Epidemiology of SARS-CoV-2 infection and SARS-CoV-2 positive hospital admissions among children in South Africa

Introduction

We describe epidemiology and outcomes of confirmed SARS‐CoV‐2 infection and positive admissions among children <18 years in South Africa, an upper‐middle income setting with high inequality.

Methods

Laboratory and hospital COVID‐19 surveillance data, 28 January ‐ 19 September 2020 was used. Testing rates were calculated as number of tested for SARS‐CoV‐2 divided by population at risk; test positivity rates were calculated as positive tests divided by total number of tests. In‐hospital case fatality ratio (CFR) was calculated based on hospitalized positive admissions with outcome data who died in‐hospital and whose death was judged SARS‐CoV‐2 related by attending physician.

Findings

315 570 children aged <18 years were tested for SARS‐CoV‐2; representing 8.9% of all 3 548 738 tests and 1.6% of all children in the country. Of children tested, 46 137 (14.6%) were positive. Children made up 2.9% (n = 2007) of all SARS‐CoV‐2 positive admissions to sentinel hospitals. Among children, 47 died (2.6% case‐fatality). In‐hospital deaths were associated with male sex [adjusted odds ratio (aOR) 2.18 (95% confidence intervals [CI] 1.08–4.40)] vs female; age <1 year [aOR 4.11 (95% CI 1.08–15.54)], age 10–14 years [aOR 4.20 (95% CI1.07–16.44)], age 15–17 years [aOR 4.86 (95% 1.28–18.51)] vs age 1–4 years; admission to a public hospital [aOR 5.07(95% 2.01–12.76)] vs private hospital and ≥1 underlying conditions [aOR 12.09 (95% CI 4.19–34.89)] vs none.

Conclusions

Children with underlying conditions were at greater risk of severe SARS‐CoV‐2 outcomes. Children > 10 years, those in certain provinces and those with underlying conditions should be considered for increased testing and vaccination.

Temperature and Latitude Correlate with SARS-CoV-2 Epidemiological Variables but not with Genomic Change Worldwide

The SARS-CoV-2 virus that causes the COVID-19 disease has spread quickly and massively around the entire globe, causing millions of confirmed cases and deaths worldwide. The disease poses a serious ongoing threat to public health. This study aims to understand the disease potential of the virus in different regions by studying how average spring temperature and its strong predictor, latitude, affect epidemiological variables such as disease incidence, mortality, recovery cases, active cases, testing rate, and hospitalization. We also seek to understand the association of temperature and geographic coordinates with viral genomics. Epidemiological data along with temperature, latitude, longitude, and preparedness index were collected for different countries and US states during the early stages of the pandemic. Our worldwide epidemiological analysis showed a significant correlation between temperature and incidence, mortality, recovery cases and active cases. The same tendency was found with latitude, but not with longitude. In the US, we observed no correlation between temperature or latitude and epidemiological variables. Interestingly, longitude was correlated with incidence, mortality, active cases, and hospitalization. An analysis of mutational change and mutational change per time in 55 453 aligned SARS-CoV-2 genome sequences revealed these parameters were uncorrelated with temperature and geographic coordinates. The epidemiological trends we observed worldwide suggest a seasonal effect for the disease that is not directly controlled by the genomic makeup of the virus. Future studies will need to determine if correlations are more likely the result of effects associated with the environment or the innate immunity of the host.

COVID-19 in the Eastern Mediterranean Region: testing frequency, cumulative cases and mortality analysis

Background

COVID-19 is now well documented in the Eastern Mediterranean Region; however, the incidence, mortality and fatality rates differ by country.

Aims

The study aimed to describe the COVID-19 pandemic in the Eastern Mediterranean Region, assessing the incidence, mortality-related and fatality rate in different countries, in comparison with the worldwide mean.

Methods

Data were sourced from the Worldometer surveillance page and from governmental reporting channels. Data were exported and analyzed using Statistical Package for Social Sciences (SPSS, version 23.0).

Results

In the Eastern Mediterranean Region, the testing frequency is heterogeneous between countries, in addition to the reported cases and death. Very few data are available from countries with political instability and security problems (Yemen, Syrian Arab Republic and Sudan), particularly for the testing frequency. Overall, despite similar rates of testing, there was a significantly lower incidence in the Eastern Mediterranean Region versus the rest of the world, in addition to a lower mortality per million-population, particularly in countries with low to moderate testing rates. However, in countries with higher testing than the world average, there is a higher incidence, a lower mortality, but an unexpected higher fatality rate.

Conclusion

The overall testing frequency was similar in the Eastern Mediterranean Region compared to the rest of the world; this would be expected to lead to a similar cumulative incidence and case fatality rate. Nevertheless, the average incidence was 70% lower than the rest of the world, and mortality per million-population was lower (90%). Moreover, in Gulf Cooperation Council high-testing countries, a similar case-fatality rate to other countries in the Region was noted, but was higher than the world average, although expected to be lower. Further studies are necessary to explain discrepancies in incidence, mortality and fatality rates among countries; principally, environmental, genetic and managerial reasons should be investigated.

COVID-19 and lockdown: Insights from Mumbai

Background

Mumbai is facing the full brunt of the COVID-19 pandemic epidemiologically and economically.

Objectives

The objective was to understand the spatial distribution and trends of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) in Mumbai during the lockdown period and draw insights for effective actions.

Methods

Spatial and trend analysis was conducted to trace the spread of the virus during the lockdown period in April 2020. The administrative divisions of Mumbai, in the form of wards and zones, have been used as units of analysis.

Results

Greater Mumbai area occupies only 0.015% of the landmass of India, but is contributing to over 20% of the SARS-CoV-2 cases in India. Cases of SARS-CoV-2 infections have increased over 375 times within 50 days of the lockdown. An analysis of trends across the wards during the 3-week period (April 4 to April 25) shows a skewed pattern, with three zones out of six contributing to the vast majority of cases in Mumbai. The wards with higher formal economic activity are relatively less affected than the other wards. The test positivity rate in Mumbai is much higher than the rest of India.

Conclusion

The study suggests that the virus had already spread to the community in Mumbai before the lockdown started.

Artificial Differences in Clostridium difficile Infection Rates Associated with Disparity in Testing

In 2015, Clostridium difficile testing rates among 30 US community, multispecialty, and cancer hospitals were 14.0, 16.3, and 33.9/1,000 patient-days, respectively. Pooled hospital onset rates were 0.56, 0.84, and 1.57/1,000 patient-days, respectively. Higher testing rates may artificially inflate reported rates of C. difficile infection. C. difficile surveillance should consider testing frequency.