Our Methodology
Transparency is at the core of what we do. Here's exactly how we collect, verify, and present mental health statistics.
Data Collection Process
1. Source Identification
We identify authoritative sources including government health agencies (SAMHSA, CDC, NIH), peer-reviewed journals, and established research institutions. We prioritize primary sources over secondary reporting.
2. Data Verification
Each statistic is verified against the original source document. We record the exact page, table, or figure where the data appears. When possible, we cross-reference with multiple sources.
3. Structured Entry
Data is entered into our database with full metadata including: numeric value, unit of measurement, time period, geographic scope, population segment, sample size (when available), and confidence intervals.
4. Citation Generation
We generate AMA-formatted citations for every statistic, ensuring proper attribution and enabling verification. Citations include author/organization, title, publisher, and publication date.
Update Frequency
Our data is updated on different schedules depending on the source:
- NSDUH Data:Annually (typically released Q4)
- CDC BRFSS:Annually
- State Data:Varies by state (6-18 months)
- Research Studies:As published
Understanding Our Data
Prevalence Rates
Expressed as percentages, these represent the proportion of a population affected by a condition during a specific time period (usually 12 months or lifetime).
Sample Sizes
When available, we display sample sizes (n=) to help you assess statistical reliability. Larger samples generally indicate more reliable estimates.
Confidence Intervals
95% confidence intervals (CI) show the range where the true value likely falls. Narrower intervals indicate more precise estimates.
Time Periods
Pay attention to the year or period each statistic covers. Mental health trends can change significantly over time.
Limitations & Considerations
- Survey-based data relies on self-reporting, which may undercount stigmatized conditions.
- State-level comparisons should account for differences in data collection methods and definitions.
- Historical data may use different diagnostic criteria than current statistics.
- Small subgroups may have wider confidence intervals due to smaller sample sizes.
