Kathleen McDavid Harrison

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 ≥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.

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.

County-Level Socioeconomic Status and Survival After HIV Diagnosis, United States

PURPOSE To estimate relative survival (RS) after human immunodeficiency virus (HIV) diagnosis, by race/ethnicity and county-level socioeconomic status (SES). METHODS We estimated 5-year RS by age, race/ethnicity, transmission category, sex, diagnosis year, CD4 count, and by county-level SES variables from the U.S. Census. Data, from the national HIV/AIDS Reporting System, were for HIV-infected persons ages > or =13 years (diagnosis during 1996-2003 and follow-up through 2005). We calculated RS proportions by using a maximum likelihood algorithm and modeled the relative risk of excess death (RR) using generalized linear models, with poverty as a random effect. RESULTS For men, RS was worse in counties with larger proportions of people living below the 2000 U.S. poverty level (87.7% for poverty of > or =20% vs. 90.1% for poverty of <5.0%) and where unemployment was greater (87.8% where unemployment > 7.1% vs. 90.5% where unemployment < 4.0%). The effects of county-level SES on RS of women were similar. In multilevel multivariate models, RR for men and women within 5 years after an HIV diagnosis was significantly worse in counties where 10.0-19.9% (compared with <5.0%) lived below the poverty level (RR = 1.3 [95% CI 1.2-1.5] and RR = 1.8 [95% CI 1.4-2.2], respectively). CONCLUSIONS RS was worse in lower SES areas. To help address the impact of county-level SES, resources for HIV testing, care, and proven economic interventions should be directed to areas with concentrations of economically disadvantaged people.

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.

Summary of CDC Consultation to Address Social Determinants of Health for Prevention of Disparities in HIV/AIDS, Viral Hepatitis, Sexually Transmitted Diseases, and Tuberculosis

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: (7) 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.

Use of Data Systems to Address Social Determinants of Health: A Need to Do More

This supplement to Public Health Reports (PHR) focuses on data systems and their use in addressing social determinants of health (SDH). This particular topic requires attention now given the evidence of increasing burden and worsening inequities in some health outcomes, in spite of decades of work to change individual behaviors, as well as the need to be efficient in our use of existing data. A holistic approach to disease prevention is urgently needed to reduce the inequities that have been perpetuated in our society for so long. Despite concerted, targeted, and coordinated efforts to reduce inequities in health outcomes, gross inequities still exist,1–4 and some evidence indicates that the gap between the best health outcomes and the worst health outcomes is growing.1,3–5 Well-meaning efforts have substantially focused on individual-related behavior changes, with less focus on wider social and structural determinants of health, which can be defined as follows:6,7 Structural factors include those physical, social, cultural, organizational, community, economic, legal, or policy aspects of the environment that impede or facilitate efforts to avoid disease transmission. Social factors include the economic and social conditions that influence the health of people and communities as a whole, and include the conditions for early childhood development, education, employment, income and job security, food security, health services, and access to services, housing, social exclusion, and stigma.8 In addition to addressing individual factors, there is an urgent need to address social and structural factors and to better understand their relationship to each other as we develop effective programs and policies to reduce inequities. A holistic approach to disease prevention involves not only addressing individual, social, structural, and environmental determinants, but also working with a wide array of sectors, such as health, education, justice, environment, and labor. Additionally, it means working with diverse kinds of data, including disease surveillance, legal, land use, marketing, workforce, education, and financial. Making the best use of a wide variety of data at the individual, neighborhood, community, and county levels, for example, can provide a more complete description of the underlying factors that may influence health outcomes than using disease surveillance data alone. As a matter of fact, using disease surveillance data alone, which often are limited to variables such as disease of interest, age, sex or gender, and race/ethnicity, can be stigmatizing and only tells part of the story. Public health professionals have an obligation to fairly and accurately describe disease occurrence in populations. As a result, we should be compelled to use data from available sources to provide a complete picture of the environment in which the disease occurs and any underlying factors contributing to its occurrence. Addressing underlying factors of health has been advocated by many health practitioners for decades.1,9–12 The Institute of Medicine Committee on Public Health Strategies to Improve Health released a report in 2010 that recommended gathering, analyzing, and communicating health information that includes not only disease-outcome data, but also data on underlying factors contributing to poor health.13 In many cases, national disease surveillance systems do not include information on underlying determinants of disease, necessitating linking to existing sources of social, structural, legal, environmental, and financial data to provide a more comprehensive description of the affected population.14 This special issue of PHRaims to reflect on the types of data we routinely gather, analyze, report, and communicate, and it calls us to take a holistic approach to data use both in the sources (e.g., United Nations, Centers for Disease Control and Prevention [CDC], Census Bureau, Department of Transportation, and Department of Justice) and kinds (e.g., disease outcome, policy, financial, land use, service usage, achievement, and segregation) of data used in public health. It calls us to be good public health stewards by challenging us to move beyond our routine analyses based mostly on individual-level data and include data from other sectors and levels in the work we do. This supplement provides examples of innovative uses and analyses of data for local, state, and national governments and organizations to consider. Promoting health equity through a holistic approach is a major strategic priority of CDCs National Center for HIV/AIDS, Viral Hepatitis, STD, and TB -Prevention (NCHHSTP).15 NCHHSTPs recent white paper entitled “Establishing a Holistic Framework to Reduce Inequities in HIV, Viral Hepatitis, STDs, and Tuberculosis in the United States” calls for a systematic approach to monitoring disease by simultaneously reporting on disease outcomes and underlying factors of poor health.16 NCHHSTP is also placing more emphasis on addressing structural determinants of health, including health policy, economic and social interventions, and cross-sectoral collaborations. The articles in this supplement clearly expand the knowledge base on social determinants and data use and are examples of the holistic approach to public health suggested in the CDC white paper.

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.

Identifying the root causes of health inequities: reflections on the 2011 National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention health equity symposium.

Ana Penman-Aguilar, PhD, MPHa,b Kathleen McDavid Harrison, PhD, MPHa Hazel D. Dean, ScD, MPHa In August 2011, the Centers for Disease Control and Prevention’s (CDC’s) National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP) held a one-day health equity symposium titled “Identifying Root Causes of Health Inequities: Using Data to Monitor and Improve Health.”1 The goal of the symposium was to strengthen CDC’s role as a leader in health equity by (1) increasing awareness, engagement, and action to address the social determinants of health (SDH); and (2) highlighting the role of data in informing and improving public health policy, practice, and research. The symposium featured keynote presentations by national experts, a question-and-answer session with the presenters, 40 scientific posters highlighting CDC’s SDH activities, and five simultaneous scientific workshops. We describe the highlights of the main messages delivered by each of several keynote speakers who are not affiliated with CDC. In offering this account of messages delivered by private citizens, CDC authors respect the autonomy and integrity of the proceedings of a public meeting and offer no judgment regarding the merits of what was discussed or the implications of specific comments for CDC and its partners.

Reflections from the CDC 2010 health equity symposium.

Twenty-six years ago, Secretary of the U.S. Department of Health and Human Services Margaret M. Heckler called for an end to health disparities among minority populations across the nation.1 Since then, the U.S. government has introduced various initiatives to reduce health disparities among our nations most marginalized populations. Despite these efforts, health disparities persist. As attempts to reduce health disparities continue, there have been major advances in the theory and research surrounding these challenges. One key development has been the renewed acknowledgment of the larger social context in contributing to the enduring gaps in health seen across vulnerable and disadvantaged groups. This notion is not brand new; in the 19th century, it was understood that the social and physical environment affected health. In 1848, Virchow concluded that poor sanitation, ignorance of basic hygiene, lack of education, and near starvation were the root problems of a typhus epidemic, and in 1855, Snow described the effects of contaminated water on spreading cholera.2,3 As this knowledge has evolved, one approach has emerged: reducing health disparities by addressing the social determinants of health (SDH). The term “social determinants of health” refers to the complex, integrated, and overlapping social structures and economic systems that include social and physical environments and health services. Adequately addressing the social and economic conditions in which people live, work, and play offers renewed hope to reduce health disparities and promote health equity.4 In 2010, the Centers for Disease Control and Prevention (CDC) hosted a symposium entitled “Establishing a Holistic Framework to Reduce Inequities in Human Immunodeficiency Virus (HIV), Viral Hepatitis, Sexually Transmitted Diseases (STDs), and Tuberculosis (TB) in the United States.” The purpose of the symposium was twofold: first, we celebrated the release of a white paper of the same name,5 and second, we offered exciting and engaging discussions with national experts on topics related to addressing SDH in public health practice, policy, and research. The day also included a frank discussion with senior staff members of the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP) and three invited speakers: Paula Braveman, MD, MPH, of the University of California, San Francisco; Scott Burris, JD, of Temple University in Philadelphia, Pennsylvania; and Johnnie “Chip” Allen, MPH, of the Ohio Department of Health in Columbus, Ohio. The discussion focused on how NCHHSTP can further incorporate an SDH approach into its work. At the start of the discussion, a number of questions and challenges were posed: How does NCHHSTP convince others that achieving health equity in the U.S. should be a public health priority? In light of the fact that resources have been declining, how do we adequately address SDH? How do we address SDH in an era with increased negativity toward groups disproportionately impacted by infectious diseases (e.g., men who have sex with men, Hispanic/Latino people, and immigrants)? As NCHHSTP is a leader in infectious disease prevention, what activities should we initiate to address both SDH and their role in HIV, hepatitis, STD, and TB prevention? What changes to our surveillance and data-collection systems should we make to measure, monitor, and collect information on SDH? How do we incorporate laws into public health surveillance research? How does synergy in programs impact the individual? How do laws fit into this? Where do we begin our focus? What is the starting point? What SDH-related variables should be a priority for annual monitoring? These questions were an important starting point to better identify CDCs role in achieving health equity. This session also reiterated the need for CDC to take the lead in reducing health disparities and promoting health equity in the U.S. and abroad.