Multiscale spatial modelling of diabetes and hypertension in Namibia
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Date
2017
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Publisher
University of Namibia
Abstract
In Namibia, non-communicable diseases are on the increase. Statistics on non-communicable diseases (cancer, diabetes, cardiovascular diseases, hypertension etc.) as a cause of morbidity and mortality indicate that it is a public health concern although population based estimates in the area are lacking. The Ministry of Health and Social Services stated that between 2004 and 2008, hospital based mortality due to cancer (all types) increased from 3.2% to 54.7%, cardiovascular diseases (all types) increased from 5.3% to 21.2% while diabetes mellitus also increased from 1.0% to 14.6%. To curb the rising trend of the burden of non-communicable diseases in Namibia, the Ministry of Health and Social Services embarked on several preventive initiatives such as raising awareness through preventive programmes, passing laws related to the use of tobacco products, developing national promotion policies as well as health promoting school initiatives. The programmes however are implemented at national level but efficient targeting of such programmes requires the identification of high risk areas of diseases to identify where the disease is most prevalent. The use of disease maps to identify areas of elevated risk for non-communicable diseases in Namibia therefore becomes important with the available limited resources. Disease mapping is one such approach that can serve as a basic tool in planning to optimize the reduction of non-communicable diseases. Furthermore, mapping of such diseases allows the study of one disease at a time or multiple disease for comprehensive programming. The study follows a quantitative cross sectional study design using multiscale disease modelling methods to describe areas of elevated risk at region, health district and constituency level in Namibia. The main aim of the study was to fit a multiscale model to identify spatial variations of diabetes and hypertension at various geographic levels in Namibia to guide better planning, monitoring and evaluation and assist in targeting of resources. The specific objectives were to; Estimate disease risk at health district, region and constituency level in Namibia; Estimate macro determinants of diabetes and hypertension; and explore various approaches to model fitting of diabetes and hypertension in Namibia. The population for the study was all persons using health facilities from 2008-2014 in Namibia. A total of 15462 cases of diabetes and 30620 cases of hypertension were reported from 444 health facilities over a period of seven years. The covariates considered for the study were safe water, wood/charcoal, main source of income: wages and salaries, main source of income: pension and education attainment: incomplete primary education. Covariates for the study were obtained from the 2011 Namibia Population and Housing Census.
The random effects were modelled using a conditional autoregressive prior distribution. A Fully Bayesian Inference based on Markov Chain Monte Carlo simulation techniques was used to overcome difficulties in calculating the posterior distribution. Results from the study showed that significant spatial variation exist for both diabetes and hypertension among the different levels considered in the study. The relative risk for diabetes was found to be highest in Kavango region suggesting an increased risk for diabetes in the area. At health district and constituency level, the relative risk for diabetes was found to be highest in Rundu health district and Rundu Urban constituency respectively suggesting that the was an increased risk of diabetes in the area. With respect to hypertension, the relative risk of hypertension was highest in Khomas region. At health district level, Andara, Rundu, Swakopmund and Windhoek had the highest relative risk indicating that there was an increased risk of hypertension in the areas. At constituency level, Rundu Urban constituency was found to exhibit the highest relative risk of hypertension suggesting an increased risk for hypertension in the areas. For the variance components, region had the highest posterior mean (1.0168 and 1.0262 for diabetes and hypertension respectively) suggesting that diabetes and hypertension was high among the different regions in Namibia compared to health districts and constituencies.
It is hoped that the study will assist policy makers especially those involved in health planning to develop comprehensive programmes or targeted interventions in areas that were found to have elevated risk of diabetes and hypertension, in turn developing programmes and strategies aiming at improving the health and well-being of the population. Moreover, the study hopes that considering spatial factors in planning of health programmes related to diabetes and hypertension could assist in the achievement of National Development Goals such as those outlined in NDP 4 in line with the progress towards achieving international goals such as UN Millennium Development Goals.
Description
A thesis submitted in partial fulfilment of the requirements for the Degree of Master of Science (Applied Statistics & Demography)
Keywords
Spatial modelling, Diabetes, Hypertension