High Resolution Mapping of Fertility and Mortality from National - PDF document

1 High Resolution Mapping of Fertility and Mortality from National Household Survey Data in Low Income Settings Alessandra Carioli 1,2 Claudio Bosco 1,2 Andrew J. Tatem 1,2 1. WorldPop, Department of Geography and Environment. University of Southampton, UK 2. Flowminder Foundation, Stockholm, Sweden Abstract

2 2 The UN sustainable development goals (SDGs), were launched in 2015 and represent an intergovernmental set of 17 aspirational goals and 169 targets to be achieved by 2030. All SDGs are based on ensuring a certain percentage of the population has access to specific services or resources, or achieves a certain level of social, economic, or physical health, and therefore there is a need for a strong and regularly updated demographic evidence base. The SDGs strive to include the social, economic and environmental dimensions, which have prominent heterogeneous characteristics at sub-national level. Moreover, a particular focus across the goals and targets is achievement 'everywhere', ensuring that no one gets left behind and that progress is monitored regularly at subnational levels to avoid national-level statistics masking local heterogeneities. Census data can provide the requisite demographic data at fine spatial scales, but such data are only collected every 10 years, and sometimes longer in many low income settings. Moreover, administrative, civil and vital registration systems are often weak, incomplete or are rarely available in low-income setting. On the other hand, National household survey data are collected more regularly to enable SDG monitoring, but are typically summarized at national or provincial level, masking substantial local heterogeneities. Recent work has however shown the potential of using spatial interpolation techniques applied to GPS-located survey cluster data in combination with geospatial covariate layers to produce high-resolution maps of key demographic and health indicators, including age structures, access to sanitation and malaria prevalence.

3 3 In this paper we apply a full Bayesian methodology to test the potential of such approaches for mapping key demographic indicators, total fertility and child mortality rates, across a set of low-income countries. We employ Development and Health Survey data and implement the Integrated Nested Laplace Approximations (INLA) (Rue et al. 2014) approach as a computationally effective tool to produce fine scale predictions for three countries: Nigeria, Nepal, and Bangladesh. Using the fitted model we obtain the 1 x 1 km grid cells with the predicted values for the two demographic indicators.

4 4 Introduction The Sustainable Development Goals (SDGs) for the 2030 Agenda are a wide ranging set of 17 different aspirational targets aimed to all countries to significantly improve life standards worldwide over a 15 years horizon. They include ending poverty and malnutrition, improving health and education, and building resilience to natural disasters and climate change. Efficient monitoring of the goals as well as country based policies directed towards the achievement of SDGs needs up to date, detailed and good quality data on populations and on key demographic indicators. In this context, knowledge of population demographics is key to understanding the direction each country is facing in fulfilling the SDGs, especially at fine scale, in order to appraise spatial heterogeneity between different regions as well as between urban and rural areas. In particular in a context of low and middle-income countries (LMIC), detailed measures at fine grid scale of fertility rates and child mortality are important key to measure and monitor spatial heterogeneity of women’s and children health and wealth. For instance, a declining fertility rate may mean the demographic dividend may take place freeing up resources (derived from a higher support ratio) that can be invested in children’s health and education, with clear long-term advantages for the living standards. Fertility in low income countries tends to be well above replacement level (set at 2.1) with important variations between macro regions, such as Western and Middle African countries 4.9 and 5.8 respectively and South-eastern Asia with 2.4 children per woman (source un.org). Similarly, child mortality in low-income settings is on average

5 5 11 times higher than in high-income countries (source: WHO). For instance, in Nigeria 1 in 8 children will not reach the age of 5, and 1 in 15 will not reach their first birthday (source: Nigeria Standard DHS 2013), while this ratio fall by more than half in Bangladesh (source: Bangladesh Standard DHS 2014). Such heterogeneity in the demographic indicators is not only appreciated at national level but also at subnational level, as there is a substantial divergence in trends across regions as well as between urban and rural areas. Thus, the availability of updated, contemporary, spatially detailed, and comparable datasets that accurately depict the distribution of fertility and child mortality is especially important for family planning and decision-making purposes. Materials and methods This study focuses on two South Asian countries, Bangladesh and Nepal, and one Sub-Saharan African country, Nigeria. All three countries are Least Developed Countries, characterized by socioeconomic underdevelopment, poverty, inequality, social exclusion and a low level of human development. However, Bangladesh and Nepal have achieved considerable human development gains over the last few decades in terms of poverty reduction, , as opposed to Nigeria, which has high infant mortality and high fertility. We estimate two demographic variables using recent DHS data, under 5 mortality and total fertility rate, employing appropriate micro data survey techniques. In order to estimate total fertility rates, we employ direct survey techniques, excluding from

6 6 the fertility prediction analysis clusters that have 0 women numbers in any of age- groups among the final exposures computed for earlier completed calendar years plus the year of women’s interview. Table 1: Number of input clusters Country Clusters Nigeria 2013 886 Bangladesh 2014 575 Nepal 2011 289 Geographical locations of the selected cluster centroids were provided in the survey and consist of 886 geolocated households for Nigeria, 575 for Bangladesh, and 289 for Nepal. In the survey, a cluster centroid of geolocation displacement was introduced to anonymize the cluster location, up to 5 km in rural areas and up to 2 km in urban areas, where urban areas are defined as settlements with more than 20000 inhabitants. Data The maps for U5MR and fertility present the predicted estimates of mortality for children under 5 years old and fertility for women aged 15-49, ⁠ 1 as a result of the geostatistical modelling. In general Northern Nigeria displays a higher incidence of child mortality as well as a higher fertility with respect to the Central and Southern regions. The Borno region (North-East) shows remarkably low child mortality and fertility, probably due to data quality (few observations).

7 7 This study uses data from household surveys available for a large number of countries and repeated fairly constantly throughout time, on average every five to three years. Its aim is to quantify relevant demographic indicators, fertility and child mortality using a Bayesian hierarchical spatiotemporal model. Looking at the constructed variables at cluster level (figure 1a and 1b), it is evident that there exists sizable subnational variation in both fertility and child mortality. Results from this study suggest that detailed and accurate depiction of important demographic indicators can be efficiently realized from survey data and mapped at fine spatial resolution, which in turn can be promptly summarized for policy intervention or resource allocation purposes. The innovative approach coupled with the use of available geolocated survey data, helps to improve the understanding of demographic dynamics. Indeed, in these countries the realization of censuses is often difficult, with data that often provide unreliable data or that are mostly out-dated due to the fast transformation of the population. Geo-located household surveys The demographic rates investigated in these analyses were computed from the DHS Program, which collects and analyses data on populations in low and middle- income countries since the mid-90s on a regular 4 years interval. DHS household surveys, adopt a multistage cluster sampling design, where the primary-sampling units (PSU) consists of pre-existing geographic areas known as census enumeration areas (EAs). Each census tract is defined by the country Census Bureau, as well as the classification into rural and urban areas.