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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.
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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
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
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)
Uses of cross-sectional studies • Individual & family care • Studies on diagnostic test • Studies of growth & development
Cross-Sectional StudiesAdvantages • 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 StudiesDisadvantages 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
Accessible population Target population: Clearly defined clinical & demographic characteristics well suited to the research question Target population Intended Sample Specifications & Sampling Example: Hypertension among adults (aged 18 years and above)
Accessible population: Specify temporal and geographic characteristics representative of target populations and easy to study Accessible population Target population Intended Sample Specifications & Sampling Example: Hypertension among adults (aged 18 years and above in the field practice area of MGIMS)
Accessible population Intended population: Design an approach to select a sample representative of accessible population & easy to do Target population Intended Sample Specifications & Sampling Example: Hypertension among adults (aged 18 years and above in the field practice area of MGIMS)
Precision & Accuracy Good precision Poor precision Good precision Poor precision Poor accuracy Good accuracy Good accuracy Poor accuracy
Confounding • Example
Criteria for confounding • The confounder must be associated with the exposure • The confounder must be associated with the disease, independent of the exposure • 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