Sociodemographic characteristics and spatial distribution of Malaria in Nigerian children

A research report submitted to the Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree ofMaster of Science in Epidemiology in the field of Epidemiology and Infectious Diseases March 2018.Background Malaria is a significant public health concern in the world. It causes mortality and morbidity especially in children under five years of age and pregnant women. Nigeria contributes about 25% to the malaria burden in Africa, with one million lives lost annually. Several factors including poverty, ownership of bed nets, socioeconomic status are associated with malaria morbidity. This study aimed to determine the factors associated with malaria morbidity and its spatial distribution in Nigerian regions among children under five years in 2010. Methods This study used cross-sectional data from the 2010 Nigeria Malaria Indicator Survey (NMIS) which was downloaded from DHS website. The primary sampling unit (PSU) which was referred as the cluster for the 2010 NMIS was defined based on the enumeration areas (EAs) from the 2006 EA census frame. The 2010 NMIS sample was selected using a stratified, two -stage cluster design consisting of 240 clusters, 83 clusters in the urban areas and 157 in the rural areas. The research was restricted to children under five years (6-59 months). The outcome variable was defined as the presence or absence of malaria. It was measured using rapid diagnostic test and microscopic examination of blood smear. Clustered adjusted Pearson’s chi-square test was used to show associations between explanatory variables and malaria. A clustered t-test was used to determine differences in the mean for continuous variables.Multilevel logistic regression models, taking into account random effects were fitted. Choropleth maps were used to display the distribution of malaria by geopolitical zones. Results There were 5137 children aged 6-59 months for this study out of 5612 children that were selected for the survey, 41.97%had malaria, and there was no difference in prevalence between males and females. The mean age was 34.96±0.33 months. There was a higher prevalence (60.19%) of malaria in the areas where there was no LLIN campaign coverage compared to areas with LLIN coverage (39.81%) (OR 0.65, 95% CI 0.43-0.97). Children from rural areas were three times more likely to have malaria than those from urban areas (OR 3.13,95%CI 2.18-4.49). The odds of malaria increased significantly with increasing age in months (OR 1.02, 95% CI 1.02-1.03, P-value<0.0001). The richest household children were less likely to have high prevalence of malaria compared to children from the poorest households (OR 0.23, 95%CI 0.15-0.37). Choropleth maps showed a high prevalence in the North-West and North-Central regions and lowest prevalence in the South-East region. Conclusion Although efforts have been made to control malaria in Nigeria, its elimination is not forthcoming. The prevalence in children under five years was high. Those who live in the rural areas, wealth index, geopolitical region and child’s age were the determining factors associated with the high prevalence of malaria in those children. There was a regional variation of malaria prevalence among the children. Children from the North-Central and North-West regions had the highest prevalence of malaria. All these factors could be as a result of policy issues, policy formulations, management, implementation, compliance and sustenance issues. However, a lot can be done in the malaria control and prevention programme in Nigeria towards vaccine development, policy formulation and implementation based on evidence, increased public health and environmental education, incorporation of the communities in activities towards malaria control, mapping of the spatial distribution of malaria as well as stepping up of ongoing control programmes. Keywords: children under five years, spatial analysis, Nigeria.LG201

Corrigendum: The intersection of age, sex, race and socio-economic status in COVID-19 hospital admissions and deaths in South Africa

The following terminology was erroneously reported: “non-white race” should be “people of colour”, or “black African, coloured and people of Indian descent”

The intersection of age, sex, race and socio-economic status in COVID-19 hospital admissions and deaths in South Africa.

Older age, male sex, and non-white race have been reported to be risk factors for COVID-19 mortality. Few studies have explored how these intersecting factors contribute to COVID-19 outcomes. This study aimed to compare demographic characteristics and trends in SARS-CoV-2 admissions and the health care they received. Hospital admission data were collected through DATCOV, an active national COVID-19 surveillance programme. Descriptive analysis was used to compare admissions and deaths by age, sex, race, and health sector as a proxy for socio-economic status. COVID-19 mortality and healthcare utilisation were compared by race using random effect multivariable logistic regression models. On multivariable analysis, black African patients (adjusted OR [aOR] 1.3, 95% confidence interval [CI] 1.2, 1.3), coloured patients (aOR 1.2, 95% CI 1.1, 1.3), and patients of Indian descent (aOR 1.2, 95% CI 1.2, 1.3) had increased risk of in-hospital COVID-19 mortality compared to white patients; and admission in the public health sector (aOR 1.5, 95% CI 1.5, 1.6) was associated with increased risk of mortality compared to those in the private sector. There were higher percentages of COVID-19 hospitalised individuals treated in ICU, ventilated, and treated with supplemental oxygen in the private compared to the public sector. There were increased odds of non-white patients being treated in ICU or ventilated in the private sector, but decreased odds of black African patients being treated in ICU (aOR 0.5; 95% CI 0.4, 0.5) or ventilated (aOR 0.5; 95% CI 0.4, 0.6) compared to white patients in the public sector. These findings demonstrate the importance of collecting and analysing data on race and socio-economic status to ensure that disease control measures address the most vulnerable populations affected by COVID-19.Significance:• These findings demonstrate the importance of collecting data on socio-economic status and race alongside age and sex, to identify the populations most vulnerable to COVID-19.• This study allows a better understanding of the pre-existing inequalities that predispose some groups to poor disease outcomes and yet more limited access to health interventions.• Interventions adapted for the most vulnerable populations are likely to be more effective.• The national government must provide efficient and inclusive non-discriminatory health services, and urgently improve access to ICU, ventilation and oxygen in the public sector.• Transformation of the healthcare system is long overdue, including narrowing the gap in resources between the private and public sectors

Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

Abstract 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

Proactive measures to combat a SARS-CoV-2 transmission among high risk patients and health care workers in an outpatient dialysis facility

BACKGROUND: End-stage-renal-failure (ESRF) patients attending clustered out-patient dialysis are susceptible to SARS-CoV-2 infection. Comorbidities render them vulnerable to severe COVID-19. Although preventative and mitigation strategies are recommended, the effect of these are unknown. A period of “potential-high-infectivity” results if a health-care-worker (HCWs) or a patient becomes infected. AIM: We describe and analyze early, universal SARS-CoV-2 real time reverse transcription polymerase chain reaction (RT-PCR) tests, biomarker monitoring and SARS-CoV-2 preventative strategies, in a single dialysis center, after a positive patient was identified. METHODOLOGY: The setting was a single outpatient dialysis center in Johannesburg, South Africa which had already implemented preventative strategies. We describe the management of 57 patients and 11 HCWs, after one of the patients tested positive for SARS-CoV-2. All individuals were subjected to RT-PCR tests and biomarkers (NeutrophilLymphocyte Ratio, C-reactive protein, and D-Dimer) within 72 h (initial-tests). Individuals with initial negative RT-PCR and abnormal biomarkers (one or more) were subjected to repeat RT-PCR and biomarkers (retest subgroup) during the second week. Additional stringent measures (awareness of viral transmission, dialysis distancing and screening) were implemented during the period of “potential high infectivity.” The patient retest subgroup also underwent clustered dialysis until retest results became available RESULTS: A second positive-patient was identified as a result of early universal RT-PCR tests. In the two positive-patients, biomarker improvement coincided with RT-PCR negative tests. We identified 13 individuals for retesting. None of these retested individuals tested positive for SARS-CoV-2 and there was no deterioration in median biomarker values between initial and retests. Collectively, none of the negative individuals developed COVID-19 symptoms during the period “potential high infectivity.” CONCLUSION: A SARS-CoV-2 outbreak may necessitate additional proactive steps to counteract spread of infection. This includes early universal RT-PCR testing and creating further awareness of the risk of transmission and modifying preventative strategies. Abnormal biomarkers may be poorly predictive of SARS-CoV-2 infection in ESRF patients due to underlying illnesses. Observing dynamic changes in biomarkers in RTPCR positive and negative-patients may provide insights into general state of health.http://frontiersin.org/Pharmacologypm2021School of Health Systems and Public Health (SHSPH

The intersection of age, sex, race and socio-economic status in COVID-19 hospital admissions and deaths in South Africa (with corrigendum)

Older age, male sex, and non-white race have been reported to be risk factors for COVID-19 mortality. Few studies have explored how these intersecting factors contribute to COVID-19 outcomes. This study aimed to compare demographic characteristics and trends in SARS-CoV-2 admissions and the health care they received. Hospital admission data were collected through DATCOV, an active national COVID-19 surveillance programme. Descriptive analysis was used to compare admissions and deaths by age, sex, race, and health sector as a proxy for socio-economic status. COVID-19 mortality and healthcare utilisation were compared by race using random effect multivariable logistic regression models. On multivariable analysis, black African patients (adjusted OR [aOR] 1.3, 95% confidence interval [CI] 1.2, 1.3), coloured patients (aOR 1.2, 95% CI 1.1, 1.3), and patients of Indian descent (aOR 1.2, 95% CI 1.2, 1.3) had increased risk of in-hospital COVID-19 mortality compared to white patients; and admission in the public health sector (aOR 1.5, 95% CI 1.5, 1.6) was associated with increased risk of mortality compared to those in the private sector. There were higher percentages of COVID-19 hospitalised individuals treated in ICU, ventilated, and treated with supplemental oxygen in the private compared to the public sector. There were increased odds of non-white patients being treated in ICU or ventilated in the private sector, but decreased odds of black African patients being treated in ICU (aOR 0.5; 95% CI 0.4, 0.5) or ventilated (aOR 0.5; 95% CI 0.4, 0.6) compared to white patients in the public sector. These findings demonstrate the importance of collecting and analysing data on race and socio-economic status to ensure that disease control measures address the most vulnerable populations affected by COVID-19. Significance: These findings demonstrate the importance of collecting data on socio-economic status and race alongside age and sex, to identify the populations most vulnerable to COVID-19. This study allows a better understanding of the pre-existing inequalities that predispose some groups to poor disease outcomes and yet more limited access to health interventions. Interventions adapted for the most vulnerable populations are likely to be more effective. The national government must provide efficient and inclusive non-discriminatory health services, and urgently improve access to ICU, ventilation and oxygen in the public sector. Transformation of the healthcare system is long overdue, including narrowing the gap in resources between the private and public sectors

Corrigendum: The intersection of age, sex, race and socio-economic status in COVID-19 hospital admissions and deaths in South Africa

Original article: https://doi.org/10.17159/sajs.2022/13323 The following terminology was erroneously reported: “non-white race” should be “people of colour”, or “black African, coloured and people of Indian descent”