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1. Using the attached template, critically appraise the following article.

    Yu et al. Hot Tea Consumption and Its Interactions With Alcohol and Tobacco Use on the Risk for Esophageal Cancer: A Population-Based Cohort Study.

Week4-Assignment4a-4b-Rubric_Template x

Rubric for Article Critique Reports

Week

4

– Assignments 4a and 4b

3

3

3

3

3

3

3

5

3

5

5

5

5

5

3

3

3

2

5

3

5

3

5

Assignment 4 (indicate 4a or 4b)

Part

Question

Answer

Points

Title

1.Title of the article, journal name, your name

3

Purpose/Research problem

2 a. What is the purpose of the study? Is it clearly identified? Is the research problem important?

5

2b. Identify the dependent variable(s)

2c. Identify the independent variable(s)

Literature review

3a. Are the cited sources relevant to the study?

3b. Does the literature review offer a balanced critical analysis of the literature?

3c. Are the cited studies recent?

Theoretical framework*

4a. Has a conceptual or theoretical framework been identified?

4b. If yes, is the framework adequately described?

Design and procedures

5a. Identify the study design used in this study? Make sure that you select the exact type of design used, i.e., one of the three discussed this week.

5b. Is the study design appropriate to answer the research question?

5c. What type of sampling design was used?

5d. Was the sample size justified on the basis of a power analysis or other rationale?

5e. Are the inclusion and exclusion criteria clearly identified? What are they?

5f. What measurement tools were used for the dependent variable(s)?

5g. What measurement tools were used for the independent variable(s)?

5h. Were validity and reliability issues discussed?

4

Ethical considerations

6a. Were the participants fully informed about the nature of the research?

6b. Were the participants protected from harm?

2

6c. Was ethical permission granted for the study?

Data analysis

7a. What type of data and statistical analysis was undertaken?

7b. Was the statistical analysis appropriate to address the research question?

Results

8. What are the results of the study? Did the results answer the research question(s)?

Discussion

9a. Were the findings linked back to the literature review?

9b. Did the authors identify study limitations? What were they?

9c. Do you think the limitations are serious enough to impact the internal and external validity** of the study?

Overall assessment

10. What is your overall assessment of the study?

Total

100

Source: Coughlan M, Cronin P, Ryan F. Step-by-step guide to critiquing research. Part 1: quantitative research.
Br J Nurs. 2007;16(11):658-63.

* A conceptual or theoretical framework/model is a representation of a concept and the relationships between this concept and other variables that might impact it or be affected by it. It provides structure to a study and a rationale for the different relationships between the variables. Not every study has to have a conceptual or theoretical framework clearly outlined. The better research questions are usually the ones informed by theory and a corresponding framework. For an example, check the following article:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934012/

** The validity of a study, in contrast to the validity of measurements, is the degree to which study results are accurate and well-founded, when account is taken of study methods, representativeness of study sample, and nature of the population from which it is drawn.

· Internal validity (results are attributed to hypothesized effect and not sample differences)

· External validity (generalizability)

Rubric for Article Critique Reports

Week

4

– Assignments 4a and 4b

3

3

3

3

3

3

3

5

3

5

5

5

5

5

3

3

3

2

5

3

5

3

5

Assignment 4 (indicate 4a or 4b)

Part

Question

Answer

Points

Title

1.Title of the article, journal name, your name

3

Purpose/Research problem

2 a. What is the purpose of the study? Is it clearly identified? Is the research problem important?

5

2b. Identify the dependent variable(s)

2c. Identify the independent variable(s)

Literature review

3a. Are the cited sources relevant to the study?

3b. Does the literature review offer a balanced critical analysis of the literature?

3c. Are the cited studies recent?

Theoretical framework*

4a. Has a conceptual or theoretical framework been identified?

4b. If yes, is the framework adequately described?

Design and procedures

5a. Identify the study design used in this study? Make sure that you select the exact type of design used, i.e., one of the three discussed this week.

5b. Is the study design appropriate to answer the research question?

5c. What type of sampling design was used?

5d. Was the sample size justified on the basis of a power analysis or other rationale?

5e. Are the inclusion and exclusion criteria clearly identified? What are they?

5f. What measurement tools were used for the dependent variable(s)?

5g. What measurement tools were used for the independent variable(s)?

5h. Were validity and reliability issues discussed?

4

Ethical considerations

6a. Were the participants fully informed about the nature of the research?

6b. Were the participants protected from harm?

2

6c. Was ethical permission granted for the study?

Data analysis

7a. What type of data and statistical analysis was undertaken?

7b. Was the statistical analysis appropriate to address the research question?

Results

8. What are the results of the study? Did the results answer the research question(s)?

Discussion

9a. Were the findings linked back to the literature review?

9b. Did the authors identify study limitations? What were they?

9c. Do you think the limitations are serious enough to impact the internal and external validity** of the study?

Overall assessment

10. What is your overall assessment of the study?

Total

100

Source: Coughlan M, Cronin P, Ryan F. Step-by-step guide to critiquing research. Part 1: quantitative research.
Br J Nurs. 2007;16(11):658-63.

* A conceptual or theoretical framework/model is a representation of a concept and the relationships between this concept and other variables that might impact it or be affected by it. It provides structure to a study and a rationale for the different relationships between the variables. Not every study has to have a conceptual or theoretical framework clearly outlined. The better research questions are usually the ones informed by theory and a corresponding framework. For an example, check the following article:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934012/

** The validity of a study, in contrast to the validity of measurements, is the degree to which study results are accurate and well-founded, when account is taken of study methods, representativeness of study sample, and nature of the population from which it is drawn.

· Internal validity (results are attributed to hypothesized effect and not sample differences)

· External validity (generalizability)

N O N – E X P E R I M E N T A L S T U D Y D E S I G N S
P A R T 1 : C R O S S – S E C T I O N A L S T U D Y D E S I G N

6310-WEEK 4

LEARNING OBJECTIVES

• Describe different types of non- experimental study
designs

• Identify the strengths and weaknesses of non-
experimental study designs

• Critically appraise the strength of evidence in non-
experimental study designs

• Utilize major national health surveys to extract local
and regional data

NON-EXPERIMENTAL STUDY DESIGNS

• Non-experimental or observational study design:
Research design in which the investigators simply
observe subjects without making any interventions.

• Goals
• Descriptive: examine the distribution of predictors and

outcomes in a population
• Analytic: examine associations between predictor and

outcome variables

MAIN NON-EXPERIMENTAL STUDY
DESIGNS

• Cross-sectional study design
• Cohort study design
• Case control study design

• Other types
• Case reports
• Case series
• Natural history studies
• Ecological studies

CROSS-SECTIONAL STUDY
DESIGN

CROSS-SECTIONAL STUDY DESIGN

• Time frame:
• All measurements for each subject are taken at the same

time.
• No follow-up period.

• Goals
• Describe variables and their distribution patterns within a

sample.
• Estimate prevalence (the proportion who have a disease or

condition at one point in time)
• Examine associations but not the direction of the

relationship.

CROSS-SECTIONAL STUDY DESIGN

Defined
Population

Exposed
Have

Disease

Exposed
Do not Have

Disease

Not Exposed
Have Disease

Not Exposed
Do not Have

Disease

Collect data on exposure and disease/condition

Four groups are possible

STATISTICAL MEASURES
IN CROSS-SECTIONAL DESIGNS

• Prevalence: the proportion who have a disease or
condition at one point in time

• Prevalent cases are existing cases at a point in time.
This is in contrast to incident cases which are new
cases over a period of time.

• Prevalence = #of people with health outcome
# of people in study population

• Example: Using self-reported information from a
sample of U.S. adults in 2017, the Centers for Disease
Control and Prevention estimates obesity
prevalence in Texas at 33%.
• For obesity prevalence by state, click here.

http://www.cdc.gov/obesity/data/prevalence-maps.html

ADVANTAGES OF CROSS-SECTIONAL
STUDIES

• Provide descriptive information about prevalence
(the proportion who have a disease or condition at
one point in time), a unique feature of cross-
sectional studies

• Short duration, inexpensive, no problem with loss to
follow-up

• Good first step for a cohort or experimental study
providing baseline characteristics and/or revealing
cross-sectional associations of interest

DISADVANTAGES OF CROSS-
SECTIONAL STUDIES

• Difficult to establish causal relationships
• Not practical for rare conditions (need large sample

size)
• Potential survivor bias (no information about

patients who died)
• Potential bias in measuring predictors

SERIAL CROSS-SECTIONAL SURVEYS

• A series of cross-sectional studies in the same
population

• Used to draw inferences about changing patterns
over time

• Longitudinal time frame
• Different from cohort studies because a new sample is

drawn each time

EXAMPLES
CROSS-SECT IONAL STUDIES

NATIONAL HEALTH AND NUTRITION
EXAMINATION SURVEY (NHANES)

• NHANES is “a program of studies designed to assess the
health and nutritional status of adults and children in the
US.”

• It collects data through a combination of interviews and
physical examinations.
• The survey interview includes demographic, socioeconomic,

dietary, and health-related questions.
• The examination component consists of medical, dental, and

physiological measurements, as well as laboratory tests.
• Findings provide prevalence estimates of chronic

conditions and assess associations between nutritional
status and health promotion and disease prevention.

• For more information about NHANES, visit their website
available here.

http://www.cdc.gov/nchs/nhanes.htm

BEHAVIORAL RISK FACTOR
SURVEILLANCE SYSTEM (BRFSS)

• BRFSS is a serial cross-sectional telephone survey
designed to monitor state-level prevalence of the
major behavioral risks among adults associated with
premature morbidity and mortality.

• It is initiated and supported by the Centers for
Disease Control and Prevention (CDC) and
conducted by state health departments.

• Findings support planning and evaluating health
promotion and disease prevention programs.

• For more information about BRFSS, visit their website
available here.

http://www.cdc.gov/brfss

• 6310-Week 4
• Learning Objectives
• Non-Experimental Study Designs
• Main Non-Experimental Study Designs
• Cross-Sectional Study Design

Cross-Sectional Study Design

Cross-Sectional Study Design

• Statistical Measures�In Cross-Sectional Designs
• Advantages of Cross-Sectional Studies
• Disadvantages of Cross-Sectional Studies
• Serial Cross-Sectional Surveys
• Examples
• National Health and Nutrition Examination Survey (NHANES)
• Behavioral Risk Factor Surveillance System (BRFSS)

Hot Tea Consumption and Its Interactions With Alcohol and Tobacco
Use on the Risk for Esophageal Cancer
A Population-Based Cohort Study
Canqing Yu, PhD*; Haijing Tang, PhD*; Yu Guo, MSc; Zheng Bian, MSc; Ling Yang, PhD; Yiping Chen, DPhil; Aiyu Tang, MD;
Xue Zhou, PhD; Xu Yang, PhD; Junshi Chen, MD; Zhengming Chen, DPhil; Jun Lv, PhD; and Liming Li, MD, MPH; on behalf of th

e

China Kadoorie Biobank Collaborative Group†

Background: Although consumption of tea at high-
temperatures has been suggested as a risk factor for esophageal
cancer, an association has not been observed consistently, and
whether any relationship is independent of alcohol and tobacco
exposure has not been evaluated.

Objective: To examine whether high-temperature tea drinking,
along with the established risk factors of alcohol consumption
and smoking, is associated with esophageal cancer risk.

Design: China Kadoorie Biobank, a prospective cohort study
established during 2004 to 2008.

Setting: 10 areas across China.

Participants: 456 155 persons aged 30 to 79 years. Those who
had cancer at baseline or who reduced consumption of tea, al-
cohol, or tobacco before baseline were excluded.

Measurements: The usual temperature at which tea was con-
sumed, other tea consumption metrics, and lifestyle behaviors
were self-reported once, at baseline. Outcome was esophageal
cancer incidence up to 2015.

Results: During a median follow-up of 9.2 years, 1731 incident
esophageal cancer cases were documented. High-temperature
tea drinking combined with either alcohol consumption or smok-

ing was associated with a greater risk for esophageal cancer than
hot tea drinking alone. Compared with participants who drank
tea less than weekly and consumed fewer than 15 g of alcohol
daily, those who drank burning-hot tea and 15 g or more of
alcohol daily had the greatest risk for esophageal cancer (hazard
ratio [HR], 5.00 [95% CI, 3.64 to 6.88]). Likewise, the HR for cur-
rent smokers who drank burning-hot tea daily was 2.03 (CI, 1.55
to 2.67).

Limitation: Tea consumption was self-reported once, at base-
line, leading to potential nondifferential misclassification and at-
tenuation of the association.

Conclusion: Drinking tea at high temperatures is associated
with an increased risk for esophageal cancer when combined
with excessive alcohol or tobacco use.

Primary Funding Source: National Natural Science Foundation
of China and National Key Research and Development Program.

Ann Intern Med. 2018;168:489-497. doi:10.7326/M17-2000 Annals.org
For author affiliations, see end of text.
This article was published at Annals.org on 6 February 2018.
* Drs. Yu and Tang contributed equally to this work.
† The members of the China Kadoorie Biobank Collaborative Group and its
steering committee are listed in the Appendix (available at Annals.org).

Esophageal cancer remains a global concern be-
cause of its increasing incidence and persistently

poor survival rates (1, 2). It poses a bigger threat to
less-developed regions and to men. Both alcohol con-
sumption and tobacco smoking are well-established
causes of esophageal squamous cell cancer (ESCC) (3),
the most common histologic subtype globally (2). Lim-
ited evidence suggests that the risk for ESCC decreases
with greater intake of vegetables and fruits and an in-
crease in physical activity and rises with higher con-
sumption of processed meat (3).

Tea, one of the most common beverages world-
wide, usually is consumed at elevated temperatures.
Existing evidence remains inconclusive regarding
whether hot tea drinking is associated with esophageal
cancer risk. Although several studies have demon-
strated inhibitory effects of tea against tumorigenesis in
the digestive tract (4), chronic thermal injury to the
esophageal mucosa may initiate carcinogenesis. The
International Agency for Research on Cancer recently
classified the intake of scalding beverages (>65 °C) as
“probably carcinogenic to humans” (3, 5). A few epide-
miologic studies addressed the association between
esophageal cancer and tea drinking with regard to fre-
quency, amount consumed, or tea temperature, with

substantially conflicting results (6–12). Except for a few
prospective investigations with limited incident cases
(13–16), the vast majority of studies followed a case–
control design. Such studies are particularly vulnerable
to several biases, including recall bias and reverse cau-
sality due to participants changing their drinking habits
after symptoms develop or they receive an esophageal
cancer diagnosis.

China is among the countries with the highest inci-
dence of esophageal cancer. Tea drinkers, especially
Chinese men, are more likely to smoke and to drink
alcohol. Tobacco smoking and alcohol consumption, as
well as the chemical compounds and adverse thermal
effect of hot tea, considerably complicate the associa-
tion between tea drinking and esophageal cancer risk.
In the China Kadoorie Biobank (CKB) study of 0.5 mil-

See also:

Editorial comment . . . . . . . . . . . . . . . . . . . . . . . . . 519
Summary for Patients . . . . . . . . . . . . . . . . . . . . . . . I-22

Web-Only
Supplement

Annals of Internal Medicine ORIGINAL RESEARCH

© 2018 American College of Physicians 489
This article has been corrected. The specific correction appears on the last page of this document. The original version (PDF) is available at Annals.org.

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lion adults, we prospectively examined the joint associ-
ation of tea-drinking metrics, especially beverage tem-
perature, and the established risk factors of smoking
and alcohol consumption with esophageal cancer risk.

METHODS
Study Population

The CKB cohort was established in 10 study re-
gions throughout China, including 5 urban and 5 rural
areas. From 2004 to 2008, the study enrolled 512 891
adults aged 30 to 79 years with valid baseline data,
including a completed questionnaire, physical mea-
surements, and written informed consent. Trained staff
entered baseline information directly into a laptop-
based data entry system developed with built-in func-
tions to avoid missing items and to minimize logic er-
rors during the interview. A more detailed description
is available elsewhere (17, 18). The Ethical Review
Committee of the Chinese Center for Disease Control
and Prevention (Beijing, China) and the Oxford Tropical
Research Ethics Committee, University of Oxford
(United Kingdom) approved the study.

For the present analysis, we excluded persons with
previously diagnosed cancer (n = 2577) and those who
had missing data for body mass index (n = 2) or were
lost to follow-up shortly after baseline (n = 1). To avoid
potential reverse causality, we further excluded per-

sons who reduced their tea (n = 11 578) or alcohol (n =
20 952) intake from at least weekly to less than weekly
and former smokers (n = 30 563) who had stopped
smoking 6 or more months ago. The final analysis in-
cluded 456 155 participants.

Assessment of Tea Consumption
Our baseline questionnaire asked participants to

report their usual frequency of tea drinking (never, only
occasionally, only at certain seasons, monthly but less
than weekly, or at least once a week) during the past 12
months. Those who drank tea less than weekly were
asked whether they had ever consumed tea weekly for
at least 1 year. Participants who reported weekly con-
sumption were asked how many days in a typical week
they drank tea (1 to 2, 3 to 5, or 6 to 7 days), how many
(300-mL) cups they consumed in 1 drinking day, the
volume of tea leaves (in grams) they added each time,
how many times they changed the leaves in 1 drinking
day, the type of tea they drank most commonly (for
example, green, oolong, or black tea), the usual tem-
perature of the tea (room temperature or warm, hot, or
burning hot), and the age at which they started drinking
tea weekly. We provided the participants with a picto-
rial guide showing a standard-sized cup and different
quantities of tea leaves in grams. The quantity of leaves
added in 1 drinking day was calculated by multiplying
the weight (in grams) of the leaves added each time by

Table 1. HRs (95% CIs) for Esophageal Cancer According to Tea Temperature Preference

Variable Less
Than
Weekly

Weekly Daily P for Trend

Warm Hot Burning Hot All* Daily†

Men (n � 164 531)
Esophageal cancer cases, n 648 60 106 188 104
PYs 697 248 151 440 209 462 290 694 118 899
Cases per 1000 PYs, n 0.93 0.40 0.51 0.65 0.87
HR (95% CI)

Age adjusted 1.00 1.04 (0.78–1.38) 1.44 (1.13–1.83) 1.60 (1.31–1.96) 1.97 (1.52–2.57) <0.001 0.058 Multivariable adjusted‡ 1.00 1.06 (0.79–1.41) 1.50 (1.17–1.92) 1.62 (1.32–1.99) 1.93 (1.48–2.52) <0.001 0.117

Further adjusted for
tobacco smoking§

1.00 1.01 (0.76–1.34) 1.38 (1.08–1.77) 1.49 (1.21–1.83) 1.75 (1.34–2.28) <0.001 0.153

Further adjusted for
alcohol consumption��

1.00 0.93 (0.70–1.24) 1.17 (0.91–1.50) 1.30 (1.05–1.59) 1.55 (1.19–2.02) <0.001 0.10

0

Women (n � 291 624)
Esophageal cancer cases, n 583 5 14 16 7
PYs 2 083 035 146 512 204 331 174 264 50 476
Cases per 1000 PYs, n 0.28 0.03 0.07 0.09 0.14
HR (95% CI)

Age adjusted 1.00 0.48 (0.20–1.17) 0.97 (0.51–1.85) 1.21 (0.69–2.10) 1.08 (0.49–2.38) 0.73 0.54
Multivariable adjusted‡ 1.00 0.51 (0.21–1.25) 1.03 (0.53–1.98) 1.29 (0.74–2.26) 1.12 (0.51–2.48) 0.55 0.60

Further adjusted for
tobacco smoking§

1.00 0.52 (0.21–1.27) 1.05 (0.54–2.02) 1.31 (0.75–2.30) 1.14 (0.52–2.52) 0.50 0.58

Further adjusted for
alcohol consumption��

1.00 0.52 (0.21–1.27) 1.04 (0.54–2.02) 1.30 (0.74–2.29) 1.13 (0.51–2.51) 0.51 0.57

HR = hazard ratio; PY = person-year.
* Calculated by assigning consecutive integers to 5 tea consumption categories.
† Restricted to daily tea consumers and calculated by assigning consecutive integers to 3 tea consumption categories.
‡ Adjusted for age (in years); education (no formal school, primary school, middle school, high school, or college or university or higher); marital
status (married, widowed, divorced/separated, or never married); household income (<2500, 2500–4999, 5000–9999, 10 000–19 999, 20 000– 34 999, or ≥35 000 Chinese renminbi/y); physical activity (in metabolic equivalent of task-hours daily); intake of red meat, fresh fruits and vegeta- bles, and preserved vegetables (in days per week, calculated by assigning participants to the midpoint of their intake category); body mass index (in kg/m2); family history of cancer (presence or absence); and menopausal status (premenopausal, perimenopausal, or postmenopausal [for women only]). § Nonsmokers or current smokers of 1–9, 10–19, 20–29, or ≥30 cigarettes or equivalents per day. �� Less than weekly; weekly; or <15, 15–29, 30–59, or ≥60 g/d of pure alcohol.

ORIGINAL RESEARCH Hot Tea, Alcohol, and Tobacco on the Risk for Esophageal Cancer

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the number of times the leaves were changed. Among
4405 weekly tea drinkers who completed the same
questionnaire twice, at an average interval of 2.6 years
between the baseline and subsequent survey (17), the
Spearman correlation coefficient was 0.35 for tea tem-
perature preference, 0.53 for cups of tea consumed,
and 0.63 for tea leaves added.

Assessment of Tobacco Smoking, Alcohol
Consumption, and Other Covariates

We asked ever-smokers how many times a day they
smoked, along with the type and amount of tobacco
used (in cigarettes or an equivalent amount of tobacco:
1 cigarette = 1 g of tobacco = 0.5 cigars) (19); we asked
former smokers how long it had been since they quit.
Participants who reported that they had stopped smok-
ing less than 6 months earlier were considered current
smokers and included in the present analysis. We
asked participants who used alcohol at least once a
week in the past 12 months how often they typically
drank, the type of alcoholic beverages they consumed
habitually, and the amount of alcohol they consumed
on a typical drinking day. On the basis of this informa-
tion, we calculated pure alcohol intake in grams on a
typical drinking day (20). We also asked participants
who drank alcohol less than weekly whether they had
ever consumed alcohol weekly for at least 1 year.

The baseline questionnaire also included other
covariates, such as sociodemographic characteristics
(age, sex, education, marital status, and household in-
come), lifestyle behaviors (physical activity and intakes
of red meat, fresh fruits and vegetables, and preserved

vegetables), menopausal status (for women), and family
history of cancer. Trained staff measured weight and
height using calibrated instruments.

Ascertainment of Esophageal Cancer Cases
We ascertained incident esophageal cancer cases

among participants from the time they enrolled in the
study by linking to local disease and death registries
and to the national health insurance system, as well as
by active follow-up (17). Trained staff, blinded to the
baseline information, coded all cases using the Interna-
tional Classification of Diseases, 10th Revision. For the
present analysis, esophageal cancer cases were de-
fined by code C15. In the CKB study, retrieval of med-
ical records from participants with incident cases is
ongoing. Trained staff review medical records for diag-
nosis validation and collect additional clinical informa-
tion, such as pathology subtype. So far, we retrieved
medical records pertaining to 870 esophageal cancer
cases reported during follow-up; of these cases, 843
(96.9%) were confirmed as esophageal cancer, 569
(65.4%) of which had pathology reports. After 37 cases
with “unknown” subtype were excluded, 91.9% of the
cases (489 of 532) were ESCC.

Statistical Analysis
We calculated person-years at risk from the base-

line date to diagnosis of esophageal cancer, death, loss
to follow-up, or 31 December 2015, whichever oc-
curred first. In the CKB study, loss to follow-up refers to
participants who moved their permanent registered
residence out of the jurisdiction of the Regional Coor-

Table 2. HRs (95% CIs) for Esophageal Cancer in Relation to Tea Temperature Preference, by Alcohol Consumption
(n = 456 155)

Variable Less Than Weekly Weekly Daily P for Trend

Warm Hot Burning Hot All* Daily†

Less than daily alcohol consumption
or <15 g/d of pure alcohol

Esophageal cancer cases, n 1095 32 43 79 61
PYs 2 665 904 264 453 353 163 372 308 140 262
Cases per 1000 PYs, n 0.41 0.12 0.12 0.21 0.43
HR (95% CI)‡

1§ 1.00 0.97 (0.67–1.39) 1.17 (0.81–1.67) 1.41 (1.09–1.84) 1.42 (1.04–1.95) 0.003 0.78
2�� 1.00 0.82 (0.57–1.18) 0.92 (0.66–1.30) 1.23 (0.96–1.59) 1.36 (1.00–1.86)

>15 g/d of pure alcohol
Esophageal cancer cases, n 136 33 77 125 50
PYs 114 379 33 498 60 630 92 651 29 113
Cases per 1000 PYs, n 1.19 0.99 1.27 1.35 1.72
HR (95% CI)‡

1§ 1.00 0.99 (0.66–1.47) 1.37 (1.00–1.88) 1.54 (1.16–2.04) 2.16 (1.49–3.14) <0.001 0.065 2�� 1.90 (1.57–2.31) 2.60 (1.79–3.76) 3.74 (2.86–4.90) 3.84 (3.06–4.83) 5.00 (3.64–6.88)

HR = hazard ratio; PY = person-year.
* Calculated by assigning consecutive integers to 5 tea consumption categories.
† Restricted to daily tea consumers and calculated by assigning consecutive integers to 3 tea consumption categories.
‡ Multivariable model was adjusted for age (in years); sex (male or female); education (no formal school, primary school, middle school, high school,
or college or university or higher); marital status (married, widowed, divorced/separated, or never married); household income (<2500, 2500–4999, 5000–9999, 10 000–19 999, 20 000–34 999, or ≥35 000 Chinese renminbi/y); physical activity (metabolic equivalent of task-hours daily); intake of red meat, fresh fruits and vegetables, and preserved vegetables (in days per week, calculated by assigning participants to the midpoint of their intake category); body mass index (kg/m2); family history of cancer (presence or absence); and tobacco smoking (nonsmokers or current smokers of 1–9, 10–19, 20–29, or ≥30 cigarettes or equivalents per day). § Calculated within strata of alcohol consumption, with participants who consumed tea less than weekly as the reference category. �� Calculated with participants who consumed tea less than weekly and consumed <15 g/d of pure alcohol as the reference category.

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dinating Center. By 31 December 2015, of the 512 891
participants, 37 289 (7.3%) had died and 4875 (<1%) were lost to follow-up. We used a Cox proportional hazards model to estimate the hazard ratio (HR) and 95% CI, with age as the underlying time scale and strat- ified jointly by 10 study areas and age at baseline in 5-year intervals. A test and graph based on Schoenfeld residuals showed that the proportional hazards as- sumption was satisfied.

Multivariable models were adjusted for age; sex;
education; marital status; household income; tobacco
smoking; alcohol consumption; physical activity; in-
takes of red meat, fresh fruits and vegetables, and pre-
served vegetables; body mass index; family history of
cancer; and menopausal status (for women). Forty
women had missing data for menopausal status; we in-
cluded an indicator for the missing-data category. No
data were missing for the other variables. We tested
the linear trend of esophageal cancer risk across vari-
ous metrics of tea consumption by modeling the levels
of ordered categorical variables as a continuous vari-
able in a separate model.

We examined whether the association between tea
temperature preference and esophageal cancer risk
differed by tobacco smoking or alcohol consumption.
We tested multiplicative interaction by using a likeli-
hood ratio test comparing models with and without
cross-product terms. We also plotted the covariate-
adjusted cumulative incidences of esophageal cancer
on the basis of the Cox model for participants with dif-
ferent combinations of tea-drinking temperature, to-
bacco use, and alcohol consumption separately, ac-
counting for death as a competing risk. Specifically,
after fitting the competing risks regression model

(stcrreg procedure), we used the stcurve procedure for
plotting (21).

We performed all statistical analyses using Stata,
version 14.2 (StataCorp).

Role of the Funding Source
The funders had no role in the study design, data

collection, data analysis and interpretation, writing of
the report, or decision to submit the manuscript for
publication.

RESULTS
Participants in the present analysis had a mean age

of 50.9 ± 10.5 years. Of 456 155 participants, 42.1% of
the men and 16.1% of the women drank tea daily. Both
men and women who reported preferring burning-hot
tea were more likely to be current smokers, consume
alcohol daily, drink more cups of tea, and add more
tea leaves per day (Supplement Table 1, available at
Annals.org).

Tea Temperature Preference, Other Metrics of
Tea Consumption, and Esophageal Cancer

During a median follow-up of 9.2 years (4.1 million
person-years), we documented 1106 incident esopha-
geal cancer cases in men and 625 in women. In the
multivariable-adjusted model of the male participants,
daily tea consumption was associated with increased
esophageal cancer risk, with greater risk seen in those
who said they preferred hotter tea (Table 1). The asso-
ciation exhibited a clear attenuation after further adjust-
ment for tobacco smoking and alcohol consumption.

Table 3. HRs (95% CIs) for Esophageal Cancer in Relation to Tea Temperature Preference, by Tobacco Smoking (n = 456 155)

Variable Less Than Weekly Weekly Daily P for Trend

Warm Hot Burning Hot All* Daily†

Nonsmokers
Esophageal cancer cases, n 781 10 28 32 15
PYs 2 316 170 186 506 248 152 226 924 63 446
Cases per 1000 PYs, n 0.34 0.05 0.11 0.14 0.24
HR (95% CI)‡

1§ 1.00 0.62 (0.33–1.18) 1.42 (0.91–2.22) 1.34 (0.90–1.99) 1.49 (0.86–2.61) 0.044 0.85
2�� 1.00 0.48 (0.25–0.90) 1.02 (0.68–1.52) 1.00 (0.69–1.45) 1.21 (0.71–2.06)

Current smokers
Esophageal cancer cases, n 450 55 92 172 96
PYs 464 113 111 445 165 642 238 034 105 928
Cases per 1000 PYs, n 0.97 0.49 0.56 0.72 0.91
HR (95% CI)‡

1§ 1.00 0.94 (0.69–1.28) 1.09 (0.83–1.42) 1.30 (1.04–1.62) 1.53 (1.15–2.03) 0.001 0.038
2�� 1.04 (0.89–1.21) 1.28 (0.94–1.75) 1.52 (1.16–1.99) 1.76 (1.41–2.21) 2.03 (1.55–2.67)

HR = hazard ratio; PY = person-year.
* Calculated by assigning consecutive integers to 5 tea consumption categories.
† Restricted to daily tea consumers and calculated by assigning consecutive integers to 3 tea consumption categories.
‡ Multivariable model was adjusted for age (in years); sex (male or female); education (no formal school, primary school, middle school, high school,
or college or university or higher); marital status (married, widowed, divorced/separated, or never married); household income (<2500, 2500–4999, 5000–9999, 10 000–19 999, 20 000–34 999, or ≥35 000 Chinese renminbi/y); physical activity (in metabolic equivalent of task-hours daily); intake of red meat, fresh fruits and vegetables, and preserved vegetables (in days per week, calculated by assigning participants to the midpoint of their intake category); body mass index (in kg/m2); family history of cancer (presence or absence); and alcohol consumption (less than weekly; weekly; or <15, 15–29, 30–59, or ≥60 g/d of pure alcohol). § Calculated within strata of smoking, with participants who consumed tea less than weekly as the reference category. �� Calculated with participants who consumed tea less than weekly and did not smoke as the reference category.

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Compared with men who drank tea less than weekly,
the HRs for esophageal cancer were 1.17 (95% CI, 0.91
to 1.50), 1.30 (CI, 1.05 to 1.59), and 1.55 (CI, 1.19 to
2.02) for daily tea drinkers who preferred their bever-
age warm, hot, and burning hot, respectively. No statis-
tically significant association was found between tea
temperature and esophageal cancer in women (P < 0.001 for interaction with sex).

We also observed a statistically significant increase
in men’s esophageal cancer risk with more cups of tea
consumed, more tea leaves added, a longer duration
of tea consumption, and green tea drinking (all P < 0.001 for interaction with sex) (Supplement Tables 2 to 5, available at Annals.org). Information on various metrics of tea consumption was available only for regular tea drinkers; therefore, we made further adjust- ments for tea temperature preference and other met- rics of tea consumption mutually among men who drank tea daily. After we mutually adjusted the metrics for one another, we found no statistically significant association between the number of cups consumed per day, volume of tea leaves added, duration of tea

drinking, type of tea consumed, or beverage tempera-
ture and the risk for esophageal cancer in men who
drank tea daily (Supplement Table 6, available at
Annals.org).

Association of Tea Temperature Preference
and Alcohol Consumption or Smoking With
Esophageal Cancer Risk

We observed important differences in the associa-
tion between tea temperature preference and esopha-
geal cancer risk across the stratum for alcohol con-
sumption (P < 0.001 for interaction) (Table 2) or tobacco smoking (P = 0.001 for interaction) (Table 3), with a stronger association in participants who drank 15 g or more of alcohol per day and in current smokers. Compared with participants who consumed tea less than weekly and drank less than 15 g of alcohol daily, those who drank burning-hot tea and 15 g or more of alcohol per day had the greatest risk for esophageal cancer (HR, 5.00 [CI, 3.64 to 6.88]). Likewise, the HR for current smokers who drank burning-hot tea daily was 2.03 (CI, 1.55 to 2.67).

Figure. Adjusted cumulative incidences of esophageal cancer for participants with different combinations of tea, alcohol, and
tobacco consumption.

30
0

0.01

C
um

ul
at

iv
e

In
ci

de
nc

e

Less than daily
Daily, warm
Daily, hot
Daily, burning hot

0.02

0.03

50 70 90

Tea-Drinking Frequency, Temperature

Age, y
30

0

0.01

C
um

ul
at

iv
e

In
ci

de
nc

e

0.02

0.03

50 70 90
Age, y

30
0

0.01

C
um

ul
at

iv
e

In
ci

de
nc

e

0.02

0.03

50 70 90
Age, y

30
0

0.01

C
um

ul
at

iv
e

In
ci

de
nc

e

0.02

0.03

50 70 90
Age, y

A. Tobacco−, Alcohol− C. Tobacco−, Alcohol+

B. Tobacco+, Alcohol− D. Tobacco+, Alcohol+

A. Nonsmokers who drank alcohol less than daily or drank fewer than 15 g of alcohol per day. B. Current smokers who drank alcohol less than daily
or drank fewer than 15 g of alcohol per day. C. Nonsmokers who drank 15 g or more of alcohol per day. D. Current smokers who drank 15 g or more
of alcohol per day. Nonsmokers who drank tea less than daily and alcohol less than daily or drank fewer than 15 g of alcohol per day were the
reference group. The multivariable model was adjusted for age; sex; study area; education; marital status; household income; physical activity;
intakes of red meat, fresh fruits and vegetables, and preserved vegetables; body mass index; and family history of cancer.

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Joint Association of Tea Temperature
Preference, Alcohol Consumption, and
Smoking With Esophageal Cancer

We further examined the joint association of the 3
factors (tea temperature, alcohol consumption, and
smoking) on esophageal cancer risk. The Figure shows
the adjusted cumulative incidences of esophageal can-
cer by combined categories of tea temperature, to-
bacco use, and alcohol consumption. In the absence of
smoking and excessive alcohol consumption, daily tea
drinking was not associated with an increased risk for
esophageal cancer, regardless of tea temperature (Ta-
ble 4) or other consumption metrics (Supplement Ta-
ble 7, available at Annals.org). The esophageal cancer
risk increased for daily tea drinkers who preferred hot
or burning-hot tea in the presence of either smoking
(HR, 1.56 [CI, 1.21 to 2.02]) or excessive alcohol con-
sumption (HR, 2.27 [CI, 1.16 to 4.45]). The strongest
association was seen in the combination of daily high-
temperature tea drinking with both smoking and exces-
sive alcohol consumption (HR, 5.01 [CI, 4.00 to 6.28]).
Other metrics of tea drinking showed less clear pat-
terns of joint association with smoking and excessive
alcohol consumption.

Sensitivity Analysis (Data Not Shown)
The association between tea temperature prefer-

ence and esophageal cancer risk persisted after exclu-
sion of patients who received an esophageal cancer
diagnosis during the first 2 (n = 339) or 4 (n = 706)
years of follow-up. The results also did not change ap-
preciably when we tried more comprehensive adjust-
ments for tobacco use by using pack-years and for
alcohol consumption by adding years of alcohol con-
sumption. When we used competing risk regression to
account for the competing risk for death, the subhazard
ratios obtained in the competing risk regression were
similar when compared with the HRs obtained in the
Cox regression.

DISCUSSION
In this large prospective Chinese cohort, we found

that the association between high-temperature tea
drinking and esophageal cancer risk was dependent on
alcohol and tobacco consumption. A synergistic asso-
ciation was found between hot tea drinking with exces-
sive alcohol consumption or smoking and the risk
for esophageal cancer. Participants who drank high-
temperature tea, consumed alcohol excessively, and
smoked had an esophageal cancer risk more than 5
times greater than those who had none of those 3 hab-
its. However, in the absence of both excessive alcohol
consumption and smoking, daily tea drinking was not
associated with esophageal cancer risk, regardless of
tea temperature or other consumption metrics.

A few prospective studies examined the associa-
tion between esophageal cancer risk and the tempera-
ture (13) or amount (14–16) of tea consumed and
showed inconsistent findings. Several systematic re-
views and meta-analyses of principally case–control
studies suggested no clear pattern of association be-
tween the amount of tea consumed and esophageal
cancer risk, although higher-temperature tea drinking
has been associated with increased esophageal cancer
risk (6–12). Previous studies also found an association
between consumption of hot food and beverages and
the risk for ESCC but not esophageal adenocarcinoma
(12, 22). In the current Chinese population, in which
ESCC is the predominant histologic subtype, we ob-
served that high-temperature tea drinking was pro-
spectively associated with esophageal cancer risk in the
presence of excessive alcohol consumption or smok-
ing; however, we found no such association in the ab-
sence of both habits.

The synergistic effects of high-temperature tea
drinking, excessive alcohol consumption, and tobacco
use on esophageal cancer risk are biologically plausi-
ble. Research has suggested that thermal injury may

Table 4. HRs (95% CIs) for Joint Associations Among Tea Temperature Preference, Tobacco Smoking, and Alcohol
Consumption and Risk for Esophageal Cancer (n = 456 155)

Tea Temperature Preference Consumption of >15 g/d of Pure Alcohol

No Yes

Esophageal
Cancer Cases, n

Cases per
1000 PYs, n

HR (95% CI)* Esophageal
Cancer Cases, n

Cases per
1000 PYs, n

HR (95% CI)*

Nonsmokers
Less than daily 775 0.31 1.00 16 0.45 1.14 (0.69–1.89)
Daily, warm 23 0.10 1.00 (0.64–1.56) 5 0.59 2.28 (0.94–5.56)
Daily, hot/burning hot 38 0.14 1.05 (0.74–1.48) 9 0.62 2.27 (1.16–4.45)

Current smokers
Less than daily 352 0.76 1.03 (0.88–1.21) 153 1.36 2.47 (2.01–3.04)
Daily, warm 20 0.18 0.93 (0.58–1.50) 72 1.38 4.59 (3.45–6.10)
Daily, hot/burning hot 102 0.43 1.56 (1.21–2.02) 166 1.55 5.01 (4.00–6.28)

HR = hazard ratio; PY = person-year.
* Multivariable model was adjusted for age (in years); sex (male or female); education (no formal school, primary school, middle school, high school,
or college or university or higher); marital status (married, widowed, divorced/separated, or never married); household income (<2500, 2500–4999, 5000–9999, 10 000–19 999, 20 000–34 999, or ≥35 000 Chinese renminbi/y); physical activity (in metabolic equivalent of task-hours daily); intake of red meat, fresh fruits and vegetables, and preserved vegetables (in days per week, calculated by assigning participants to the midpoint of their intake category); body mass index (in kg/m2); and family history of cancer (presence or absence).

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damage the epithelium and impair barrier function,
subsequently augmenting the risk for damage from
other risk factors, such as excessive alcohol consump-
tion and smoking (22). The release of N-nitroso com-
pounds, which are formed as a result of inflammatory
processes associated with chronic thermal irritation of
the esophageal mucosa, also may contribute to esoph-
ageal cancer development (22, 23). The mechanism for
the joint association of hot tea drinking, excessive alco-
hol consumption, and tobacco use with esophageal
cancer warrants further elucidation.

Various tea-drinking metrics were interrelated in
the population studied, making it difficult to identify the
most relevant metric in relation to esophageal cancer
risk. The present study found evidence of increased
esophageal cancer risk with higher tea temperatures.
Additional studies are needed to confirm our findings.

In contrast to the strong and relatively consistent
evidence from experimental studies showing potential
anticarcinogenic properties of bioactive compounds in
tea (4), results from epidemiologic studies have not
convincingly demonstrated that tea drinking has pre-
ventive effects against esophageal cancer in humans
(6–12). Most of the inverse associations between tea
drinking and esophageal cancer risk were found in
case–control studies in East Asian countries, especially
China. The protective effects of tea drinking, if any, on
esophageal cancer development were thought to be
overshadowed by alcohol consumption and smoking
as well as the thermal effect of hot tea (24). However, in
the present population, when we restricted our analy-
ses to the subgroup of nonsmokers who were not
heavy alcohol users, daily tea drinking, regardless of
consumption metrics or tea type, was not associated
with a decreased risk for esophageal cancer. In an early
randomized controlled trial with 200 participants from
Henan, China, decaffeinated green tea did not show a
benefit in alleviating precancerous lesions or abnormal
cell proliferation patterns after 11 years of follow-up
(25). A nested case–control study in a cohort of men
from Shanghai also found no association between uri-
nary biomarkers of tea polyphenol and the risk for
esophageal cancer (26). The reason for the difference
between the results from animal research and those
from human studies with high-quality evidence is prob-
ably that humans were exposed to tea polyphenols at
levels 1 to 2 orders of magnitude lower than those used
in animal studies (4).

To the best of our knowledge, this study is the first
to provide compelling evidence of joint associations
between high-temperature tea drinking with estab-
lished risk factors of excessive alcohol and tobacco use
and esophageal cancer risk. Strengths of the study in-
clude its prospective design, a population geographi-
cally spread across urban and rural China, and careful
adjustment for potential confounders. To avoid reverse
causality bias, we excluded persons who had reduced
their consumption of tea, alcohol, or tobacco and, sub-
sequently, participants who received an esophageal
cancer diagnosis during the first several years of follow-
up. The CKB study collected detailed information on

several metrics of tea consumption, including fre-
quency, amount, duration, type of tea, and qualitative
gradation of tea temperature, allowing us to analyze
them comprehensively and to mutually adjust them for
one another.

Some limitations also warrant mention. Tea-
drinking patterns were self-reported and collected
once, at baseline, although consumption habits may
change over time. Nevertheless, misclassification of ex-
posure may have been nondifferential with regard to
subsequent disease status, attenuating our findings.
Tea temperatures relied on qualitative self-report data
and were not validated by actual measurement. We did
not ask participants about sip size, which together with
initial tea temperature, determines intraesophageal
temperature (27); this may have led to differences in
subjective perception of temperature. Obtaining a
valid and reliable estimate of the temperature at which
study participants typically drink tea is a challenge for
most epidemiologic research. The present study lacked
information regarding the histologic subtype of each
esophageal cancer case. However, ESCC accounts for
more than 90% of the subset of esophageal cancer
cases documented in the CKB study population as well
as in China (28). Hot tea drinking may be correlated
with a preference for consuming other beverages and
foods at high temperature, but we did not collect this
information or adjust for it in our analyses. Thus, thermal
injury from other beverages and foods may have contrib-
uted to the observed associations. Despite the large sam-
ple size, few women in the CKB cohort smoked or drank
alcohol, leading to wide CIs for the effect estimates and
inconclusive results for women and precluding further
sex-specific joint analysis of relevant factors. In addition,
when we further examined the 3-way associations—that
is, the association among tea temperature, alcohol con-
sumption, and tobacco smoking and tea temperature,
frequency of tea drinking per day, and alcohol con-
sumption (or tobacco smoking)—the cases were too
small to obtain reliable effect estimates for the 3 cate-
gories of tea temperature.

Our findings show a noticeable increase in esoph-
ageal cancer risk associated with a combination of
high-temperature tea drinking, excessive alcohol con-
sumption, and tobacco smoking. They suggest that ab-
staining from hot tea might be beneficial for preventing
esophageal cancer in persons who drink alcohol exces-
sively or smoke. More prospective studies are war-
ranted to confirm the interactions observed in this
study. Studies that directly measure tea temperature
are particularly encouraged.

From Peking University Health Science Center, Beijing, China;
Beijing Institute of Technology, Beijing, China; Chinese Acad-
emy of Medical Sciences, Beijing, China; University of Oxford,
Oxford, United Kingdom; Suzhou Center for Disease Control
and Prevention, Suzhou, Jiangsu, China; Heilongjiang Center
for Disease Control and Prevention, Harbin, Heilongjiang, Chi-
na; China National Center for Food Safety Risk Assessment,
Beijing, China; Peking University Health Science Center and
Peking University Institute of Environmental Medicine, Beijing,

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China; and Peking University Health Science Center and Chi-
nese Academy of Medical Sciences, Beijing, China.

Acknowledgment: The authors thank the study participants
and members of the survey teams in each of the 10 regional
centers, as well as the project development and management
teams based in Beijing, Oxford, and the regional centers.

Grant Support: By the National Natural Science Foundation
of China (grants 81530088, 81390540, 81390544, and
81390541) and National Key Research and Development Pro-
gram of China (grants 2016YFC0900500, 2016YFC0900501,
and 2016YFC0900504). The CKB baseline survey and first re-
survey were supported by a grant from the Kadoorie Charita-
ble Foundation in Hong Kong. The long-term follow-up is sup-
ported by Wellcome Trust, United Kingdom (grants 202922/
Z/16/Z, 088158/Z/09/Z, and 104085/Z/14/Z), and the Chinese
Ministry of Science and Technology (grant 2011BAI09B01).

Disclosures: Authors have disclosed no conflicts of interest.
Forms can be viewed at www.acponline.org/authors/icmje
/ConflictOfInterestForms.do?msNum=M17-2000.

Reproducible Research Statement: Study protocol: Cohort
description and questionnaires are available at www
.p3gobservatory.org/questionnaire/list.htm. Statistical code:
Available from Dr. Lv (e-mail, lvjun@bjmu.edu.cn). Data set:
See study Web site (www.ckbiobank.org) for data access pol-
icy and procedures.

Requests for Single Reprints: Jun Lv, PhD, or Liming Li, MD,
MPH, Department of Epidemiology and Biostatistics, Peking
University Health Science Center, 38 Xueyuan Road, Beijing
100191, China; e-mail, lvjun@bjmu.edu.cn or lmlee@vip.163
.com.

Current author addresses and author contributions are avail-
able at Annals.org.

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demiol. 2013;42:816-27. [PMID: 23918852] doi:10.1093/ije/dyt078
21. Cleves M, Gould W, Gutierrez RG, Marchenko YV. An Introduc-
tion to Survival Analysis Using Stata. 3rd ed. College Station, TX:
Stata Pr; 2010.
22. Andrici J, Eslick GD. Hot food and beverage consumption and
the risk of esophageal cancer: a meta-analysis. Am J Prev Med. 2015;
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23. Mirvish SS. Role of N-nitroso compounds (NOC) and
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http://www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M17-2000

http://www.p3gobservatory.org/questionnaire/list.htm

http://www.p3gobservatory.org/questionnaire/list.htm

mailto:lvjun@bjmu.edu.cn

http://www.ckbiobank.org

mailto:lvjun@bjmu.edu.cn

mailto:lmlee@vip.163.com

mailto:lmlee@vip.163.com

http://www.annals.org

http://wcrf.org/sites/default/files/CUP%20OESOPHAGEAL_WEB

http://wcrf.org/sites/default/files/CUP%20OESOPHAGEAL_WEB

http://www.oecd.org/els/health-systems/Table-of-Content-Metadata-OECD-Health-Statistics-2017

http://www.oecd.org/els/health-systems/Table-of-Content-Metadata-OECD-Health-Statistics-2017

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25. Wang LD, Zhou Q, Feng CW, Liu B, Qi YJ, Zhang YR, et al.
Intervention and follow-up on human esophageal precancerous le-
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geal cancer. Gan To Kagaku Ryoho. 2002;29 Suppl 1:159-72. [PMID:
11890101]

26. Sun CL, Yuan JM, Lee MJ, Yang CS, Gao YT, Ross RK, et al.
Urinary tea polyphenols in relation to gastric and esophageal can-
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2002;23:1497-503. [PMID: 12189193]
27. De Jong UW, Day NE, Mounier-Kuhn PL, Haguenauer JP. The
relationship between the ingestion of hot coffee and intraoesopha-
geal temperature. Gut. 1972;13:24-30. [PMID: 5060664]
28. Arnold M, Soerjomataram I, Ferlay J, Forman D. Global inci-
dence of oesophageal cancer by histological subtype in 2012. Gut.
2015;64:381-7. [PMID: 25320104] doi:10.1136/gutjnl-2014-308124

AD LIBITUM
Provisions

I spot you first in floral, reaching past purple gladiolas
and petite roses to score a sassy bunch of sunflowers.
On to bakery to claim a glossy cake—your daughter’s
favorite, I overhear—announcing her name in yellow icing.

Our carts cross again in produce, where, over dusky eggplants
and delicate lettuces, we exchange a glancing smile—a nod
to roles and rituals, we keepers of the celebration even after
decades have distanced us from our homegrown progeny.

The final sighting at checkout, where I grin as you fasten
three roiling balloons to your basket. You chat with
the checker, offer the bagger an encouraging aside—
something about perseverance, the doors it can open.

The I Love You balloon bobs merrily above the others
as you swipe your card. Then, one last remark
to the checkout team before you turn to go: “So glad
it’s pretty today. That cemetery gets muddy in the rain.”

Outside, in sunlight, you’re on the other side of the wide
parking lot. I return your brief wave as you wrangle balloons,
now dancing madly to escape backseat confinement. Then
you’re gone, pulling away, a silent riot of color in your wake.

Sylvia S. Villarreal, MEd, MPH
McGovern Center for Humanities and Ethics
Houston, Texas

Current Author Address: Sylvia S. Villarreal, MEd, MPH;
e-mail, Sylvia.S.Villarreal@uth.tmc.edu.

© 2018 American College of Physicians

Hot Tea, Alcohol, and Tobacco on the Risk for Esophageal Cancer ORIGINAL RESEARCH

Annals.org Annals of Internal Medicine • Vol. 168 No. 7 • 3 April 2018 497

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mailto:Sylvia.S.Villarreal@uth.tmc.edu

http://www.annals.org

Current Author Addresses: Drs. C. Yu, J. Lv, and L. Li: Depart-
ment of Epidemiology and Biostatistics, Peking University
Health Science Center, 38 Xueyuan Road, Beijing 100191,
China.
Drs. H. Tang and X. Yang: School of Software, Beijing Institute
of Technology, 5 South Zhongguancun Street, Beijing
100081, China.
Drs. Y. Guo and Z. Bian: Chinese Academy of Medical Sci-
ences, Beijing 102300, China.
Drs. L. Yang, Y. Chen, and Z. Chen: Clinical Trial Service Unit
and Epidemiological Studies Unit (CTSU), Nuffield Depart-
ment of Population Health, University of Oxford, Richard
Doll Building, Old Road Campus, Oxford OX3 7LF, United
Kingdom.
Dr. A. Tang: Suzhou Center for Disease Control and Preven-
tion, Suzhou 215006, China.
Dr. X. Zhou: Heilongjiang Center for Disease Control and Pre-
vention, Harbin 150030, China.
Dr. J. Chen: China National Center for Food Safety Risk As-
sessment, Beijing 100738, China.

Author Contributions: Conception and design: J. Lv, L. Li.
Analysis and interpretation of the data: C. Yu, H. Tang, X.
Yang, J. Lv.
Drafting of the article: C. Yu, J. Lv.
Critical revision for important intellectual content: J. Lv, L. Li.
Final approval of the article: C. Yu, H. Tang, Y. Guo, Z. Bian, L.
Yang, Y. Chen, A. Tang, X. Zhou, X. Yang, J. Chen, Z. Chen, J.
Lv, L. Li.
Obtaining of funding: J. Chen. Z. Chen, L. Li.
Administrative, technical, or logistic support: Y. Guo, J. Chen,
Z. Chen, L. Li.
Collection and assembly of data: Y. Guo, Z. Bian, L. Yang, Y.
Chen, A. Tang, X. Zhou.

APPENDIX: CKB COLLABORATIVE GROUP
Members of the CKB International Steering Com-

mittee are Junshi Chen, Zhengming Chen (Principal In-
vestigator [PI]), Robert Clarke, Rory Collins, Yu Guo,
Liming Li (PI), Jun Lv, Richard Peto, and Robin Walters.

The CKB Collaborative Group members who
authored this work are Canqing Yu (National Co-
ordinating Centre, Beijing), Yu Guo (National Co-
ordinating Centre, Beijing), Zheng Bian (National
Co-ordinating Centre, Beijing), Ling Yang (International
Co-ordinating Centre, Oxford), Yiping Chen (Interna-
tional Co-ordinating Centre, Oxford), Aiyu Tang (Su-
zhou CDC), Xue Zhou (Heilongjiang Provincial CDC),
Junshi Cheng, Zhengming Chen (PI, International Co-
ordinating Centre, Oxford), Jun Lv (National Co-
ordinating Center, Beijing), and Liming Li (PI, National
Co-ordinating Center, Beijing).

The CKB Collaborative Group members who con-
tributed to this work but did not author it are as follows:

International Co-ordinating Centre, Oxford
Daniel Avery, Ruth Boxall, Derrick Bennett, Yumei

Chang, Robert Clarke, Huaidong Du, Simon Gilbert,
Alex Hacker, Mike Hill, Michael Holmes, Andri Iona,
Christiana Kartsonaki, Rene Kerosi, Ling Kong, Om

Kurmi, Garry Lancaster, Sarah Lewington, Kuang Lin,
John McDonnell, Iona Millwood, Qunhua Nie, Jay-
akrishnan Radhakrishnan, Paul Ryder, Sam Sansome,
Dan Schmidt, Paul Sherliker, Rajani Sohoni, Becky Ste-
vens, Iain Turnbull, Robin Walters, Jenny Wang, Lin
Wang, Neil Wright, and Xiaoming Yang.

National Co-ordinating Centre, Beijing
Xiao Han, Can Hou, Pei Pei, Chao Liu, and Yunlong

Tan.
Regional Co-ordinating Centres
Qingdao CDC: Zengchang Pang, Ruqin Gao, Shan-

peng Li, Shaojie Wang, Yongmei Liu, Ranran Du, Yajing
Zang, Liang Cheng, Xiaocao Tian, Hua Zhang, Yaoming
Zhai, Feng Ning, Xiaohui Sun, and Feifei Li.

Licang CDC: Silu Lv, Junzheng Wang, and Wei
Hou.

Heilongjiang Provincial CDC: Mingyuan Zeng and
Ge Jiang.

Nangang CDC: Liqiu Yang, Hui He, Bo Yu, Yanjie Li,
Qinai Xu, Quan Kang, and Ziyan Guo.

Hainan Provincial CDC: Dan Wang, Ximin Hu, Jin-
yan Chen, Yan Fu, Zhenwang Fu, and Xiaohuan Wang.

Meilan CDC: Min Weng, Zhendong Guo, Shukuan
Wu, Yilei Li, Huimei Li, and Zhifang Fu.

Jiangsu Provincial CDC: Ming Wu, Yonglin Zhou,
Jinyi Zhou, Ran Tao, Jie Yang, and Jian Su. Suzhou
CDC: Fang Liu, Jun Zhang, Yihe Hu, Yan Lu, Liangcai
Ma, Shuo Zhang, Jianrong Jin, and Jingchao Liu.

Guangxi Provincial CDC: Zhenzhu Tang, Naying
Chen, and Ying Huang.

Liuzhou CDC: Mingqiang Li, Jinhuai Meng, Rong
Pan, Qilian Jiang, Jian Lan, Yun Liu, Liuping Wei, Liyuan
Zhou, Ningyu Chen, Ping Wang, Fanwen Meng, Yulu
Qin, and Sisi Wang.

Sichuan Provincial CDC: Xianping Wu, Ningmei
Zhang, Xiaofang Chen, and Weiwei Zhou.

Pengzhou CDC: Guojin Luo, Jianguo Li, Xiaofang
Chen, Xunfu Zhong, Jiaqiu Liu, and Qiang Sun.

Gansu Provincial CDC: Pengfei Ge, Xiaolan Ren,
and Caixia Dong.

Maiji CDC: Hui Zhang, Enke Mao, Xiaoping Wang,
Tao Wang, and Xi Zhang.

Henan Provincial CDC: Ding Zhang, Gang Zhou,
Shixian Feng, Liang Chang, and Lei Fan. Huixian CDC:
Yulian Gao, Tianyou He, Huarong Sun, Pan He, Chen
Hu, Xukui Zhang, Huifang Wu, and Pan He.

Zhejiang Provincial CDC: Min Yu, Ruying Hu, and
Hao Wang.

Tongxiang CDC: Yijian Qian, Chunmei Wang, Kaixu
Xie, Lingli Chen, Yidan Zhang, Dongxia Pan, and Qijun
Gu.

Hunan Provincial CDC: Yuelong Huang, Biyun
Chen, Li Yin, Huilin Liu, Zhongxi Fu, and Qiaohua Xu.

Liuyang CDC: Xin Xu, Hao Zhang, Huajun Long,
Xianzhi Li, Libo Zhang, and Zhe Qiu.

Annals of Internal Medicine • Vol. 168 No. 7 • 3 April 2018 Annals.org

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CORRECTION: HOT TEA CONSUMPTION AND ITS INTERACTIONS

WITH ALCOHOL AND TOBACCO USE ON THE RISK FOR

ESOPHAGEAL CANCER: A POPULATION-BASED COHORT STUDY

“Effect of” was incorrectly added to the title of the pre-
ceding article (1) during the copyediting process. It was
not our intent to imply causality, and we apologize for any
confusion.

Reference
1. Yu C, Tang H, Guo Y, Bian Z, Yang L, Chen Y, et al. Hot tea consumption

and its interactions with alcohol and tobacco use on the risk for esophageal

cancer: a population-based cohort study. Ann Intern Med. 2018;168:487-97.

[PMID: 29404576] doi: 10.7326/M17-2000

Annals.org Annals of Internal Medicine • Vol. 168 No. 7 • 3 April 2018

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http://www.annals.org

N O N – E X P E R I M E N T A L S T U D Y D E S I G N S
P A R T 2 : C O H O R T S T U D Y D E S I G N

6310-WEEK 4

MAIN NON-EXPERIMENTAL STUDY
DESIGNS

• Cross-sectional study design
• Cohort study design
• Case-control study design

COHORT STUDY DESIGN

COHORT STUDY DESIGN

• Cohort: a group of subjects followed over time
• Cohort design: A non-experimental design in which a defined

group of people (a cohort) is followed over time to study
outcomes for subsets of the cohorts
• Data is collected at baseline to assess exposure/characteristic
• Data is collected again at later point(s) in time to examine the

development of a disease or condition
• Time frame: longitudinal
• Advantages:

• Allows calculation of incidence (number of new cases of a condition
occurring over time)

• Establishes the time sequence of variable  strengthens the process of
inferring the causal basis of an association

• Types
• Prospective
• Retrospective
• Multiple cohort

COHORT STUDY DESIGN

Over time

Baseline

Gather data at: Defined
Population

Exposed

Disease No Disease

Not
Exposed

Disease No Disease

STEPS IN A PROSPECTIVE
COHORT STUDY

• Define selection criteria and recruit sample from the
population (cohort).

• At baseline, measure predictor variables and, if
appropriate, baseline level of outcome variable(s).

• Follow cohort over time, minimizing loss to follow-up.
• Measure outcome variable(s) at follow-up.

STEPS IN A RETROSPECTIVE
COHORT STUDY

• Identify an existing cohort that has some predictor
information already recorded

• Assess loss to follow-up that has occurred
• Measure outcome variable(s) that have already

occurred.

MULTIPLE-COHORT DESIGN

• Two or more separate samples: one with exposure to a
potential risk factor (predictor) and one or more with no
exposure.

• Next steps: measure other predictors; follow up; assess
outcomes

• Note that a double-cohort design is different from the
use of two samples in a case-control design
• Double-cohort: two groups chosen based on level of predictor
• Case-control: two groups chosen based on presence or

absence of an outcome
• Strengths: Feasible approach to study rare exposures to

environmental and occupational hazards
• Weaknesses: Confounding since the cohorts are

assembled from separate populations.

STATISTICAL MEASURES
IN COHORT DESIGNS

• Cohort study results are usually reported in
measures that reflect the concept of being at risk.*
•

Risk

•

Odds

•

Rate

*See the example in Hulley’s textbook and Table 7.2 on page 93 for a good
example on the calculation of these measures.

RISK

• In the context of cohort studies, risk refers to the
number of new cases who develop the health
outcome among those at risk, over a specified time
period.

• It refers to the probability that a health outcome will
occur.

• It is usually expressed as a percentage (ranging
from 0% to 100%).

CALCULATING RISK

• Define the population at risk
• Determine the number of new cases (those who

develop the outcome/disease)
• Specify the time period

Risk = Number of new cases In specified time period

Population at risk

EXAMPLE*: RISK CALCULATION

• 15,000 children, ages 2 to 8, who live in an area
around high-voltage power lines were followed for
10 years or until the development of a childhood
leukemia. Fifty cases were identified over the 10-
year period.

• Risk = 50/15,000 = .0033 or .33% or 3.3 people per
1000 over 10 years

*This is a hypothetical example and does not reflect the actual risk of
childhood leukemia. There is also no consistent body of evidence on
an association between living near high-voltage power lines and
childhood leukemia.

ODDS

• Odds refers to the probability (p) of an event occurring to the
probability that it will not occur (1-p).

• Odds of an event = p/(1-p)
• Odds ratios (OR) are used to compare the relative odds of the

occurrence of the outcome of interest (e.g. disease or
disorder), given exposure to the variable of interest (e.g.
health characteristic, aspect of medical history). The odds
ratio can also be used to determine whether a particular
exposure is a risk factor for a particular outcome, and to
compare the magnitude of various risk factors for that
outcome.
• OR=1 Exposure does not affect odds of outcome
• OR>1 Exposure associated with higher odds of outcome
• OR<1 Exposure associated with lower odds of outcome

Sources:
Polit DF, Beck CT. 2012. Nursing Research: Generating and Assessing Evidence for Nursing

Practice.
Szumilas M. Explaining Odds Ratios. Journal of the Canadian Academy of Child and

Adolescent Psychiatry. 2010;19(3):227-229.

RATE

• Rate refers to the number of subjects who develop an
outcome (new cases) divided by the person-time at risk

• Rate accounts for the reality of a changing population
through the person-time concept

• Person-time is an estimate of the actual time each
person remains at risk for the health outcome (in years,
months, or days). It is the sum of each participant’s time
at risk before developing the outcome, leaving the
study, being lost to follow-up, or dying.

Rate = Number of new cases
Person-time at risk

ADDITIONAL RESOURCES

• For more information on prevalence, incidence, risk
and rate, click here.

• For more information on the concept of person-
year, watch the following video, available here
(6:09 minutes).

https://sph.unc.edu/files/2015/07/nciph_ERIC1

ISSUES TO CONSIDER WHEN
EVALUATING COHORT STUDIES

• Subjects are:
• appropriate to research question,
• available for follow-up, and
• representative of the population to which findings will be

generalized.
• Number of subjects provides adequate power.
• Measurements of predictor and outcome variables

are precise and accurate.
• Potential confounders are measured.
• Loss to follow-up is minimized.

BIAS SPECIFIC TO
COHORT STUDY DESIGNS

• Bias specific to cohort study designs
• Attrition bias resulting from losing people to follow-up

• To assess the extent of attrition bias, compare baseline
characteristics of those who were available and not available
for follow-up

• To minimize attrition bias,
• Use incentives
• Collect multiple contact information items
• Incorporate additional contact attempts
• Include follow-ups between data collection points

EXAMPLES
COHORT STUDIES

FRAMINGHAM HEART STUDY

• Objective: To identify risk factors for cardiovascular disease (CVD) by
following its development over a long period of time in a large group of
participants who had not yet developed overt symptoms of CVD or
suffered a heart attack or stroke.

• Sample: 5,209 men and women between the ages of 30 and 62 from the
town of Framingham, Massachusetts.

• Extensive physical examinations and lifestyle interviews to analyze
common patterns related to CVD development.
• Since 1948, subjects return to the study every two years.
• In 1971, the Study enrolled a second generation: 5,124 of the original participants’

adult children and their spouses.
• In 1994, a different cohort was enrolled to reflect a more diverse community of

Framingham (Omni cohort of the

Framingham Heart Study

).
• In April 2002 the Study enrolled a third generation of participants (grandchildren of

Original Cohort). In 2003, a second group of Omni participants was enrolled.
• Results: identification of major CVD risk factors – high blood pressure, high

blood cholesterol, smoking, obesity, diabetes, and physical inactivity.
• Click here if you are interested in more information on the Framingham

Heart Study.

Home

NURSES HEALTH STUDY

• Objective: To investigate factors that influence
women’s health with a primary focus on cancer
prevention.

• 1976 baseline sample: 122,000 registered nurses
ages 30 to 55 years.

• Results: diet, physical activity and other lifestyle
factors can promote better health.

• Click here if you are interested in more information
on the

Nurses Health Study

.

http://www.nurseshealthstudy.org/

HISPANIC EPESE

• Hispanic Established Populations for the Epidemiologic Study of
the Elderly

• Objectives
• Estimate the prevalence of key physical and mental health conditions and

functional impairments in older Mexican Americans.
• Investigate predictors of physical and mental health conditions and

functional status at baseline.
• Study changes in health and functioning among survivors
• Examine changes in health behaviors and key social mediators of health

status (social networks and support, various key transitions such as changes
in living arrangements, widowhood, etc.).

• Sample: 1993-94 representative sample of community-dwelling
Mexican-American elderly, aged 65 years and older, residing in
the five southwestern states of Arizona, California, Colorado, New
Mexico, and Texas.
• Five follow-ups.
• N = 3,050 participants with an additional 902 added at 4th follow-up.

• Note that analysis of baseline data serves as a cross-sectional
study.

• Click here if you interested in more information on the H-EPESE.

https://www.icpsr.umich.edu/icpsrweb/ICPSR/series/546

• 6310-Week 4
• Main Non-Experimental Study Designs
• Cohort Study Design

Cohort Study Design

Cohort Study Design

• Steps in a Prospective �Cohort Study
• Steps in a Retrospective �Cohort Study
• Multiple-Cohort Design
• Statistical Measures �in Cohort Designs

Risk

• Calculating Risk
• Example*: Risk Calculation

Odds

Rate

• Additional Resources
• Issues to Consider when Evaluating Cohort Studies
• Bias Specific to �Cohort study designs
• Examples

Framingham Heart Study

Nurses Health Study

• Hispanic EPESE

PREVENTING CHRONIC DISEASE
P U B L I C H E A L T H R E S E A R C H , P R A C T I C E , A N D P O L I C Y
Volume 13, E181 DECEMBER 2016

ORIGINAL RESEARCH

Association Between Sitting Time and
Cardiometabolic Risk Factors After

Adjustment for Cardiorespiratory Fitness,
Cooper Center Longitudinal Study,

2010–2013
Carolyn E. Barlow, PhD1,2; Kerem Shuval, PhD3; Bijal A. Balasubramanian, MBBS, PhD2,4;

Darla E. Kendzor, PhD5,6; Nina B. Radford, MD7; Laura F. DeFina, MD1;
Kelley Pettee Gabriel, PhD8

Suggested citation for this article: Barlow CE, Shuval K,
Balasubramanian BA, Kendzor DE, Radford NB, DeFina LF, et
al. Association Between Sitting Time and Cardiometabolic Risk
Factors After Adjustment for Cardiorespiratory Fitness, Cooper
Center Longitudinal Study, 2010–2013. Prev Chronic Dis 2016;
13:160263. DOI: https://doi.org/10.5888/pcd13.160263.

PEER REVIEWED

Abstract

Introduction
Objective estimates, based on waist-worn accelerometers, indicate
that adults spend over half their day (55%) in sedentary behaviors.
Our study examined the association between sitting time and car-
diometabolic risk factors after adjustment for cardiorespiratory fit-
ness (CRF).

Methods
A cross-sectional analysis was conducted with 4,486 men and
1,845 women who reported daily estimated sitting time, had meas-
ures for adiposity, blood lipids, glucose, and blood pressure, and
underwent maximal stress testing. We used a modeling strategy
using logistic regression analysis to assess CRF as a potential ef-
fect modifier and to control for potential confounding effects of
CRF.

Results
Men who sat almost all of the time (about 100%) were more likely
to be obese whether defined by waist girth (OR, 2.61; 95% CI,

1.25–5.47) or percentage of body fat (OR, 3.33; 95% CI,
1.35–8.20) than were men who sat almost none of the time (about
0%). Sitting time was not significantly associated with other cardi-
ometabolic risk factors after adjustment for CRF level. For wo-
men, no significant associations between sitting time and cardi-
ometabolic risk factors were observed after adjustment for CRF
and other covariates.

Conclusion
As health professionals struggle to find ways to combat obesity
and its health effects, reducing sitting time can be an initial step in
a total physical activity plan that includes strategies to reduce
sedentary time through increases in physical activity among men.
In addition, further research is needed to elucidate the relation-
ships between sitting time and CRF for women as well as the un-
derlying mechanisms involved in these relationships.

Introduction
Prolonged sitting time characterizes the daily lifestyle patterns of
most people living in developed countries (1). Estimates of medi-
an reported sitting time for US adults range between 6.5 to 8 hours
per day (2). Objective estimates, based on waist-worn accelero-
meters, indicate that adults spend over half their day (55%) in
sedentary behaviors (3). Several studies demonstrate direct, inde-
pendent associations between sedentary behavior and cardiometa-
bolic risk factors such as adiposity and fasting blood glucose level
after adjustment for the beneficial effect of moderate-intensity to
vigorous-intensity physical activity (MVPA), accumulated mostly
during leisure or discretionary periods of the day (4). However,
within a 24-hour period, people spend a significant proportion of

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health

and Human Services,

the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.

www.cdc.gov/pcd/issues/2016/16_0263.htm • Centers for Disease Control and Prevention 1

www.cdc.gov/pcd/issues/2016/16_0263.htm

https://1.35�8.20

https://1.25�5.47

https://doi.org/10.5888/pcd13.160263

PREVENTING CHRONIC DISEASE VOLUME 13, E181

PUBLIC HEALTH RESEARCH, PRACTICE, AND POLICY DECEMBER 2016

waking hours in sedentary behaviors or light-intensity physical
activities relative to time spent in MVPA (1). Therefore, investig-
ators recently argued that accounting for an individual’s total
physical activity level during the entire waking period, not just
during isolated segments of the day (eg, time spent sitting or time
spent highly active), is essential to understanding the complex re-
lationships between physical activity behavior and cardiometabol-
ic risk factors (5). Furthermore, objectively measured total activ-
ity level per day appears to be more strongly associated with cardi-
ometabolic risk factors than is MVPA per day (6).

Given that cardiorespiratory fitness (CRF) reflects a person’s ha-
bitual physical profile and overall general health, the primary goal
of our study was to determine whether among adult men and wo-
men time spent sitting was associated with elevated levels of waist
girth, body mass index, body fat percentage, total cholesterol, low-
density lipoprotein (LDL) cholesterol, triglycerides, glucose, and
resting systolic blood pressure; low levels of high-density lipopro-
tein (HDL) cholesterol; and the presence of metabolic syndrome.
Secondary goals were to 1) examine whether CRF confounded or
modified the associations between sitting time and cardiometabol-
ic risk factors and 2) explore whether the role of CRF differed by
sex.

Methods
Participants included in this cross-sectional analysis received a
preventive medical examination at the Cooper Clinic in Dallas,
Texas, during 2010 through 2013 and provided written consent to
participate in the Cooper Center Longitudinal Study (CCLS). Par-
ticipants in CCLS are generally healthy and self-referred or re-
ferred by their employers to the Cooper Clinic for preventive med-
ical examinations that include a physician-administered medical
examination, fasting laboratory studies, body composition meas-
urements, and a maximal treadmill graded exercise test. For our
analysis, to eliminate the potential for a disease condition that
could affect the exposure of interest (eg, a stroke may result in in-
creased sitting time), participants were excluded if they reported a
personal history of cardiovascular disease (n = 51), stroke (n =
27), or diabetes (n = 582) or if they did not reach 85% of their pre-
dicted maximal heart rate on the treadmill test (n = 137). Parti-
cipants were also excluded if their data for some covariates were
missing (n = 332). These criteria resulted in an analytic sample of
1,845 women and 4,486 men aged 20 to 79 years. Each year, the
Cooper Institute’s institutional review board reviewed and ap-
proved the overall study. Our study also received exempt status
from the University of Texas Health Science Center at Houston’s
Committee for the Protection of Human Subjects.

Sitting time was based on participants’ responses to a question on
the medical history questionnaire completed before their clinical
examination. The sitting question, derived from the Canada Fit-
ness Survey (7), assessed the proportion of time spent sitting dur-
ing work, school, and housework during waking hours on a typic-
al day. Response options were 1) almost none of the time (about
0%), 2) approximately one-quarter of the time (about 25%), 3) ap-
proximately half of the time (about 50%), 4) approximately three-
quarters of the time (about 75%), and 5) almost all of the time
(about 100%).

Cardiometabolic risk factors (primary dependent
measures)

Body mass index (BMI, kg/m2) and body composition measure-
ments (% body fat, waist girth) were measured during the prevent-
ive medical examination. These measurements were taken accord-
ing to standard procedures by trained technicians and described
previously (8). Briefly, BMI was computed as weight in kilo-
grams divided by height in meters squared measured on a sta-
diometer and a standard physician’s scale. Participants with BMI
of 30 kg/m2 or higher were classified as obese (9). Waist girth
(cm) was measured with a plastic tape at the level of the umbil-
icus following a normal exhalation. An elevated waist girth for
men was 102 cm or greater and for women was 88 cm or greater
(10). Percentage of body fat was determined by measuring 7 skin-
fold sites (axilla, chest, abdomen, triceps, hip, thigh, and back)
with calipers and inserting the sum of these skinfold measure-
ments in a generalized body density equation to estimate percent-
age of body fat (11). Sex-specific cut points of percentage of body
fat (<25% or ≥25% for men and <32% or ≥32% for women) were used to classify patients as obese (12).

Serum samples taken after patients fasted for 12 hours were ana-
lyzed for lipids by using automated bioassays in accordance with
standard procedures. Elevated lipid levels were defined by using
the following cut points: total cholesterol higher than 200 mg/dL;
LDL cholesterol higher than 100 mg/dL; HDL cholesterol less
than 40 mg/dL for men and less than 50 mg/dL for women; trigly-
cerides 150 mg/dL or higher; and fasting blood glucose 100 mg/
dL or higher (10).

Resting blood pressure was auscultated as the first and fifth
Korotkoff sounds according to a standard sphygmomanometer
protocol (13). Elevated blood pressure was defined as a systolic
blood pressure 130 mm Hg or higher or diastolic blood pressure
85 mm Hg or higher, or both (10).

Using the criteria of the American Heart Association and the Na-
tional Heart, Lung, and Blood Institute, we defined metabolic syn-
drome as meeting 3 or more of the following criteria: abdominal

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PREVENTING CHRONIC DISEASE VOLUME 13, E181

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obesity (waist girth: ≥102 cm for men and ≥88 cm for women);
high triglycerides (≥150 mg/dL); low HDL (<40 mg/dL for men and <50 mg/dL for women); high blood pressure (systolic blood pressure ≥130 mm Hg, or diastolic blood pressure ≥85 mm Hg, or physician-diagnosed history of hypertension); and high glucose (fasting blood glucose ≥100 mg/dL or physician-diagnosed his- tory of high glucose) (10).

Covariates

CRF was assessed by using the time to complete a treadmill-
graded exercise test and the modified Balke protocol described
previously (14). Duration on the treadmill is highly correlated with
measured oxygen consumption (VO2) (r = 0.92 for men [15] and r
= 0.94 for women [16]). A value for maximal metabolic equival-
ent of tasks (METs) was estimated from the final speed and grade
of the treadmill test (17).

Participants were asked to report the frequency and duration of 11
specific physical activity types: walking, running, treadmill, swim-
ming, stationary cycling, bicycling, elliptical, aerobic dance, rack-
et sports, vigorous sports, and other activity. These 11 activity
types represent high-intensity MVPA. Summary estimates were
computed by weighting the product of the reported frequency and
duration (in minutes per week [min/wk−1]) by a standardized es-
timate of the MET of each activity type (18), which was then
summed across all activities performed. The leisure-time physical
activity estimate was expressed as a log transformation of MET/
min/wk−1.

On the basis of literature, we included additional covariates from
the medical history questionnaire: age, sex, alcohol consumption,
and smoking status. Alcohol consumption was calculated as the
combined number of drinks per week of beer, wine, and hard li-
quor. Smoking status was categorized as current smoker or
nonsmoker based on self-reported behavior. Three variables were
created to indicate current medication use (yes/no) for hyperten-
sion, diabetes, or hyperlipidemia; a fourth variable, hormone re-
placement therapy, was created for women only. Medication use
was reported by the patient to the study physician who conducted
the medical examination.

Statistical analysis

Descriptive characteristics of the study sample are presented by
sex and for the total sample. To examine crude associations, we
tested for linear trends reflecting the prevalence of each outcome
for each sex across increasing categories of self-reported sitting
time (ie, about 0% of the time to about 100% of the time). First,
the potential effect modification of CRF on self-reported sitting
time and each cardiometabolic risk factor was explored with the
addition of an interaction term to a logistic regression model in

which sitting time and CRF were used to predict each outcome.
Next, CRF was added to the fully adjusted model to control for
confounding effects after we determined that the effect size in-
creased more than 10% with its inclusion in the fully adjusted
model. Results are presented for each risk factor regressed against
self-reported sitting time 1) adjusted for age (y) (model A); 2) ad-
justed for age and cardiorespiratory fitness (METs) (model B); and
3) adjusted for all covariates in model B and for self-reported
physical activity (MET-minutes per week), alcohol consumption
(drinks per week), smoking status (yes/no), waist girth (in models
with lipids, glucose, or blood pressure as the outcome), and medic-
ation use associated with the outcome (model C). The presence of
multicollinearity between self-reported physical activity and CRF
was assessed and found to be weakly correlated (r = 0.34). Ana-
lyses were performed using SAS/STAT version 9.4 (SAS Institute,
Inc). All significance testing was 2-sided with a P value of less
than .05 considered significant.

Results
The average age of the analytic sample (n = 6,331) was 50.7 (SD
10.0) years old and consisted of mostly men (71%) (Table 1).
Eight percent of patients reported current smoking. Alcohol con-
sumption was moderate (median [25th, 75th percentile], 4 [1, 9]
drinks per week). A higher percentage of men (41%) than women
(13%) reported sitting most or all of the time (≥75% of the time)
during a usual day. The average CRF level was 11.6 (SD 2.2)
METs for men and 9.8 (SD 1.9) METs for women.

For men, high self-reported sitting time was significantly associ-
ated with high prevalence of cardiometabolic risk factors, includ-
ing elevated waist girth, percentage of body fat, and obesity (all P
for linear trend < .05) (Table 2). No associations were observed for the other risk factors or metabolic syndrome. Similarly, for women, high self-reported sitting time was significantly associ- ated with high prevalence of elevated waist girth and percentage of body fat, obesity, and metabolic syndrome (all P for linear trend < .001). In addition, the more women sat, the higher their levels of triglycerides and the lower their levels of HDL cholesterol (both P for linear trend < .001). For women, no associations were ob- served between self-reported sitting time and total cholesterol, LDL cholesterol, glucose, or blood pressure.

Next, we assessed the role of CRF as an effect-modifying variable
by adding a self-reported sitting time × CRF interaction term to
the models for each separate cardiometabolic outcome. This inter-
action term was not significant for any of the cardiometabolic risk
factors after adjustment for covariates for either men or women
(all P > .05).

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For men, the crude associations that were observed between self-
reported sitting time and each measure of adiposity remained sig-
nificant after covariate adjustment, including CRF (Table 3). More
specifically, in model C, men who reported sitting about 100% of
the time were more than twice as likely to be obese whether
defined by waist girth (OR, 2.61; 95% CI, 1.25–5.47), or percent-
age of body fat (OR, 3.33; 95% CI, 1.35–8.20) relative to men
who sat about 0% of the time. Similar to the results for men, asso-
ciations between self-reported sitting time and each measure of
adiposity were seen among women (Table 3) when adjusted for
age (model A). However, unlike men, when CRF was added to the
model (model C), these associations for women were no longer
significant. Self-reported sitting time was not associated with the
remaining risk factors among men or women (Appendix).

Discussion
Our findings suggest that prolonged sitting is associated with high
levels of adiposity among men even after accounting for their CRF
level. However, this relationship between self-reported sitting time
and adiposity was not found for women. Furthermore, for men,
other cardiometabolic risk factors (elevated lipids, blood glucose,
triglycerides, and blood pressure; low levels of HDL; and the pres-
ence of metabolic syndrome) were not significantly associated
with sitting time. For women, self-reported sitting time was not as-
sociated with any individual cardiometabolic risk factor or the
presence of metabolic syndrome.

Previous cross-sectional studies report significant associations
between sedentary behavior and various cardiometabolic risk
factors after controlling for MVPA (19,20). However, these stud-
ies probably suffer from incomplete ascertainment of an individu-
al’s exposure to physical activity given that only a small portion of
the day was examined (ie, 3% of their day assuming 30 minutes
per day of MVPA during 16 waking hours), which in turn could
explain the significant associations found in published study res-
ults. In their study of National Health and Nutrition Examination
Survey (NHANES) participants, Maher et al found high-sensitiv-
ity C-reactive protein and triglycerides to be the only cardiometa-
bolic risk factors associated with sedentary behavior when con-
trolling for total physical activity time as assessed with accelero-
meters, which produce information about activity throughout the
day (5). Although these associations reached statistical signific-
ance, the relationships were weak and not of clinical significance.
In addition, a prospective study of men in the CCLS cohort found
that prolonged TV viewing and time spent in a car were detriment-
ally linked only to a marker of insulin sensitivity (but not to other
cardiometabolic risk factors) when CRF was taken into account
(21).

Similar to the results from NHANES (5), our study found that
self-reported sitting time was not associated with cardiometabolic
risk factors other than obesity for men when reported physical
activity level or cardiorespiratory fitness level are taken into ac-
count. However, little evidence exists of studies having explored
the potential role of CRF in the relationship between estimates of
total sitting time and cardiometabolic risk factors. The role of CRF
appeared to differ for men and for women, and this finding also
deserves further study. More specifically, for men, CRF confoun-
ded the relationship between sitting time and cardiometabolic risk
factors: men had higher levels of muscle mass (70 kg) than wo-
men (50 kg), which might protect men against the adverse effect
of prolonged sitting on lipids, glucose, and blood pressure, but not
against the accumulation of body fat. For women, CRF may have
confounded the effect of sitting time on some risk factors, but it
did not modify this relationship. A previous cross-sectional study
of the CCLS cohort found that the more women sat, the lower
their fitness level (22). Therefore, high levels of time sitting dur-
ing the day could lower fitness levels and lower total daily caloric
expenditures, which could lead to increases in women’s body fat.
For different levels of CRF, we found no sex-related difference in
the relationship between sitting time and cardiometabolic risk
factors.

Our study findings have public health and clinical implications:
they indicate that, among men, increased self-reported long sitting
time is related to a higher likelihood of obesity. These results
along with other published study results point to a relationship
between prolonged sedentary time and increased risk for chronic
conditions and premature mortality among both men and women
(23,24). Reducing total sitting time and incorporating activity
breaks into one’s daily schedule lowers cardiometabolic risk (25).
The American Cancer Society Guidelines on Nutrition and Physic-
al Activity for Cancer Prevention underscores the need to reduce
total sitting time along with habitually engaging in MVPA (26).
Therefore, developing and implementing programs specifically to
reduce and break up sitting time at home and work is paramount.
Primary care providers can play an important role in encouraging
their patients to change their sedentary behavior. One study found
that physicians were significantly more likely counsel their pa-
tients about the value of physical activity than to counsel them
about the risks associated with sedentary behavior (27). Tools,
such as the Rapid Assessment Disuse Index specifically tailored
for use at the point of care, can be used by physicians to assess pa-
tients with high levels of sitting and low levels of physical activ-
ity and provide pertinent and effective counseling (27). In addi-
tion, the 5As model (28), which has been used successfully to pro-
mote physical activity in primary care, can be applied to sedentary
behavior counseling.

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Strengths of this study include a direct estimate of CRF, a compre-
hensive analytic approach, and a large sample size with numerous
clinical covariates. Limitations of note were the self-reported
measure of sitting time (which has not been validated), character-
istics of the sample, and cross-sectional study design. More spe-
cifically, participants were asked to report estimates of time spent
sitting during a typical day in broad categories which could result
in misclassification of the exposure. In addition, participants were
generally healthy, predominantly non-Hispanic white, and well-
educated. The homogeneous nature of the cohort decreased the
ability to generalize these results to more diverse populations.
However, the socioeconomic homogeneity of this cohort reduced
the likelihood of confounding by unmeasured factors such as oc-
cupation, income, and other socioeconomic indicators known to
influence health. The cross-sectional study design limited report-
ing to the description of associations and thus results do not imply
causality.

The more men sat, the more likely they were to be obese by any
definition (ie, BMI, percentage of fat, waist circumference), but no
other cardiometabolic risk factors were significantly associated
with sitting time. For women, after adjustment for CRF and other
covariates, no significant associations were observed between sit-
ting time and cardiometabolic risk factors. Our results support
physicians who work with their male patients to control risk
factors by advising them to reduce sitting time to avoid obesity
and its associated health conditions. The reduction and interrup-
tion of sitting time can be an initial step in developing a total phys-
ical activity plan that includes strategies to reduce sedentary time
through increases in physical activity. Assessment of the entire in-
tensity spectrum of behaviors from sleep to vigorous-intensity
physical activity will provide health professionals with the inform-
ation needed to tailor physical activity plans for risk reduction and
health promotion.

Acknowledgments
D.E.K. was supported, in part, by an American Cancer Society
grant (MRSGT-10-104-01-CPHPS). We thank Kenneth H.
Cooper, MD, MPH, for establishing the Cooper Center Longitud-
inal Study, the Cooper Clinic physicians and staff for collecting
clinical data, and The Cooper Institute for maintaining the data-
base.

Author Information
Corresponding Author: Carolyn E. Barlow, PhD, The Cooper
Institute, 12330 Preston Rd, Dallas, TX 75230. Telephone: 972-
341-3246. Email: bwright@cooperinst.org.

Author Affiliations: 1The Cooper Institute, Dallas, Texas.
2University of Texas Health Science Center at Houston School of
Public Health — Dallas Campus, Dallas, Texas. 3Department of
Intramural Research, American Cancer Society, Atlanta, Georgia.
4University of Texas Southwestern Medical Center — Harold C.
Simmons Cancer Center, Dallas, Texas. 5Department of Family
and Preventive Medicine, University of Oklahoma Health
Sciences Center, Oklahoma City, Oklahoma. 6Oklahoma Tobacco
Research Center, Stephenson Cancer Center, Oklahoma City,
Oklahoma. 7Cooper Clinic, Dallas, Texas. 8University of Texas
Health Science Center at Houston School of Public Health —
Austin Campus, Austin, Texas.

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The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services,

the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.

www.cdc.gov/pcd/issues/2016/16_0263.htm • Centers for Disease Control and Prevention 5

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Tables

Characteristic Men Women Total

N 4,486 1,845 6,331

Age, y 51.2 (9.8) 49.4 (10.4) 50.7 (10.0)

Waist girth, cm 94.2 (10.9) 77.4 (10.8) 89.3 (13.3)

Elevated waist girth 21 16 19

BMI, kg/m2 27.8 (4.7) 24.6 (5.0) 26.9 (5.0)

Obese (BMI ≥30) 23 11 20

Percentage of body fat 22.0 (5.5) 25.5 (6.3) 23.0 (6.0)

Elevated body fatb 18 10 16

Total cholesterol, mg/dLb 185.1 (36.4) 194.8 (34.3) 188.0 (36.0)

Total cholesterol >200 mg/dLb 32 42 35

LDL cholesterol, mg/dLb 108.4 (33.1) 105.5 (30.2) 107.6 (32.3)

LDL cholesterol >100 mg/dLb 58 53 56

Use of lipid lowering medicationb 31 12 26

HDL cholesterol, mg/dLb 53.1 (14.8) 70.3 (18.9) 58.0 (17.9)

HDL cholesterol <40 mg/dL for men and <50 mg/dL for women b

12 17 15

Triglycerides, mg/dLb 118.2 (58.1) 95.3 (48.1) 111.5 (56.3)

Triglycerides ≥150 mg/dLb 22 12 19

Glucose, mg/dLb 95.6 (9.8) 90.2 (8.5) 94.0 (9.7)

Glucose ≥100 mg/dLb 27 11 23

Resting SBP, mm Hgb 119.5 (12.0) 111.6 (13.1) 117.2 (12.9)

Resting DBP, mm Hgb 80.0 (8.9) 75.2 (8.4) 78.6 (9.0)

Blood pressure ≥130/85 mm Hgb 34 17 29

Use of hypertension medicationb 25 13 22

Metabolic syndromeb 15 6 12

Time spent sittingb,c

About 0% 11 17 15

About 25% 21 37 32

About 50% 27 34 32

About 75% 29 11 16

Table 1. Selected Characteristics of Participants in the Cooper Center Longitudinal Study of Sitting Time and Cardiometabolic Risk Factors, by Sex, 2010–2013a

Abbreviation: BMI, body mass index; DBP, diastolic blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein; METs, metabolic equivalent of tasks;
SBP, systolic blood pressure.
a Values are mean (SD) or percentage of participants with the characteristic unless otherwise noted.
b Information was available for a subset of the dataset. Men, n = 2,816; women, n = 1,140; total, n = 3,956.
c Response options were 1) almost none of the time (about 0%), 2) approximately one-quarter of the time (about 25%), 3) approximately half of the time (about
50%), 4) approximately three-quarters of the time (about 75%), and 5) almost all of the time (about 100%).

(continued on next page)

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(continued)

Characteristic Men Women Total

About 100% 12 2 5

Cardiorespiratory fitness (METs) b 11.6 (2.2) 9.8 (1.9) 11.1 (2.3)

Physical activity (MET-minutes/week), median (25th, 75th
percentile)b

960 (382, 1,799) 892 (255, 1,750) 960 (340, 1,785)

Current smokerb 10 3 8

Alcohol intake (drinks/wk), median (25th, 75th percentile)b 5 (2, 10) 3 (1, 7) 4 (1,9)

Table 1. Selected Characteristics of Participants in the Cooper Center Longitudinal Study of Sitting Time and Cardiometabolic Risk Factors, by Sex, 2010–2013a

Abbreviation: BMI, body mass index; DBP, diastolic blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein; METs, metabolic equivalent of tasks;
SBP, systolic blood pressure.
a Values are mean (SD) or percentage of participants with the characteristic unless otherwise noted.
b Information was available for a subset of the dataset. Men, n = 2,816; women, n = 1,140; total, n = 3,956.
c Response options were 1) almost none of the time (about 0%), 2) approximately one-quarter of the time (about 25%), 3) approximately half of the time (about
50%), 4) approximately three-quarters of the time (about 75%), and 5) almost all of the time (about 100%).

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services,

the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.

Centers for Disease Control and Prevention • www.cdc.gov/pcd/issues/2016/16_0263.htm 8

www.cdc.gov/pcd/issues/2016/16_0263.htm

PREVENTING CHRONIC DISEASE VOLUME 13, E181

PUBLIC HEALTH RESEARCH, PRACTICE, AND POLICY DECEMBER 2016

Characteristic

Sitting Timea

About 100% About 75% About 50% About 25% About 0%
P Value for

Trend

Men

n 757 1,669 1,511 469 80 —

Elevated waist girth (≥102 cm) 24 21 21 19 13 .014

Obese (BMI ≥30) 28 23 22 19 15 <.001

Elevated percentage of fat (≥25%) 36 29 27 22 17 <.001

Elevated total cholesterol (>200 mg/dL) 32 33 33 31 34 .97

Elevated LDL cholesterol (>100 mg/dL) 58 59 58 62 55 .052

Low HDL cholesterol (<40 mg/dL) 18 17 16 16 10 .14

Elevated triglycerides (≥150 mg/dL) 24 22 22 21 21 .26

Elevated glucose (≥100 mg/dL) 26 28 27 31 27 .29

Elevated blood pressure (≥130/85 mm Hg) 19 16 17 17 22 .56

Metabolic syndrome 16 15 15 13 11 .22

Women

n 204 390 496 535 220 —

Elevated waist girth (≥88 cm) 26 18 16 13 10 <.001

Obese (BMI ≥30) 23 14 11 7 7 <.001

Elevated percentage of fat (≥32%) 23 22 13 13 11 <.001

Elevated total cholesterol (>200 mg/dL) 43 39 43 41 44 .49

Elevated LDL cholesterol (>100 mg/dL) 56 52 54 53 52 .69

Low HDL cholesterol (<50 mg/dL) 4 4 2 2 1 <.001

Elevated triglycerides (≥150 mg/dL) 16 14 11 10 7 <.001

Elevated glucose (≥100 mg/dL) 12 11 12 11 10 .63

Elevated blood pressure (≥130/85 mm Hg) 9 7 7 6 6 .30

Metabolic syndrome 11 6 5 5 2 <.001

Table 2. Percentage of Participants With Detrimental Levels of Cardiometabolic Risk Factors According to Sitting Time Categories, Cooper Center Longitudinal
Study, 2010–2013

Abbreviations: —, not applicable; BMI, body mass index; HDL, high density lipoprotein; LDL, low density lipoprotein.
a Response options were 1) almost none of the time (about 0%), 2) approximately one-quarter of the time (about 25%), 3) approximately half of the time (about
50%), 4) approximately three-quarters of the time (about 75%), and 5) almost all of the time (about 100%).

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services,

the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.

www.cdc.gov/pcd/issues/2016/16_0263.htm • Centers for Disease Control and Prevention 9

www.cdc.gov/pcd/issues/2016/16_0263.htm

PREVENTING CHRONIC DISEASE VOLUME 13, E181

PUBLIC HEALTH RESEARCH, PRACTICE, AND POLICY DECEMBER 2016

Characteristic/Modelb

Sitting Timea

About 100%, Odds
Ratio (95% CI)

About 75%, Odds Ratio
(95% CI)

About 50%, Odds Ratio
(95% CI)

About 25%, Odds Ratio
(95% CI) About 0%

Men

Elevated waist girth (≥102 cm)

Model A 2.17 (1.12–4.20) 1.77 (0.92–3.39) 1.73 (0.90–3.31) 1.49 (0.76–2.93) 1 [Reference]

Model B 2.54 (1.29–5.00) 2.09 (1.08–4.07) 1.95 (1.00–3.80) 1.60 (0.80–3.20) 1 [Reference]

Model C 2.61 (1.25–5.47) 2.27 (1.10–4.69) 2.09 (1.01–4.32) 1.68 (0.79–3.58) 1 [Reference]

Body mass index

Model A 2.17 (1.15–4.10) 1.72 (0.92–3.21) 1.57 (0.84–2.94) 1.31 (0.68–2.52) 1 [Reference]

Model B 2.53 (1.32–4.84) 2.01 (1.07–3.81) 1.76 (0.92–3.33) 1.38 (0.70–2.69) 1 [Reference]

Model C 2.51 (1.24–5.05) 2.08 (1.05–4.14) 1.74 (0.88–3.47) 1.34 (0.65–2.76) 1 [Reference]

Elevated percentage of body fat (≥25%)

Model A 3.38 (1.47–7.76) 2.48 (1.09–5.62) 2.11 (0.92–4.80) 1.40 (0.59–3.31) 1 [Reference]

Model B 3.74 (1.61–8.67) 2.78 (1.21–6.40) 2.28 (0.99–5.24) 1.54 (0.64–3.68) 1 [Reference]

Model C 3.33 (1.35–8.20) 2.66 (1.09–6.48) 2.06 (0.84–5.02) 1.22 (0.48–3.11) 1 [Reference]

Women

Elevated waist girth (≥88 cm)

Model A 3.54 (2.06–6.10) 2.20 (1.32–3.67) 1.74 (1.05–2.87) 1.29 (0.78–2.16) 1 [Reference]

Model B 3.07 (1.75–5.41) 1.94 (1.14–3.29) 1.61 (0.96–2.71) 1.30 (0.77–2.20) 1 [Reference]

Model C 1.77 (0.95–3.29) 1.30 (0.73–2.30) 1.21 (0.69–2.11) 1.11 (0.63–1.95) 1 [Reference]

Body mass index

Model A 4.04 (2.18–7.51) 2.27 (1.25–4.12) 1.63 (0.90–2.96) 1.11 (0.60–2.05) 1 [Reference]

Model B 3.51 (1.85–6.66) 1.95 (1.05–3.60) 1.47 (0.80–2.71) 1.07 (0.57–2.00) 1 [Reference]

Model C 1.63 (0.79–3.35) 1.06 (0.54–2.10) 0.91 (0.46–1.79) 0.76 (0.38–1.51) 1 [Reference]

Elevated percentage of body fat (≥32%)

Model A 2.58 (1.29–5.15) 2.25 (1.21–4.18) 1.23 (0.65–2.31) 1.26 (0.68–2.33) 1 [Reference]

Model B 1.98 (0.97–4.06) 1.83 (0.97–3.48) 1.09 (0.57–2.08) 1.21 (0.64–2.27) 1 [Reference]

Model C 1.15 (0.51–2.60) 1.12 (0.54–2.33) 0.85 (0.41–1.76) 1.03 (0.51–2.11) 1 [Reference]

Table 3. Association Between Sitting Time and the Prevalence of Detrimental Levels of Cardiometabolic Risk Factors, Men and Women, Cooper Center Longitudinal
Study, 2010–2013

a Response options were 1) almost none of the time (about 0%), 2) approximately one-quarter of the time (about 25%), 3) approximately half of the time (about
50%), 4) approximately three-quarters of the time (about 75%), and 5) almost all of the time (about 100%).
b Model A, adjusted for age; model B, adjusted for age and cardiorespiratory fitness (metabolic equivalent of tasks [METs]); and model C, adjusted for all covari-
ates in model B plus physical activity (MET-minutes per week), alcohol consumption (drinks per week), current smoking status, waist girth (in models with lipids,
glucose, or blood pressure as the outcome), and hormone replacement therapy (women only).

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services,

the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.

10 Centers for Disease Control and Prevention • www.cdc.gov/pcd/issues/2016/16_0263.htm

www.cdc.gov/pcd/issues/2016/16_0263.htm

PREVENTING CHRONIC DISEASE VOLUME 13, E181

PUBLIC HEALTH RESEARCH, PRACTICE, AND POLICY DECEMBER 2016

Appendix. Association Between Sitting Time and the Prevalence of Detrimental
Levels of Other Cardiometabolic Risk Factors

Characteristic/Modelb

Sitting Timea

About 100%, Odds
Ratio (95% CI)

About 75%, Odds Ratio
(95% CI)

About 50%, Odds Ratio
(95% CI)

About 25%, Odds Ratio
(95% CI) About 0%

Men

Elevated total cholesterol (>200 mg/dL)

Model A 0.78 (0.48–1.27) 0.81 (0.50–1.30) 0.83 (0.52–1.34) 0.85 (0.51–1.40) 1 [Reference]

Model B 0.69 (0.41–1.17) 0.74 (0.45–1.23) 0.78 (0.47–1.30) 0.86 (0.50–1.47) 1 [Reference]

Model C 0.70 (0.41–1.18) 0.75 (0.45–1.24) 0.78 (0.47–1.31) 0.86 (0.50–1.48) 1 [Reference]

Elevated LDL cholesterol (>100 mg/dL)

Model A 0.93 (0.58–1.49) 0.96 (0.60–1.52) 0.96 (0.60–1.52) 0.92 (0.56–1.50) 1 [Reference]

Model B 0.86 (0.51–1.46) 0.92 (0.55–1.54) 0.96 (0.57–1.60) 0.99 (0.58–1.60) 1 [Reference]

Model C 0.86 (0.51–1.46) 0.93 (0.56–1.55) 0.96 (0.57–1.60) 0.99 (0.58–1.71) 1 [Reference]

Low HDL cholesterol (<40 mg/dL)

Model A 1.82 (0.85–3.86) 1.73 (0.82–3.64) 1.68 (0.80–3.53) 1.74 (0.80–3.77) 1 [Reference]

Model B 1.53 (0.70–3.33) 1.57 (0.73–3.37) 1.57 (0.73–3.38) 1.62 (0.73–3.59) 1 [Reference]

Model C 1.57 (0.72–3.42) 1.62 (0.75–3.48) 1.60 (0.74–3.44) 1.63 (0.74–3.62) 1 [Reference]

Elevated triglycerides (≥150 mg/dL)

Model A 1.10 (0.63–1.94) 0.97 (0.56–1.68) 1.01 (0.58–1.75) 0.99 (0.56–1.77) 1 [Reference]

Model B 0.94 (0.52–1.68) 0.87 (0.49–1.54) 0.92 (0.52–1.62) 0.93 (0.51–1.69) 1 [Reference]

Model C 0.96 (0.54–1.74) 0.91 (0.51–1.61) 0.93 (0.52–1.65) 0.93 (0.51–1.71) 1 [Reference]

Elevated glucose (≥100 mg/dL)

Model A 1.15 (0.68–1.95) 1.26 (0.75–2.10) 1.11 (0.66–1.86) 1.12 (0.66–1.93) 1 [Reference]

Model B 1.07 (0.62–1.84) 1.21 (0.71–2.05) 1.05 (0.62–1.79) 1.11 (0.64–1.94) 1 [Reference]

Model C 1.08 (0.62–1.86) 1.23 (0.72–2.09) 1.06 (0.62–1.81) 1.12 (0.64–1.95) 1 [Reference]

Elevated blood pressure (≥130/85 mm Hg)

Model A 0.90 (0.52–1.57) 0.69 (0.40–1.19) 0.83 (0.48–1.43) 0.72 (0.40–1.28) 1 [Reference]

Model B 0.81 (0.46–1.44) 0.65 (0.37–1.13) 0.78 (0.45–1.36) 0.70 (0.39–1.27) 1 [Reference]

Model C 0.82 (0.46–1.46) 0.66 (0.38–1.15) 0.79 (0.45–1.38) 0.71 (0.39–1.28) 1 [Reference]

Metabolic syndrome

Model A 1.55 (0.76–3.20) 1.40 (0.69–2.85) 1.45 (0.71–2.94) 1.20 (0.57–2.52) 1 [Reference]

Model B 1.79 (0.86–3.73) 1.64 (0.80–3.38) 1.63 (0.79–3.35) 1.30 (0.61–2.76) 1 [Reference]

Model C 1.64 (0.76–3.53) 1.62 (0.77–3.43) 1.60 (0.75–3.38) 1.28 (0.58–2.80) 1 [Reference]

Abbreviation: HDL, high-density lipoprotein; LDL, low-density lipoprotein; METs, metabolic equivalent of tasks.
a Response options were 1) almost none of the time (about 0%), 2) approximately one-quarter of the time (about 25%), 3) approximately half of the time (about
50%), 4) approximately three-quarters of the time (about 75%), and 5) almost all of the time (about 100%).
b Model A, adjusted for age; model B, adjusted for age and cardiorespiratory fitness (metabolic equivalent of tasks [METs]); and model C, adjusted for all covariates
in model B plus physical activity (MET-minutes per week), alcohol consumption (drinks per week), current smoking status, waist girth (in models with lipids, glucose,
or blood pressure as the outcome), and hormone replacement therapy (women only).

(continued on next page)

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services,

the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.

www.cdc.gov/pcd/issues/2016/16_0263.htm • Centers for Disease Control and Prevention 11

www.cdc.gov/pcd/issues/2016/16_0263.htm

PREVENTING CHRONIC DISEASE VOLUME 13, E181

PUBLIC HEALTH RESEARCH, PRACTICE, AND POLICY DECEMBER 2016

(continued)

Characteristic/Modelb

Sitting Timea

About 100%, Odds
Ratio (95% CI)

About 75%, Odds Ratio
(95% CI)

About 50%, Odds Ratio
(95% CI)

About 25%, Odds Ratio
(95% CI) About 0%

Women

Elevated total cholesterol (>200 mg/dL)

Model A 1.04 (0.70–1.54) 0.85 (0.60–1.20) 0.92 (0.66–1.27) 0.83 (0.60–1.14) 1 [Reference]

Model B 0.88 (0.58–1.33) 0.76 (0.53–1.09) 0.86 (0.61–1.21) 0.78 (0.56–1.09) 1 [Reference]

Model C 0.85 (0.56–1.28) 0.74 (0.52–1.06) 0.84 (0.60–1.19) 0.77 (0.55–1.07) 1 [Reference]

Elevated LDL cholesterol (>100 mg/dL)

Model A 1.29 (0.88–1.90) 1.04 (0.75–1.46) 1.11 (0.80–1.53) 1.05 (0.77–1.44) 1 [Reference]

Model B 0.90 (0.60–1.37) 0.81 (0.57–1.16) 0.95 (0.67–1.34) 0.95 (0.68–1.33) 1 [Reference]

Model C 0.87 (0.57–1.32) 0.78 (0.55–1.12) 0.93 (0.66–1.31) 0.93 (0.67–1.31) 1 [Reference]

Low HDL cholesterol (<40 mg/dL)

Model A 1.94 (1.06–3.54) 1.70 (0.97–2.94) 1.31 (0.76–2.26) 1.11 (0.64–1.93) 1 [Reference]

Model B 0.89 (0.46–1.74) 1.00 (0.55–1.80) 0.85 (0.47–1.52) 0.85 (0.47–1.52) 1 [Reference]

Model C 0.89 (0.45–1.73) 0.99 (0.55–1.79) 0.84 (0.47–1.51) 0.84 (0.47–1.51) 1 [Reference]

Elevated triglycerides (≥150 mg/dL)

Model A 2.62 (1.39–4.91) 2.10 (1.17–3.76) 1.63 (0.91–2.91) 1.45 (0.81–2.60) 1 [Reference]

Model B 1.53 (0.77–3.02) 1.50 (0.81–2.79) 1.26 (0.68–2.31) 1.30 (0.71–2.38) 1 [Reference]

Model C 1.43 (0.72–2.83) 1.42 (0.76–2.64) 1.21 (0.66–2.22) 1.26 (0.69–2.32) 1 [Reference]

Elevated glucose (≥100 mg/dL)

Model A 1.70 (0.92–3.16) 1.32 (0.75–2.32) 1.23 (0.72–2.10) 1.14 (0.67–1.93) 1 [Reference]

Model B 1.35 (0.70–2.60) 1.18 (0.66–2.12) 1.16 (0.68–2.00) 1.17 (0.68–2.01) 1 [Reference]

Model C 1.29 (0.67–2.49) 1.14 (0.64–2.06) 1.13 (0.65–1.96) 1.15 (0.67–1.98) 1 [Reference]

Elevated blood pressure (≥130/85 mm Hg)

Model A 1.81 (0.87–3.75) 1.28 (0.65–2.52) 1.12 (0.58–2.14) 1.01 (0.53–1.93) 1 [Reference]

Model B 1.53 (0.72–3.24) 1.17 (0.59–2.33) 1.06 (0.55–2.04) 1.00 (0.52–1.92) 1 [Reference]

Model C 1.52 (0.72–3.22) 1.17 (0.58–2.32) 1.05 (0.54–2.03) 1.00 (0.52–1.92) 1 [Reference]

Metabolic syndrome

Model A 5.43 (2.00–14.75) 3.20 (1.20–8.51) 2.52 (0.96–6.64) 2.08 (0.79–5.51) 1 [Reference]

Model B 4.42 (1.57–12.43) 2.68 (0.98–7.34) 2.27 (0.84–6.13) 2.17 (0.80–5.91) 1 [Reference]

Model C 2.44 (0.84–7.06) 1.66 (0.59–4.64) 1.63 (0.59–4.48) 1.80 (0.66–4.95) 1 [Reference]

Abbreviation: HDL, high-density lipoprotein; LDL, low-density lipoprotein; METs, metabolic equivalent of tasks.
a Response options were 1) almost none of the time (about 0%), 2) approximately one-quarter of the time (about 25%), 3) approximately half of the time (about
50%), 4) approximately three-quarters of the time (about 75%), and 5) almost all of the time (about 100%).
b Model A, adjusted for age; model B, adjusted for age and cardiorespiratory fitness (metabolic equivalent of tasks [METs]); and model C, adjusted for all covariates
in model B plus physical activity (MET-minutes per week), alcohol consumption (drinks per week), current smoking status, waist girth (in models with lipids, glucose,
or blood pressure as the outcome), and hormone replacement therapy (women only).

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services,

the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.

12 Centers for Disease Control and Prevention • www.cdc.gov/pcd/issues/2016/16_0263.htm

www.cdc.gov/pcd/issues/2016/16_0263.htm

Template for Article Critique Reports
Week 4 – Assignment 4a – Barlow

Part Question Answer Points
Title Title of the article, journal name, your

name
Title (1): Association Between Sitting Time and Cardiometabolic Risk
Factors After Adjustment for Cardiorespiratory Fitness, Cooper Center
Longitudinal Study, 2010–2013
Journal (1): Preventing Chronic Disease
Your name (1) 3

Purpose/Research
problem

What is the purpose of the study? Is it
clearly identified? Is the research problem
important?

Primary goal
To examine the association between sitting time and cardiometabolic
risk factors after adjustment for cardiorespiratory fitness (CRF) (3)
Secondary goals
– To examine whether CRF confounded or modified the associations

between sitting time and cardiometabolic risk factors (1)
– To explore whether the role of CRF differed by sex (1)

Yes the purpose is clearly identified (0.25 bonus).
Yes, this is an important research problem given the high prevalence of
extended sitting times in our society (deskwork, TVs, computers, video
games, etc.) (0.25 bonus) 5

Identify the dependent variable(s) Cardiometabolic risk factors 3
Identify the independent variable(s) Sitting time 3

Literature review Are the cited sources relevant to the
study?

Yes.
3

Does the literature review offer a balanced
critical analysis of the literature?

Yes.
3

Are the cited studies recent? Yes. All are all from the past 10 years. 3
Theoretical
framework

Has a conceptual or theoretical framework
been identified?

No theoretical framework was identified.
3

If yes, is the framework adequately
described?

Not applicable.
3

Design and
procedures

Identify the study design used in this
study?

Cross sectional
5

Is the study design appropriate to answer
the research question?

Yes, given that the objective is to assess the association and not the
causality. 3

What type of sampling design was used? Not clearly indicated but it seems like a convenience sample (no
indication of random selection of participants or consecutive sampling) 5

Was the sample size justified on the basis
of a power analysis or other rationale?

No, sample size was not justified on the basis of a power analysis.
5

Are the inclusion and exclusion criteria
clearly identified?

Inclusion criteria
– Adults (0.5)
– Generally healthy Cooper clinic patients (0.5)
– Receiving preventive medical examination (0.5)
Exclusion criteria
– personal history of cardiovascular disease (0.5), stroke (0.5), or

diabetes (0.5)
– not reaching 85% of predicted maximal heart rate on the treadmill

test (1)
– missing data on some of the covariates (1) 5

What measurement tools were used for
the dependent variable(s)?

Cardiometabolic risk factors were measured using the following:
– Body mass index: height measured on a stadiometer; weight on a

standard physician’s scale; BMI = weight in kilograms divided by
height in meters squared; BMI of 30 kg/m2 or higher classified as
obese (0.5)

– Waist girth measured with a plastic tape at the level of the umbilicus
following a normal exhalation; elevated waist girth ≥102 cm for men
and ≥88 cm for women (0.5)

– Percentage of body fat measured at 7 skinfold sites with calipers and
inserting the sum of these skinfold measurements in a generalized
body density equation to estimate percentage of body fat. Sex-
specific cut points of percentage of body fat (<25% or ≥25% for men and <32% or ≥32% for women) were used to classify patients as obese (0.5)

– Lipids:
o Elevated total cholesterol higher than 200 mg/dL (0.5)
o Elevated LDL cholesterol higher than 100 mg/dL (0.5)
o Low HDL cholesterol less than 40 mg/dL for men and less

than 50 mg/dL for women (0.5)
o Elevated triglycerides ≥ 150 mg/dL; (0.5)
o Elevated glucose if fasting blood glucose ≥100 mg/dL (0.5)

– Elevated blood pressure: defined as a systolic blood pressure 130
mm Hg or higher or diastolic blood pressure 85 mm Hg or higher, or
both (0.5)

– Metabolic syndrome defined as meeting 3 or more of the following
criteria: abdominal obesity; high triglycerides; low HDL; high blood
pressure; and high glucose (0.5) 5

What measurement tools were used for
the independent variable(s)?

Sitting time was measured by a question, assessing the proportion of
time spent sitting during work, school, and housework during waking
hours on a typical day (3.5). Response options were (1.5)
1) almost none of the time (about 0%) 5

2) approximately one-quarter of the time (about 25%)
3) approximately half of the time (about 50%)
4) approximately three quarters of the time (about 75%)
5) almost all of the time (about 100%).

Were validity and reliability issues
discussed?

Dependent variables: measurements were taken according to standard
(1) procedures by trained (1) technicians. This enhances the validity and
reliability of the measures.
Independent variable: Sitting time question was derived from the Canada
Fitness Survey. However, that measure has not been validated (1). The
reliability of the measure was not addressed (1). 4

Ethical
considerations

Were the participants fully informed about
the nature of the research?

Given the IRB approval, it is assumed that the participants were informed
about the nature of the study. 3

Were the participants protected from
harm?

Reporting on sitting time and having standard health measurements
collected in a clinical environment is not likely to result in harm to
participants. This is supported by the exempt status of the study, which
implies that there is minimal risk associated with participation. 2

Was ethical permission granted for the
study?

Yes. The Cooper Institutes institutional review board reviewed and
approved the overall study. The study also received exempt status from
the University of Texas Health Science Center at Houston’s Committee
for the Protection of Human Subjects. 3

Data analysis What type of data and statistical analysis
was undertaken?

Logistic regressions models adjusted for various covariates.
3

Was the statistical analysis appropriate to
address the research question?

Yes. Logistic regressions allow for examining associations between
sitting time and cardiometabolic risk factors. It also allows for the
calculation of odds ratios.

Note: It is OK if the answer is not clear since evaluation of these
advanced statistical analyses is beyond the scope of this course. 2

Results What are the results of the study? Did the
results answer the research question(s)?

– Men: Prolonged sitting is associated with high levels of adiposity after
accounting for cardiorespiratory fitness. Other cardiometabolic risk
factors (elevated lipids, blood glucose, triglycerides, and blood
pressure; low levels of HDL; and the presence of metabolic
syndrome) were not significantly associated with sitting time.

– Women: The relationship between self-reported sitting time and
adiposity as well as with any individual cardiometabolic risk factor
was not found. 5

Discussion Were the findings linked back to the
literature review?

Yes. The authors critically explore the similarities and differences in
findings between their study and the literature. 3

Did the authors identify study limitations?
What were they?

The authors identify the following limitations:
– Self-reported measure of sitting time has not been validated (1.5) 5

– Sample characteristics (healthy, non-Hispanic whites, well educated)
are not representative of the population. This may limit the
generalizability of the findings (1.5)

– Cross-sectional study design does not establish causality (2)
Do you think the limitations are serious
enough to impact the internal and external
validity* of the study?

The limitations of the study do not pause a serious threat to the internal
validity of the study (1.5). However, the homogeneous sample does
pause questions about the generalizability of study findings to other
population groups (minorities, people with health conditions, those with
lower educational attainment, etc.) (1.5).

*From your Week 3 PPT slides: The validity of a study, in contrast to the
validity of measurements, is the degree to which study results are
accurate and well-founded, when account is taken of study methods,
representativeness of study sample, and nature of the population from
which it is drawn.
– Internal validity (results are attributed to hypothesized effect and not

sample differences)
– External validity (generalizability) 3

Overall What is your overall assessment of the *Answers may vary.
assessment study? This study addresses a very important research question given our

society’s sedentary lifestyle that supports extended periods of sitting
down. The large sample size supports the inferences made. However,
there is a need to replicate this study in different population groups and
to utilize a different research design that supports establishing causality
rather than mere associations. 5

Total 100

Source: Coughlan M1, Cronin P, Ryan F. Step-by-step guide to critiquing research. Part 1: quantitative research. Br J Nurs. 2007;16(11):658-63.

N O N – E X P E R I M E N T A L S T U D Y D E S I G N S
P A R T 3 : C A S E – C O N T R O L S T U D Y D E S I G N

6310-WEEK 4

MAIN NON-EXPERIMENTAL STUDY
DESIGNS

• Cross-sectional study design
• Cohort study design
• Case-control study design

CASE-CONTROL STUDIES

CASE-CONTROL RESEARCH DESIGN

• A non-experimental research design involving the
comparison of a “case” (person with disease/condition
of interest) and a “matched control” (similar person
without the condition).

• Retrospective study design: A group of subjects with the
outcome (cases) and another without the outcome
(controls) are identified. The investigator then works
backward to find differences in predictor variables that
may be associated with the outcome.

• Advantages:
• Inexpensive and efficient for studying rare diseases/conditions

CASE CONTROL STUDY DESIGN

Cases

Disease

Exposed Not
Exposed

Controls

No
Disease

Exposed Not
Exposed

STEPS: CASE CONTROL STUDIES

• Develop a research question
• Select a sample from a population of people with the

outcome of interest or disease (cases)
• Select a sample from a population at risk without the

outcome of interest or disease (controls)
• Measure predictor variables

• Note that the use of two samples in a case-control
design is different from a double-cohort design
• Double-cohort: two groups chosen based on level of predictor
• Case-control: two groups chosen based on presence or

absence of an outcome

STATISTICAL MEASURES
IN CASE-CONTROL DESIGNS

• Odds ratio (OR) is a measure of association between an
exposure and an outcome. The OR represents the odds
that an outcome will occur given a particular exposure,
compared to the odds of the outcome occurring in the
absence of that exposure.

• OR is used to determine whether an exposure is a risk
factor for an outcome, and to compare the magnitude
of various risk factors for that outcome.
• OR=1 Exposure does not affect odds of outcome
• OR>1 Exposure associated with higher odds of outcome
• OR<1 Exposure associated with lower odds of outcome

Source: Szumilas M. Explaining Odds Ratios. Journal of the Canadian Academy of
Child and Adolescent Psychiatry. 2010;19(3):227-229.

STRENGTHS OF CASE CONTROL
STUDIES

• Efficient for rare diseases and those with long latent
periods between exposure and disease

• Inexpensive
• Small sample size
• Ability to examine a large number of predictor

variables
• Short duration

WEAKNESSES OF CASE-CONTROL
STUDIES

• One outcome (presence or absence of the disease
that is the criterion for drawing the two samples)

• No causal relations
• Cannot yield estimates for the incidence or

prevalence of a disease
• Susceptibility to bias

• Sampling bias
• Measurement bias

SOURCES OF BIAS IN
CASE-CONTROL STUDIES

• Sampling bias
• Cases are sampled from patients in whom the disease has

already been diagnosed and who are available for study.
This can lead to a potentially non-representative sample
(sample does not include those who are undiagnosed,
misdiagnosed, unavailable for study, or dead).

• Strategies for sampling controls to minimize
sampling bias
• Clinic- or hospital-based controls
• Population-based samples of cases and controls
• Two or more control groups
• Matching

SOURCES OF BIAS IN
CASE-CONTROL STUDIES

• Measurement bias
• Due to retrospective approach to measuring the predictor

variables or exposure
• Nondifferential misclassification of exposure (similar in cases and

controls)  difficulty in finding associations
• Differential misclassification of exposure (recall bias: different

recollection of exposure among cases)  unpredictable effects
on associations

• Strategies to minimize measurement bias
• Use data recorded before the outcome occurred
• Use blinding

EXAMPLES
CASE-CONTROL STUDIES

1952 CASE-CONTROL STUDY:
SMOKING AND LUNG CANCER

• Research question: Do patients with carcinoma of
the lung differ from other persons, either in their
smoking habits or in some way which might be
related to the theory that atmospheric pollution is
responsible for the development of the disease?

• Sample: 1,465 lung cancer cases & 1,465 controls
with no cancer in 20 hospitals in England.
• Cases matched in age, sex, hospital

• Results: There is a relationship between the number
of cigarettes smoked daily and developing lung
cancer in men.

Source: Doll R, Hill AB. A study of the aetiology of carcinoma of the lung. Br Med
J. 1952 ;13;2(4797):1271-86.

• 6310-Week 4
• Main Non-Experimental Study Designs
• Case-Control Studies
• Case-Control Research Design
• Case Control Study Design
• Steps: Case Control Studies
• Statistical Measures�in Case-Control Designs
• Strengths of Case Control Studies
• Weaknesses of Case-Control Studies
• Sources of Bias in �Case-Control Studies

Sources of Bias in �Case-Control Studies

• Examples
• 1952 Case-Control Study:�Smoking and Lung Cancer