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 Yet many poverty maps are out of date or exist only at very coarse levels of granularity.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 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 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 Breastfeeding support in low and middle-income countries: Secondary analysis of national survey data

Midwifery ◽  
2020 ◽  
Vol 82 ◽  
pp. 102601 ◽  
Author(s):  
Kimberly Peven ◽  
Edward Purssell ◽  
Cath Taylor ◽  
Debra Bick ◽  
Velma K. Lopez
Keyword(s):  
Survey Data ◽  
National Survey ◽  
Secondary Analysis ◽  
Breastfeeding Support ◽  
Middle Income ◽  
Middle Income Countries ◽  
Low And Middle Income

scholarly journals Age patterns in overweight and wasting prevalence of under 5-year-old children from low- and middle-income countries

2021 ◽  
Author(s):  
Luiza I. C. Ricardo ◽  
Giovanna Gatica-Domínguez ◽  
Inácio Crochemore-Silva ◽  
Paulo A. R. Neves ◽  
Juliana dos Santos Vaz ◽  
...  
Keyword(s):  
Low Income ◽  
Age Groups ◽  
Demographic And Health Surveys ◽  
Older Children ◽  
Middle Income ◽  
Middle Income Countries ◽  
Multiple Indicator ◽  
Standard Deviations ◽  
Nationally Representative ◽  
Low And Middle Income

Abstract Objectives To describe how overweight and wasting prevalence varies with age among children under 5 years in low- and middle-income countries (LMICs). Methods We used data from nationally representative Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Overweight and wasting prevalence were defined as the proportions of children presenting mean weight for length/height (WHZ) more than 2 standard deviations above or below 2 standard deviations from the median value of the 2006 WHO standards, respectively. Descriptive analyses include national estimates of child overweight and wasting prevalence, mean, and standard deviations of WHZ stratified by age in years. National results were pooled using the population of children aged under 5 years in each country as weight. Fractional polynomials were used to compare mean WHZ with both overweight and wasting prevalence. Results Ninety national surveys from LMICs carried out between 2010 and 2019 were included. The overall prevalence of overweight declined with age from 6.3% for infants (aged 0–11 months) to 3.0% in 4 years olds (p = 0.03). In all age groups, lower prevalence was observed in low-income compared to upper-middle-income countries. Wasting was also more frequent among infants, with a slight decrease between the first and second year of life, and little variation thereafter. Lower-middle-income countries showed the highest wasting prevalence in all age groups. On the other hand, mean WHZ was stable over the first 5 years of life, but the median standard deviation for WHZ decreased from 1.39 in infants to 1.09 in 4-year-old children (p < 0.001). For any given value of WHZ, both overweight and wasting prevalence were higher in infants than in older children. Conclusion The higher values of WHZ standard deviations in infants suggest that declining prevalence in overweight and wasting by age may be possibly due to measurement error or rapid crossing of growth channels by infants.

scholarly journals New estimations of child marriage: Evidence from 98 low- and middle-income countries

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258378
Author(s):  
Mengjia Liang ◽  
Sandile Simelane ◽  
Satvika Chalasani ◽  
Rachel Snow
Keyword(s):  
Survival Analysis ◽  
Child Marriage ◽  
Demographic And Health Surveys ◽  
Hazard Rates ◽  
Middle Income ◽  
The Sustainable Development ◽  
Marriage Rates ◽  
Middle Income Countries ◽  
Nationally Representative ◽  
Low And Middle Income

The Sustainable Development Goals include a target on eliminating child marriage, a human rights abuse. Yet, the indicator used in the SDG framework is a summary statistic and does not provide a full picture of the incidence of marriage at different ages. This paper aims to address this limitation by providing an alternative method of measuring child marriage. The paper reviews recent data on nuptiality and captures evidence of changes in the proportion married and in the age at marriage, in 98 low- and middle-income countries (LMICs). Using data collected from nationally representative Demographic and Health Surveys and Multiple Indicator Cluster Surveys, survival analysis is applied to estimate (a) age-specific marriage hazard rates among girls before age 18; and (b) the number of girls that were married before age 18 in 2020. Results show that the vast majority of girls remain unmarried until age 10. Child marriage rates increase gradually until age 14 and accelerate significantly thereafter at ages 15–17. By accounting for both single-year-age-specific child marriage hazard rates and the age structure of the population with a survival analysis approach, lower estimates in countries with a rapid decrease in child marriage and higher estimates in countries with constant or slightly rising child marriage rates relative to the direct approach are obtained.

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