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      Monitoring Inequalities in the Health Workforce: The Case Study of Brazil 1991–2005

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          Abstract

          Introduction

          Both the quantity and the distribution of health workers in a country are fundamental for assuring equitable access to health services. Using the case of Brazil, we measure changes in inequalities in the distribution of the health workforce and account for the sources of inequalities at sub-national level to identify whether policies have been effective in decreasing inequalities and increasing the density of health workers in the poorest areas between 1991 and 2005.

          Methods

          With data from Datasus 2005 and the 1991 and 2000 Census we measure the Gini and the Theil T across the 4,267 Brazilian Minimum Comparable Areas (MCA) for 1991, 2000 and 2005 to investigate changes in inequalities in the densities of physicians; nurse professionals; nurse associates; and community health workers by states, poverty quintiles and urban-rural stratum to account for the sources of inequalities.

          Results

          We find that inequalities have increased over time and that physicians and nurse professionals are the categories of health workers, which are more unequally distributed across MCA. The poorest states experience the highest shortage of health workers (below the national average) and have the highest inequalities in the distribution of physicians plus nurse professionals (above the national average) in the three years. Most of the staff in poor areas are unskilled health workers. Most of the overall inequalities in the distribution of health workers across MCA are due to inequalities within states, poverty quintiles and rural-urban stratum.

          Discussion

          This study highlights some critical issues in terms of the geographical distribution of health workers, which are accessible to the poor and the new methods have given new insights to identify critical geographical areas in Brazil. Eliminating the gap in the health workforce would require policies and interventions to be conducted at the state level focused in poor and rural areas.

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          Most cited references29

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          Human resources and health outcomes: cross-country econometric study.

          Only a few studies have investigated the link between human resources for health and health outcomes, and they arrive at different conclusions. We tested the strength and significance of density of human resources for health with improved methods and a new WHO dataset. We did cross-country multiple regression analyses with maternal mortality rate, infant mortality rate, and under-five mortality rate as dependent variables. Aggregate density of human resources for health was an independent variable in one set of regressions; doctor and nurse densities separately were used in another set. We controlled for the effects of income, female adult literacy, and absolute income poverty. Density of human resources for health is significant in accounting for maternal mortality rate, infant mortality rate, and under-five mortality rate (with elasticities ranging from -0.474 to -0.212, all p values < or = 0.0036). The elasticities of the three mortality rates with respect to doctor density ranged from -0.386 to -0.174 (all p values < or = 0.0029). Nurse density was not associated except in the maternal mortality rate regression without income poverty (p=0.0443). In addition to other determinants, the density of human resources for health is important in accounting for the variation in rates of maternal mortality, infant mortality, and under-five mortality across countries. The effect of this density in reducing maternal mortality is greater than in reducing child mortality, possibly because qualified medical personnel can better address the illnesses that put mothers at risk. Investment in human resources for health must be considered as part of a strategy to achieve the Millennium Development Goals of improving maternal health and reducing child mortality.
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            China's human resources for health: quantity, quality, and distribution.

            In this paper, we analyse China's current health workforce in terms of quantity, quality, and distribution. Unlike most countries, China has more doctors than nurses-in 2005, there were 1.9 million licensed doctors and 1.4 million nurses. Doctor density in urban areas was more than twice that in rural areas, with nurse density showing more than a three-fold difference. Most of China's doctors (67.2%) and nurses (97.5%) have been educated up to only junior college or secondary school level. Since 1998 there has been a massive expansion of medical education, with an excess in the production of health workers over absorption into the health workforce. Inter-county inequality in the distribution of both doctors and nurses is very high, with most of this inequality accounted for by within-province inequalities (82% or more) rather than by between-province inequalities. Urban-rural disparities in doctor and nurse density account for about a third of overall inter-county inequality. These inequalities matter greatly with respect to health outcomes across counties, provinces, and strata in China; for instance, a cross-county multiple regression analysis using data from the 2000 census shows that the density of health workers is highly significant in explaining infant mortality.
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              Socio-economic and ethnic group inequities in antenatal care quality in the public and private sector in Brazil.

              Socio-economic inequalities in maternal and child health are ubiquitous, but limited information is available on how much the quality of care varies according to wealth or ethnicity in low- and middle-income countries. Also, little information exists on quality differences between public and private providers. Quality of care for women giving birth in 2004 in Pelotas, Brazil, was assessed by measuring how many of 11 procedures recommended by the Ministry of Health were performed. Information on family income, self-assessed skin colour, parity and type of provider were collected. Antenatal care was used by 98% of the 4244 women studied (mean number of visits 8.3), but the number of consultations was higher among better-off and white women, who were also more likely to start antenatal care in the first trimester. The quality of antenatal care score ranged from 0 to 11, with an overall mean of 8.3 (SD 1.7). Mean scores were 8.9 (SD 1.5) in the wealthiest and 7.9 (SD 1.8) in the poorest quintiles (P < 0.001), 8.4 (SD 1.6) in white and 8.1 (SD 1.9) in black women (P < 0.001). Adjusted analyses showed that these differences seemed to be due to attendance patterns rather than discrimination. Mean quality scores were higher in the private 9.3 (SD 1.3) than in the public sector 8.1 (SD 1.6) (P < 0.001); these differences were not explained by maternal characteristics or by attendance patterns. Special efforts must be made to improve quality of care in the public sector. Poor and black women should be actively encouraged to start antenatal care early in pregnancy so that they can fully benefit from it. There is a need for regular monitoring of antenatal attendances and quality of care with an equity lens, in order to assess how different social groups are benefiting from progress in health care.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                27 March 2012
                : 7
                : 3
                : e33399
                Affiliations
                [1 ]Department for Health Systems Policies and Workforce, World Health Organization, Geneva, Switzerland
                [2 ]Center for Population and Development Studies, Harvard School of Public Health, Boston, Massachusetts, United States of America
                [3 ]Institute of Social Medicine, University of the State of Rio de Janeiro, Rio de Janeiro, Brazil
                [4 ]Dental School, Pontifical Catholic University, Minas Gerais, Brazil
                University of Colorado Denver, United States of America
                Author notes

                Conceived and designed the experiments: AS MRDP. Performed the experiments: AS. Analyzed the data: AS. Contributed reagents/materials/analysis tools: AS. Wrote the paper: AS MRDP CLC. Planned the data analysis: AS. Interpreted the results: AS MRDP Produced the first draft of the manuscript: AS. Contributed to the interpretation of results: AS MRDP CLC. Read and approved the final manuscript: AS MRDP CLC.

                Article
                PONE-D-11-18205
                10.1371/journal.pone.0033399
                3314019
                22479392
                b95e85cc-7567-4434-af79-d66cda564af9
                Sousa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 16 September 2011
                : 13 February 2012
                Page count
                Pages: 7
                Categories
                Research Article
                Medicine
                Non-Clinical Medicine
                Health Care Policy
                Health Care Providers
                Public Health
                Science Policy
                Research Assessment
                Social and Behavioral Sciences
                Economics
                Health Economics

                Uncategorized
                Uncategorized

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