Last data update: May 13, 2024. (Total: 46773 publications since 2009)
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Query Trace: Harrison KM[original query] |
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Reflections from the CDC 2010 Health Equity Symposium
Colbert SJ , Harrison KM . Public Health Rep 2011 126 Suppl 3 38-40 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 nation's 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 |
Use of data systems to address social determinants of health: a need to do more
Harrison KM , Dean HD . Public Health Rep 2011 126 Suppl 3 1-5 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. |
Identifying the impact of social determinants of health on disease rates using correlation analysis of area-based summary information
Song R , Hall HI , Harrison KM , Sharpe TT , Lin LS , Dean HD . Public Health Rep 2011 126 Suppl 3 70-80 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
Beltran VM , Harrison KM , Hall HI , Dean HD . Public Health Rep 2011 126 Suppl 3 41-53 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
Page MJ , Harrison KM , Wei X , Hall HI . Public Health Rep 2010 125 (5) 718-27 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
Sharpe TT , Harrison KM , Dean HD . Public Health Rep 2010 125 Suppl 4 11-5 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
Harrison KM , Song R , Zhang X . J Acquir Immune Defic Syndr 2009 53 (1) 124-30 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. |
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