Analyzing Epidemiological Data

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Required Documents

• Dataset or data extract with codebook/data dictionary
• Population denominators from Census Bureau (intercensal or postcensal estimates) or denominator files from the data system
• CSTE or program-specific case definitions applied during data collection
• Prior analytic reports for the same topic (for consistency in methods)
• Data-use agreement and IRB approval documentation if using restricted-access data
• Healthy People 2030 targets for the relevant indicators (if benchmarking)

Step 1 — Assess Data Quality

Before any calculation:

• Completeness: Tabulate missing values for each key variable. Flag variables with > 15% missing for sensitivity analysis.
• Accuracy: Cross-validate a sample of records against source documents or laboratory confirmations.
• Timeliness: Determine reporting lag — median days from event to record entry. Note if provisional data may change.
• Consistency: Check for coding changes across years (ICD-9 to ICD-10 transition, CSTE case definition revisions).
• Representativeness: Assess whether the data capture the full population at risk or only a sentinel subset.

Document all data quality findings. They constrain the interpretive strength of any downstream analysis.

Step 2 — Calculate Core Epidemiologic Measures

Incidence

• Incidence rate (person-time) = New cases / Person-time at risk. Express per 100,000 person-years for chronic diseases, per 100,000 population for annual surveillance.
• Cumulative incidence (risk) = New cases / Population at risk at start of period. Used for fixed-cohort analyses.
• Attack rate = Cases / Population at risk during an outbreak. A special case of cumulative incidence.

Prevalence

• Point prevalence = Existing cases at a point in time / Total population at that time.
• Period prevalence = Existing cases during a defined period / Mid-period population.

Mortality

• Crude death rate = Deaths / Mid-year population × 100,000.
• Cause-specific mortality rate = Deaths from cause / Mid-year population × 100,000.
• Case fatality rate = Deaths from disease / Cases of disease × 100. (Not a true rate — a proportion.)

Measures of Association

• Relative risk (RR) = Incidence in exposed / Incidence in unexposed. Used in cohort studies.
• Odds ratio (OR) = (a × d) / (b × c) from a 2×2 table. Used in case-control studies; approximates RR when disease is rare.
• Attributable risk (AR) = Incidence in exposed − Incidence in unexposed. Quantifies excess risk due to exposure.
• Population attributable fraction (PAF) = (Incidence total − Incidence unexposed) / Incidence total. Quantifies public health impact.

Step 3 — Standardize and Adjust Rates

Direct age-adjustment (preferred when stratum-specific rates are stable):

• Apply observed age-specific rates to the 2000 U.S. standard population (19 age groups per NCHS).
• Sum the expected counts across strata and divide by the total standard population.
• Report as age-adjusted rate per 100,000.

Indirect age-adjustment (when stratum-specific rates are unstable due to small numbers):

• Apply a reference population's age-specific rates to the study population's age distribution.
• Calculate the Standardized Mortality Ratio (SMR) = Observed deaths / Expected deaths.
• SMR > 1.0 indicates excess mortality relative to the reference.

Always specify the standard population and method in footnotes. Never compare a directly adjusted rate to an indirectly adjusted rate.

Step 4 — Test Hypotheses and Quantify Uncertainty

• Calculate 95% confidence intervals for all rates, ratios, and proportions.
• For rate comparisons: use normal approximation for large counts, exact Poisson for counts < 20.
• For 2×2 tables: chi-square test (expected cells ≥ 5) or Fisher's exact test (any expected cell < 5).
• For trend analysis: Joinpoint regression (NCI Joinpoint software) to identify statistically significant changes in annual percent change (APC).
• For multivariable analysis: logistic regression (binary outcome), Poisson/negative binomial regression (count outcome), Cox proportional hazards (time-to-event).
• For survey data (BRFSS, NHANES): apply survey weights, strata, and PSU using appropriate procedures (PROC SURVEYFREQ, svydesign in R).

Report p-values alongside confidence intervals, but emphasize magnitude of effect and public health significance over statistical significance alone.

Step 5 — Visualize and Communicate Results

• Tables: Present rates with numerators, denominators, and 95% CIs. Suppress cells with counts 1-4 per NCHS reliability standards (RSE > 30% = unreliable; RSE 20-30% = flag).
• Figures: Epi curves, trend lines with confidence bands, choropleth maps with appropriate class intervals (quantile or natural breaks).
• Comparisons: Always include the referent group, the comparison period, and the standard population in table footnotes.
• Context: Benchmark against Healthy People 2030 targets, national MMWR data, or peer jurisdictions.
• Interpretation: State findings in plain language for non-epidemiologist audiences. Distinguish correlation from causation.

Checkpoint B — Analysis Review

• All measures calculated with explicit numerator, denominator, and multiplier documented
• Age-adjustment method and standard population specified
• Confidence intervals reported for every point estimate
• Small-cell suppression rules applied consistently
• Survey weights applied for complex survey designs
• Data limitations section included in report
• All code/queries saved and version-controlled for reproducibility
• Results reviewed by senior epidemiologist before release

Quality Audit

• Denominator source documented with Census vintage year
• ICD code version consistent across the entire analysis period (no mixed ICD-9/ICD-10 without crosswalk)
• Rate calculations match when manually verified for at least one stratum
• Joinpoint or trend test results include APC and p-value for each segment
• BRFSS/YRBSS analyses account for complex survey design (weighted, stratified, clustered)
• Mortality analyses distinguish underlying cause from contributing cause coding
• PAF calculations use correct formula variant for the study design (cohort vs. cross-sectional)
• All outputs labeled DRAFT until senior review complete

Guidelines

• Never compare crude rates across populations with different age structures. Always age-adjust or present stratum-specific rates.
• Always report the absolute number of cases alongside rates. A rate of 500 per 100,000 based on 5 cases in a population of 1,000 is unreliable.
• Distinguish incidence from prevalence in all outputs. Incidence measures new cases; prevalence measures existing cases. Confusing them leads to incorrect intervention targeting.
• When using BRFSS data, acknowledge the shift from landline-only to dual-frame (landline + cell) sampling that occurred in 2011. Pre-2011 and post-2011 BRFSS data are not directly comparable without raking adjustment.
• For Healthy People 2030 benchmarking, use the data source and methodology specified in the objective's data documentation — not a proxy data source.
• Mark any AI-generated analysis output as [DRAFT — REQUIRES EPIDEMIOLOGIST REVIEW] until a credentialed professional has validated the methods and interpretation.