A critical analysis of discovery health's claims-based risk adjustment of mortality rates in South African private sector hospitals

In 2019, Discovery Health published a risk adjustment model to determine standardised mortality rates across South African private hospital systems, with the aim of contributing towards quality improvement in the private healthcare sector. However, the model suffers from limitations due to its design and its reliance on administrative data. The publication's aim of facilitating transparency is unfortunately undermined by shortcomings in reporting. When designing a risk prediction model, patient-proximate variables with a sound theoretical or proven association with the outcome of interest should be used. The addition of key condition-specific clinical data points at the time of hospital admission will dramatically improve model performance. Performance could be further improved by using summary risk prediction scores such as the EUROSCORE II for coronary artery bypass graft surgery or the GRACE risk score for acute coronary syndrome. In general, model reporting should conform to published reporting standards, and attempts should be made to test model validity by using sensitivity analyses. In particular, the limitations of machine learning prediction models should be understood, and these models should be appropriately developed, evaluated and reported.

Household Cost of Antenatal Care and Delivery Services in a Rural Community of Kaduna State; Northwestern Nigeria

Maternal mortality remains a leading cause of death among women of reproductive age. While Nigeria has only two percent of the global population; it contributes 10 to the global maternal mortality burden. Antenatal care (ANC) reduces the incidence of maternal mortality. However; financial capability affects access to antenatal care. Thus; the rural poor are at a higher risk of maternal mortality. Materials and Methods: A cross-sectional descriptive study involving 135 women (pregnant women and those who are 6 weeks postpartum). Structured interviewer-administered questionnaires were used for data collection. Data analysis was carried out using statistical package for social sciences software (version 17). Results: The average amount spent on booking and initial laboratory investigations were N77 (half a dollar) and N316 ($2); respectively. Per ANC visit; average amount spent on drugs and transportation were N229 ($1.5) and N139 ($0.9) respectively. For delivery; the average amount spent was N1500 ($9.6). On an average; ANC plus delivery cost about N3;365.00 ($22). There was a statistically significant association between husband's income and ANC attendance (X 2 = 2.451; df = 2; P = 0.048). Conclusion: Cost of Antenatal care and delivery services were not catastrophic but were a barrier to accessing antenatal care and facility-based delivery services in the study area. ANC attendance was associated with the income of household heads. Pro-poor policies and actions are needed to address this problem; as it will go a long way in reducing maternal mortality in this part of the country