Spatial Patterns and Determinants of Demographic Outcomes

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.

Whereas the coverage of prenatal care in much of rural sub-Saharan Africa has greatly increased, institutional deliveries continue to lag behind as a substantial share of rural women give birth outside clinic settings and without professional obstetric care. This study uses unique longitudinal data from rural southern Mozambique to examine both the probability of having an institutional delivery and the choice of clinic for institutional delivery as a function of individual and household characteristics and of location and characteristics of maternal and child health facilities. Spatial and multivariate regression analyses are employed to determine both additive and interactive effects of the two groups of factors in the context of high HIV prevalence and a massive scale-up of HIV testing, prophylaxis, and treatment services.

In this paper, we explore the impact on mortality of income inequality in residential neighbourhoods and municipalities among elderly 65-84 years in the year 2004, using Swedish longitudinal micro-data covering the entire Swedish population for the period 1970 – 2006. Preliminary cross-sectional multi-level analyses are now complemented by longitudinal analyses of long-term residential histories with exposure to equal/unequal municipalities and neighbourhoods and the long-term impact on mortality. We investigate the association between mortality and income inequality at place of residence at different time lags and the effect of a summary measure of previous exposures to environments characterised by different inequality levels. We also compare groups that have different experiences of residential characteristics, i.e. those that have resided in unequal or equal places and those that have changed from equal to unequal residences or vice versa. Preliminary results from a cross-sectional analysis on 2006, show that income inequality in the municipality of residence had an independent effect on mortality in the age group 65-74 years

This paper analyses the causes of geographical fertility differences within metropolitan areas, by focusing on the metropolitan regions of Barcelona and Madrid. Our main hypothesis is that fertility differences among municipalities would be based on two main elements: 1) the degree to which the suburbanization process has developed –which, in the Spanish case, has rapidly expanded and has been highly selective– and 2) foreign immigration’s local level impact. Results from the descriptive analysis show that despite regional fertility levels have become increasingly similar within Spain, there are increasingly strong fertility variations within Barcelona and Madrid metropolitan regions. While the core cities have low, late and more stable fertility levels, the suburban periphery municipalities have earlier and higher fertility levels. This would confirm the fact that different areas within the metropolitan regions are increasingly specializing in a particular function –productive or residential. These settlement preferences are in turn dependent on nationality and the life cycle stage. Factor analysis using Movimiento Natural de la Población (vital statistics) data allow us to investigate the causes of such differences.

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%.

It has been argued that social conditions are the fundamental determinants of health for those agents who possess them, and this argument has been bolstered by both individual and ecological studies. However, little is known about whether social conditions also benefit others nearby. Using the US county mortality data, we fill this gap by first theorizing the relationships between the social conditions of a county and the mortality of its neighbors with spatial spillover and social relativity perspectives. We then measured social conditions with income inequality, social capital, social affluence and concentrated disadvantage and used spatial Durbin modeling and spatial partitioning technique to examine the effects of these variables on mortality across space. The analytic results suggested that (1) social conditions of a specific county are not only related to its own mortality but also the mortality in neighboring counties; (2) The partitioning results provide evidence for spatial feedback, which underscores the importance of spatial structure underlying the data; and (3) The immediate neighbors (those shared the same boundaries or a vertex) play a more important role in understanding the direct impacts of social conditions on mortality than those neighbors far away. These findings provide new insight to mortality research.

In the context of current discussions regarding the human dimensions of climatic change it is essential the location and characterization of the populations in situations of risk and the evaluation of how vulnerable such populations are to extreme climatic events.
To perform the analysis we consider the data relative to the area susceptible to landslides and its respective volume of population resident and potentially vulnerable to geological accidents. This data was plotted in a Lorentz Curve to allow an evaluation of the relationship between the volumes of population in relation to the extension of the vulnerable area.
The integration of the environmental dimension with the human dimension gives us a product that can be used to help in the creation of policies to prevent and to mitigate the impact resulting from extreme climatic events and also allows the comparison between different regions.

We used District Level Household Survey round 3 conducted in India in 2007-08 to test the following hypotheses: 1) Child mortality is positively associated with fertility; 2) primary infertility is negatively associated with fertility; and 3) son preference is positively associated with fertility. For analysis purpose, we divided India into 85 natural regions based on the agro-climatic scheme proposed by the census of India. We estimated all the independent variables and the dependent variable for each of the 85 natural regions of India. Bivariate LISA, Moran’s I, ordinary least squares (OLS), and instrumental variable (IV) regression were used to test the hypotheses. Improved water and improved sanitation were used as instruments in the IV regression. Primary infertility, use of family planning methods, and female literacy were negatively associated with TFR in the OLS (child mortality was not included in OLS). Instrumental variable regression (also included child mortality) confirmed the findings of the OLS. But, the statistical significance of female literacy reduced slightly in the IV regression. Child mortality and son preference were not statistically associated with TFR in the IV regression.

For many countries, in particular in sub-Saharan Africa, Demographic and Health Surveys (DHS) are the main national source of data (depending on the subject). Several DHS collect latitude and longitude of surveyed clusters but the sampling method is not appropriate to derive local estimates: sample size is not large enough for a direct spatial interpolation.
We develop a methodological approach for estimating a proportion by using kernel density estimators with adaptive bandwidths of equal number of persons surveyed. The method was tested by creating a fictitious country from which survey datasets were produced. We compared the prevalence surface estimated from survey data with the model’s original prevalence surface.
This method makes it possible to achieve a smoothing effect that adapts to the high irregularity of spatial distribution among the survey clusters. The surfaces thus generated are relatively accurate for densely populated areas and strongly smoothed in sparsely surveyed areas. Although local variations were filtered out, the regional component in the spatial variation of prevalence was reproduced, and the estimated prevalence surfaces could be interpreted as regional trend surfaces.
Furthermore, this approach could be easily applied using prevR, a dedicated package for the statistical software R.

Malaria is a major obstacle to socio-economic development in Sub Sahara Africa, with about 90% of all recorded cases worldwide. The disease accounted for nearly one million deaths in 2008, mostly among children living in Africa. Furthermore malaria is a leading cause of under-five deaths in SSA where a child dies every 45 seconds of Malaria. It is of high importance to properly identify risk factors that are associated with the incidence of Malaria. Analyzing spatial data must be done with caution as observations may now be correlated, hence ordinary statistical methods assuming independence of observations are no longer valid. Ignoring the structure of the data may result in asymptotically biased parameter estimates. A crucial step in modeling spatial data is the specification of the spatial dependency, by choosing the correlation function. However, often the choice for a particular application is unclear and diagnostic tests will have to be carried out following fitting of a model. To resolve this problem, we adopt a more robust method for modeling spatial correlation by simultaneously solving the combined estimating equations using different working correlation structures. We illustrate our method by modeling the spatial correlation of malaria incidence in Liberia and Madagascar.