EQUITABLY COLLECT, ANALYZE, AND DISSEMINATE INJURY AND VIOLENCE DATA

The very foundation of public health is data. Like other public health agencies, state IVP programs have always been data-driven. Given the wide range of injury types and risk factors, multiple data sources are required to develop a comprehensive and accurate picture of injury and violence trends.

Equitable Data Collection, Analysis, and Dissemination

Currently, inequity is ingrained into how we collect and analyze data. Data that define and highlight inequities are collected or suppressed without considering historical and social contexts that impact health. This context clarifies and highlights root causes of inequities, without which key insight is missed and inequities can persist. Aggregate data, suppressed data, and the lack of data on upstream driving factors contribute to the gaps in data that make it difficult to inform equitable interventions and policies. In some cases, populations can be made invisible and/or classified as other races; for example, American Indians/American Natives are often classified as white or other. It is commonplace that within federal, state, and local privacy and data collection laws, results for small populations are aggregated into others; because populations are not accurately represented it is difficult to collect, protect, and accurately use data and determine social vulnerability indexes.

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IVP programs should collect data on SDOHs, risk and protective factors, Adverse Childhood Experiences (ACES), and injury and violence-related health indicators by race/ethnicity, gender, sexual orientation, and/or community subgroups. Epidemiologists can provide technical expertise on data sources to query, data to collect, and the framing of surveillance methodologies to account for various sub-populations in a statistically accurate and equitable way. They should also be able to provide context around data and detail the limitations in current data being used and reported. Epidemiologists can consider alternative data sources as these might provide additional insight for communities of interest. Examples of these sources include qualitative studies, focus groups, social media, and others.

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