Crude rates such as the crude death rate are functions of both the age-specific rates and the age composition of a population. However, differences in the age structure between two populations or two time periods can result in specious differences in the corresponding crude rates making direct comparisons between populations or across time inappropriate. Therefore, when comparing crude rates between populations, it is desirable to eliminate or minimize the influence of age composition. This task is accomplished by using a standard age structure yielding an age-standardized rate. This paper proposes an updated International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) standard for use in low- and middle-income countries (LMICs) based on newly available data from the health and demographic surveillance system site members of the INDEPTH network located throughout Africa and southern Asia. The updated INDEPTH standard should better reflect the age structure of LMICs and result in more accurate health indicators and demographic rates. We demonstrate use of the new INDEPTH standard along with several existing ‘world’ standards and show how resulting age-standardized crude deaths rates differ when using the various standard age compositions.

Background: Cardiovascular diseases (CVDs) are part of the leading causes of mortality and morbidity in developing countries, including South Africa, where they are a major public health issue. Understanding the joint spatial clustering of CVDs and associated risk factors to determine areas in need of enhanced integrated interventions would help develop targeted, cost-effective and productive mediations. We estimated joint spatial associations and clustering patterns of 2 CVDs (stroke and heart attack) and 3 risk factors (hypertension, high blood cholesterol (HBC) and smoking) among adults in South Africa. Methods: We used cross-sectional secondary adult (15–64-year olds) health data from the South African Demographic Health Survey 2016. Age and gender standardized disease incidence ratios were analyzed using joint spatial global and local bivariate Moran’s Index statistics. Results: We found significantly positive univariate spatial clustering for stroke (Moran; s Index = 0.128), smoking (0.606) hypertension (0.236) and high blood cholesterol (0.385). Smoking and high blood cholesterol (0.366), smoking and stroke (0.218) and stroke and high blood cholesterol (0.184) were the only bivariate outcomes with significant bivariate clustering. There was a joint stroke-smoking local “hot spots” cluster among four districts in the urban western part of the country (City of Cape Town; Cape Winelands; Overberg and Eden) and a joint “cold spots” cluster in the rural north-western part of the country. Similar joint “hot spots” clustering was found for stroke and high blood cholesterol, which also had “cold spots” cluster in the rural east-central part of the country. Smoking and high blood cholesterol had a “hot spots” cluster among five districts in the urban western part of the country (City of Cape Town; Cape Winelands; Overberg; Eden, and West Coast) and “cold spots” around the rural districts in east-southern parts of the country. Conclusions: Our study showed that districts tended to co-cluster based on the rates of CVDs and risk factors, where higher rates were found in urban places than in rural areas. These findings are suggestive of a more contagious and spatial diffusion process among interdependent districts in urban districts. Urbanization or rurality needs to be considered when intervention initiatives are implemented with more general approaches in rural areas. The finding of “hot spot” co-clusters in urban areas means that integrated intervention programmes aimed at reducing the risk of CVDs and associated risk factors would be cost-effective and more productive.

South Africa is experiencing an increasing burden of noncommunicable diseases (NCDs). There is evidence of co-morbidity of several NCDs at small geographical areas in the country. However, the extent to which this applies to joint spatial autocorrections of NCDs is not known. The objective of this study was to derive and quantify multivariate spatial autocorrections for NCDrelated mortality in South Africa. The study used mortality attributable to cerebrovascular, ischaemic heart failure and hypertension captured by the country’s Department of Home Affairs for the years 2001, 2007 and 2011. Both univariate and pairwise spatial clustering measures were derived using observed, empirical Bayes smoothed and age-adjusted standardised mortality rates. Cerebrovascular and ischaemic heart co-clustering was significant for the years 2001 and 2011. Cerebrovascular and hypertension co-clustering was significant for the years 2007 and 2011, while hypertension and ischaemic heart co-clustering was significant for the year 2011. Co-clusters of cerebrovascular-ischaemic heart disease are the most profound and located in the south-western part of the country. It was successfully demonstrated that bivariate spatial autocorrelations can be derived for spatially dependent mortality rates as exemplified by mortality rates attributed to three cardiovascular conditions. The identified co-clusters of spatially dependent health outcomes may be targeted for an integrated intervention and monitoring programme.

Abstract

The ongoing highly contagious coronavirus disease 2019 (COVID-19) pandemic, which started in Wuhan, China, in December 2019, has now become a global public health problem. Using publicly available data from the COVID-19 data repository of Our World in Data, we aimed to investigate the influences of spatial socio-economic vulnerabilities and neighbourliness on the COVID-19 burden in African countries. We analyzed the first wave (January–September 2020) and second wave (October 2020 to May 2021) of the COVID-19 pandemic using spatial statistics regression models. As of 31 May 2021, there was a total of 4,748,948 confirmed COVID-19 cases, with an average, median, and range per country of 101,041, 26,963, and 2191 to 1,665,617, respectively. We found that COVID-19 prevalence in an Africa country was highly dependent on those of neighbouring Africa countries as well as its economic wealth, transparency, and proportion of the population aged 65 or older (p-value < 0.05). Our finding regarding the high COVID-19 burden in countries with better transparency and higher economic wealth is surprising and counterintuitive. We believe this is a reflection on the differences in COVID-19 testing capacity, which is mostly higher in more developed countries, or data modification by less transparent governments. Country-wide integrated COVID suppression strategies such as limiting human mobility from more urbanized to less urbanized countries, as well as an understanding of a county’s social-economic characteristics, could prepare a country to promptly and effectively respond to future outbreaks of highly contagious viral infections such as COVID-19.