeism and missed nursing care together, an inverse relationship between the mean scores of the scales was expected to be found through Pearson Correlation ( r > -0.2, weak), with α = 0.05 and (1-β) = 0.80 power in the 95% confidence interval. Therefore, a minimum of 208 nurses were required for the study.

The study’s inclusion criteria required that participants be nurses currently employed at the selected hospitals with a minimum of one year of experience. At the time of data collection, a total of 945 nurses were employed at these hospitals, with 836 having been in their roles for over a year. The researchers were not affiliated with the nursing staff at the hospitals in question. The objectives of the study, the data collection forms, and the methodology were presented to the nursing directors of the hospitals in person. The researchers requested the email addresses of those nurses who expressed interest in participating in the study and met the established inclusion criteria. By providing their email addresses, the participants thereby consented to receive the data collection forms. Digital forms were distributed online to 836 nurses employed at two public hospitals that had been authorized to participate in the data collection. All responses were anonymized and treated confidentially in accordance with national policies; access to the data was restricted to the research team and not shared with other groups. A total of 229 nurses completed the forms, representing 27.4% of the eligible nurse population. This participation rate met the requirements of the sample size calculation. Post hoc power analysis indicated an effect size of 0.380 and a statistical power of 92% for the study.

Data collection

Online digital instruments were distributed to a cohort of 836 nurses employed in two public hospitals, selected through a simple random sampling method. The study utilized three distinct online instruments for data collection. Before collecting the data, we conducted a pilot study with 20 nurses to test the comprehensibility of the data collection forms and we did not include these results in the study data.

Data collection tools

The descriptive characteristics form consisting of 1 In post hoc power analyses, the impact size was found to be 0.380, and the theoretical power of the study was determined to be 95%. 5 items was created based on insights from the literature review [ 5 , 7 , 9 , 14 , 16 ]. The form was used to collect descriptive information and details regarding the working conditions of the study participants.

Stanford Presenteeism Scale-Short Form (SP-6)

The Turkish validity and reliability study of the SP-6, developed by Koopman et al. (2002) to determine participants’ presenteeism tendencies, was conducted by Teoman and Seren (2022) [ 23 , 24 ]. The scale’s original 6-item structure was revised to form the Stanford Presenteeism Scale-Short (SPS-Turkish short form), comprising 4 items. The SPS-Turkish short form reflects the experiences of individuals in the work environment over the past month. The scale comprises one dimension and four items, utilizing a 5-point Likert-type scale with responses ranging from ‘Strongly Disagree’ to ‘Strongly Agree’. Responses are coded from 1 to 5, progressing from ‘Strongly Disagree’ to ‘Strongly Agree’. As the score obtained from the scale increases, the level of presenteeism also increases.

Missed Nursing Care Survey (MISSCARE) : The scale, developed by Kalisch and Williams (2009) [ 25 ], was adapted into Turkish by Sönmez et al. (2012) [ 26 ]. The MISSCARE scale is comprised of two parts. Part A (MISSCARE-A) assesses the quantity of missed care needs using a five-point Likert scale ranging from ‘Rarely not given’ (1) to ‘Not applicable’ (5). Part B (MISSCARE-B) evaluates the reasons for missed care needs using a four-point Likert scale ranging from ‘Significant reason’ (1) to ‘Not a reason for care not being given’ (4). Part B of the scale consists of three dimensions: workforce resources (1, 2, 3, 4), material resources (7, 10, 11), and communication (5, 6, 8, 12, 13, 14, 15, 16, 17). The scale does not include any reverse-coded items. Higher scores in Part A indicate an increase in the quantity of missed nursing care needs, while higher scores in Part B indicate the reasons for missed nursing care needs.

Data analysis

Statistical analysis was performed using IBM SPSS for Windows 22.0 version. Descriptive statistics were presented in numbers, percentages, and Mean ± SD. The normality of the distribution was assessed using the Shapiro-Wilk test. Student’s t-test and one-way ANOVA were used to compare the means of the scale scores. The Kruskal-Wallis test was used for non-parametric comparisons between group. Bonferroni correction was used to identify the source of observed differences between the groups. Pearson correlation analysis was used to examine the relationships between the scales. A significance level of p  < 0.05 was considered statistically significant for all statistical decisions.

Descriptive characteristics of the nurses

All of the participants, 74.2% were female, 43.2% were married, and 81.2% held a bachelor’s degree. A majority of the nurses (62.4%) reported inadequate staffing levels in their units. Furthermore, almost half (48.5%) considered leaving the profession, while over four-fifths (81.7%) expressed dissatisfaction with their working conditions (refer to Table  1 for more information). (Table  1 )

Comparison of scale scores based on descriptive characteristics

Significant differences were found in the total scores of the Stanford Presenteeism Scale based on participants’ gender (t = 3.387, p  = 0.001). Female nurses experienced more intense presenteeism compared to male nurses. Nurses who perceived a shortage of colleagues in their units (t = 2.548, p  = 0.01), contemplated leaving the profession (t = 2.107, p  = 0.036), and were dissatisfied with their working conditions (t = 3.176, p  = 0.002) scored higher on the presenteeism scale than their counterparts. For income status, no statistically significant difference was found in the Stanford Presenteeism Scale scores ( X² =3.143, p  = 0.208) or in the MISSCARE survey scores ( X² =5.709, p  = 0.058). Additionally, nurses who experienced difficulties in providing care due to material shortages had higher average scores in both presenteeism (F = 9.610, p  < 0.001, a-b) and missed nursing care (F = 3.496, p  = 0.032, a-b). Furthermore, nurses who were unable to provide care or delayed care due to workload in their clinics showed significant differences in both presenteeism ( X²= 9.760, p  = 0.008) and missed nursing care ( X²= 7.019, p  = 0.030). A significant difference was found between the wards where the nurses participating in the study the total scores and they obtained from both scales. As a result of “Bonferroni” correction, it was determined that nurses working in intensive care units received higher scores than their colleagues working in other services.

Nurses participating in the study had mean scores of 13.33 ± 3.82 on the Stanford Presenteeism Scale, 67.05 ± 17.93 on missed care quantity (MISSCARE A), and 30.82 ± 7.30 on reasons for missed care (MISSCARE B) (Table  2 ).

Relationship between presenteeism and MNC scale score

A statistically significant and positively moderate relationship was found between the total score of the Stanford Presenteeism Scale and the Missed Nursing Care Needs Quantity ( r  = 0.542) and the Reasons for Missed Nursing Care Needs ( r  = 0.444; p  < 0.001) (Table  3 ). Furthermore, a statistically significant and strong positive correlation was found between the quantity of missed nursing care needs and the reasons.

Presenteeism is a multidimensional phenomenon and is highly prevalent among healthcare workers [ 27 ]. It is important to know these levels since presenteeism, which affects the motivation, effectiveness and efficiency of nurses, and missing nursing care have important consequences, especially risking patient safety.

In this study, it was found that nurses experienced moderate levels of presenteeism. Other studies on the subject have shown that the total scores of the presenteeism scale are similar to our results [ 28 , 29 , 30 ]. A review of the literature reveals that healthcare workers, especially nurses, have high rates of presenteeism. A high rate of presenteeism decreases job satisfaction and triggers an increase in absenteeism and turnover. In addition, it is reported that presenteeism negatively affects work efficiency in nurses, which reduces the quality of care [ 31 , 32 ]. The study found that the nurses exhibited less than levels of MNC compared to the average. In studies conducted in different countries and other regions of Turkey, the results were found to be varied. [ 26 , 33 , 34 , 35 ]. One of the reasons for differences in MNC is how society perceives the nursing role [ 36 ]. In Turkey, relatives or paid caregivers usually accompany the patient during hospitalization, help with basic care practices and facilitate communication between the patient and the nurse. As a result, it is expected that in places with a predominantly traditional culture, such as Turkey and the Southeastern Anatolia Region of Turkey, the rates of MNC would be lower than in countries with less traditional cultures, such as European countries.

In the present research, it was found that female nurses had higher SP-6 and MISSCARE Survey scores than male nurses. In a study conducted by Çelmeçe and Menekay with 240 health care workers, it was stated that female nurses had higher stress levels than male nurses because female nurses had more responsibilities outside the workplace and therefore work stress increased even more [ 37 ]. Similarly, there are studies reporting that female nurses are more affected by presenteeism [ 38 , 39 , 40 ]. The higher rate of missed nursing care and presenteeism among female nurses may be due to more stress outside of work.

The study shows that most nurses perceived the number of nurses in their units as inadequate and delayed or neglected care due to lack of supplies or workload. In addition, those who perceived nurse staffing as inadequate and those who neglected or delayed care due to lack of supplies or workload were found to have higher mean scores on both the SPS-6 and MISSCARE Survey. Related studies have reported that nursing care is skipped more frequently when the nurse-patient ratio is low and the relevant personnel are insufficient [ 8 , 35 , 41 ]. It is thought that the unexpected increase in the number of patients cared for or patient needs, the inability to obtain the necessary materials or the inoperability of the devices may further negatively affect the intensive workload of nurses and increase the unmet nursing care.

In our research results, it was determined that nurses over the age of 30 had higher SP-6 total scores, even though no statistical significance was found between age and presenteeism. In similar studies evaluating the presenteeism among nurses, it was found that the susceptibility to presenteeism increased as the average year of work/age increased [ 17 , 29 ]. As a result of the reviewed literature, it is seen that working in the field of nursing for a long time increases the susceptibility to presenteeism.

Nurses’ satisfaction with their working conditions and overall job satisfaction have a significant impact on the quality of nursing care and the occurrence and extent of nursing care lapses [ 12 , 13 ]. This study found that over half of the nurses were dissatisfied with their working conditions, and almost half expressed a desire to leave the profession. High rates of presenteeism are known to reduce job satisfaction, leading to increased absenteeism and turnover [ 42 ], which is consistent with this study findings. It is considered that the health policies implemented by the countries, the density of health institutions and the differences between the working-leave processes of the nurses affect the presenteeism of the nurses and the results of the study vary accordingly.

Assessments of MNC and presenteeism based on descriptive characteristics revealed that nurses working in intensive care units had higher averages than those in other units. Intensive care units, which deal with complex and high-acuity patients, inherently face an increased workload for nurses. It is estimated that the increased incidence of unmet care in ICUs is likely due to a combination of increased workload and inadequate staffing in these critical care settings [ 14 , 43 ].

The most important outcome of this study was that nurses’ presenteeism was significantly positively associated with MNC. In another study investigating nurses’ care behaviors and presenteeism, it was found that presenteeism status was significantly associated with self-reported quality of care score [ 29 ]. Work productivity, absenteeism and job satisfaction decrease in nurses with presenteeism. All these cause a decrease in the quality of care required for the nursing profession and put patients at risk.

Limitations

The study’s generalizability is limited due to its focus on nurses employed in only two specific public hospitals located within designated city centers. In addition, slightly more than a quarter of the nurses who met the inclusion criteria responded. Within the framework of the methodology applied, the data was collected only through an online questionnaire. This approach may not have captured the views of respondents who did not prefer to participate in online surveys or had internet access restrictions, which may limit the overall validity of the study. Furthermore, the use of self-report data from nurses, along with the research’s limited timeframe, further restricts the broader applicability of the findings. Additionally, nurses may be hesitant to openly discuss the concepts of presenteeism and missed care. There is a potential for concealing these phenomena and portraying missed care as ‘unmissed’, with participants responding to scale questions while concealing this circumstance.

The study shows that nurses who face challenges related to heavy paperwork and inadequate care provision are more likely to engage in presenteeism. The correlation between presenteeism and instances of missed care highlights the impact of presenteeism behaviors on the quality of patient care. Factors such as material shortages and high paperwork intensity contribute to the difficulties faced by nurses, potentially leading to an increase in both presenteeism and instances of missed care. To address overlooked nursing tasks, it is recommended to maintain reasonable patient-to-nurse ratios, establish appropriate nursing care delivery approaches tailored to institutional and patient characteristics, rectify deficiencies in patient care equipment, ensure equitable utilization of available resources, provide essential training for nurses in using new devices and tools, and encourage the development of innovative tools, instruments, and care methods. Expanding research in this area is advised by employing diverse research methodologies, including quantitative, qualitative, and mixed methods, and by incorporating more extensive and diverse participant groups.

Data availability

The author confirms that all data generated or analyzed during this study are included in this published article. Additionally, primary and secondary sources and data supporting the findings of this study can be provided by the authors, ensuring the protection of students’ personal data, upon request by the editor.

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We would like to express our gratitude to the nursing staff who participated in this study for their time and valuable insights.

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Ezgi Dirgar

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Soner Berşe

Department of Nursing, Faculty of Health Sciences, Harran University, Sanliurfa, Turkey

Faculty of Nursing, Hacettepe University, Ankara, Turkey

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Author Contributions: Conceptualization (ED, SB, AŞ) ; data collection (ED, SB, AŞ); formal analysis (BT, ED); methodology (BT, ED, SB) ; roles/writing - original draft (ED, BT, SB, AŞ,JL); writing - review & editing (ED, BT, SB, AŞ,JL); Supervision (ED, BT, JL)Conflict Interest Statement: Authors declared no conflict interest.

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The study was started after receiving the required permissions from the Harran University Non-Interventional Research Ethics Committee (Decision No: HRÜ/23.01.20), Faculty of Health Sciences, Harran University. We conducted according to the ethics guidelines set out in the Declaration of Helsinki. All the participant participating in the study were informed about the study, their written/verbal consents were taken, and they were also informed that they could leave the study at any time. The authors declare that they have no competing interests.

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Dirgar, E., Berşe, S., Şahin, A. et al. Presenteeism and missed nursing care: a descriptive, correlational and observational study. BMC Nurs 23 , 652 (2024). https://doi.org/10.1186/s12912-024-02253-9

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Genetically predicted dietary intake and risks of colorectal cancer: a Mendelian randomisation study

• Tung Hoang 1 , 2 , 3 ,
• Sooyoung Cho 4 ,
• Ji-Yeob Choi 5 , 7 , 8 ,
• Daehee Kang 1 , 5 , 6 , 8 &
• Aesun Shin   ORCID: orcid.org/0000-0002-6426-1969 1 , 2 , 4 , 6 , 8  

BMC Cancer volume  24 , Article number:  1153 ( 2024 ) Cite this article

Metrics details

Effects of confounders on associations between diet and colorectal cancer (CRC) in observational studies can be minimized in Mendelian randomization (MR) approach. This study aimed to investigate observational and genetically predicted associations between dietary intake and CRC using one-sample MR.

Using genetic data of over 93 million variants, we performed a genome-wide association study to find genomic risk loci associated with dietary intake in participants from the UK Biobank. Then we calculated genetic risk scores of diet-related variants and used them as instrumental variables in the two-stage least square MR framework to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for associations. We also performed observational analyses using age as a time-scale in Cox proportional hazard models.

Allele scores were calculated from 399 genetic variants associated with the consumption of of red meat, processed meat, poultry, fish, milk, cheese, fruits, vegetables, coffee, tea, and alcohol in participants from the UK Biobank. In MR analysis, genetically predicted fruit intake was significantly associated with a 21% decreased risk of CRC (HR = 0.79, 95% CI = 0.66–0.95), and there was a marginally inverse association between vegetable intake and CRC (HR = 0.85, 95% CI = 0.71–1.02). However, null findings were observed in multivariable analysis, with HRs (95% CIs) of 0.99 (0.98–1.01) and 0.99 (0.98–1.00) per increment of daily servings of fruits and vegetables, respectively.

Dietary habits were attributable to genetic variations, which can be used as instrumental variables in the MR framework. Our study supported a causal relationship between fruit intake and a decreased risk of CRC and suggested an effective strategy of consuming fruits in the primary prevention of CRC.

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Introduction

With a global burden of 1.9 million new cases and 0.9 million deaths estimated in 2020, colorectal cancer (CRC) is the third most common cancer type and the second most common cancer death due to this malignancy in the world [ 1 ]. Regarding the prevention of CRC, the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) launched the guidance every 10 years based on up-to-date systematic reviews and meta-analyses and reported the level of evidence for the association of different dietary factors with CRC risk [ 2 ]. Observational studies may be vulnerable to residual confounding by factors that cannot be measured, and this may limit it in interpreting such an observed association as a causal relationship [ 3 , 4 ]. In the meantime, by examining genetic variants such as single nucleotide polymorphisms (SNPs) as instrument variables (IVs) that act as proxies for environmental factors, Mendelian randomisation (MR) was suggested to provide a useful approach to minimise the bias of the effect estimate between risk factors and CRC risk [ 5 , 6 , 7 ].

A previous MR study comprehensively examined the causal inference of modifiable factors with the CRC risk [ 8 ]. Among 39 risk factors, only coffee consumption was included in the analysis due to unavailable or unsuitable SNPs for the use as instrumental variables (IVs) for other dietary factors [ 8 ]. Given a substantial proportion of the preference for foods was explained by genetic variations, individual food preferences and dietary habits have been identified to be affected by the senses of taste and smell and metabolic processes [ 9 , 10 , 11 ]. Additionally, a previous comprehensive genome-wide association study (GWAS) reported hundreds of significant loci for single foods and dietary patterns in participants of the UK Biobank [ 12 ]. However, underlying biological mechanisms contributing to genetic variations for the intake of several food items (e.g., pork vs. beef vs. lamb/mutton, oily vs. nonoily fish, fresh vs. dried fruits, cooked vs. raw vegetables) have been still unclear. Therefore, we first carried out a GWAS of food intake to identify genetic variants associated with the intake of total red meat, processed meat, poultry, total fish, milk, cheese, total fruits, total vegetables, coffee, tea, and alcohol, using updated data of more than double number of SNPs compared to the previous study. We then performed a one-sample MR study to elucidate the association between genetically predicted dietary intake and CRC risk using GWAS-identified genomic risk loci as IVs.

Materials and Methods

Study population.

The UK Biobank is a prospective cohort study that included 502,389 participants aged 37–73 years who resided within 25 miles of 22 recruiting centers between 2006 and 2010. The study was approved by the North West Multi-centre Research Ethics Committee. The methodological details and rationale of the UK Biobank have been published elsewhere [ 13 , 14 , 15 ].

In the present study, we mutually excluded participants without genetic information ( N  = 15,208), sex mismatch ( N  = 367), putative sex chromosome aneuploidy ( N  = 651), and those who were either genetically identified or self-reported as having ethnic backgrounds other than White British (including White, British, Irish, and any other White backgrounds) ( N  = 78,378). After exclusion, the sample available for the genome-wide association analysis was restricted to 408,093 individuals. Finally, we excluded participants who were diagnosed with any cancers at enrolment ( N  = 34,078) and those who withdrew from the study during the follow-up ( N  = 11), leaving a total of 374,001 individuals (Fig.  1 ).

Flow diagrams of study participants and analytical framework

Genotyping and quality control

Genotyping was performed using either the custom UK Biobank Axiom Array or the Affymetrix Axiom Array, as described elsewhere [ 14 , 15 ]. Genotyping data were imputed using both the UK10K and 1000 Genomes Phase 3 and the Haplotype Reference Consortium reference panel, which resulted in a total of 93,095,623 markers [ 14 ]. Following the quality control procedure, we excluded SNPs with low imputation quality (imputed score < 0.3, n  = 15,368,777), high missingness (geno > 0.05, n  = 909,502), low minor allele frequency (maf < 0.0002, n  = 55,398,429) and those that deviated from the expected Hardy–Weinberg equilibrium ( p  < 1e-6, n  = 8,717,604) [ 16 ]. A total of 27,503,596 SNPs that passed the quality filtering remained.

Dietary intake assessment

A touchscreen food frequency questionnaire (FFQ) was used to assess food and beverage intake in the preceding year [ 17 ]. Details of the questionnaire were publicly available [ 18 ]. In this study, we included foods that were documented in the WCRF report for their associations with CRC risk at various levels of evidence. We also selected foods for which consumption could reasonably be attributed to genetic variations (Additional file 1: eInformation). A linear mixed model was applied to adjust for familial relatedness in genome-wide association analysis of food intake; thus, we converted dietary outcomes into quantitative traits (Table  1 ). Of these, frequency traits of beef, pork, lamb, processed meat, poultry, oily fish, nonoily fish, cheese, and alcohol intake, and quantitative traits of fresh and dried fruits, cooked and raw vegetables, and coffee and tea consumption were included in our analyses. For categorical phenotypes, we used the corresponding numeric values (times/week) for the analysis. To justify the selection of dietary factors, we combined food items into more common food groups which are similar to those from the WCRF report. We grouped single items to obtain the total intake of red meat (including pork, beef, and lamb), total fish (including oily and nonoily fish), total fruits (including fresh and dried fruits), and total vegetables (including cooked and raw vegetables) [ 19 ]. Milk consumption (mL/day) was estimated based on the type of milk, breakfast cereal, coffee, and tea intake [ 19 ]. The 24-h dietary data were used to validate the estimation of milk intake, and 94% of the total milk consumption was found to come from milk added to breakfast cereal, coffee, and tea [ 19 ]. Overall, the Shapiro–Wilk test was applied to assess the normality of the data, and for data not following a normal distribution, the median and interquartile range was reported for data that did not follow normal distribution.

Outcome ascertainment

Incident CRC cases were determined via the ICD-10 code, in which CRC was defined as either colon cancer (C18.0-C18.9) or rectal cancer (C19 and C20). Time to follow-up was defined as the date of study enrolment until the date of CRC diagnosis, death, lost-to-follow-up, or end of follow-up (June 25, 2021), whichever came first.

Instrumental variables for dietary phenotype

To identify genetic variants associated with dietary traits, we performed a GWAS for food intake (Additional file 1: eMethod). In brief, we performed a genome-wide association analysis under the linear mixed model approach [ 20 ]. We incorporated age, sex, and the first 6 first principal component scores released by the UK Biobank [ 14 ] as covariates. In the large-scale UK Biobank dataset, more than 30% of study participants were genetically defined to relate with another participant [ 14 ]. Therefore, we further adjusted for the cryptic relatedness among participants by calculating the sparse genetic relatedness matrix (GRM) using genotyping data of 93,183 SNPs, which were used for the final kinship inference of the released UK Biobank data [ 14 , 21 , 22 ]. The list of genomic risk loci and their functions were determined under the SNP2GENE and GENE2FUNC functions of the web-based FUMA tool [ 23 ].

In sensitivity analysis, we excluded genetic variants, which were associated with more than two dietary traits from the list of IVs for dietary intake to minimise the possibility of horizontal pleiotropy. Additionally, for the exclusion restriction assumption, we further excluded SNPs that were associated with CRC risks ( p -value < 0.05) from the list of candidate IVs to minimise the possibility of genetic variants affecting CRC other than through dietary intake. Details on the estimation of beta coefficients for the effect of variants on CRC risks adjusting for familial relatedness were available at Additional file 1: eMethod.

The internally weighted allele score for each participant was calculated by multiplying the number of effect alleles that the participant carried by the corresponding beta-coefficient of the association between the genetic variant and dietary intake estimated from the genome-wide association. Then we summed up the weighted allele score of individual genetic variants and used them as IVs in the MR analysis.

To assess the weak instrument problem, an F -statistic was implemented for IVs of allele scores and their corresponding individual genetic variants [ 24 ]. F -statistic was approximated by a squared estimate for IVs on dietary intake frequency divided by its variance.

Mendelian randomisation analysis

We carried out a one-sample MR in the UKB to assess the effect of dietary intake on CRC using the two-stage least square method [ 25 , 26 ]. In the first stage, we regressed each food frequency consumption on its respective allele score using a linear regression model to obtain a set of fitted values for exposure of interest. In the second stage, we regressed the CRC outcome on the fitted values obtained in stage 1 using an age-scale Cox proportional hazard model. Additionally, we used the MR pleiotropy residual sum and outlier test (MR-PRESSO) to detect the presence of pleiotropy [ 27 ] and the MR-Egger regression to identify whether directional pleiotropy may influence the causal estimates [ 28 ]. Subgroup analyses were conducted by sex and CRC subsites.

In sensitivity analysis, we carried out a multivariable MR, which included multiple dietary factors which their allele scores were substantially correlated or had relatively high genetic correlations.

Observational association

We sought to evaluate the association between dietary intake (in a continuous form) and CRC risk using age as a time-scale in Cox proportional hazard models. In the multivariable analysis, we adjusted for confounders, including sex, family history of CRC, household income, smoking, alcohol consumption (except for alcohol consumption exposure), body mass index, and physical activity, which were associated with CRC risk in the univariate analysis.

Study population characteristics

Table 2 summarises the general characteristics and dietary habits of 174,576 men and 199,428 women without any cancers at enrolment. At recruitment, participants were aged 56.6 years (mean ages 56.5 years for men and 56.8 years for women). After a median follow-up of 12.4 years (interquartile range 11.6–13.1 years), 3,131 colon cancer and 1,555 rectal cancer cases were newly detected.

Loci and annotation of SNPs related to dietary intake

The results from the genome-wide association analysis for significant SNPs ( p  < 5 × 10 –8 ) associated with food intake are presented as Manhattan plots (Fig.  2 ). We identified a total of 402 genomic risk loci for the consumption of red meat ( n  = 15), processed meat ( n  = 12), poultry ( n  = 1), total fish ( n  = 28), milk ( n  = 50), cheese ( n  = 59), total fruits ( n  = 82), total vegetables ( n  = 50), coffee ( n  = 33), tea ( n  = 40), and alcohol ( n  = 57) in the linear mixed model adjusting for familial relatedness (Additional file 2: Table S1). Of these, variants rs2199936 (chromosome 4, ABCG2 gene), rs139797380 (chromosome 6, SLC35D3 gene), and rs4410790 (chromosome 7, AC003075.4 gene) were associated with milk, coffee, and tea consumption. Variant 2:27,748,992 (chromosome 2, GCKR gene) was associated with the consumption of milk, coffee, and alcohol. Variant rs8103840 (chromosome 19, FUT1 gene) was associated with the intake of processed meat, fish, and fruits. In addition, some SNPs were associated with two dietary factors, including rs201406724 (milk and tea), rs11940694 (milk and alcohol), rs2465018 (milk and tea), rs17685 (milk and tea), rs4726481 (tea and alcohol), rs7012814 (cheese and tea), 8:73,433,232 (milk and tea), rs11032362 (processed meat and fruits), 12:11,271,915 (coffee and tea), rs12591786 (milk and tea), rs12909335 (milk and tea), rs9937521 (tea and alcohol), rs12459249 (milk and coffee), and rs429358 (fish and fruits).

Manhattan plot of genome-wide association analyses of A red meat, B processed meat, C poultry, D fish, E milk, F cheese, G fruit, H vegetable, I coffee, J tea, and K alcohol consumption using linear mixed model. X-axis shows chromosome positions, Y-axis shows -log10 of p -values. Red dashed lines indicate significant threshold ( p  = 5e-8)

Biological processes, molecular functions, and Wikipathways that may involve in insights into genetic effects on the intake of fish, milk, cheese, fruits, coffee, tea, and alcohol are presented in Additional file 2: Figures S1-S7. Overall, the heritability was highest for the consumption of cheese (h 2  = 10.48%), alcohol (h 2  = 9.71%), and milk (h 2  = 9.01%), followed by tea (h 2  = 8.34%) and fruits (h 2  = 7.83%). Other foods had a heritability of approximately 5%-6%, except poultry (h 2  = 3.50%) (Additional file 2: Table S2). Furthermore, we found a relatively high genetic relationship for the intake between milk and tea (r = 0.86), fish and vegetables (r = 0.52), fruits and vegetables (r = 0.49), red meat and processed meat (r = 0.48), processed meat and fruits (r = -0.46), cheese and alcohol (r = 0.44), and red meat and poultry (r = 0.43) (Additional file 2: Figure S8). The highest Pearson correlation coefficients between food consumption were found for coffee and tea (r = -0.32) and milk and tea (r = 0.30) (Additional file 2: Figure S8).

Mendelian randomisation analysis of dietary intake and colorectal cancer risk

All genetic instruments of SNPs and allele scores predicted dietary intake frequency, with F -statistics greater than 10, are presented in Tables 3 , Additional file 2: Tables S1 and S3. Since only one variant was associated with poultry intake, we did not calculate the MR estimate for the effect of poultry intake on CRC.

Table 4 shows the estimates of the causal effect of dietary intake on CRC risks in the one-sample MR approach using the full lists of genetic variants. Overall, genetically proxied fruit intake was associated with 21% decreased risks of both CRC (HR = 0.79, 95% CI = 0.66–0.95) and colon cancer (HR = 0.79, 95% CI = 0.63–0.99). Findings for other dietary factors were not significant: red meat (HR = 0.72, 95% CI = 0.40–1.28), processed meat (HR = 0.57, 95% CI = 0.29–1.11), fish (HR = 1.05, 95% CI = 0.72–1.53), milk (HR = 1.19, 95% CI = 0.86–1.63), cheese (HR = 0.98, 95% CI = 0.78–1.23), coffee (HR = 1.16, 95% CI = 0.96–1.40), tea (HR = 0.95, 95% CI = 0.82–1.11), and alcohol (HR = 1.01, 95% CI = 0.86–1.20). These associations remained after excluding genetic variants associated with more than one dietary phenotype or related to CRC risks (Additional file 2: Table S4). In sex-specific subgroups, CRC reduction was only observed in women for an increment of 1 serving/day of consuming fruits in both the main analysis of including all eligible variants and the sensitivity analysis of the reduced list of variants, with HRs (95% CIs) of 0.72 (0.53–0.98) and 0.69 (0.50–0.96), respectively. Furthermore, genetically proxied alcohol consumption was associated with a 22% increased risk of CRC in men. However, this association disappeared in the sensitivity analysis using the reduced list of variants.

Marginally inverse associations were found for vegetable intake and CRC (HR = 0.85, 95% CI = 0.71–1.02) and colon cancer (HR = 0.80, 95% CI = 0.64–1.01) risks. Using genetic variants associated with a single dietary phenotype and not related to CRC, the magnitude of associations was similar to that of all eligible variants, with HRs (95% CIs) of 0.84 (0.70–1.01) and 0.80 (0.63–1.01) for CRC and colon cancer, respectively.

In the sensitivity analysis of using multivariable MR with the inclusion of multiple dietary factors which their allele scores were substantially correlated (r > 0.10, Additional file 2: Figure S9A) or had relatively high genetic correlations (r > 0.30, Additional file 2: Figure S9B), the sets of red meat and processed meat; fish, total fruit, and total vegetables; milk, tea, and coffee; and cheese and alcohol were considered in the model. Accordingly, genetically predicted consumption of red meat, processed meat, and cheese was associated with an increased risk of CRC, with HRs (95% CIs) of 1.30 (1.19–1.43), 1.29 (1.18–1.41), and 1.36 (1.21–1.53), respectively (Additional file 2: Table S9). Furthermore, inverse associations were observed for associations between genetically predicted vegetable (HR = 0.94, 95% CI = 0.90–0.98) and tea (HR = 0.97, 95% CI = 0.95–0.99) consumption (Additional file 2: Table S9).

Evaluation of pleiotropy effects

Although MR-PRESSO global tests suggested a possible bias from horizontal pleiotropy in associations of processed meat intake in men and coffee consumption in women with rectal cancer (Table  3 , p pleiotropy  = 0.01), the estimates after correcting for outliers remained in similar directions of associations, with HRs (95% CIs) of 0.30 (0.03–3.21) and 0.72 (0.35–1.47), respectively. The MR-PRESSO distortion test showed that the distortion in the effect estimates before and after removing outliers was not significant. These possible pleiotropy effects disappeared in our sensitivity analysis of restricting genetic variants for IVs (Additional file 2: Table S4).

Additional file 2: Table S5 shows the observational effect of dietary intake on the risk of CRC. Red meat (HR = 1.05, 95% CI = 1.03–1.07, per 1 time/week), processed meat (HR = 1.03, 95% CI = 1.01–1.05, per 1 time/week), and alcohol consumption (HR = 1.03, 95% CI = 1.01–1.04, per 1 time/week) were positively associated with CRC risks. In contrast, more frequently milk (HR = 0.95, 95% CI = 0.92–0.97, per 100 mL/day) and tea (HR = 0.98, 95% CI = 0.97–0.99, per 1 cup/day) consumers had decreased risk of CRC. However, null associations were observed in multivariable analysis, with HRs (95% CIs) of 0.99 (0.98–1.01) and 0.99 (0.98–1.00) per increment of daily servings of fruits and vegetables, respectively. When stratified by sex, the effects of red meat, processed meat, and alcohol consumption remained for the men subgroup, whereas only the inverse association between milk intake and CRC risk was observed in women. Nevertheless, null findings were observed in multivariable analysis, with HRs (95% CIs) of 0.99 (0.98–1.01) and 0.99 (0.98–1.00) per increment of daily servings of fruits and vegetables, respectively.

In the analysis by CRC subsites, positive associations of red meat intake and inverse associations of milk and tea consumption were observed with both colon cancer and rectal cancer (Additional file 2: Tables S6-S7). Furthermore, processed meat (HR = 1.03, 95% CI = 1.01–1.05, per 1 time/day) and alcohol (HR = 1.03, 95% CI = 1.01–1.04, per 1 time/day) consumption showed an increased risk of colon cancer.

In this study, we identified 399 genomic risk loci for self-reported traits reflecting daily consumption of food items included in the WCRF report for CRC prevention (Additional file 2: Figure S10). Using these genomic risk loci in the one-sample MR framework, we found that genetically predicted dietary intake of fruits was associated with a lower risk of CRC, with a similar magnitude of an inverse association with colon cancer. Additionally, marginally inverse associations between vegetable intake with CRC and colon cancer were observed in the total study population. When compared with our observational analysis of a prospective cohort study design, these associations appeared to be weaker and did not reach the level of significance (Additional file 2: Figure S11).

When we searched PubMed up to September 2023 for the GWAS of dietary traits, a total of 23 GWAS were identified, and seven studies included the population of the UK Biobank (Additional file 2: Table S8). Our study extended to the previous research by accounted for familial relatedness, which was not adjusted in most previous GWAS. Besides, to justify the selection of dietary factors, we combined food items into more common food groups that underlying biological mechanisms contributing to genetic variations existed. In addition, we analysed updated data with more than double SNPs from the most comprehensive GWAS for dietary intake [ 12 ]. Moreover, we carried out functional analyses to inform possible biological mechanisms between genetic factors and food consumption. A detailed comparison of the identified variants and the heritability of genetic factors between our present GWAS and Cole’s study is further provided in Additional file 3: Appendix.

By obtaining dietary habits from the questionnaire, we considered the amount of food consumption in the continuous form and applied the linear mixed model. A previous study converted food-liking traits into numerical values (range 0–9) without justification [ 29 ]. Given the transformation of food preference phenotypes into the hedonic scale into numeric values is not appropriate, the proportional odds logistic mixed model (POLMM) has been shown to handle ordinal categorical phenotypes, especially when the phenotype is extremely imbalanced [ 30 ]. The authors applied the POLMM for the frequent consumption of food items (never or almost never, once every few months, once a month, once a week, 2–4 times per week, and almost daily) in the UK Biobank without converting into numeric values [ 30 ]. In our present study, modelling dietary intake frequencies as continuous variables may violate the assumption of linearity relationship between SNPs and food consumption due to the restriction of outcome variable ranges. Nevertheless, findings on the top 10 genes were similar to those identified from our current study (e.g., CCDC171 for beef, pork, and lamb, XKR6 for processed meat, LY6H for poultry, and MLLT10 for oily fish).

The anti-cancer effects of fruits and vegetables were suggested due to their bioactive compounds, such as fiber, folate, vitamins, minerals, and flavonoids [ 31 ]. Of these, fiber is fermented by several bacteria to produce short-chain fatty acids (SCFAs), including acetate (central appetite regulation), propionate (gluconeogenesis and satiety signaling regulation), and butyrate (a main energy source for human colonocytes) [ 32 , 33 ]. Higher fiber intake was associated with the increase of SCFAs, and SCFA-producing bacteria, which regulate the immune system and metabolism and reduce the CRC risk [ 33 ]. According to the WCRF/AICR, there was limited evidence for the effect of fruit and non-starchy vegetable intake on CRC prevention [ 34 ]. According to pooled estimates from prospective cohort studies, per daily 100 g of fruit and vegetable intakes were associated with a decreased risk of CRC by 4% (relative risk (RR) 0.96, 95% CI = 0.93–0.99) and 2% (RR = 0.98, 95% CI = 0.96–0.99), respectively [ 35 ]. However, individual studies tended to show null associations. A previous case–control analysis of nine observational studies within the Genetics Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry did not observe any significant associations between fruit (odds ratio (OR) 1.04, 95% CI = 0.93–1.15) and vegetable (OR = 0.92, 95% CI = 0.82–1.03) intakes with overall CRC risk [ 36 ]. Similarly, nonsignificant associations between fruit (HR = 1.00, 95% CI = 0.94–1.05) and vegetable (HR = 1.01, 95% CI = 0.93–1.11) intakes and CRC risks were recently reported in a prospective cohort analysis of the UK Biobank [ 19 ]. These inconsistent findings with our MR estimates may be partly due to differences in study design and analytical framework. In general, observational studies are more prone to residual confounding, reverse causation, and measurement error than MR analyses, which randomly assign the exposure of interest-related IVs among individuals [ 4 , 26 ]. Such sources of bias may attenuate associations toward the null [ 4 , 26 ]. Furthermore, while the MR estimates reflect the effect of lifelong perturbations in risk factors, observational results may reflect more acute effects, during the follow-up period since the enrolment time point of a cohort) [ 37 ]. Our present observational analysis with a longer follow-up period (12.4 vs. 5.7 years) suggested stronger favorable effects of fruits (HR = 0.99, 95% CI = 0.91–1.01) and vegetables (HR = 0.99, 95% CI = 0.98–1.00), thus supports the evidence of long-term beneficial effects [ 19 ].

Among dietary factors, the International Agency for Research on Cancer classified processed meat as a human carcinogen (Group 1) and red meat as a probable carcinogen (Group 2A) [ 38 ]. Carcinogenic effects of red meat and processed meat were introduced via several chemicals such as N -nitroso compounds, heterocyclic aromatic amines, and polycyclic aromatic hydrocarbons formed in red meat and when cooking meat at high temperatures [ 39 ]. The WCRF/AICR also reported probable to convincing evidence of red meat and processed meat intake in association with CRC risks [ 34 ]. However, our present study observed the association between red meat and processed meat with CRC risk in observational analyses and multivariable MR. Besides differences in study design and analytical framework, the explained variation of IVs for the exposure of interest may affect our estimates. Although the allele score IVs explained variations of dietary intake (F-statistics greater than 90), the number of SNPs used for the calculation of allele scores for red meat and processed meat was relatively small, which may not allow us to detect any significant associations. We further observed an inverse association between processed meat intake and rectal cancer risk. These findings disappeared in sex-specific subgroups and need to be interpreted cautiously, possibly due to the small proportion of rectal cancer cases among whole study participants.

To date, very few MR studies reported the effect of dietary factors on CRC risk. Most of them considered blood concentrations of nutrients (carotenoids, calcium, copper, fatty acids, folate, iron, magnesium, methionine, phosphorus, selenium, sodium, vitamin B6, vitamin B12, vitamin D, vitamin E, and zinc) as exposure of interest [ 8 , 40 , 41 , 42 , 43 , 44 ]. Only the MR study conducted by Cornish et al. examined the causal estimate between diet consumption of coffee and CRC risk. Although we used much more SNPs in the allele score calculation, our study revealed a similar direction of the estimates (33 SNPs, HR = 1.16, 95% CI = 0.96–1.40 in the current study vs. 4 SNPs, OR = 1.17, 95% CI = 0.88–1.55 in the previous study) [ 8 ].

Furthermore, we found inconclusive evidence of the MR estimates of total fish, milk, cheese, coffee, tea, and alcohol consumption on CRC. Of these, pooled estimates from observational studies showed significantly or suggestively inverse associations of fish (RR = 0.89, 95% CI = 0.80–0.99), milk (RR = 0.94, 95% CI = 0.92–0.96), cheese (RR = 0.94, 95% CI = 0.87–1.02), coffee (RR = 1.00, 95% CI = 0.99–1.02), tea (RR = 0.99, 95% CI = 0.97–1.01), and alcohol (RR = 1.07, 95% = 1.05–1.08) intake with CRC risk [ 35 ]. Compared to observational analysis, estimates from MR may commonly have wider CIs and thus toward null findings [ 37 ].

This study has several strengths. Having large-scale individual-level data with much more genetic information of imputed SNPs compared to earlier GWAS, we applied the recent methodology to account for confounding effects of both population stratification and cryptic relatedness to identify loci associated with food intake. We also performed a comprehensive MR analysis to suggest evidence for the causal estimate of dietary intake and CRC risk. Genetic variants had adequate strengths; thus, bias due to small F -statistics or small sample size can be minimised. Undertaking sensitivity analyses to evaluate the plausibility of IV assumptions and robustness to pleiotropy and outliers, our findings from MR analyses may be less biased by residual confounding and reverse causation than observational results. Additionally, combining many SNPs into a single allele score may increase the power of the analysis and reduce the risk of bias from possible weak instruments [ 26 ]. Furthermore, available data for one-sample MR analysis allowed us to consider the effect estimate in several subgroups, such as sex and CRC subsites.

Despite providing new evidence about the causal effect of dietary intake on CRC risk, this study has some limitations that need to be addressed. One limitation of the study is the fact that we analysed CRC risk only using the dietary information measured at a single time point, which may not reflect the lifelong dietary intake, thus, our findings were based on the assumption that such dietary habits might not change or be equally changed during follow-up. The effect of dietary factors might be underestimated due to random measurement errors [ 45 ]. Previous study investigated the reproducibility of the touchscreen questionnaire of average diet over the previous 12 months used in the current study with the 24-h dietary assessment [ 45 ]. Overall, the intra-correlation of food groups was reported to range between 0.38 to 0.63, which was comparable with the overall reproducibility of FFQs in nutritional epidemiology studies (macronutrients: 0.44–0.79; micronutrients: 0.51–0.74) [ 46 ]. However, among all participants completed the touchscreen questionnaire, only approximately 42% study participants provided the 24-h dietary assessment [ 45 ]. Nevertheless, our findings were limited for 24-h dietary data. Besides, given that disparities in dietary intake according to different ethnic groups may exist due to cultural knowledge and food-related skills [ 47 , 48 ], analyses for individuals from ethnic backgrounds other than White British require additional investigations. Furthermore, we derived SNPs and weights for IVs in all participants after quality control and performed the two-stage least square analysis in participants without any cancer at baseline. There could still be a winner’s curse on our estimate due to the overlap between the dataset in which genetic variants were selected and the dataset in which genetically predicted associations were determined [ 49 ]. However, the winner’s curse bias in our study can be mitigated by selecting more stringent SNPs based on not only significant threshold but also linkage disequilibrium among variants. Moreover, to obtain GWAS-identified variants for the MR analysis, our study assumed linear associations between dietary intake and risk of developing CRC.

In summary, the present study comprehensively assessed the influence of genetic variants and their functional mechanisms on the dietary behaviors of participants in the UK Biobank. By cautiously accounting for population stratification and cryptic relatedness in this large-scale of recently released imputation data, we identified several loci for food consumption. These genetic variants associated were used as IVs in the MR framework to address the relationship between dietary intake and CRC risk. Our findings supported a relationship between fruit intake and a decreased risk of CRC and suggested an effective strategy of consuming fruits in the primary prevention of CRC. Further studies in individuals from ethnic backgrounds other than White British are needed to validate our findings.

Availability of data and materials

The UK Biobank is an open access resource, available at https://www.ukbiobank.ac.uk/researchers/ . Data used in this project can be obtained from the UK Biobank by submitting a data request proposal.

Abbreviations

• Genome-wide association study
• Mendelian randomisation

Instrumental variable

World Cancer Research Fund

American Institute for Cancer Research

Food frequency questionnaire

Single nucleotide polymorphism

proportional odds logistic mixed model

Hazard ratio

Relative risk

Confidence interval

Principal component

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Acknowledgements

We thank Professor Seunggeun Lee (Shawn), from Seoul National University Graduate School of Data Science, for his useful comments on this study.

This work was supported by the grant from the National Research Foundation of Korea (NRF) (No: 2022R1A2C1004608).

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Hoang, T., Cho, S., Choi, JY. et al. Genetically predicted dietary intake and risks of colorectal cancer: a Mendelian randomisation study. BMC Cancer 24 , 1153 (2024). https://doi.org/10.1186/s12885-024-12923-1

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Resistant hypertension (RH) may be associated with microalbuminuria (MAU), a marker of cardiovascular risk and target organ damage, and both may be related to microvascular damage. Laser speckle contrast imaging (LSCI) is an innovative approach for noninvasively evaluating systemic microvascular endothelial function useful in the context of RH with or without MAU. Microalbuminuria was defined as a urine albumin-to-creatinine ratio between 30 and 300 mg/g. Microvascular reactivity was evaluated using LSCI to perform noninvasive measurements of cutaneous microvascular perfusion changes. Pharmacological (acetylcholine [ACh], or sodium nitroprusside [SNP]) and physiological (postocclusive reactive hyperemia [PORH]) stimuli were used to evaluate vasodilatory responses. Thirty-two patients with RH and a normal urine albumin-to-creatinine ratio (RH group) and 32 patients with RH and microalbuminuria (RH + MAU) were evaluated. Compared with patients without MAU, patients with RH + MAU showed reduced endothelial-dependent systemic microvascular reactivity, as demonstrated by an attenuation of microvascular vasodilation induced by PORH. On the other hand, ACh-induced vasodilation did not differ between groups. The results also revealed reduced endothelial-independent (SNP-induced) microvascular reactivity in hypertensive patients with MAU compared with patients without MAU. In this study, there was evidence of endothelial dysfunction associated with impaired microvascular smooth muscle function in patients with RH + MAU. This may suggest that patients with RH need more intensive therapeutic strategies for the control of blood pressure to avoid further vascular damage and the resulting consequences.

The study was registered at ClinicalTrials.gov ( https://register.clinicaltrials.gov ) under protocol # NCT05464849, initial release 12/07/2022.

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Data availability.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank Marcio Marinho Gonzalez for his excellent technical assistance.

This investigation was supported by grants from CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, E.T. grant #311680/2021-6) and FAPERJ (Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro), E.T. grant #E-26/200.976/2022.

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Vinicius Crahim, Valéria Verri, Andrea De Lorenzo & Eduardo Tibirica

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ET, VC, ADL, and VV contributed to the conception and design of the study, and to the analysis and interpretation of data; ET, VC, ADL, and VV were also involved in the drafting of the manuscript, and literature review. All authors have given final approval of the version to be published and are publicly responsible for its content.

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The present study was performed in accordance with the Helsinki Declaration of 1975, revised in 2000, and was approved by the Institutional Review Board (IRB) of the National Institute of Cardiology, Rio de Janeiro, Brazil, under protocol # CAAE 53946021.8.0000.5272. Once deemed eligible to participate in this study, all subjects read and signed an informed consent document approved by the IRB. The study was registered at ClinicalTrials.gov ( https://register.clinicaltrials.gov ) under protocol # NCT05464849, initial release 12/07/2022.

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Crahim, V., Verri, V., De Lorenzo, A. et al. Reduced systemic microvascular function in patients with resistant hypertension and microalbuminuria: an observational study. J Hum Hypertens (2024). https://doi.org/10.1038/s41371-024-00958-7

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Received : 24 March 2024

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DOI : https://doi.org/10.1038/s41371-024-00958-7

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Measles—clinical and biological manifestations in adult patients, including a focus on the hepatic involvement: results from a single-center observational cohort study from romania.

1. Introduction

2. materials and methods, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Bîrluțiu, V.; Bîrluțiu, R.-M. Measles—Clinical and Biological Manifestations in Adult Patients, Including a Focus on the Hepatic Involvement: Results from a Single-Center Observational Cohort Study from Romania. J. Clin. Med. 2024 , 13 , 5535. https://doi.org/10.3390/jcm13185535

Bîrluțiu V, Bîrluțiu R-M. Measles—Clinical and Biological Manifestations in Adult Patients, Including a Focus on the Hepatic Involvement: Results from a Single-Center Observational Cohort Study from Romania. Journal of Clinical Medicine . 2024; 13(18):5535. https://doi.org/10.3390/jcm13185535

Bîrluțiu, Victoria, and Rares-Mircea Bîrluțiu. 2024. "Measles—Clinical and Biological Manifestations in Adult Patients, Including a Focus on the Hepatic Involvement: Results from a Single-Center Observational Cohort Study from Romania" Journal of Clinical Medicine 13, no. 18: 5535. https://doi.org/10.3390/jcm13185535

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Informing Healthcare Decisions with Observational Research Assessing Causal Effect. An Official American Thoracic Society Research Statement

Rationale: Decisions in medicine are made on the basis of knowledge and reasoning, often in shared conversations with patients and families in consideration of clinical practice guideline recommendations, individual preferences, and individual goals. Observational studies can provide valuable knowledge to inform guidelines, decisions, and policy.

Objectives: The American Thoracic Society (ATS) created a multidisciplinary ad hoc committee to develop a research statement to clarify the role of observational studies—alongside randomized controlled trials (RCTs)—in informing clinical decisions in pulmonary, critical care, and sleep medicine.

Methods: The committee examined the strengths of observational studies assessing causal effects, how they complement RCTs, factors that impact observational study quality, perceptions of observational research, and, finally, the practicalities of incorporating observational research into ATS clinical practice guidelines.

Measurements and Main Results: There are strengths and weakness of observational studies as well as RCTs. Observational studies can provide evidence in representative and diverse patient populations. Quality observational studies should be sought in the development of ATS clinical practice guidelines, and medical decision-making in general, when 1 ) no RCTs are identified or RCTs are appraised as being of low- or very low-quality ( replacement ); 2 ) RCTs are of moderate quality because of indirectness, imprecision, or inconsistency, and observational studies mitigate the reason that RCT evidence was downgraded ( complementary ); or 3 ) RCTs do not provide evidence for outcomes that a guideline committee considers essential for decision-making (e.g., rare or long-term outcomes; “ sequential” ).

Conclusions: Observational studies should be considered in developing clinical practice guidelines and in making clinical decisions.

Introduction

•  Participants in the Ad Hoc Committee
•  Evidence Review and Discussion Results
•  Strengths and Limitations of Observational Studies
•  Diversity and Health Equity
•  Observational Study Quality
•  Common Perceptions Surrounding Observational Studies
•  Pragmatic RCTs
•  Recommendations When Incorporating Observational Research into Clinical Practice Guidelines
•  Recommendation Reevaluation

Conclusions

Decisions in medicine are made on the basis of knowledge and reasoning, often in shared conversations with patients and families in consideration of individual preferences and goals. Randomized controlled trials (RCTs) are generally considered to have the best study design for making inferences about the causal effect of an intervention on outcomes. Observational studies, however, can also offer valuable information and complement RCTs in many ways. This research statement summarizes the work of an ad hoc diverse multidisciplinary committee of the American Thoracic Society (ATS) to provide recommendations on the role of observational studies—alongside RCTs—in informing clinical and policy decisions in pulmonary, critical care, and sleep medicine. This statement has a specific focus on the role of observational studies for informing practice guidelines.

• 1. Observational studies have strengths as well as limitations. Many of their strengths complement those of RCTs. Whereas observational studies tend to have stronger external validity, RCTs tend to have stronger internal validity.
• 2. By studying larger and more representative samples of patients under real-world conditions, observational studies contribute to our knowledge of patients of diverse backgrounds and settings.
• 3. When assessing quality, the individual merits of each observational study should be considered, rather than discounting studies simply because of their observational nature.
• • When RCTs do not provide evidence for outcomes that the guideline committee considers essential (e.g., because they would be considered unethical or not feasible to conduct), observational evidence should be sought.
• • When RCTs are appraised as being of low or very low quality, observational evidence should be sought to “ replace ” RCT evidence.
• • When RCTs are appraised to be of moderate quality because of indirectness , imprecision , or inconsistency of the RCT evidence, observational studies should be sought to “ complement ” RCT evidence.
• • When RCTs do not provide the best evidence for outcomes that the guideline committee considers essential for decision-making, such as rare or long-term outcomes, observational studies should be sought to be “ sequential ” to RCTs.

As a patient service and advocacy organization, Respiratory Health Association is aware of the systematic underrepresentation of women and certain sub-populations (e.g., minorities, people with fewer socioeconomic resources) in randomized clinical studies. While this is a situation that needs to be remedied, increased use of observational studies can also help to address systematic underrepresentation for the benefit of the full demographic spectrum of patients. —Joel J. Africk, President and Chief Executive Officer of Respiratory Health Association

Decisions in medicine are made on the basis of knowledge and reasoning, often in shared conversations with patients and families that consider individual preferences and goals. Clinical practice guidelines inform clinical decisions about medical care by using structured syntheses of evidence to formulate recommendations on the optimal course of action to prevent, diagnose, and manage disease—ultimately to improve health. Policies and programs also use evidence to inform best care for patients.

RCTs are generally considered to have the best study design for making inferences about the causal effect of an intervention on outcomes because there is random distribution of measured and unmeasured confounding characteristics ( Table 1 ). Efficacy RCTs seek to enroll relatively homogeneous groups of individuals, with interventions delivered using a study protocol to which adherence is strict. Therefore, observed treatment effects in efficacy trials may not be seen in real-world clinical conditions ( 1 ). These considerations have led to growing interest in pragmatic RCTs (also known as effectiveness or practical RCTs, which are designed to evaluate the effectiveness of an intervention in real-world practice conditions) that evaluate healthcare options in populations that more closely approximate people who receive care in routine healthcare settings. However, some clinical questions cannot be feasibly studied with RCT designs because of ethical issues or other factors (e.g., lack of time or other resources). Furthermore, RCTs can take a long time to complete and may not focus on rare or long-term outcomes. Various factors may also limit participation in clinical trials—including knowledge about clinical trials, perceived burden and personal benefit of participation, level of altruism, concerns about safety or being assigned to a less effective treatment, trust in healthcare providers, and access to transportation and health care—which may vary according to socioeconomic resources ( 2 – 9 ). Finally, evidence from RCTs may only be of very low, low, or moderate quality because of bias (e.g., low rates of completed follow-up visits, publication bias), lack of generalizability, imprecision, and inconsistency of evidence; in such cases, evidence from well-designed observational studies can be helpful in filling evidence gaps.

Potential Strengths and Limitations of RCTs and Observational Studies That Examine Causal Associations

Definition of abbreviation : RCT = randomized controlled trial.

In contrast to RCTs, observational (or “nonexperimental” or “nonrandomized”) studies are those in which individuals are observed for outcomes of interest, usually in the course of routine medical care. Although researchers make no attempt to actively assign patients to different treatments, such studies can also estimate causal effects. Situations in which a “dose response” can be observed and/or in which plausible residual confounding would be expected to attenuate the effect estimate can contribute to compelling evidence for causal inference ( 10 ). The Hill criteria are also used to help determine whether observed associations are causal ( 11 ). Observational studies comparing the effectiveness of treatment options using existing data (e.g., claims data) can generally be completed more quickly than RCTs. Thus, although observational studies are not without their own limitations, they can complement the shortcomings of RCTs. Observational studies also often capture more diverse patient populations and practice settings, allowing generalizability that even well-conducted RCTs rarely provide. Organizations like the Food and Drug Administration and the European Medicines Agency use population-based observational studies of real-world data to support regulatory decision-making.

The purpose of this research statement is to describe the ways in which observational studies and RCTs can be used to inform clinical decisions in pulmonary, critical care, and sleep medicine, with a specific focus on the role and inclusion of observational studies for informing practice guidelines. Although observational methods can be applied to answer a wide variety of questions (both descriptive and predictive), this research statement focuses on observational studies designed for the purpose of making causal inferences about the effects of interventions (treatments). It also focuses mainly on studies that use existing and/or secondary data—retrospectively or prospectively obtained—that were not collected to address the specific research question being studied. This includes studies comparing effectiveness of interventions (termed “comparative-effectiveness studies”). The current research statement will review strengths and weaknesses of observational studies, methods of evaluating observational study quality, and how observational studies align with pragmatic RCTs. This will provide context for the practical recommendations provided for the incorporation of observational studies into clinical practice guidelines. This document is intended for anyone who synthesizes evidence or who uses synthesized evidence and is concerned about how that synthesis is informed.

Participants in the Ad Hoc Committee

The ad hoc committee included ATS members and nonmembers of different gender and professional backgrounds with clinical expertise in pulmonary, critical care, and sleep medicine, as well as individuals with expertise in observational research study design, RCTs, pragmatic controlled trials, clinical practice guidelines, quality improvement, population health, behavioral health, epidemiology, health services research, patient-centered care, comparative-effectiveness research, implementation science, biostatistical methods, and health economics. A caregiver also participated. Two decision-makers provided comment. Potential conflicts of interest, including intellectual and financial conflicts, were disclosed and managed in accordance with the policies and procedures of the ATS.

Evidence Review and Discussion

Committee participants were divided into working groups that focused on the following areas: 1 ) the strengths of observational studies and how they complement RCTs—specifically how they address diversity and health equity; 2 ) perceptions of observational research; and, finally, 3 ) the practicalities of incorporating observational research into evidence syntheses for medical decision-making, specifically for ATS clinical practice guidelines.

Participants were provided with articles from a targeted literature search to facilitate discussions within the working groups. From February to May of 2018, each working group was tasked with summarizing the literature and formulating provisional conclusions and recommendations for further discussion at an in-person meeting on May 19, 2018, during the ATS International Conference in San Diego, California. At the meeting, co-chairs led discussions by the working groups. During the meeting, participants believed that because pragmatic RCTs share some similar features with observational studies, brief discussion of their utility should be included in the final statement. After the meeting, discussions to refine ideas continued through teleconferences.

Drafts of the research statement were written, revised, and circulated to all members of the committee to seek further feedback. Additional teleconferences were held, and suggestions were incorporated until consensus was reached among the committee members. Two policy-makers were also invited to provide comments. The revised draft was submitted to the ATS. The report underwent peer review and revision, with all committee members reviewing before finalization. The final version of the research statement was presented to the ATS Board of Directors for approval.

Strengths and Limitations of Observational Studies

Beyond generally taking less time and money to conduct, observational studies have a number of strengths, as outlined in Table 1 ( 12 ). They have the potential to provide high-quality evidence. They can complement evidence from efficacy and pragmatic RCTs when concerns exist about the representativeness of patient and provider experience or conditions under which the trial was conducted (e.g., intervention rigidly implemented or study cohort not representative of disease populations). Observational studies have the ability to incorporate experiences of vast numbers of patients treated across a wide range of real-world practice settings, including academic and community settings. Observational studies can also be used to assess interventions that would be unethical to study with an RCT because of lack of equipoise or prevailing restrictions and attitudes toward the risk of harms and/or potential benefits. Furthermore, they can address questions that are not under the control of the investigator, such as questions about genetic markers. Finally, the large sample sizes and extended follow-up periods typical of many observational studies provide the time and statistical power needed to identify rarer exposures or outcomes—like adverse effects or later-emerging benefits/harms that could affect clinical practice guideline recommendations.

Observational studies also have limitations that are important to recognize ( Table 1 ). First, without randomization, comparison groups—including those who have and have not received an intervention—are likely to differ in ways that are associated with the outcomes of interest. Although methods exist that can adjust for observed differences between treated and untreated individuals, it is challenging for them (although not impossible through methods such as instrumental variable analysis and Mendelian randomization) to account for unobserved differences or unmeasured confounders ( 12 , 13 ). Observational studies may also select patients for study entry or exposure classification in ways that cause spurious effect estimates, even in the absence of confounding. Some common examples of biases that are not due to confounding include collider-stratification bias induced by study entry based on an effect of the exposure of interest, immortal time bias arising from improper classification of time before exposure, and unintentional conditioning on effect mediators ( 14 , 15 ). Challenges to identifying, measuring, and adjusting for all potential confounders and accounting for all selection biases make observational studies more susceptible to bias than RCT designs. Second, many large observational studies rely on secondary data that were not collected for research purposes. Such data may lack desirable granular clinical detail—such as pulmonary-function and other test-result data—that would help identify patient characteristics or patient-reported outcomes, such as symptoms and quality of life. This could make these types of studies vulnerable to misclassification (if the investigators tried to categorize a variable that was not reliably measured because of lack of detail), unmeasured confounding, and conclusions based on outcomes that are not the most relevant to patients.

Observational studies have strengths and weaknesses that tend to complement those of RCTs. For example—as discussed above—whereas high-quality observational studies tend to have potential for stronger external validity, RCTs tend to have stronger internal validity. Other complementary features are shown in Table 1 . Thus, the best medical evidence on which to base clinical decisions is that which is determined to be of the highest quality in both designs.

Diversity and Health Equity

Because active participation is not required, and a waiver of informed consent can be obtained from research ethics boards, observational studies are more likely to include representative and diverse patient populations. With larger and more representative samples, observational studies provide an opportunity to explore treatment effect heterogeneity at multiple levels, including by sex, race/ethnicity, comorbidity, adherence to therapy, and access to care. Such information is fundamental to understand and overcome factors that contribute to health inequity.

In contrast to observational studies, RCTs often include participants who are not representative of the populations affected by the diseases they examine. Studies have shown that approximately 6% of patients with asthma ( 16 ) and 27% of patients with chronic obstructive pulmonary disease ( 17 , 18 ) meet eligibility criteria of contemporary RCTs in these areas. A recent systematic review found that African Americans—a group disproportionately at risk of asthma morbidity and mortality—were underrepresented in RCTs assessing adherence to asthma medications ( 19 ). Barriers that prevent people from being recruited and participating in RCTs, such as strict eligibility criteria ( 20 – 22 ), difficulties contacting potential participants or their surrogate decision-makers, large participant burden in terms of time and effort, beliefs about research, and need for informed consent ( 23 ), tend to disproportionately impact minorities, patients of lower socioeconomic status, and patients with mental health conditions or other comorbidities ( 24 ). Having limited access to healthcare providers who participate in clinical trials and structural racism and research abuses that have led people to, understandably, distrust the medical establishment also reduce the involvement of underrepresented groups in research ( 25 ). Although we must strive to overcome such obstacles in the conduct of RCTs, observational studies may overcome such barriers to participation by underrepresented groups.

Observational Study Quality

Weaknesses of observational studies may be mitigated with rigorous study design and the use of causal diagrams. Observational study methods that reduce confounding and strengthen causal inference have developed greatly in the past 15 years and can be conducted by knowledgeable researchers ( 12 , 26 , 27 ). One of these is targeted trial emulation, which is the application of design principles from randomized trials to the design and analysis of observational studies. This has been shown to help researchers identify and avoid unnecessary biases and provide a clear means for articulating the trade-offs that need to be made in observational studies ( 28 , 29 ). In addition, reports indicate that outcomes of high-quality observational studies and RCTs often do not differ ( 30 – 32 ). When assessing study quality, the individual merits of each observational study should be considered, rather than discounting studies simply because of their observational nature. Tools for assessing the quality of observational studies—including those that investigate causal inference ( 26 )—include the Newcastle-Ottawa Scale and ROBINS-I (Risk of Bias in Non-Randomized Studies–of Interventions) tool. The Newcastle-Ottawa Scale was developed to assess the quality of observational studies ( http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp ), and the ROBINS-I tool was developed to address the risk of bias in observational studies. Other guides also exist.

Common Perceptions Surrounding Observational Studies

Although observational studies are becoming better understood and more accepted by the scientific community, there is still some mistrust of their validity by clinicians that can lead to exclusion or discounting of their results during evidence syntheses and clinical decision-making ( 33 ). Such distrust has been perpetuated by generalizations that, although they might apply to some observational studies, are taken by some to be absolute. Table 2 outlines some of the perceptions surrounding observational studies.

Perceptions and Generalizations Surrounding Observational Research by Some in the Medical Community

Definition of abbreviations : GRADE = Grading of Recommendations Assessment, Development and Evaluation; RECORD = Reporting of Studies Conducted Using Observational Routinely-Collected Health Data; RCT = randomized controlled trial; STROBE = Strengthening the Reporting of Observational Studies in Epidemiology.

Pragmatic RCTs

Pragmatic RCTs occupy a space between traditional efficacy RCTs and observational studies. This trial design prioritizes design decisions such that study results may more closely mimic those observed in routine clinical conditions ( 34 ). Such design decisions include eligibility criteria that rely on data collected during routine care, research embedded in clinical practice, and some flexibility in intervention delivery. Although pragmatic RCTs attempt to reflect real-world circumstances, studies employing pragmatic trial designs often require informed consent and other forms of active patient participation (e.g., completing study questionnaires) that could limit the applicability of study results to real-world clinical populations. Observational studies can also evaluate interventions in real-world conditions. When done well, because they can include more people, they have the potential to address external validity in a way that pragmatic RCTs cannot.

Recommendations When Incorporating Observational Research into Clinical Practice Guidelines

According to the National Academy of Medicine, clinical practice guidelines make recommendations informed by the best available evidence identified by systematic reviews ( 35 ). It is imperative that clinical guidelines that influence the care of millions of people reflect evidence of best-known care. Appropriate inclusion of observational studies in evidence syntheses can contribute to this.

Currently, many guideline groups use a stepwise approach to identify the best evidence. RCTs are initially sought and, as a group, assessed for quality; if RCTs are identified and are determined to be of “sufficient” or “adequate” quality, they are used to inform a recommendation. What constitutes sufficient quality is left to the discretion of the guideline panel. If no RCTs are identified or if the available RCTs are determined to be of insufficient quality, then observational studies are sought. Observational studies of sufficient quality are first used to inform the recommendation. If no observational studies are identified or if the available observational studies are determined to be of insufficient quality, then indirect evidence is sought. Finally, if sufficient indirect evidence does not exist, then either no recommendation is made or uncontrolled studies or expert opinions (i.e., clinical knowledge and experience) are used to inform recommendations. This stepwise approach, which saves time and effort, means that observational studies are considered only on an as-needed basis, rather than being considered routinely.

Although this “as-needed” approach to the inclusion of observational studies in practice guidelines is practical and efficient, it means that the totality of evidence is often not used to inform patient care. Varying approaches used by other guideline developers may lead to the selection of different studies, resulting in inconsistent estimates of effects and different recommendations across guidelines. A more unified, widely accepted approach to the use of observational studies is desirable.

In 2005, the ATS adopted the GRADE ( 36 ) approach, a dynamic paradigm for appraising and summarizing evidence, as well as for formulating, writing, and grading recommendations ( 37 ). GRADE is also endorsed by the World Health Organization, Cochrane Collaboration, American College of Physicians, and other guideline-developing organizations ( http://gradeworkinggroup.org ). GRADE recognizes that study type is not the only indicator of study quality, as all study designs can be of variable quality. GRADE uses study design to make an initial assumption about the quality of evidence and then provides criteria that warrant upgrading the quality of a body of evidence (e.g., large magnitude of effect, dose–response gradient, plausible confounders contributing to opposite effect) or downgrading the quality of a body of evidence (e.g., risk of bias, indirectness, inconsistency, imprecision, and publication bias).

The GRADE working group is developing guidance for the use of observational studies in the development of clinical practice guidelines ( 10 , 38 , 39 ). An algorithm for including observational studies in the development of guidelines is provided in Figure 1 .

Algorithm for including observational research in medical decision-making. GRADE = Grading of Recommendations Assessment, Development and Evaluation.

An observational study can provide higher-quality evidence than an RCT. When no RCTs are identified or RCTs are appraised as being of low or very low quality, observational studies are sought; in this case, observational evidence is considered “ replacement ” evidence because it may substitute for RCT evidence or its quality may surpass RCT evidence ( 38 ). When moderate-quality RCT evidence exists, the justification for the moderate-quality rating should be reviewed. Observational studies may be sought when concerns such as indirectness, imprecision, or inconsistency have led to downgrading the RCT evidence from being of high quality to being of moderate quality; in this instance, observational evidence is considered “ complementary ” because it provides additional information.

Indirectness refers to situations in which the studies included patient populations, interventions, comparators, or outcomes that were different from those in the question posed by the guideline panel ( 36 ). Indirectness is often used to refer to a lack of generalizability. As an example, if a guideline panel asks about vaccinations in the elderly, but all relevant studies enrolled younger volunteers, indirectness of the population exists. Imprecision indicates that the confidence interval (CI) of the estimated effect is too wide to definitively answer the question asked by the guideline panel (i.e., the ends of the CI would lead to different clinical decisions). As an example, if a guideline panel decided a priori that a 5% mortality reduction is necessary to use a drug and the studies estimate that the drug reduces mortality by 7% with a 95% CI of 3–11%, then imprecision exists because at one end of the CI you would use the drug and at the other end you would not. Inconsistency exists if there is variability in the direction or magnitude of effect across studies; this determination may be subjective or may use the I 2 statistic or a P value for heterogeneity. Indirectness, imprecision, and inconsistency are the causes of downgrading evidence from having high quality to having moderate quality that warrant seeking complementary evidence because these are the limitations that may be overcome by observational evidence. As examples, consider the following: if RCTs are limited by indirectness, then observational studies that directly address the guideline question may be found; if RCTs are limited by imprecision, then large observational studies with narrow CIs may be found; and if RCTs are limited by inconsistency, then multiple consistent observational studies may be found. In contrast—although this is controversial—it is less certain that observational studies can overcome RCTs with risk of bias because observational studies, according to GRADE, also have a risk of bias.

Finally, observational studies may be sought when a guideline panel surmises that RCTs do not provide the best evidence for outcomes that the guideline committee considers essential for decision-making, such as when rare or long-term outcomes are judged as critical. In this situation, observational evidence is considered “ sequential ” because necessary information is not available from RCTs, so it must be obtained from observational studies. Sequential evidence and replacement evidence are frequently confused. Generally speaking, replacement evidence is from observational studies that are sought because there is no RCT evidence or very poor RCT evidence, whereas sequential evidence is sought because, although adequate quality RCT evidence exists, the RCT evidence may be incomplete or too narrow to be informative. As an example, if a guideline committee was addressing a bronchoscopic intervention for asthma, there might be high- or moderate-quality RCTs reporting short-term outcomes, but the guideline committee might also be interested in long-term outcomes not reported by the RCTs and, therefore, might seek observational studies as sequential evidence.

Observational evidence is unnecessary when RCT evidence that examines outcomes that the guideline committee considers essential for decision-making (i.e., critical outcomes) is appraised as being of high quality (i.e., observational evidence is not needed to replace, complement, or provide sequential evidence).

Recommendation Reevaluation

Our recommendations on when to integrate observational study evidence in clinical guidelines and evidence synthesis should be evaluated, and updates should be made as we learn about its benefits (e.g., how often observational evidence changes guideline recommendations). In these evaluations, consideration should be given to the evolution of the GRADE approach as well as to the added resources needed to search for and identify observational studies, review and assess their quality, and make decisions about their suitability for inclusion.

Observational research is important to guide medical decisions about guide patient care, programs, and policy. Its importance will likely grow as we seek knowledge to guide personalized medicine; as real-world data and real-world evidence are increasingly requested by decision-makers ( 40 ); as rich data sources from electronic medical records, for example, become more plentiful; as RCTs get more expensive; and as we increase our commitment to diversity and health equity. Quality clinical practice guidelines are instrumental in synthesizing evidence and making recommendations that improve health outcomes for millions of people around the world. The strongest medical evidence is often supported by both observational studies and RCTs; thus, both observational and randomized studies are key to informing decisions and maximizing the health of our patients.

Acknowledgments

This official research statement was prepared by an ad hoc subcommittee of the ATS Assembly on Behavioral Science and Health Services Research.

Members of the subcommittee are as follows:

A ndrea S . G ershon, M . D ., M . S c . 1 ( Co-Chair )

J erry A . K rishnan, M . D ., P h. D . 2 ( Co-Chair )

P eter K . L indenauer, M . D ., M . S c. 3 ( Co-Chair )

J oel J . A frick, A . B ., J . D. 4

K evin J . A nstrom, P h. D. 5

D avid H . A u, M . D ., M . S c . 6

B ruce G . B ender, M . D. 7

M . A lan B rookhart, P h. D. 8

R aed A . D weik, M . D. 9

M ei L an K . H an, M . D ., M . S. 10

M in J . J oo, M . D ., M . P . H. 2

V alery L avergne, M . D ., M . S c . 11

A nuj B . M ehta , M.D. 12

M arc M iravitlles , M.D. 13

R ichard A . M ularski , M.D., M.S.H.S, M.C.R. 14

E yal O ren, M.S., P h .D. 15

K ristin A . R iekert, P h. D. 16

N icolas R oche, M . D. 17

L ouise R ose, R . N ., B . N ., P h. D. 18

M ohsen S adatsafavi, M . D ., P h. D. 19

N oah C . S choenberg, M . D. 20

T herese A . S tukel, P h. D . 21,22

A llan J . W alkey, M . D ., M . S c. 23

C urtis H . W eiss, M . D ., M . S . 24

K evin C . W ilson, M . D . 23

H annah W unsch, M . D ., M . S c. 25,26

1 Department of Medicines, Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Ontario, Canada; 2 Division of Pulmonary, Critical Care, Sleep and Allergy, Department of Medicine, University of Illinois at Chicago, Chicago, Illinois; 3 Institute for Healthcare Delivery and Population Science, University of Massachusetts Medical School-Baystate, University of Massachusetts, Springfield, Massachusetts; 4 Respiratory Health Association, Chicago, Illinois; 5 Department of Biostatistics and Bioinformatics and 8 Department of Population Health Sciences, Duke University, Durham, North Carolina; 6 Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington; 7 Division of Pediatric Behavioral Health, Center for Health Promotion, and 12 Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, National Jewish Health, Denver, Colorado; 9 Respiratory Institute, Cleveland Clinic, Cleveland, Ohio; 10 Division of Pulmonary & Critical Care, University of Michigan, Ann Arbor, Michigan; 11 Department of Microbiology, Infectious Medicine, and Immunology, University of Montreal, Montreal, Quebec, Canada; 13 Department of Pneumology, Vall d'Hebron University Hospital, Barcelona, Spain; 14 Department of Pulmonary and Critical Care Medicine, Center for Health Research, Kaiser Permanente Northwest, Portland, Oregon; 15 Division of Epidemiology & Biostatistics, School of Public Health, San Diego State University, San Diego, California; 16 Division of Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine, John Hopkins University, Baltimore, Maryland; 17 Department of Respiratory Medicine, Cochin Hospital and Institute, APHP Center University, Paris, France; 18 Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King’s College London, London, United Kingdom; 19 Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada; 20 Department of Internal Medicine, Pulmonary and Critical Care Division, Beth Israel Deaconess Medical Center, Boston, Massachusetts; 21 ICES, Toronto, Ontario, Canada; 22 Institute of Health Policy, Management and Evaluation, 25 Department of Anesthesia, and 26 Interdepartmental Division of Critical Care, University of Toronto, Toronto, Ontario, Canada; 23 Division of Pulmonary, Allergy, Sleep, and Critical Care Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts; and 24 NorthShore University Health System, Evanston, Illinois

Acknowledgment

The authors thank Ms. Anne Hayes, Director, Research, Analysis and Evaluation Branch, Ontario Ministry of Health, and Ms. Sarah Burke Dimitrova, Lead, Quality Standards, Ontario Health, for their thoughtful comments and suggestions. They thank Dr. Carlos A. Cuello-Garcia, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada, for his guidance.

Supported by the American Thoracic Society.

T his official research statement of the A merican T horacic S ociety was approved S eptember 2020

Author Disclosures: J.A.K. served on a data safety and monitoring board for Sanofi; and received research support from Inogen, PCORI, National Institutes of Health, and ResMed. K.J.A. served on an advisory committee for Promedior; served as a consultant for AstraZeneca, Janssen, and Promedior; served on a data safety and monitoring board for Boehringer Ingelheim and Promedior; and received research support from Boehringer Ingelheim and Bristol Myers Squibb. D.H.A. served as a consultant for Gilead; served on a data safety and monitoring board for Novartis; and received research support from the American Lung Association. M.A.B. served on an advisory committee for AbbVie, Amgen, Atara Biotherapeutics, Brigham and Women’s Hospital, Merck, and Vertex; and has equity interest in NoviSci. M.K.H. served as a consultant for AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Merck, and Mylan; served on a data and safety monitoring board and as a speaker for Novartis; and received research support from Novartis and Sunovion. V.L. received research support from Fonds de Recherche du Québec–Santé and is employed as the Senior Methodologist and Editor for the department of Clinical Affairs and Practice Guideline. M.M. served as a consultant for AstraZeneca, Bial, Boehringer Ingelheim, Chiesi, CSL Behring, Ferrer, Gebro, GlaxoSmithKline, Grifols, Kamada, Laboratorios Esteve, Mereo, Novartis, pH Pharma, Sanofi, Spin Therapeutics, Teva, and Verona; served as a speaker for AstraZeneca, Bial, Boehringer Ingelheim, Chiesi, Cipla, CSL Behring, Grifols, Menarini, Novartis, Rovi, Sandoz, and Zambon; and received research support from GlaxoSmithKline and Grifols. R.A.M. received research support from GlaxoSmithKline. K.A.R. served on an advisory committee for Gilead; and received royalties from Springer Publishing. N.R. served on an advisory committee for AstraZeneca, Boehringer Ingelheim, Chiesi, Novartis, Sanofi, and Teva; served as a speaker for AstraZeneca, Chiesi, Novartis, Teva, and Zambon; received research support from Boehringer Ingelheim, Novartis, Pfizer, and Zambon; and received personal fees from GlaxoSmithKline and Pfizer. M.S. served on an advisory committee for AstraZeneca and GlaxoSmithKline; served as a speaker for Boehringer Ingelheim and Teva; and received research support from AstraZeneca and Boehringer Ingelheim. A.J.W. received royalties from UpToDate. C.H.W. served on an advisory committee and as a speaker for Mylan/Theravance; and received research support from the National Institutes of Health. K.C.W. is employed by the American Thoracic Society as Chief of Documents and Documents Editor with interest in the success of ATS guidelines. A.S.G., P.K.L., J.J.A., B.G.B., R.A.D., M.J.J., A.B.M., E.O., L.R., N.C.S., T.A.S., and H.W. reported no commercial or relevant noncommercial interests.