Socioecologically informed use of remote sensing data to predict rural household poverty.
ABSTRACT: Tracking the progress of the Sustainable Development Goals (SDGs) and targeting interventions requires frequent, up-to-date data on social, economic, and ecosystem conditions. Monitoring socioeconomic targets using household survey data would require census enumeration combined with annual sample surveys on consumption and socioeconomic trends. Such surveys could cost up to $253 billion globally during the lifetime of the SDGs, almost double the global development assistance budget for 2013. We examine the role that satellite data could have in monitoring progress toward reducing poverty in rural areas by asking two questions: (i) Can household wealth be predicted from satellite data? (ii) Can a socioecologically informed multilevel treatment of the satellite data increase the ability to explain variance in household wealth? We found that satellite data explained up to 62% of the variation in household level wealth in a rural area of western Kenya when using a multilevel approach. This was a 10% increase compared with previously used single-level methods, which do not consider details of spatial landscape use. The size of buildings within a family compound (homestead), amount of bare agricultural land surrounding a homestead, amount of bare ground inside the homestead, and the length of growing season were important predictor variables. Our results show that a multilevel approach linking satellite and household data allows improved mapping of homestead characteristics, local land uses, and agricultural productivity, illustrating that satellite data can support the data revolution required for monitoring SDGs, especially those related to poverty and leaving no one behind.
Project description:BACKGROUND:Trachoma is widely considered a disease of poverty. Although there are many epidemiological studies linking trachoma to factors normally associated with poverty, formal quantitative data linking trachoma to household economic poverty within endemic communities is very limited. METHODOLOGY/PRINCIPAL FINDINGS:Two hundred people with trachomatous trichiasis were recruited through community-based screening in Amhara Region, Ethiopia. These were individually matched by age and gender to 200 controls without trichiasis, selected randomly from the same sub-village as the case. Household economic poverty was measured through (a) A broad set of asset-based wealth indicators and relative household economic poverty determined by principal component analysis (PCA, (b) Self-rated wealth, and (c) Peer-rated wealth. Activity participation data were collected using a modified 'Stylised Activity List' developed for the World Bank's Living Standards Measurement Survey. Trichiasis cases were more likely to belong to poorer households by all measures: asset-based analysis (OR = 2.79; 95%CI: 2.06-3.78; p<0.0001), self-rated wealth (OR, 4.41, 95%CI, 2.75-7.07; p<0.0001) and peer-rated wealth (OR, 8.22, 95% CI, 4.59-14.72; p<0.0001). Cases had less access to latrines (57% v 76.5%, p = <0.0001) and higher person-to-room density (4.0 v 3.31; P = 0.0204) than the controls. Compared to controls, cases were significantly less likely to participate in economically productive activities regardless of visual impairment and other health problems, more likely to report difficulty in performing activities and more likely to receive assistance in performing productive activities. CONCLUSIONS/SIGNIFICANCE:This study demonstrated a strong association between trachomatous trichiasis and relative poverty, suggesting a bidirectional causative relationship possibly may exist between poverty and trachoma. Implementation of the full SAFE strategy in the context of general improvements might lead to a virtuous cycle of improving health and wealth. Trachoma is a good proxy of inequality within communities and it could be used to target and evaluate interventions for health and poverty alleviation.
Project description:To quantify the association between socioeconomic status (SES) and type 2 diabetes in India.Nationally representative cross-sectional household survey.Urban and rural areas across 29 states in India.168?135 survey respondents aged 18-49 years (women) and 18-54 years (men).Self-reported diabetes status.Markers of SES were social caste, household wealth and education. The overall prevalence of self-reported diabetes was 1.5%; this increased to 1.9% and 2.5% for those with the highest levels of education and household wealth, respectively. In multilevel logistic regression models (adjusted for age, gender, religion, marital status and place of residence), education (OR 1.87 for higher education vs no education) and household wealth (OR 4.04 for richest quintile vs poorest) were positively related to self-reported diabetes (p<0.0001). In a fully adjusted model including all socioeconomic variables and body mass index, household wealth emerged as positive and statistically significant with an OR for self-reported diabetes of 2.58 (95% credible interval (CrI): 1.99 to 3.40) for the richest quintile of household wealth versus the poorest. Nationally in India, a one-quintile increase in household wealth was associated with an OR of 1.31 (95% CrI 1.20 to 1.42) for self-reported diabetes. This association was consistent across states with the relationship found to be positive in 97% of states (28 of 29) and statistically significant in 69% (20 of 29 states).The authors found that the highest SES groups in India appear to be at greatest risk for type 2 diabetes. This raises important policy implications for addressing the disease burdens among the poor versus those among the non-poor in the context of India, where >40% of the population is living in poverty.
Project description:Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1?×?1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data-ideally to be made free and publicly available-and offer lay descriptions of some of the difficulties in generating such data products.
Project description:The main determinants of agricultural employment are related to households' access to private assets and the influence of inherited social-economic stratification and power relationships. However, despite the recommendations of rural studies which have shown the importance of multilevel approaches to rural poverty, very few studies have explored quantitatively the effects of common-pool resources and household livelihood capitals on agricultural employment. Understanding the influence of access to both common-pool resources and private assets on rural livelihoods can enrich our understanding of the drivers of rural poverty in agrarian societies, which is central to achieving sustainable development pathways. Based on a participatory assessment conducted in rural communities in India, this paper differentiates two levels of livelihood capitals (household capitals and community capitals) and quantifies them using national census data and remotely sensed satellite sensor data. We characterise the effects of these two levels of livelihood capitals on precarious agricultural employment by using multilevel logistic regression. Our study brings a new perspective on livelihood studies and rural economics by demonstrating that common-pool resources and private assets do not have the same effect on agricultural livelihoods. It identifies that a lack of access to human, financial and social capitals at the household level increases the levels of precarious agricultural employment, such as daily-wage agricultural labour. Households located in communities with greater access to collective natural capital are less likely to be agricultural labourers. The statistical models also show that proximity to rural centres and access to financial infrastructures increase the likelihood of being a landless agricultural labourer. These findings suggest that investment in rural infrastructure might increase livelihood vulnerability, if not accompanied by an improvement in the provisioning of complementary rural services, such as access to rural finance, and by the implementation of agricultural tenancy laws to protect smallholders' productive assets.
Project description:BACKGROUND:Water, sanitation and hygiene (WASH) are essential for a healthy and dignified life. International targets to reduce inadequate WASH coverage were set under the Millennium Development Goals (MDGs, 1990-2015) and now the Sustainable Development Goals (SDGs, 2016-2030). The MDGs called for halving the proportion of the population without access to adequate water and sanitation, whereas the SDGs call for universal access, require the progressive reduction of inequalities, and include hygiene in addition to water and sanitation. Estimating access to complete WASH coverage provides a baseline for monitoring during the SDG period. Sub-Saharan Africa (SSA) has among the lowest rates of WASH coverage globally. METHODS:The most recent available Demographic Household Survey (DHS) or Multiple Indicator Cluster Survey (MICS) data for 25 countries in SSA were analysed to estimate national and regional coverage for combined water and sanitation (a combined MDG indicator for 'improved' access) and combined water with collection time within 30 minutes plus sanitation and hygiene (a combined SDG indicator for 'basic' access). Coverage rates were estimated separately for urban and rural populations and for wealth quintiles. Frequency ratios and percentage point differences for urban and rural coverage were calculated to give both relative and absolute measures of urban-rural inequality. Wealth inequalities were assessed by visual examination of coverage across wealth quintiles in urban and rural populations and by calculating concentration indices as standard measures of relative wealth related inequality that give an indication of how unevenly a health indicator is distributed across the wealth distribution. RESULTS:Combined MDG coverage in SSA was 20%, and combined basic SDG coverage was 4%; an estimated 921 million people lacked basic SDG coverage. Relative measures of inequality were higher for combined basic SDG coverage than combined MDG coverage, but absolute inequality was lower. Rural combined basic SDG coverage was close to zero in many countries. CONCLUSIONS:Our estimates help to quantify the scale of progress required to achieve universal WASH access in low-income countries, as envisaged under the water and sanitation SDG. Monitoring and reporting changes in the proportion of the national population with access to water, sanitation and hygiene may be useful in focusing WASH policy and investments towards the areas of greatest need.
Project description:OBJECTIVES:Equality is a central component of the Sustainable Development Goals (SDGs). We took one SDG indicator and benchmark-percent of family planning demand met with modern contraceptives, with a benchmark of at least 75% in all countries-as a case study to illuminate recommendations for monitoring equality. Specifically, we assessed levels, patterns, and trends in disparity by key background characteristics and identified disparity measures that are programmatically relevant and easy to interpret. METHODS:Data were from the Demographic and Health Surveys in 55 countries that have conducted at least 2 surveys since 1990. We calculated absolute difference among subgroups, disaggregated by age, education, household wealth quintile, urban/rural residence, subnational region/administrative unit, and marital status. Our unit of analysis was survey, and we conducted largely descriptive analyses. To understand trends in disparity, we used a fixed-effect linear regression model to estimate an annual rate of change in absolute differences. RESULTS:A significant level of disparity existed across various background characteristics, ranging from a median difference of 5 percentage points by marital status to 32 percentage points by administrative unit. On average across the study countries, national level of met demand has increased over time while disparity has declined in most disaggregates including by education, wealth, residence, and age. We found statistically significant positive correlations among 4 disparity measures-education, wealth, residence, and administrative unit. Disparities by wealth quintile were easiest to interpret over time and across countries. CONCLUSIONS:At the global level, we recommend monitoring disparity in met demand by wealth quintile, which is strongly correlated with disparity by education, residence, and region and comparable across countries and over time. For monitoring by individual countries and for programmatic purposes, we further recommend monitoring disparity by first-level administrative unit, which can provide direct programmatic relevance.
Project description:OBJECTIVES:Lack of wealth (poverty) impacts almost every aspect of human biology. Accordingly, many studies include its assessment. In almost all cases, approaches to assessing poverty are based on lack of success within cash economies (eg, lack of income, employment). However, this operationalization deflects attention from alternative forms of poverty that may have the most substantial influence on human wellbeing. We test how a multidimensional measure of poverty that considers agricultural assets expands the explanatory power of the construct of household poverty by associating it with one key aspect of wellbeing: symptoms of mental health. METHODS:We used the case of three highly vulnerable but distinctive communities in Haiti-urban, town with a rural hinterland, and rural. Based on survey responses from adults in 4055 geographically sampled households, linear regression models were used to predict depression and anxiety symptom levels controlling for a wide range of covariates related to detailed measures of material poverty, including cash-economy and agricultural assets, income, financial stress, and food insecurity. RESULTS:Household assets related to the cash economy were significantly associated with lower (ie, better) depression scores (-0.7, [95% CI: -1.2 to, -0.1]) but unrelated to anxiety scores (-0.3 [95% CI: -0.8 to 0.3]). Agricultural wealth was significantly-and more strongly-associated with both reductions in depression symptoms (-1.4 [95% CI: -2.2 to -0.7]) and anxiety symptoms (-1.8 [95% CI: -2.6 to -1.0]). These associations were consistent across the three sites, except in the fully urban site in Port-au-Prince where level of depression symptoms was not significantly associated with household agricultural wealth. CONCLUSIONS:Standard measures of poverty based on success in the cash economy can mask important associations between poverty and wellbeing, in this case related to household-level subsistence capacity and crucial food-producing household assets.
Project description:BACKGROUND: There are growing concerns regarding inequities in health, with poverty being an important determinant of health as well as a product of health status. Within the People's Republic of China (P.R. China), disparities in socio-economic position are apparent, with the rural-urban gap of particular concern. Our aim was to compare direct and proxy methods of estimating household wealth in a rural and a peri-urban setting of Hunan province, P.R. China. METHODS: We collected data on ownership of household durable assets, housing characteristics, and utility and sanitation variables in two village-wide surveys in Hunan province. We employed principal components analysis (PCA) and principal axis factoring (PAF) to generate household asset-based proxy wealth indices. Households were grouped into quartiles, from 'most wealthy' to 'most poor'. We compared the estimated household wealth for each approach. Asset-based proxy wealth indices were compared to those based on self-reported average annual income and savings at the household level. RESULTS: Spearman's rank correlation analysis revealed that PCA and PAF yielded similar results, indicating that either approach may be used for estimating household wealth. In both settings investigated, the two indices were significantly associated with self-reported average annual income and combined income and savings, but not with savings alone. However, low correlation coefficients between the proxy and direct measures of wealth indicated that they are not complementary. We found wide disparities in ownership of household durable assets, and utility and sanitation variables, within and between settings. CONCLUSION: PCA and PAF yielded almost identical results and generated robust proxy wealth indices and categories. Pooled data from the rural and peri-urban settings highlighted structural differences in wealth, most likely a result of localized urbanization and modernization. Further research is needed to improve measurements of wealth in low-income and transitional country contexts.
Project description:Women's empowerment is associated with improved child nutrition, and both underpin the achievement of multiple Sustainable Development Goals (SDGs). We examined pathways by which women's empowerment influences child nutritional status. We pooled nationally representative data from Demographic and Health Surveys (2011-2016) collected from married women with children aged 6-24 months in Ethiopia, Kenya, Rwanda, Tanzania, and Uganda (n?=?13,780). We operationalized child nutritional status using anemia, height-for-age z-score (HAZ), and weight-for-age z-score (WHZ). We operationalized women's empowerment using a validated measure comprised of three latent domains: social/human assets ("assets"), intrinsic agency (attitudes about intimate partner violence), and instrumental agency (influence in household decision making). We used structural equation models with latent constructs to estimate hypothesized pathways from women's empowerment to child nutritional status with further mediation by maternal body mass index (BMI) and stratification by wealth. Women's empowerment domains were directly and positively associated with maternal BMI (estimate±SE: assets, 0.17?±?0.03; intrinsic agency, 0.23?±?0.03; instrumental agency, 0.03?±?0.01). Maternal BMI was directly and positively associated with child HAZ (0.08?±?0.04) and child WHZ (0.35?±?0.03). Assets were indirectly associated with child HAZ and WHZ through intrinsic agency and maternal BMI. In the lowest wealth category, the direct effects from women's empowerment to child nutritional status were significant (assets and instrumental agency were associated with anemia; intrinsic agency associated with HAZ). In the highest wealth category, direct effects from women's empowerment on child nutritional status were significant (intrinsic and instrumental agency associated with WHZ). Improving women's empowerment, especially intrinsic agency, in East Africa could improve child nutrition directly and via improved maternal nutrition. These findings suggest that efforts to realize SDG 5 may have spillover effects on other SDGs. However, strategies to improve nutrition through empowerment approaches may need to also address household resource constraints.
Project description:In Low and Lower-Middle-Income countries, the prevalence of anaemia in infancy remains high. In early childhood anaemia cause irreversible cognitive deficits and represents a higher risk of child mortality. The consequences of anaemia in infancy are a major barrier to overcome poverty traps. The aim of this study was to analyse, based on a multi-level approach, different factors associated with anaemia in children 6?23 months old based on recent available Standard Demographic Health Surveys (S-DHS). We identified 52 S-DHS that had complete information in all covariates of interest in our analysis between 2005 and 2015. We performed traditional logistic regressions and multilevel logistic regression analyses to study the association between haemoglobin concentrations and household, child, maternal, socio-demographic variables. In our sample, 70% of the 6?23 months-old children were anaemic. Child anaemia was strongly associated with maternal anaemia, household wealth, maternal education and low birth weight. Children fed with fortified foods, potatoes and other tubers had significantly lower rates of anaemia. Improving overall household living conditions, increasing maternal education, delaying childbearing and introducing iron rich foods at six months of age may reduce the likelihood of anaemia in toddlerhood.