Identifying the impact of social determinants of health on disease rates using correlation analysis of area-based summary information.

OBJECTIVES: We developed a statistical tool that brings together standard, accessible, and well-understood analytic approaches and uses area-based information and other publicly available data to identify social determinants of health (SDH) that significantly affect the morbidity of a specific disease. METHODS: We specified AIDS as the disease of interest and used data from the American Community Survey and the National HIV Surveillance System. Morbidity and socioeconomic variables in the two data systems were linked through geographic areas that can be identified in both systems. Correlation and partial correlation coefficients were used to measure the impact of socioeconomic factors on AIDS diagnosis rates in certain geographic areas. RESULTS: We developed an easily explained approach that can be used by a data analyst with access to publicly available datasets and standard statistical software to identify the impact of SDH. We found that the AIDS diagnosis rate was highly correlated with the distribution of race/ethnicity, population density, and marital status in an area. The impact of poverty, education level, and unemployment depended on other SDH variables. CONCLUSIONS: Area-based measures of socioeconomic variables can be used to identify risk factors associated with a disease of interest. When correlation analysis is used to identify risk factors, potential confounding from other variables must be taken into account.

Collection of social determinant of health measures in U.S. national surveillance systems for HIV, viral hepatitis, STDs, and TB.

Challenges exist in the study of social determinants of health (SDH) because of limited comparability of population-based U.S. data on SDH. This limitation is due to differences in disparity or equity measurements, as well as general data quality and availability. We reviewed the current SDH variables collected for HIV, viral hepatitis, sexually transmitted diseases, and tuberculosis at the Centers for Disease Control and Prevention through its population-based surveillance systems and assessed specific system attributes. Results were used to provide recommendations for a core set of SDH variables to collect that are both feasible and useful. We also conducted an environmental literature scan to determine the status of knowledge of SDH as underlying causes of disease and to inform the recommended core set of SDH variables.

Federal funding for reporting cases of HIV infection in the United States, 2006.

OBJECTIVE: The Centers for Disease Control and Prevention (CDC) provides funding for human immunodeficiency virus (HIV) surveillance in 65 areas (states, cities, and U.S. dependent areas). We determined the amount of CDC funding per reported case of HIV infection and examined factors associated with differences in funding per reported case across areas. METHODS: We derived HIV data from the HIV/AIDS Reporting System (HARS) database. Budget numbers were based on award letters to health departments. We performed multivariate linear regression for all areas and for areas of low, moderate, and moderate-to-high morbidity. RESULTS: Mean funding per case reported was $1,520, $441, and $411 in areas of low, moderate, and moderate-to-high morbidity, respectively. In low morbidity areas, funding per case decreased as log total cases increased (p < 0.001). For moderate and moderate-to-high morbidity areas, funding per case fell as log total cases increased (p < 0.001), but increased in accordance with an area's population (p < 0.05) and the proportion of that population residing in an urban setting (p < 0.05). The models for low, moderate, and moderate-to-high morbidity predicted funding per case as $1490, $423, and $390, respectively. CONCLUSIONS: Economies of scale were evident. The amount of CDC core surveillance funding per case reported was significantly associated with the total number of cases in an area and, depending on morbidity, with total population and percentage of that population residing in an urban setting.

Summary of CDC consultation to address social determinants of health for prevention of disparities in HIV/AIDS, viral hepatitis, sexually transmitted diseases, and tuberculosis. December 9-10, 2008.

In December 2008, the Centers for Disease Control and Prevention (CDC) convened a meeting of national public health partners to identify priorities for addressing social determinants of human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), viral hepatitis, sexually transmitted diseases (STDs), and tuberculosis (TB). The consultants were divided into four working groups: (1) public health policy, (2) data systems, (3) agency partnerships and prevention capacity building, and (4) prevention research and evaluation. Groups focused on identifying top priorities; describing activities, methods, and metrics to implement priorities; and identifying partnerships and resources required to implement priorities. The meeting resulted in priorities for public health policy, improving data collection methods, enhancing existing and expanding future partnerships, and improving selection criteria and evaluation of evidence-based interventions. CDC is developing a national communications plan to guide and inspire action for keeping social determinants of HIV/AIDS, viral hepatitis, STDs, and TB in the forefront of public health activities.

Life expectancy after HIV diagnosis based on national HIV surveillance data from 25 states, United States.

INTRODUCTION: We estimate life expectancy and average years of life lost (AYLL) after an HIV diagnosis using population-based surveillance data from 25 states that have had name-based HIV surveillance since 1996. METHODS: We used US national HIV surveillance data (cases > or = 13 years old) to model life expectancy after an HIV diagnosis using the life table approach. We then compared life expectancy at HIV diagnosis with that in the general population of the same age, sex, and race/ethnicity in the same calendar year using vital statistics data to estimate the AYLL due to an HIV diagnosis. RESULTS: Average life expectancy after HIV diagnosis increased from 10.5 to 22.5 years from 1996 to 2005. Life expectancy (years) was better for females than for males but improved less for females (females: 12.6-23.6 and males: 9.9-22.0). In 2005, life expectancy for black males was shortest, followed by Hispanic males and then white males. AYLL for cases diagnosed in 2005 was 21.1 years (males: 19.1 and females: 22.7) compared with 32.9 years in 1996. CONCLUSIONS: Disparity in life expectancy for females and both black and Hispanic males, compared with males and white males, respectively, persists and should be addressed.

HIV behavioural surveillance among refugees and surrounding host communities in Uganda, 2006.

We used a standardised behavioural surveillance survey (BSS), modified to be directly relevant to populations in conflict and post-conflict settings as well as to their surrounding host populations, to survey the populations of a refugee settlement in south-western Uganda and its surrounding area. Two-stage probability sampling was used to conduct 800 interviews in each population. The BSS questionnaire adapted for displaced populations was administered to adults aged 15-59 years. It collected information on HIV knowledge, attitudes and practices; issues before, during and after displacement; level of interaction and sexual exploitation among the refugees and host communities (i.e., nationals). Population parameters were compared and 95% confidence intervals were calculated for core HIV indicators. The demographic characteristics were similar (except for educational achievement), and HIV awareness was very high (>95%) in both populations. The refugees reported more-accepting attitudes towards persons with HIV than did nationals (19% versus 13%; p < 0.01). More refugees than nationals reported ever having had transactional sex (10% versus 6%; p < 0.01), which mostly occurred post-displacement. Five percent of females among both the refugees and nationals reported experiencing forced sex, which mostly occurred post-displacement and after the arrival of refugees, respectively. Nationals reported more frequent travel to refugee settlements than reported by refugees to national villages (22% versus 11%; p < 0.01). The high mobility and frequent interactions of these two populations suggest that integrated HIV programmes should be developed and would be an efficient use of resources. Evidence suggesting that female refugees may be at elevated risk for HIV infection, due to forced sex, transactional sex and other vulnerabilities, warrants further examination through qualitative research. The findings indicate a need for additional, focused HIV-prevention programmes, such as youth education, for both refugees and Ugandan nationals.

Risk factor redistribution of the national HIV/AIDS surveillance data: an alternative approach.

OBJECTIVE: The purpose of this study was to assess an alternative statistical approach-multiple imputation-to risk factor redistribution in the national human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) surveillance system as a way to adjust for missing risk factor information. METHODS: We used an approximate model incorporating random variation to impute values for missing risk factors for HIV and AIDS cases diagnosed from 2000 to 2004. The process was repeated M times to generate M datasets. We combined results from the datasets to compute an overall multiple imputation estimate and standard error (SE), and then compared results from multiple imputation and from risk factor redistribution. Variables in the imputation models were age at diagnosis, race/ethnicity, type of facility where diagnosis was made, region of residence, national origin, CD-4 T-lymphocyte cell count within six months of diagnosis, and reporting year. RESULTS: In HIV data, male-to-male sexual contact accounted for 67.3% of cases by risk factor redistribution and 70.4% (SE = 0.45) by multiple imputation. Also among males, injection drug use (IDU) accounted for 11.6% and 10.8% (SE = 0.34), and high-risk heterosexual contact for 15.1% and 13.0% (SE = 0.34) by risk factor redistribution and multiple imputation, respectively. Among females, IDU accounted for 18.2% and 17.9% (SE = 0.61), and high-risk heterosexual contact for 80.8% and 80.9% (SE = 0.63) by risk factor redistribution and multiple imputation, respectively. CONCLUSIONS: Because multiple imputation produces less biased subgroup estimates and offers objectivity and a semiautomated approach, we suggest consideration of its use in adjusting for missing risk factor information.