Using Generalized Structured Additive Regression Models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa (preprint)

Background The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors.Methods COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian.Results The risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR = 4.16; 95% Credible Interval: 4.05–4.27), being on oxygen (aOR = 1.49; 95% Credible Interval: 1.46–1.51) and on invasive mechanical ventilation (aOR = 3.74; 95% Credible Interval: 3.61–3.87). Being admitted in a public hospital (aOR = 3.16; 95% Credible Interval: 3.10–3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts.Conclusion The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.

Using Generalized Structured Additive Regression Models to Determine Factors Associated with and Clusters for COVID-19 Hospital Deaths in South Africa (preprint)

Background: The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus as well as deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors. Methods: COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD), who together with the South African National Department of Health (SANDoH) collected hospital admissions data through DATCOV, an active electronic hospital surveillance system for COVID-19. We used the generalized structured additive logistic regression model that allows for modelling spatial correlation to realistically estimate risk factors for hospital COVID-19 deaths. The model included patient demographic and clinical factors as well as time in months which accounted for different waves. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian. Results: The risk of COVID-19 in-hospital mortality increased with patient age as well as with admission to intensive care unit (ICU) (aOR=4.16; 95% Credible Interval: 4.05-4.27), being on oxygen (aOR=1.49; 95% Credible Interval: 1.46-1.51) and on invasive mechanical ventilation (aOR=3.74; 95% Credible Interval: 3.61-3.87). Being admitted in a public hospital (aOR= 3.16; 95% Credible Interval: 3.10-3.21) was also a significant risk factor for mortality. Risk of deaths also increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts. Conclusion: The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. This highlights the importance of modelling spatial patterns simultaneously with fixed and nonlinear effects of continuous covariates to identify clusters at high risk of health outcome. The flexible approach to modelling data that has spatial patterns helps to account for possible loss of efficiency due to spatial correlation that spatial patterns can induce in data. Our analysis suggests notable COVID-19 hospital deaths clustering in some districts in Limpopo and Eastern Cape provinces and this information can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes.

Assessing the Impacts of COVID-19 and Social Isolation on Mental Health in the United States of America (preprint)

The COVID-19 pandemic has had a devastating impact on the world at large with over 500 million cases and over 6 million deaths reported thus far. Of those, over 85 million cases and 1 million deaths have occurred in the United States of America (USA). The mental health of the general population has been impacted by several aspects of the pandemic including lockdowns, media sensationalism, social isolation, and spread of the disease. In this paper, we examine the effect that social isolation and COVID-19 infection and related death had on the prevalence of anxiety and depression in the general population of the USA in a state-by-state multiple time-series analysis. Vector Error Correction Models are estimated and we subsequently evaluated the coefficients of the estimated models and calculated their impulse response functions for further interpretation. We found that variables related to COVID-19 overall led to an increase both anxiety and depression across the studied period, while variables related to social isolation had a varied effect depending on the state being considered. Both conclusions have important implications for future pandemics.

Population Based SARS-CoV-2 Sero-Epidemiological Survey and Estimated Infection Incidence and Fatality Risk in Gauteng Province, South Africa (preprint)

Background: Limitations in laboratory testing capacity undermine the ability to quantify the overall burden of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection. We undertook a cross-sectional population based sero-survey for SARS-CoV-2 infection in 26 sub-districts, Gauteng Province (population 15·9 million), South Africa. Furthermore, we estimated SARS-CoV-2 mortality risk triangulating seroprevalence, recorded COVID-19 deaths and excess mortality data.Methods: We employed multi-stage random household sampling with selection probability proportional to sub-district size, stratifying sub-district census-sampling frame by housing type and selecting clusters within household type strata. Serum SARS-CoV-2 receptor binding domain (RBD) Immunoglobulin G (IgG) was measured using a quantitative assay on Luminex platform.Findings: Overall RBD IgG seroprevalence was 19·1% (95%Confidence interval [CI]: 18·1-20·1%), being similar in children and adults. Seroprevalence varied from 5·5% to 43·2% across sub-districts. Conservatively, there were 2 897 120 (95%CI: 2 743 907-3 056 866) SARS-CoV-2 infections, yielding an incidence of 19 090 per 100 000 until January 9, 2021, when 330 336 COVID-19 cases were recorded. The estimated mortality risk using recorded COVID-19 deaths (n=8198) was 0·28% (95%CI: 0·27-0·30) and 0·67% (95%CI: 0·64-0·71) assuming 90% of modelled natural excess deaths were due to COVID-19 (n=21 582). Notably, 53·8% (65/122) of individuals with previous self-reported confirmed SARS-CoV-2 infection were RBD IgG sero-negative.Interpretation: The imputed number of SARS-CoV-2 infections was 8·8 fold greater than recorded number of COVID-19 cases. The imputed SARS-CoV-2 infection mortality risk varied 2·39 fold when calculated using reported COVID-19 deaths (0·28%) compared with excess mortality derived COVID-19 attributable deaths (0·67%). Waning of RBD IgG may have inadvertently under-estimated number of SARS-CoV-2 infections, and conversely over-estimated mortality risk, by a factor of two. Funding Information: Bill and Melinda Gates Foundation.Declaration of Interests: We declare no competing interests.Ethics Approval Statement: The University of the Witwatersrand Human Research Ethics Committee granted a waiver for formal approval of the survey, which was deemed to be part of public-health good and surveillance to manage the COVID-19 pandemic. Electronic signed informed consent was administered to individuals older than 15 years age, parental consent obtained for children <12 years of age, and assent and parental consent for adolescents 12-15 years old.