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How are health inequalities articulated across urban and rural spaces in Tanzania? This research paper explores the variations, differences, and inequalities, in Tanzania’s health outcomes—to question both the idea of an urban advantage in health and the extent of urban–rural inequalities in health. The three research objectives aim to understand: what are the health differences (morbidity and mortality) between Tanzania’s urban and rural areas; how are health inequalities articulated within Tanzania’s urban and rural areas; and how are health inequalities articulated across age groups for rural–urban Tanzania? By analyzing four national datasets of Tanzania (National Census, Household Budget Survey, Demographic Health Survey, and Health Demographic Surveillance System), this paper reflects on the outcomes of key health indicators across these spaces. The datasets include national surveys conducted from 2009 to 2012. The results presented showcase health outcomes in rural and urban areas vary, and are unequal. The risk of disease, life expectancy, and unhealthy behaviors are not the same for urban and rural areas, and across income groups. Urban areas show a disadvantage in life expectancy, HIV prevalence, maternal mortality, children’s morbidity, and women’s BMI. Although a greater level of access to health facilities and medicine is reported, we raise a general concern of quality and availability in health services; what data sources are being used to make decisions on urban–rural services, and the wider determinants of urban health outcomes. The results call for a better understanding of the sociopolitical and economic factors contributing to these inequalities. The urban, and rural, populations are diverse; therefore, we need to look at service quality, and use, in light of inequality: what services are being accessed; by whom; for what reasons?
Two research scientists from Ifakara Health Institute conducted this research synthesis. Three national datasets were selected to identify the differences between urban and rural health outcomes (Table (Table2).2). Indicators of morbidity and mortality are included. The national datasets enable us to synthesis, and subsequently analyze, health across different stages of the life course’—from pre-natal to older ages. This synthesis paper therefore focuses on the inequalities of morbidity and mortality across Tanzania’s rural and urban areas. The focus is placed on who is at risk, where they are, and what risks emerge, as per national data reported. In focusing on morbidity, we differentiate between the outcome of diseases and potential outcome (risk) of diseases—from negative unhealthy behaviors by inquiring datasets on lifestyle choices, and access to care (see Appendix Table Table77 for a full list of definitions applied). National datasets included within the research Indicators of mortality and morbidity for urban and rural Tanzania *Children born in reference period THMIS: Tanzania HIV and Malaria Indicator Survey [30] The three datasets (Table (Table2)2) were selected based on the meeting the following inclusion criteria: (1) Is the dataset collected from national representative survey or specific sites? (2) Does the dataset collect urban and rural health outcomes? (3) Is the data open source? (4) Is the data updated and recently collected—within the last 5 years? (5) The dataset has a wealth index applied? (6) Finally, does the data use the statistical definition of “urban” and “rural” boundaries? In Tanzania, there are three key definitions of “urban”—the first is the statistical perspective used by the National Bureau of Statistics (NBS); the second definition is the politico-administrative definition based on administered boundaries and used by the President’s Office-Regional Administration and Local Government (PO-RALG); and the third is the human settlement definition used by the Ministry of Lands and Human Settlement (MLHSD). Each use a different spatial unit of analysis: enumeration areas; local government authorities politico-administrative boundaries; and settlements, respectively [20]. The definitions influence how resources are allocated. These definition variations in boundaries present challenges in comparing datasets. Therefore to control for this, all national data sets used (Census, DHS, and HBS) use the same definition of urban and rural boundaries to ensure comparability: the statistical definition, based on smaller-scale enumeration areas (EA) (areas composed of 300–900 people). An EA is defined as urban when located in a urban ward or had urban characteristics (exceeded a certain size-density criterion, was occupied by non-agricultural activities and non-domestic buildings), containing 300–500 people, and having access to their own market and social services (ibid., pp. 4–5). The inclusion criteria meant site-specific datasets, such as the Health Demographic Surveillance Survey (HDSS) comparing rural and urban Ifakara, were excluded from the synthesis. Each of the data sources applies a wealth index in generating wealth classes. The index is based on ownership of assets and housing characteristics. Household assets identified in the surveys include possession of a television, bicycle, or car, and information on housing characteristics includes having access to a source of drinking water, the quality of sanitation facilities, and type of materials used for the dwelling construction. Wealth data was used to compare health outcomes across socioeconomic groups. Fourteen indicators were selected from the three datasets to represent indicators of morbidity and mortality across urban–rural spaces (see Appendix: Table Table7).7). Three categories were made: nutritional status; access to care; and disease outcome. Across the categories, differentiation is found on identifying actual morbidity or mortality, and the potential risk. “Nutritional status” includes the physical manifestation of morbidity, mainly linked to diet and malnourishment. “Disease outcomes” includes a specific focus on the diseases identified and mortality rates. “Access to care” includes indicators of access to care, care that may reduce the risk of morbidity and mortality. This does not take into account the underlying factors and conditions that interplay to influence this. Published data was extracted and compiled for these indicators from the three datasets. Data was compiled in Excel and evaluated based on urban and rural classifications and wealth class. The 14 indicators show health outcome advantage and disadvantage. Advantage is defined by a positive health outcome from reported data; this does not however take into account structural and social factors, issues concerning access or quality. Statistical significances were tested on only eight indicators; NBS provides sampling error estimates on few selected indicators.
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