By Adam Wagstaff, Owen O'Donnell, Eddy Van Doorslaer, Magnus Lindelow
Have gaps in future health results among the terrible and higher off grown? Are they better in a single nation than one other? Are future health quarter subsidies extra both allotted in a few international locations than others? Are healthiness care funds extra innovative in a single well-being care financing procedure than one other? What are catastrophic funds and the way can they be measured? How a long way do overall healthiness care funds impoverish families?
Answering questions resembling those calls for quantitative research. This in flip depends upon a transparent figuring out of the way to degree key variables within the research, resembling overall healthiness results, well-being bills, desire, and dwelling criteria. It additionally calls for set quantitative equipment for measuring inequality and inequity, progressivity, catastrophic costs, poverty effect, and so on.
This publication offers an summary of the major concerns that come up within the dimension of health and wellbeing variables and residing criteria, outlines and explains crucial instruments and techniques for distributional research, and, utilizing labored examples, indicates how those instruments and strategies should be utilized within the well-being zone. The ebook seeks to supply the reader with either an exceptional snatch of the rules underpinning distributional research, whereas whilst delivering hands-on information on the best way to circulate from rules to perform.
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Extra info for Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and their Implementation
World Bank, Washington, DC. , R. Pande, M. Mahy, and G. Jones. 1999. Trends in Child Mortality in the Developing World: 1960 to 1996. New York: UNICEF. International Monetary Fund, OECD, United Nations, and World Bank. 2000. A Better World for All: Progress Towards the International Development Goals. Washington, DC: International Monetary Fund, OECD, United Nations, and the World Bank. UNICEF. 2001. Progress Since the World Summit for Children: A Statistical Review. New York: UNICEF. United Nations.
18 Chapter 2 Routine data: health information systems and censuses Some forms of routine data may be suitable for health equity analysis. Health information systems (HIS) collect a combination of health data through ongoing data collection systems. ). HIS data are used primarily for management purposes, for example, for planning, needs assessments, resource allocation, and quality assessments. However, in some contexts, HIS data include demographic or socioeconomic variables that permit equity analysis.
If this were the case, analysts should be cautious in interpreting results and drawing inferences about the general population. In some cases, it may be possible to correct for this bias by modeling nonresponse. Other potential sources of nonsampling bias include errors in recording or data entry. Source: Authors. 18 Chapter 2 Routine data: health information systems and censuses Some forms of routine data may be suitable for health equity analysis. Health information systems (HIS) collect a combination of health data through ongoing data collection systems.