This study investigated district-level variations in the impact of COVID-19 on maternal, neonatal and child healthcare (MNCH) service utilisation, delivery and health outcomes in Gauteng, one of the hardest-hit provinces in South Africa.

A cross-sectional quantitative study.

We collected District Health Information System data for MNCH services from all 493 public healthcare facilities across all five districts in Gauteng province. We applied simple linear regression to assess key performance indicators before (March 2019 to February 2020) and during (March 2020 to February 2021) the COVID-19 pandemic. A pooled multiple linear regression model compared the impact of the pandemic in each district with that of the Johannesburg reference district. The regression models followed the bootstrap approach. Analyses were performed in Stata V.17.0.

Regarding service utilisation, primary headcount under 5 years (n) significantly decreased in all five districts during COVID-19. The effect was greater in Johannesburg (–20 954.5, 95% CI –28 913.3 to –12 995.7; p

In Gauteng province, the COVID-19 pandemic caused a heterogeneous adverse impact on MNCH service utilisation, delivery and health outcomes across the districts. Recognising the geographical differences in the effects of outbreaks and pandemics is critically important for informed decision-making to support healthcare services recovery in affected areas and for planning against future crises.

Type 2 diabetes (T2D) is a complex disease with a heterogeneous clinical presentation. Recently, five distinct clusters of T2D have been identified in the Emirati population of long-standing T2D with complications. This study aimed to validate these clusters in newly diagnosed T2D patients without any complications and determine whether severe and mild phenotypes are detectable early in the disease course.

Retrospective, cross-sectional, non-interventional study.

Primary healthcare centres in Dubai, UAE.

A total of 451 adults, including both Emiratis and expatriates, diagnosed with T2D in the last 5 years and without T2D-related complications at the time of visit, were enrolled. Patients with complications, incomplete clinical data or higher duration of T2D were excluded from the study.

Identification of distinct T2D clusters using machine learning-based clustering analysis. Five clinical variables: age at diagnosis, body mass index, glycated haemoglobin, fasting serum insulin and fasting blood glucose served as predictors. Overlap between clusters was assessed via the Silhouette Index and Bayesian probability.

Five clusters were identified, replicating prior findings: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity-related diabetes (MOD) and mild early-onset diabetes (MEOD). As confirmed by a Silhouette Index and Bayesian probability of 1, 55.43% of the patients showed cluster-exclusiveness, while 44.56% of the cohort showed overlap between clusters. The highest overlap was recorded for mild forms of T2D in the order MOD>MARD>MEOD.

The study confirms that both severe and mild T2D phenotypes are present in newly diagnosed, complication-free patients, supporting the applicability of cluster-based classification early in disease. These results highlight the potential for personalised treatment strategies to optimise management and prevent complications. Future studies should investigate longitudinal outcomes and therapeutic response across clusters.