Developing, low-income countries not only confront the challenge of resource constraints, an ageing population and urbanisation, but also face the double burden of communicable diseases such as HIV/ AIDS, which is currently the African continent's leading cause of death, and diabetes mellitus. [1] According to global estimations, it is predicted that by the year 2030, developing countries will experience as much as a 69% increase in the number of adults diagnosed with diabetes. [2] Diabetes caused more than 68 thousand deaths in South Africa (SA) in 2013. [3] Census information for the year 2013 indicates that in KwaZulu-Natal (KZN), a subtropical province of SA with the country's second-largest population, diabetes was the fourth-largest cause of death. [4,5] More than any other continent, Africa suffers from the combined presence and effects of diabetes together with HIV/AIDS and tuberculosis (TB). The interactions between these conditions and their treatment pose many challenges. Patients with diabetes have been found to have an increased general risk of infection and a two to three times increased risk of developing TB. HIV has been found to promote recurrence of latent TB and drastically increases the progression to active disease. [6] Some antiretrovirals cause glucose intolerance, predisposing the HIV-positive patient to developing diabetes. Drug interactions between the medications used to treat diabetes and TB reduce each other's effectiveness, making it difficult to treat both conditions in one patient. [7] The International Diabetes Federation's 2013 Diabetes Atlas [3] indicates that SA is home to more than two and a half million diagnosed diabetics, and an approximate further million who are undiagnosed. It has been estimated that another 2.6 million South Africans have impaired glucose tolerance, an early metabolic Background. The global increase in the prevalence of diabetes mellitus is most marked in African countries. The District Health Information System (DHIS) is the primary data collection system of the Department of Health in KwaZulu-Natal Province (KZN), South Africa. Data are routinely collected at all public healthcare facilities in the province and are aggregated per facility. Objective. To investigate the distribution, incidence and prevalence of diabetes in the public healthcare sector of KZN. Methods. Data collected by the DHIS for all patients with diabetes in KZN from 1 January 2010 to 31 December 2014 inclusive were analysed. Additional open-source databases were accessed to enable further exploration of the data collected. Results. The study showed that the majority (38.7%) of patients with diabetes on the public sector register were from the district of eThekwini. Positive correlations were found between the prevalence of diabetes, the mortality rate and the number of defaulters (patients with diabetes who did not return for regular treatment). Conclusions. Provincial estimates of the prevalence of diabetes in this study were higher than the known national prevalence. This may be ...

Recent studies have highlighted a high burden of disease in some deprived districts of KwaZulu-Natal (KZN) Province. [7] Diabetes was recently identified as the major natural cause of death in the district of Nelson Mandela Bay in Eastern Cape (EC) Province. [4] However, there is very limited research detailing the diabetes burden in this high-risk district. Our study aims to create a diabetes profile of all EC districts. Bridging the current data gap will allow for more evidencebased initiatives, policy development and tailored clinical guidelines to manage diabetes within the urban African framework. Methods Context The EC, home to the area formerly known as Transkei, has been identified as one of the poorest provinces in SA. This can be attributed partly to high levels of poverty in the former homelands. [8] The province is divided into two metropolitan municipalities and six district municipalities, with 63% of the area scoring a rank of 1 (most deprived) in the SA Index of Multiple Deprivation (SAIMD) study. [4] The District Health Information System (DHIS) is tasked with the storage of primary data collected by allocated information officers and healthcare workers from each district. Submitted data are formatted within specific data collection fields, as prescribed by the provincial DoH. Study design This descriptive study involves the collection, review and analysis of data from the DHIS, which is the primary data source among the 257 districts of the nine provinces of the DoH. This open-access article is distributed under Creative Commons licence CC-BY-NC 4.0.

This study analysed diabetes-related information routinely collected by the KwaZulu-Natal (KZN) Department of Health (DOH). Methods: Primary data were obtained for all public health facilities through the District Health Information System (DHIS) for the period 2006-2016 inclusive (11 years). Additional open source data on population estimates were obtained from Statistics South Africa. Quantitative analysis of DHIS data was performed using Microsoft Excel before graphical representations were generated using the ThinkCell software. Results: The number of clinical visits by diabetic patients in KZN increased by 305% in the 10 years between 2006 and 2015. According to the data collected by the Department of Health, a large majority of patients diagnosed with diabetes are seeking medical care in the more populated district of eThekwini. The number of patients not returning for scheduled treatment (defaulters) has reduced since recording began in 2012. According to the data, the incidence of diabetes in KZN is oscillating; however, a strong correlation is found between incidence and patient screening. Conclusion: The largest number of diabetic patients were seen in the highly urbanised district of eThekwini. The screening of high-risk patients has increased in frequency and exhibits strong correlations with incidence, further supporting the effectiveness of screening and its inclusion in a new primary healthcare protocol. There was a sharp reduction in number of defaulting patients in 2016, probably indicating improved compliance. The inconsistency of data input is a limitation of the study. However, this study within these constraints highlights the importance of 'big data' for healthcare policy and more effective health care in KZN.

Background:The need for greater information on the burden of diabetes has never been more significant than at present, especially when considering the association between diabetes and the severity of COVID-19. Statistics South Africa has identified diabetes mellitus (DM) as the leading cause of death in the Western Cape (WC) province, South Africa. Aims: This study aimed to analyse diabetes-related data collected on patient visits, screening frequency, age proportion and distribution of new patients at primary health levels in the WC public healthcare sector. Methods: An eight-year (2012-2019) audit was conducted of all diabetes-related public health data routinely collected using the WC District Health Information System (DHIS). The data were analysed using Excel® 2016. Time-series and cross-sectional analyses were made possible using pivot tables to gain insight into data trends and incidence rates. Results: This study found that the eight-year crude incidence rate for diabetes increased by 2% between 2012 and 2019. In addition, the incidence rate of diabetes increased by an average of 21% when private institutions were excluded. The recorded number of patients diagnosed with type 1 DM (T1DM) decreased annually between 2013 and 2017 (796 vs. 217, respectively). This decreasing trend could be due to the late onset of T1DM in patients from the African continent or possible data misinterpretation and inadequate training at a primary collection level. The cumulative number of patients screened for diabetes within the WC public health sector (2016-2019) depicts a compound annual growth rate of 16%. A strong positive correlation (p = 0.98) was found between patients screened and the frequency of patients newly diagnosed with DM. The majority (64%) of clinical visits by patients registered with a confirmed diagnosis of diabetes were seen in the metropolitan municipality of 'The City of Cape Town'. Conclusions: The incidence of DM in the WC province, as in South Africa and globally, is increasing. Intensified screening translates into improved 'pick-up' rates and decreases the overall prevalence of undiagnosed DM with its complications. The findings of this study have implications for the development of public healthcare policies and guidelines. Personnel training and resources are suggested to improve the quality of the clinical data and strengthen the DHIS.

Background. The growing burden of diabetes has long been under the radar in developing countries such as South Africa (SA). In recent years, there has been an unprecedented and exponential increase in recorded and undiagnosed diabetes mellitus (DM) cases. Unreliable data collection, overburdened health systems and poor infrastructure have all proved to be barriers to achieving optimum disease management. The District Health Information System (DHIS) serves as the data collection tool for the SA public healthcare sector. It is used in all nine SA provinces to gather data without individual patient identifiers. Objective. To analyse and compare the DM data collected by the DHIS in the Western Cape (WC), Eastern Cape (EC), KwaZulu-Natal (KZN) and Gauteng provinces of SA. Methods. An audit of diabetes-related data from the DHIS for 2016 was conducted. The data were then analysed using Excel. Time-series and cross-sectional analyses were made possible using pivot tables. Graphics were designed using Thinkcell software. Results. Of the four provinces surveyed, Gauteng recorded the highest incidence of DM, 67% higher than the reported global DM incidence estimate, while the WC had the lowest incidence. A similar pattern was also noted regarding the incidence of DM in people aged <18 years, with Gauteng having the highest and WC the lowest prevalence results. When comparing the number of DM-related consultations conducted in each province, the metropolitan districts were highlighted as hotspots of activity for DM care. This study found a moderate inversely proportional relationship between the incidence of DM in all provinces and education deprivation (p<0.05). Among the provinces that collected data on screening (excluding EC), KZN recorded the highest number of diabetic screenings. Conclusion. Metropolitan areas were highlighted as areas to be targeted, further reinforcing the current connection observed between urbanisation and DM in SA. The presence and recording of screening efforts is an excellent step in the right direction for the SA public healthcare sector and the DHIS. With improved interprovincial co-ordination regarding standardisation of the criteria and specifications of data collection fields, and enhanced training for data officers and primary collection agents, good quality and rich data is a very close possibility.