Siroli Lily: State Flower of Manipur

Cross- sectional Study Subodh S Gupta MGIMS, Sewagram

Simplest research questions ► What is the total population of Imphal? ► What proportion of married men in Imphal help their wives in kitchen. ► What proportion of patients attending OPD in Imphal hospital and research center come for psychiatric disorders? ► What is the prevalence of underweight among under-five children in Ukhrul? ► What proportion of residents of Imphal above 30 years of age do exercise regularly?

More research questions ► Do married men younger than 40 years help their wives in household chores more often than those above 40 years? ► Is the prevalence of hypertension higher in those who exercise regularly than those who do not exercise regularly?

Types of study design Grimes and Schulz. Lancet 2002

Snapshot observation Vs longitudinal observation

Cross-sectional studies ► Descriptive, or ► Analytical, or ► Both

Gynecomastia in a drug company ► ► Puerto Rico pharmaceutical company: Survey showed that employees had gynecomastia ► ► OC pills; oestrogen dust might be the cause ► ► Dust control measures; epidemic disappeared Harrington et al. Arch Environ Health 1978; 33: 12-15

Demographic surveys: a type of cross-sectional studies ► National Family Health Survey ► District level Health Survey ► NNMB Survey ► Sentinel surveillance for HIV

HIV prevalence in India by district, 2005

Multiple cross-sections help in giving a whole picture?

Estimated adult HIV prevalence & number of PLHA, India,

HIV estimation 2010

Uses of cross-sectional studies Public Health ► Community diagnosis  Health status  Determinants of health & disease  Association between variables  Identification of groups requiring special care ► Surveillance ► Evaluation of community’s health care (coverage evaluation)

► Individual & family care ► Studies on diagnostic test ► Studies of growth & development Uses of cross-sectional studies

Cross-Sectional Studies Advantages ► Cheap and quick studies. ► Data is frequently available through current records or statistics. ► Ideal for generating new hypothesis ► Generalizable results if population based sample ► Study multiple outcomes and exposures ► Can measure prevalence ► Hypothesis generating for causal links ► Serial surveys

Cross-Sectional Studies Disadvantages The importance of the relationship between the cause and the effect cannot be determined. ► Temporal weakness:  Cannot determine if cause preceded the effect or the effect was responsible for the cause.  The rules of contributory cause cannot be fulfilled. ► Impractical for rare diseases if pop based sample Prone to bias (selection, measurement)

Sampling methods ► Probability sampling  Simple random sampling  Systematic sampling  Stratified random sampling  Cluster sampling ► Non-probability sampling  Consecutive sampling  Convenience sampling  Purposive (Judgmental) sampling

Target population Accessible population Intended Sample Target population: Clearly defined clinical & demographic characteristics well suited to the research question Specifications & Sampling Example: Hypertension among adults (aged 18 years and above)

Target population Accessible population Intended Sample Accessible population: Specify temporal and geographic characteristics representative of target populations and easy to study Specifications & Sampling Example: Hypertension among adults (aged 18 years and above in the field practice area of MGIMS)

Target population Accessible population Intended Sample Intended population: Design an approach to select a sample representative of accessible population & easy to do Specifications & Sampling Example: Hypertension among adults (aged 18 years and above in the field practice area of MGIMS)

Precision & Accuracy Good precisionPoor precisionGood precisionPoor precision Poor accuracyGood accuracyGood accuracyPoor accuracy

Confounding ► Example

Criteria for confounding 1. The confounder must be associated with the exposure 2. The confounder must be associated with the disease, independent of the exposure 3. The confounder must not be part of the causal pathway connecting the exposure to the disease.

Example ► Crude analysis

Criteria 1 ► Stratified analysis

Example: ► Criteria 1: The confounder must be associated with the exposure

Example: ► Criteria 2: The confounder must be associated with the disease, independent of the exposure

Bias in cross-sectional studies Selection Bias (eg, NSSP study) Is study population representative of target population? Is there systematic increase or decrease of RF? Measurement Bias Outcome ► Misclassified (dead, misdiagnosed, undiagnosed) ► Length-biased sampling  Cases overrepresented if illness has long duration and are underrepresented if short duration.(Prev = k x I x duration) Risk Factor ► Recall bias ► Prevalence-incidence bias  RF affects disease duration not incidence eg, HLA-A2

Analysis ► Analysis plan  Depending on objectives of the study  Dummy tables

Analysis- Descriptive CS study ► Objective:  To describe the disease in time, place and person  To generate hypothesis ► Analysis  Means & SD  Median & percentile  Proportions – Prevalence  Ratios  Age, sex or other group specific analysis

Analysis – Analytical CS study ► Objective:  Is there any association?  What is the strength of association? ► Analysis:  Is there any association?  What is the strength of association? ► Correlations ► Regression coefficients ► Differences between mean ► Odds ratio ► Risk ratio ► Risk difference

Other analysis ► Stratified analysis ► Logistic regression