We examined factors associated with in-hospital COVID-19 mortality in South Africa between March 2020 and March 2022, adjusting for spatial autocorrelation at district level, fixed effects and some non-linear temporal and age effects. Besides confirming that older age, male gender, admission in ICU, being treated with supplemental oxygen and invasive mechanical ventilation, predisposed hospitalized COVID-19 patients to a higher risk of in-hospital mortality, our study highlighted substantial heterogeneity in mortality across districts.
Our study describes the spatial distribution of COVID-19 in-hospital deaths in the whole population in South Africa adjusting for several factors including month of admission as a proxy for epidemic waves. The impact of spatial neighbourhood dependents and heterogeneity have not previously been explored at higher resolution in South Africa regarding COVID-19 in-hospital mortality.
To respond effectively to new epidemics, it is essential to unpack the spatial dynamics of in-hospital deaths at higher spatial resolution such as at subdistrict or district level in order to identify priority areas for intervention to improve health outcomes. Our spatial model adjusted for temporal effects, suggested that age had a nonlinear effect on in-hospital mortality with increasing risk for increasing age as was determined in other previous studies (6, 12, 26–29).
The study also provides evidence that in-hospital mortality was clustered geographically with several districts in Eastern Cape and Limpopo showing elevated risk. Previous studies showed that hospitalized COVID-19 patients in the Eastern Cape and Limpopo provinces were at higher odds of death (6), however, our study goes further in identifying the districts in those provinces which predisposed patients to higher odds of in-hospital mortality. A study in Brazil similarly explored the spatial distribution of COVID-19 cases and deaths in paediatric population and showed that forty municipalities had higher mortality, and these were observed in regions with poor socio-economic indicators and greatest health disparities (19). Another study in South Africa assessed spatial distribution of deaths at provincial level and other COVID-19 outcomes showed that Eastern Cape had higher risk for deaths (30, 31).
Understanding spatial heterogeneity in relation to the socio-environmental determinants and COVID-19 related outcomes is central to targeting interventions for vulnerable populations (1). Our results indicate substantial geographical variations in the distribution of COVID-19 in-hospital mortality across South Africa districts. In South Africa, 21% of the population have access to private health care while the remainder rely on public health care support (6). There are also inequities in health care access in South Africa, with poorer access and availability of health facilities in rural provinces such as Eastern Cape, KwaZulu-Natal and Limpopo. Quality of care has also historically been poor in many public health facilities (14).
A study that assessed inequalities in access to health care in South Africa showed large differentials in private health care admissions between people living in the poorer, more rural provinces like Limpopo, Eastern Cape and Mpumalanga compared to more urban provinces like Gauteng (32). We provide evidence of spatial clustering and spatial heterogeneity in in-hospital mortality in South Africa. Highlighting districts at excess risk of COVID-19 in-hospital mortality can help guide local health care system policies to better protect vulnerable population subgroups. Health care access inequality is not a new aspect in South Africa, however, when it contributes to some groups of the population left at higher risk of in-hospital mortality, this calls for immediate action for implementation of policies that enhance equal access to and improved delivery of health care(6, 14, 32).
Besides showing spatial structure in the COVID-19 in-hospital mortality in South Africa, our study using the non-linear effects of months as a proxy for temporal evolution of the epidemic highlight higher risk of deaths during the second and third waves. Jassat et al similarly reveal in their study that in-hospital case-fatality ratio during the Omicron wave was 10·7%, compared with 21·5% during the first wave, 28·8% during the second wave, and 26·4% during the third wave (7, 8, 33). The Beta and Delta VOCs drove the second and third COVID-19 waves in South Africa. Our study reveals an ebbing off in the in-hospital deaths during the last months which were driven by the latter variants of Omicron. Omicron marked a change in the SARS-CoV-2 epidemic curve, clinical profile, and deaths in South Africa (33) Davies et al showed similar patterns in the Western Cape province (9).
Major strengths of this study include the application of flexible structured additive logistic regression model within the Bayesian framework which allows for exploration of spatial association with health outcomes at higher spatial resolutions as well as allow for inclusion of non-linear effects and fixed effects. In addition, this study utilizes a COVID-19 hospital admissions high-quality national data repository with their associated individual level outcomes ensuring ample admission sample data as well as outcomes for analysis at district level. This study is not without some limitations. For this analysis, we adjusted for some of the known risk factors for COVID-19 in-hospital deaths, however, there may be other covariates which we may not have included. A further limitation may be that the DATCOV database does not distinguish between patients who were admitted with coincidental positive SARS-CoV-2 test and those with COVID-19. As highlighted in the preceding limitations, there was limited information on other important variables and ancillary predictors that can be used to model in-hospital mortality. Therefore, there is a need to utilize the same modelling framework to account for the impact of these and other factors especially aggregated at subdistrict or district levels.
As in one Brazilian study (19), our findings confirmed the higher burden of COVID-19 in-hospital mortality in some districts in Limpopo and Eastern Cape provinces. This may be linked to poor health care access including limited access to private health care (32) due to unaffordability of medical insurance. To reduce the burden of in-hospital deaths due to potentially new epidemics and COVID-19 in particular, the South African government and private stakeholders need to build strong and resilient health care infrastructure which can be accessed equally by both the poor and rich independent of class or race especially in vulnerable districts. Modelling the effects of underlying factors and in-hospital disease mortality at small spatial scale is essential to plan effective control strategies for disease outcome risks (20). The relationship between COVID-19 in-hospital mortality rates and sociodemographic, clinical and districts spatial dependents can consequently guide the development of specific intervention actions for these places.