Spatial approaches to estimation of demographic rates

In the Egyptian context, delayed fertility transition compared to neighboring countries, can be in part ascribed to the delay in the fall of infant mortality rates. Infant mortality was high in Egypt till the 1980s. Since then, infant mortality recorded significant progress: in 2001, the number of deaths per 1000 births was 38 against 97 in 1984. However differences are still significant between governorates: in urban governorates, the 2008 level is 29 deaths per 1,000 births. In rural Upper Egypt, mortality was about 39 ‰. No previous studies had attempted to estimate infant and child mortality in Egypt for small geographical areas. Strong socio-economics differences and inequalities exist between urban and rural setting, Upper and Lower Egypt and even between small area in the same region or city. Those differences justify the need to calculate infant and child mortality rates at the local level. We will account for this problem using a Bayesian hierarchical model for small area: model-based estimators will be derived and their precisions compared with alternative estimators proposed in literature. We use data from Egyptian Demographic and Health Surveys (1995 and 2005), Egyptian population register and Egyptian Population and Housing Census (1996 and 2006).

Population projections at a small area level are sensitive to random departures in temporal trends of population change components. In order to have robust trends for the county level projections in Costa Rica, the team in charge of forecasting decided to determine county clusters based on the past trends. In this paper, we compare three descriptive techniques used to build space-time clusters for the Crude Birth Rate: Functional Data Analysis (FDA), the scan statistic, and Geographically Weighted Regression GWR. GWR was the technique that yielded clusters related to the diffusion paradigm for fertility decline. However, the scan statistic provided a more parsimonious set of clusters that are more tractable for population projections.

This study examines the spatial relationship to the maternal-health-care utilization focusing on institutional births which direct related to maternal-mortality.Socio-economically weak states (Empowered Action Group)was studied. Some facility adequacy indices were prepared for the purpose using DLHS-3 data and reliability test found to be good(0.7<alpha<1.0).Inequality measures showed maximum number of districts belongs to UP and Bihar where lowest adequacy of all indices of infrastructures at PHCs. Though very uneven pattern was seen for the adequacy at HSCs. Correlation-matrix showed health personnel adequacy index were highly correlated with physical-infrastructure-index at PHC. Spatial dependence for delivery care captured the better acceptability to describe through several tests of spatial diagnosis over dependents, independents and error term. Some covariates disappeared its influence on independents once spatial-lag parameter incorporated in the OLS model like availability of doctors at PHC, proportion of SC/ST and urban population. Low infrastructure adequacy, distant health facility providing ANC/delivery care and proportion of lowest quintile have significantly reduced the probability of institutional births while receipt of 3or more ANC, connectivity of village to the health center and women literacy have encouraged.

Sub-national estimates of under-5 mortality are useful for evaluating within-country inequality, tracking progress, and identifying areas of greatest need. We estimate under-5 mortality for each of Zambia's 72 districts annually 1980-2010, using summary birth history data from censuses and complete birth history data from Demographic and Health surveys to fit a series of small area models. We consider a variety of generalized linear mixed models that differ in how spatial trends, temporal trends, and spatial-temporal interactions are introduced. All models suggest considerable heterogeneity in levels of under-5 mortality, with the worst off districts experiencing mortality risks 2-3 times as great as those in the best off districts. Distinct spatial trends are also apparent: districts in the northeast and southwest experience noticeably higher mortality than districts in the central part of the country. Progress in decreasing mortality over the past 30 years has also been variable: while there is some evidence of decline in most districts, our models suggest that a subset of districts have experienced decreases in mortality exceeding 50%.