scholarly journals How to do (or not to do) … a health financing incidence analysis

2018 ◽  
Vol 33 (3) ◽  
pp. 436-444 ◽  
Author(s):  
John E Ataguba ◽  
Augustine D Asante ◽  
Supon Limwattananon ◽  
Virginia Wiseman
Keyword(s):  
Health Services ◽  
Survey Data ◽  
Household Survey ◽  
Health Financing ◽  
Financing System ◽  
Middle Income ◽  
Middle Income Countries ◽  
Cardinal Measure ◽  
Household Survey Data ◽  
Incidence Analysis

Abstract Financing incidence analysis (FIA) assesses how the burden of health financing is distributed in relation to household ability to pay (ATP). In a progressive financing system, poorer households contribute a smaller proportion of their ATP to finance health services compared to richer households. A system is regressive when the poor contribute proportionately more. Equitable health financing is often associated with progressivity. To conduct a comprehensive FIA, detailed household survey data containing reliable information on both a cardinal measure of household ATP and variables for extracting contributions to health services via taxes, health insurance and out-of-pocket (OOP) payments are required. Further, data on health financing mix are needed to assess overall FIA. Two major approaches to conducting FIA described in this article include the structural progressivity approach that assesses how the share of ATP (e.g. income) spent on health services varies by quantiles, and the effective progressivity approach that uses indices of progressivity such as the Kakwani index. This article provides some detailed practical steps for analysts to conduct FIA. This includes the data requirements, data sources, how to extract or estimate health payments from survey data and the methods for assessing FIA. It also discusses data deficiencies that are common in many low- and middle-income countries (LMICs). The results of FIA are useful in designing policies to achieve an equitable health system.

scholarly journals Microestimates of wealth for all low- and middle-income countries

2022 ◽  
Vol 119 (3) ◽  
pp. e2113658119
Author(s):  
Guanghua Chi ◽  
Han Fang ◽  
Sourav Chatterjee ◽  
Joshua E. Blumenstock
Keyword(s):  
Survey Data ◽  
Household Survey ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Middle Income ◽  
Middle Income Countries ◽  
Economic Development And Growth ◽  
Household Survey Data ◽  
Nationally Representative ◽  
Low And Middle Income

Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop microestimates of the relative wealth and poverty of the populated surface of all 135 low- and middle-income countries (LMICs) at 2.4 km resolution. The estimates are built by applying machine-learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, and topographic maps, as well as aggregated and deidentified connectivity data from Facebook. We train and calibrate the estimates using nationally representative household survey data from 56 LMICs and then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each microestimate to facilitate responsible downstream use. These estimates are provided free for public use in the hope that they enable targeted policy response to the COVID-19 pandemic, provide the foundation for insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of sustainable development.

scholarly journals Gridded Population Survey Sampling: A Review of the Field and Strategic Research Agenda

2020 ◽  
Author(s):  
Dana R. Thomson ◽  
Dale A. Rhoda ◽  
Andrew J. Tatem ◽  
Marcia C. Castro
Keyword(s):  
Population Survey ◽  
Household Survey ◽  
R Package ◽  
Population Based ◽  
Survey Sampling ◽  
Population Estimates ◽  
Population Surveys ◽  
Middle Income ◽  
Middle Income Countries ◽  
Household Survey Data

Objective: In low- and middle-income countries (LMICs), household survey data are a main source of information for planning, evaluation, and decision-making. Standard surveys are based on censuses, however, for many LMICs it has been more than ten years since their last census and they face high urban growth rates. Over the last decade, survey designers have begun to use modelled gridded population estimates as sample frames. We summarize the state of the emerging field of gridded population survey sampling, focussing on LMICs. Methods: We performed a systematic review and identified 43 national and sub-national gridded population-based household surveys implemented across 29 LMICs. Findings: Gridded population surveys used automated and manual approaches to derive clusters from WorldPop and LandScan gridded population estimates. After sampling, many surveys interviewed all households in each cluster or segment, though some sampled households from larger clusters. Tools to select gridded population survey clusters include the GridSample R package, Geo-sampling tool, and GridSample.org. In the field, gridded population surveys generally relied on geographically accurate maps based on satellite imagery or OpenStreetMap, and a tablet or GPS technology for navigation. Conclusions: For gridded population survey sampling to be adopted more widely, several strategic questions need answering regarding cell-level accuracy and uncertainty of gridded population estimates, the methods used to group/split cells into sample frame units, design effects of new sample designs, and feasibility of tools and methods to implement surveys across diverse settings.

scholarly journals Leveraging household survey data to measure barriers to health services access in the Americas

2020 ◽  
Vol 44 ◽  
pp. 1
Author(s):  
Ernesto Báscolo ◽  
Natalia Houghton ◽  
Amalia Del Riego
Keyword(s):  
Health Services ◽  
Survey Data ◽  
Household Survey ◽  
Wealth Quintile ◽  
Cross Sectional Study ◽  
Access Barriers ◽  
Wealth Status ◽  
Cross Sectional ◽  
Household Survey Data ◽  
Barriers To Health

Objective. To identify advantages and challenges of using household survey data to measure access barriers to health services in the Americas and to report findings from most recent surveys. Methods. Descriptive cross-sectional study using data retrieved from publicly available nationally representative household surveys carried out in 27 countries of the Americas. Values for indicators of access barriers for forgone care were generated using available datasets and reports from the countries. Results were disaggregated by wealth quintiles according to income or asset-based wealth levels. Results. Most surveys were similar in general approach and in the categories of their content. However, country-specific questionnaires varied by country, which hindered cross-country comparisons. On average, about one-third of people experienced multiple barriers to forgone appropriate care. There was great variability between countries in the experience of these barriers, although disparities were relatively consistent across countries. People in the poorest wealth quintile were more likely to experience barriers related to acceptability issues, financial and geographic access, and availability of resources. Conclusions. The analysis indicates major inequalities by wealth status and uneven progress in multiple access barriers that hinder progress towards the goals of equity as part of the Sustainable Development Goals and universal health in the Americas. Access barriers were multiple, which highlights the need for integrated and multisectoral approaches to tackle them. Given the variability between instruments across countries, future efforts are needed to standardize questionnaires and improve data quality and availability for regional monitoring of access barriers.

scholarly journals How to do (or not to do) … a benefit incidence analysis

2010 ◽  
Vol 26 (2) ◽  
pp. 174-182 ◽  
Author(s):  
Di McIntyre ◽  
John E Ataguba
Keyword(s):  
Health Service ◽  
Health Services ◽  
Health System ◽  
Economic Status ◽  
Methodological Approach ◽  
Household Survey ◽  
Middle Income ◽  
Unit Costs ◽  
Benefit Incidence Analysis ◽  
Incidence Analysis

Abstract Benefit incidence analysis (BIA) considers who (in terms of socio-economic groups) receive what benefit from using health services. While traditionally BIA has focused on only publicly funded health services, to assess whether or not public subsidies are ‘pro-poor’, the same methodological approach can be used to assess how well the overall health system is performing in terms of the distribution of service benefits. This is becoming increasingly important in the context of the growing emphasis on promoting universal health systems. To conduct a BIA, a household survey dataset that incorporates both information on health service utilization and some measure of socio-economic status is required. The other core data requirement is unit costs of different types of health service. When utilization rates are combined with unit costs for different health services, the distribution of benefits from using services, expressed in monetary terms, can be estimated and compared with the distribution of the need for health care. This paper aims to provide an introduction to the methods used in the ‘traditional’ public sector BIA, and how the same methods can be applied to undertake an assessment of the whole health system. We consider what data are required, potential sources of data, deficiencies in data frequently available in low- and middle-income countries, and how these data should be analysed.

scholarly journals Gridded Population Survey Sampling: A Review of the Field and Strategic Research Agenda

2019 ◽  
Author(s):  
Dana R. Thomson ◽  
Dale A. Rhoda ◽  
Andrew J. Tatem ◽  
Marcia C. Castro
Keyword(s):  
Population Survey ◽  
Household Survey ◽  
R Package ◽  
Population Based ◽  
Survey Sampling ◽  
Population Estimates ◽  
Population Surveys ◽  
Middle Income ◽  
Middle Income Countries ◽  
Household Survey Data

Objective: In low- and middle-income countries (LMICs), household survey data are a main source of information for planning, evaluation, and decision-making. Standard surveys are based on censuses, however, for many LMICs it has been more than ten years since their last census and they face high urban growth rates. Over the last decade, survey designers have begun to use modelled gridded population estimates as sample frames. We summarize the state of the emerging field of gridded population survey sampling, focussing on LMICs. Methods: We performed a systematic review and identified 23 national and sub-national gridded population-based household surveys implemented across 18 LMICs. Findings: Gridded population surveys used automated and manual approaches to derive clusters from WorldPop and LandScan gridded population estimates. After sampling, many surveys interviewed all households in each cluster or segment, though some sampled households from larger clusters. Tools to select gridded population survey clusters include the GridSample R package, Geo-sampling tool, and GridSample.org. In the field, gridded population surveys generally relied on geographically accurate maps based on satellite imagery or OpenStreetMap, and a tablet or GPS technology for navigation. Conclusions: For gridded population survey sampling to be adopted more widely, several strategic questions need answering regarding cell-level accuracy and uncertainty of gridded population estimates, the methods used to group/split cells into sample frame units, design effects of new sample designs, and feasibility of tools and methods to implement surveys across diverse settings.

Equity in Irish health care financing: measurement issues

2010 ◽  
Vol 5 (2) ◽  
pp. 149-169 ◽  
Author(s):  
Samantha Smith
Keyword(s):  
Health Care ◽  
Previous Analysis ◽  
Health Care Financing ◽  
Household Survey ◽  
Health Financing ◽  
Financing System ◽  
Equity In Health ◽  
Measurement Issues ◽  
Household Survey Data ◽  
Equity In Health Care

AbstractThis paper employs widely used analytic techniques for measuring equity in health care financing to update Irish results from previous analysis based on data from the late 1980s. Kakwani indices are calculated using household survey data from 1987/88 to 2004/05. Results indicate a marginally progressive financing system overall. However, interpretation of the results for the private sources of health financing is complicated. This problem is not unique to Ireland but it is argued that it may be relatively more important in the context of a complex health financing system, illustrated in this paper by the Irish system. Alternative options for improving the analysis of equity in health care financing are discussed.

Sign in / Sign up

Export Citation Format

Share Document