Public data, responsibly interpreted.
The California Elder Fraud Observatory is an independent research initiative analyzing county-level elder financial abuse reporting across California using Adult Protective Services administrative data and U.S. Census demographics.
About the Observatory
Counties are compared against a statistically expected reporting rate derived from a multivariable demographic model — not against the statewide average. That distinction matters. A rural county with 30% seniors and a large urban county with 15% seniors are expected to look different; comparing both to a single statewide average obscures more than it reveals.
The observatory publishes every metric, every dataset, and every modeling choice so that policymakers, journalists, researchers, and the public can interpret county-level reporting patterns with appropriate context. A reporting difference above or below the model-expected rate is a signal for further inquiry, not evidence of over- or under-reporting.
Founder of the California Elder Fraud Observatory, an independent research initiative using public administrative data and demographic modeling to better understand county-level variation in elder financial abuse reporting across California.
Laia Chandran-Moles is a student researcher at Los Gatos High School interested in applying statistics and data science to public health and public policy. She founded the California Elder Fraud Observatory to make county-level elder financial abuse reporting more transparent, interpretable, and accessible to policymakers, researchers, journalists, and community organizations.
The observatory combines publicly available Adult Protective Services administrative data with demographic modeling to help readers interpret reporting differences within their appropriate statistical context rather than relying on simple statewide rankings.
Data Sources
- California Department of Social Services (CDSS)Adult Protective Services SOC 242 administrative records, fiscal years 2019–2024, aggregated to the county level.
- U.S. Census BureauAmerican Community Survey (ACS) 5-year estimates for the six demographic variables used as model inputs.
- USDA Economic Research ServiceRural–Urban Continuum Codes (RUCC 2023) for rural / urban classification.
Acknowledgements
Thanks to the California Department of Social Services APS Bureau and to county APS agencies whose reporting makes this analysis possible, and to the community of open-data practitioners whose tools this observatory builds on.
A reporting difference above or below the model-expected rate is a signal for further inquiry, not evidence of over- or under-reporting. The observatory publishes context alongside every rate and encourages readers to consult the Methodology page.