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).

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.

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.

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.

© 2018 American Journal of Preventive Medicine. Pu reserved.

RESEARCH ARTICLE

From the 1Cleve Reserve Univers Research, Medici ter for Behavior Cleveland Clinic,

Address corr Clinic Main Cam 44195. E-mail: ch

0749-3797/$3 https://doi.org

blished by Elsevier Inc. All

Associations Between Unhealthy Weight-Loss

Strategies and Depressive Symptoms

D1X XAlexander Chaitoff, D2X XMPH,1 D3X XCarol Swetlik, D4X XBA,1 D5X XCatherine Ituarte, D6X XBS,1 D7X XElizabeth Pfoh, D8X XPhD, MPH,2

D9X XLing-Ling Lee, D10X XBS,1 D11X XLeslie J. Heinberg, D12X XPhD,3 D13X XMichael B. Rothberg, D14X XMD, MPH2

Introduction: There appears to be a link between weight loss and improved mental health, but less is known about how using unhealthy weight-loss strategies impacts the odds of reporting depression.

Methods: This study includes respondents from the National Health and Nutrition Examination Survey from 2005 to 2014 who attempted to lose weight over the past year. Analysis occurred in 2017. Multivariable logistic regression was used to describe associations between all weight-loss strategies, including those grouped as unhealthy (smoking, vomiting, laxatives, skipping meals, and using diet pills), and the adjusted odds of depression (Patient Health Questionnaire−9 score ≥10). The model was then stratified by BMI, sex, race, and antidepressant use to compare the effect of using at least one unhealthy weight-loss strategy and depression within certain populations.

Results: The sample included 6,765 respondents (weighted n=59.2 million, 95% CI=55.5, 62.9 mil- lion). Of these respondents, 18.0%

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