Association Between County-Level Social Vulnerability and Centers for Disease Control and Prevention-Funded HIV
Wei Song1, Mesfin S Mulatu, Nicole Crepaz
1Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA.
Journal of Acquired Immune Deficiency Syndromes (1999)
|January 9, 2025
Summary
Community-level social vulnerability impacts HIV testing outcomes. While CDC-funded programs reach vulnerable areas, disparities in HIV positivity, care linkage, and prevention referrals persist, requiring targeted resource allocation.
Area of Science:
- Public Health
- Epidemiology
- Social Determinants of Health
Background:
- Community-level social vulnerabilities significantly influence HIV outcomes.
- This study examined the association between county-level social vulnerability and HIV testing program results funded by the Centers for Disease Control and Prevention (CDC).
Purpose of the Study:
- To assess the relationship between county-level social vulnerability and key performance indicators of CDC-funded HIV testing programs.
- To identify disparities in HIV testing outcomes based on social vulnerability metrics.
Main Methods:
- Utilized HIV testing data from 60 state/local health departments and 119 community-based organizations (2020-2022).
- Integrated HIV testing data with the Minority Health Social Vulnerability Index (SVI) at the county level.
- Calculated disparity measures for HIV positivity, linkage to care, partner services, and pre-exposure prophylaxis (PrEP) referrals between high and low SVI counties.
Main Results:
- Over 85% of 4.9 million HIV tests occurred in high social vulnerability counties.
- Higher HIV positivity (1.1%) and linkage to care (77.5%) were observed in high SVI counties.
- Lower rates of partner services (72.1%) and PrEP referrals (48.1%) were found in high SVI counties, with disparities varying by demographics and site type.
Conclusions:
- CDC-funded HIV testing programs effectively reach vulnerable populations.
- HIV testing program outcomes, including linkage and prevention referrals, demonstrate variability influenced by social vulnerability, demographics, and testing site.
- Ongoing monitoring of social vulnerability's impact on HIV testing is crucial for resource allocation and ending the HIV epidemic.
Related Concept Videos
Statistical Methods for Analyzing Epidemiological Data
299
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
299
Bias in Epidemiological Studies
151
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
151
Prevalence and Incidence
342
In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
342
Causality in Epidemiology
289
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
289
Hazard Ratio
85
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
For example, in a clinical trial...
85
Longitudinal Research
11.8K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
11.8K


