Earned Income Tax Credit Receipt By Hispanic Families With Children: State Outreach And Demographic Factors.

The Earned Income Tax Credit (EITC), the largest refundable tax credit for low-to-middle-income US families with children, has been shown to improve maternal and child health and reduce public spending on health. However, many eligible families do not receive it. This study used 2014 Survey of Income and Program Participation data to explore predictors of EITC receipt among Hispanic families, an understudied segment of the eligible population. We found lower likelihoods of receipt among Hispanic income-eligible families, even those who were eligible US citizens by naturalization, compared with their peers. Parent self-employment and lower English language proficiency were also associated with lower EITC receipt. With new data collected on state policies, we found that states' granting of drivers' licenses to undocumented people, availability of government information in Spanish, and employer mandates to inform employees were associated with greater EITC receipt among all income-eligible families, including Hispanic families. These findings showcase ways in which information and outreach at the state level can support the equitable receipt of tax refunds and similar types of benefits distributed through the tax system.

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia.

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.

Population Estimates of GFR and Risk Factors for CKD in Guatemala.

INTRODUCTION: Chronic kidney disease (CKD) is an emerging public health priority in Central America. However, data on the prevalence of CKD in Guatemala, Central America's most populous country, are limited, especially for rural communities. METHODS: We conducted a population-representative survey of 2 rural agricultural municipalities in Guatemala. We collected anthropometric data, blood pressure, serum and urine creatinine, glycosylated hemoglobin, and urine albumin. Sociodemographic, health, and exposure data were self-reported. RESULTS: We enrolled 807 individuals (63% of all eligible, 35% male, mean age 39.5 years). An estimated 4.0% (95% confidence interval [CI] 2.4-6.6) had CKD, defined as an estimated glomerular filtration rate (eGFR) less than 60 ml/min per 1.73 m2. Most individuals with an eGFR below 60 ml/min per 1.73 m2 had diabetes or hypertension. In multivariable analysis, the important factors associated with risk for an eGFR less than 60 ml/min per 1.73 m2 included a history of diabetes or hypertension (adjusted odds ratio [aOR] 11.21; 95% CI 3.28-38.24), underweight (body mass index [BMI] <18.5) (aOR 21.09; 95% CI 2.05-217.0), and an interaction between sugar cane agriculture and poverty (aOR 1.10; 95% CI 1.01-1.19). CONCLUSIONS: In this population-based survey, most observed CKD was associated with diabetes and hypertension. These results emphasize the urgent public health need to address the emerging epidemic of diabetes, hypertension, and CKD in rural Guatemala. In addition, the association between CKD and sugar cane in individuals living in poverty provides some circumstantial evidence for existence of CKD of unknown etiology in the study communities, which requires further investigation.

Addressing Unintentional Exclusion of Vulnerable and Mobile Households in Traditional Surveys in Kathmandu, Dhaka, and Hanoi: a Mixed-Methods Feasibility Study.

The methods used in low- and middle-income countries' (LMICs) household surveys have not changed in four decades; however, LMIC societies have changed substantially and now face unprecedented rates of urbanization and urbanization of poverty. This mismatch may result in unintentional exclusion of vulnerable and mobile urban populations. We compare three survey method innovations with standard survey methods in Kathmandu, Dhaka, and Hanoi and summarize feasibility of our innovative methods in terms of time, cost, skill requirements, and experiences. We used descriptive statistics and regression techniques to compare respondent characteristics in samples drawn with innovative versus standard survey designs and household definitions, adjusting for sample probability weights and clustering. Feasibility of innovative methods was evaluated using a thematic framework analysis of focus group discussions with survey field staff, and via survey planner budgets. We found that a common household definition excluded single adults (46.9%) and migrant-headed households (6.7%), as well as non-married (8.5%), unemployed (10.5%), disabled (9.3%), and studying adults (14.3%). Further, standard two-stage sampling resulted in fewer single adult and non-family households than an innovative area-microcensus design; however, two-stage sampling resulted in more tent and shack dwellers. Our survey innovations provided good value for money, and field staff experiences were neutral or positive. Staff recommended streamlining field tools and pairing technical and survey content experts during fieldwork. This evidence of exclusion of vulnerable and mobile urban populations in LMIC household surveys is deeply concerning and underscores the need to modernize survey methods and practices.

Feasibility of satellite image and GIS sampling for population representative surveys: a case study from rural Guatemala.

BACKGROUND: Population-representative household survey methods require up-to-date sampling frames and sample designs that minimize time and cost of fieldwork especially in low- and middle-income countries. Traditional methods such as multi-stage cluster sampling, random-walk, or spatial sampling can be cumbersome, costly or inaccurate, leading to well-known biases. However, a new tool, Epicentre's Geo-Sampler program, allows simple random sampling of structures, which can eliminate some of these biases. We describe the study design process, experiences and lessons learned using Geo-Sampler for selection of a population representative sample for a kidney disease survey in two sites in Guatemala. RESULTS: We successfully used Epicentre's Geo-sampler tool to sample 650 structures in two semi-urban Guatemalan communities. Overall, 82% of sampled structures were residential and could be approached for recruitment. Sample selection could be conducted by one person after 30 min of training. The process from sample selection to creating field maps took approximately 40 h. CONCLUSION: In combination with our design protocols, the Epicentre Geo-Sampler tool provided a feasible, rapid and lower-cost alternative to select a representative population sample for a prevalence survey in our semi-urban Guatemalan setting. The tool may work less well in settings with heavy arboreal cover or densely populated urban settings with multiple living units per structure. Similarly, while the method is an efficient step forward for including non-traditional living arrangements (people residing permanently or temporarily in businesses, religious institutions or other structures), it does not account for some of the most marginalized and vulnerable people in a population-the unhoused, street dwellers or people living in vehicles.

Gridded population survey sampling: a systematic scoping review of the field and strategic research agenda.

INTRODUCTION: 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 10 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 scoping review in Scopus of specific gridded population datasets and "population" or "household" "survey" reports, and solicited additional published and unpublished sources from colleagues. RESULTS: We identified 43 national and sub-national gridded population-based household surveys implemented across 29 LMICs. Gridded population surveys used automated and manual approaches to derive clusters from WorldPop and LandScan gridded population estimates. After sampling, some survey teams interviewed all households in each cluster or segment, and others 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.

A grid-based sample design framework for household surveys.

Traditional sample designs for household surveys are contingent upon the availability of a representative primary sampling frame. This is defined using enumeration units and population counts retrieved from decennial national censuses that can become rapidly inaccurate in highly dynamic demographic settings. To tackle the need for representative sampling frames, we propose an original grid-based sample design framework introducing essential concepts of spatial sampling in household surveys. In this framework, the sampling frame is defined based on gridded population estimates and formalized as a bi-dimensional random field, characterized by spatial trends, spatial autocorrelation, and stratification. The sampling design reflects the characteristics of the random field by combining contextual stratification and proportional to population size sampling. A nonparametric estimator is applied to evaluate the sampling design and inform sample size estimation. We demonstrate an application of the proposed framework through a case study developed in two provinces located in the western part of the Democratic Republic of the Congo. We define a sampling frame consisting of settled cells with associated population estimates. We then perform a contextual stratification by applying a principal component analysis (PCA) and k-means clustering to a set of gridded geospatial covariates, and sample settled cells proportionally to population size. Lastly, we evaluate the sampling design by contrasting the empirical cumulative distribution function for the entire population of interest and its weighted counterpart across different sample sizes and identify an adequate sample size using the Kolmogorov-Smirnov distance between the two functions. The results of the case study underscore the strengths and limitations of the proposed grid-based sample design framework and foster further research into the application of spatial sampling concepts in household surveys.

From Rags to Riches: Assessing poverty and vulnerability in urban Nepal.

Urbanisation brings with it rapid socio-economic change with volatile livelihoods and unstable ownership of assets. Yet, current measures of wealth are based predominantly on static livelihoods found in rural areas. We sought to assess the extent to which seven common measures of wealth appropriately capture vulnerability to poverty in urban areas. We then sought to develop a measure that captures the characteristics of one urban area in Nepal. We collected and analysed data from 1,180 households collected during a survey conducted between November 2017 and January 2018 and designed to be representative of the Kathmandu valley. A separate survey of a sub set of households was conducted using participatory qualitative methods in slum and non-slum neighbourhoods. A series of currently used indices of deprivation were calculated from questionnaire data. We used bivariate statistical methods to examine the association between each index and identify characteristics of poor and non-poor. Qualitative data was used to identify characteristics of poverty from the perspective of urban poor communities which were used to construct an Urban Poverty Index that combined asset and consumption focused context specific measures of poverty that could be proxied by easily measured indicators as assessed through multivariate modelling. We found a strong but not perfect association between each measure of poverty. There was disagreement when comparing the consumption and deprivation index on the classification of 19% of the sample. Choice of short-term monetary and longer-term capital approaches accounted for much of the difference. Those who reported migrating due to economic necessity were most likely to be categorised as poor. A combined index was developed to capture these dimension of poverty and understand urban vulnerability. A second version of the index was constructed that can be computed using a smaller range of variables to identify those in poverty. Current measures may hide important aspects of urban poverty. Those who migrate out of economic necessity are particularly vulnerable. A composite index of socioeconomic status helps to capture the complex nature of economic vulnerability.

Stigma and Acceptance of Sierra Leone's Child Soldiers: A Prospective Longitudinal Study of Adult Mental Health and Social Functioning.

OBJECTIVE: To investigate the associations of war and postconflict factors with mental health among Sierra Leone's former child soldiers as adults. METHOD: In 2002, we recruited former child soldiers from lists of soldiers (aged 10-17 years) served by Disarmament, Demobilization, Reintegration centers and from a random door-to-door sample in 5 districts of Sierra Leone. In 2004, self-reintegrated child soldiers were recruited in an additional district. At 2016/2017, 323 of the sample of 491 former child soldiers were reassessed. Subjects reported on war exposures and postconflict stigma, family support, community support, anxiety/depression, and posttraumatic stress symptoms. RESULTS: Of the subjects, 72% were male, with a mean age of 28 years. In all, 26% reported killing or injuring others; 67% reported being victims of life-threatening violence; 45% of female subjects and 5% of male subjects reported being raped; and 32% reported death of a parent. In 2016/2017 (wave 4), 47% exceeded the threshold for anxiety/depression, and 28% exceeded the likely posttraumatic stress disorder threshold. Latent class growth analysis yielded 3 trajectory groups based on changes in stigma and family/community acceptance; "Improving Social Integration" (n = 77) fared nearly as well as the "Socially Protected" (n = 213). The "Socially Vulnerable" group (n = 33) had increased risk of anxiety/depression above the clinical threshold and possible PTSD, and were around 3 times more likely to attempt suicide. CONCLUSION: Former child soldiers had elevated rates of mental health problems. Postconflict risk and protective factors related to outcomes long after the end of conflict. Targeted social inclusion and family interventions could benefit the long-term mental health of former child soldiers.

Correction to: Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs.

Readers should note an additional Acknowledgment for this article: Dana Thomson is funded by the Economic and Social Research Council grant number ES/5500161/1.

Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs.

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.

Self-Regulation in Low- and Middle-Income Countries: Challenges and Future Directions.

Self-regulation is developed early in life through family and parenting interactions. There has been considerable debate on how to best conceptualize and enhance self-regulation. Many consider self-regulation as the socio-emotional competencies required for healthy and productive living, including the flexibility to regulate emotions, control anger, maintain calm under pressure, and respond adaptively to a variety of situations. Its enhancement is the focus of many child and family interventions. An important limitation of the self-regulation field is that most empirical and conceptual research comes from high-income countries (HICs). Less is known about the manifestation, measurement and role of self-regulation in many collectivistic, rural, or less-developed contexts such as low- and middle-income countries (LMICs). This position paper aims to present an initial review of the existing literature on self-regulation in LMICs, with a focus on parenting, and to describe challenges in terms of measurement and implementation of self-regulation components into existing interventions for parents, children and adolescents in these settings. We conclude by establishing steps or recommendations for conducting basic research to understand how self-regulation expresses itself in vulnerable and low-resource settings and for incorporating components of self-regulation into services in LMICs.

Improving household surveys and use of data to address health inequities in three Asian cities: protocol for the Surveys for Urban Equity (SUE) mixed methods and feasibility study.

INTRODUCTION: As rapid urbanisation transforms the sociodemographic structures within cities, standard survey methods, which have remained unchanged for many years, under-represent the urban poorest. This leads to an overly positive picture of urban health, distorting appropriate allocation of resources between rural and urban and within urban areas. Here, we present a protocol for our study which (i) tests novel methods to improve representation of urban populations in household surveys and measure mental health and injuries, (ii) explores urban poverty and compares measures of poverty and 'slumness' and (iii) works with city authorities to understand, and potentially improve, utilisation of data on urban health for planning more equitable services. METHODS AND ANALYSIS: We will conduct household surveys in Kathmandu, Hanoi and Dhaka to test novel methods: (i) gridded population sampling; (ii) enumeration using open-access online maps and (iii) one-stage versus two-stage cluster sampling. We will test reliability of an observational tool to categorise neighbourhoods as slum areas. Within the survey, we will assess the appropriateness of a short set of questions to measure depression and injuries. Questionnaire data will also be used to compare asset-based, consumption-based and income-based measures of poverty. Participatory methods will identify perceptions of wealth in two communities in each city. The analysis will combine quantitative and qualitative findings to recommend appropriate measures of poverty in urban areas. We will conduct qualitative interviews and establish communities of practice with government staff in each city on use of data for planning. Framework approach will be used to analyse qualitative data allowing comparison across city settings. ETHICS AND DISSEMINATION: Ethical approvals have been granted by ethics committees from the UK, Nepal, Bangladesh and Vietnam. Findings will be disseminated through conference papers, peer-reviewed open access articles and workshops with policy-makers and survey experts in Kathmandu, Hanoi and Dhaka.

Using routinely collected laboratory data to identify high rifampicin-resistant tuberculosis burden communities in the Western Cape Province, South Africa: A retrospective spatiotemporal analysis.

BACKGROUND: South Africa has the highest tuberculosis incidence globally (781/100,000), with an estimated 4.3% of cases being rifampicin resistant (RR). Control and elimination strategies will require detailed spatial information to understand where drug-resistant tuberculosis exists and why it persists in those communities. We demonstrate a method to enable drug-resistant tuberculosis monitoring by identifying high-burden communities in the Western Cape Province using routinely collected laboratory data. METHODS AND FINDINGS: We retrospectively identified cases of microbiologically confirmed tuberculosis and RR-tuberculosis from all biological samples submitted for tuberculosis testing (n = 2,219,891) to the Western Cape National Health Laboratory Services (NHLS) between January 1, 2008, and June 30, 2013. Because the NHLS database lacks unique patient identifiers, we performed a series of record-linking processes to match specimen records to individual patients. We counted an individual as having a single disease episode if their positive samples came from within two years of each other. Cases were aggregated by clinic location (n = 302) to estimate the percentage of tuberculosis cases with rifampicin resistance per clinic. We used inverse distance weighting (IDW) to produce heatmaps of the RR-tuberculosis percentage across the province. Regression was used to estimate annual changes in the RR-tuberculosis percentage by clinic, and estimated average size and direction of change was mapped. We identified 799,779 individuals who had specimens submitted from mappable clinics for testing, of whom 222,735 (27.8%) had microbiologically confirmed tuberculosis. The study population was 43% female, the median age was 36 years (IQR 27-44), and 10,255 (4.6%, 95% CI: 4.6-4.7) cases had documented rifampicin resistance. Among individuals with microbiologically confirmed tuberculosis, 8,947 (4.0%) had more than one disease episode during the study period. The percentage of tuberculosis cases with rifampicin resistance documented among these individuals was 11.4% (95% CI: 10.7-12.0). Overall, the percentage of tuberculosis cases that were RR-tuberculosis was spatially heterogeneous, ranging from 0% to 25% across the province. Our maps reveal significant yearly fluctuations in RR-tuberculosis percentages at several locations. Additionally, the directions of change over time in RR-tuberculosis percentage were not uniform. The main limitation of this study is the lack of unique patient identifiers in the NHLS database, rendering findings to be estimates reliant on the accuracy of the person-matching algorithm. CONCLUSIONS: Our maps reveal striking spatial and temporal heterogeneity in RR-tuberculosis percentages across this province. We demonstrate the potential to monitor RR-tuberculosis spatially and temporally with routinely collected laboratory data, enabling improved resource targeting and more rapid locally appropriate interventions.

Early changes in intervention coverage and mortality rates following the implementation of an integrated health system intervention in Madagascar.

INTRODUCTION: The Sustainable Development Goals framed an unprecedented commitment to achieve global convergence in child and maternal mortality rates through 2030. To meet those targets, essential health services must be scaled via integration with strengthened health systems. This is especially urgent in Madagascar, the country with the lowest level of financing for health in the world. Here, we present an interim evaluation of the first 2 years of a district-level health system strengthening (HSS) initiative in rural Madagascar, using estimates of intervention coverage and mortality rates from a district-wide longitudinal cohort. METHODS: We carried out a district representative household survey at baseline of the HSS intervention in over 1500 households in Ifanadiana district. The first follow-up was after the first 2 years of the initiative. For each survey, we estimated maternal, newborn and child health (MNCH) coverage, healthcare inequalities and child mortality rates both in the initial intervention catchment area and in the rest of the district. We evaluated changes between the two areas through difference-in-differences analyses. We estimated annual changes in health centre per capita utilisation from 2013 to 2016. RESULTS: The intervention was associated with 19.1% and 36.4% decreases in under-five and neonatal mortality, respectively, although these were not statistically significant. The composite coverage index (a summary measure of MNCH coverage) increased by 30.1%, with a notable 63% increase in deliveries in health facilities. Improvements in coverage were substantially larger in the HSS catchment area and led to an overall reduction in healthcare inequalities. Health centre utilisation rates in the catchment tripled for most types of care during the study period. CONCLUSION: At the earliest stages of an HSS intervention, the rapid improvements observed for Ifanadiana add to preliminary evidence supporting the untapped and poorly understood potential of integrated HSS interventions on population health.

Impact of a health system strengthening intervention on maternal and child health outputs and outcomes in rural Rwanda 2005-2010.

INTRODUCTION: Although Rwanda's health system underwent major reforms and improvements after the 1994 Genocide, the health system and population health in the southeast lagged behind other areas. In 2005, Partners In Health and the Rwandan Ministry of Health began a health system strengthening intervention in this region. We evaluate potential impacts of the intervention on maternal and child health indicators. METHODS: Combining results from the 2005 and 2010 Demographic and Health Surveys with those from a supplemental 2010 survey, we compared changes in health system output indicators and population health outcomes between 2005 and 2010 as reported by women living in the intervention area with those reported by the pooled population of women from all other rural areas of the country, controlling for potential confounding by economic and demographic variables. RESULTS: Overall health system coverage improved similarly in the comparison groups between 2005 and 2010, with an indicator of composite coverage of child health interventions increasing from 57.9% to 75.0% in the intervention area and from 58.7% to 73.8% in the other rural areas. Under-five mortality declined by an annual rate of 12.8% in the intervention area, from 229.8 to 83.2 deaths per 1000 live births, and by 8.9% in other rural areas, from 157.7 to 75.8 deaths per 1000 live births. Improvements were most marked among the poorest households. CONCLUSION: We observed dramatic improvements in population health outcomes including under-five mortality between 2005 and 2010 in rural Rwanda generally and in the intervention area specifically.

Next wave of interventions to reduce under-five mortality in Rwanda: a cross-sectional analysis of demographic and health survey data.

BACKGROUND: Sustained investments in Rwanda's health system have led to historic reductions in under five (U5) mortality. Although Rwanda achieved an estimated 68% decrease in the national under U5 mortality rate between 2002 and 2012, according to the national census, 5.8% of children still do not reach their fifth birthday, requiring the next wave of child mortality prevention strategies. METHODS: This is a cross-sectional study of 9002 births to 6328 women age 15-49 in the 2010 Rwanda Demographic and Health Survey. We tested bivariate associations between 29 covariates and U5 mortality, retaining covariates with an odds ratio p < 0.1 for model building. We used manual backward stepwise logistic regression to identify correlates of U5 mortality in all children U5, 0-11 months, and 12-59 months. Analyses were performed in Stata v12, adjusting for complex sample design. RESULTS: Of 14 covariates associated with U5 mortality in bivariate analysis, the following remained associated with U5 mortality in multivariate analysis: household being among the poorest of the poor (OR = 1.98), child being a twin (OR = 2.40), mother having 3-4 births in the past 5 years (OR = 3.97) compared to 1-2 births, mother being HIV positive (OR = 2.27), and mother not using contraceptives (OR = 1.37) compared to using a modern method (p < 0.05 for all). Mother experiencing physical or sexual violence in the last 12 months was associated with U5 mortality in children ages 1-4 years (OR = 1.48, p < 0.05). U5 survival was associated with a preceding birth interval 25-50 months (OR = 0.67) compared to 9-24 months, and having a mosquito net (OR = 0.46) (p < 0.05 for both). CONCLUSIONS: In the past decade, Rwanda rolled out integrated management of childhood illness, near universal coverage of childhood vaccinations, a national community health worker program, and a universal health insurance scheme. Identifying factors that continue to be associated with childhood mortality supports determination of which interventions to strengthen to reduce it further. This study suggests that Rwanda's next wave of U5 mortality reduction should target programs in improving neonatal outcomes, poverty reduction, family planning, HIV services, malaria prevention, and prevention of intimate partner violence.

Maternal Support of Children's Early Numerical Concept Learning Predicts Preschool and First-Grade Math Achievement.

The primary goal in this study was to examine maternal support of numerical concepts at 36 months as predictors of math achievement at 4½ and 6-7 years. Observational measures of mother-child interactions (n = 140) were used to examine type of support for numerical concepts. Maternal support that involved labeling the quantities of sets of objects was predictive of later child math achievement. This association was significant for preschool (d = .45) and first-grade math (d = .49), controlling for other forms of numerical support (identifying numerals, one-to-one counting) as well as potential confounding factors. The importance of maternal support of labeling set sizes at 36 months is discussed as a precursor to children's eventual understanding of the cardinal principle.