Trends and Dynamics of COVID-19 in Zimbabwe: Implications for Selected Sustainable Development Goals

This study analysed the spatial and temporal trends and dynamics of COVID-19 to understand their implications on Sustainable Development Goals (SDGs) in Zimbabwe. Data on daily cases and mortality rates of COVID-19 were collected from the Worldometer website, whilst data on lockdown measures and travel restrictions were collected from Zimbabwe's Ministry of Health and Child Care. Exploratory and confirmatory analyses were employed on statistical data. COVID-19 statistical data were first tested for normality using the Kolmogorov-Smirnov test. Subsequently, the non-parametric Mann-Kendal (M-K) test was performed to determine the monthly average number of new cases and deaths trend from March 2020 to February 2022 using XLSTAT (2020). The study shows a significant increase (p = 0.00, α= 0.05) in COVID-19 cases between March 2020 and February 2022. The trend is characterised by sharp increases associated with wave periods. Although the results show no correlation between stringency index and COVID-19 cases, periods of high stringency are associated with a slightly lower number of cases. The spatial trends show that highly populated areas have high numbers of patient cases. Indeed, the lockdown measures put in place, among other factors, contributed to controlling the spread of the virus. The trends and dynamics of COVID-19 in Zimbabwe have implications for achieving SDG 1, SDG 2, SDG 3 and SDG 6. Thus, there is a need to factor in the temporal and spatial realities of COVID-19 in making a policy framework for effective control of the pandemic and promotion of sustainable development. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Short-term association between COVID-19 related deaths, hospitalized patients and air pollution during the first lockdown in the four largest cities in Germany

Identifying risk factors of coronavirus disease 2019 (COVID-19) for its clinical course became important during the current global health crisis. This study explores the relationship between ambient air quality and the clinical course of COVID-19 during the first German lockdown. Daily air pollutant concentrations (PM10, O3 and NO2) and confirmed COVID-19-related hospital cases and deaths in the four largest German cities are used for multivariate regression analysis to study the correlation of air pollutants with COVID-19-related hospitalised or ICU patients and deaths. A significant positive association of O3 with patients and deaths is observed. NO2 shows a positive correlation with hospitalised and ICU patients. A 10 µg/m3 increase in O3 is linked to a 27% (95% CI: 15% to 39%) increase of COVID-19-related deaths. However, PM10 is associated with a decrease in deaths. Altogether, these findings indicate that air pollutants seem to have a significant influence on the course of COVID-19 infections. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Excess deaths from non-COVID-19-related causes in Japan and 47 prefectures from January 2020 through May 2021 by place of death.

Excess deaths, including all-causes mortality, were confirmed for the first time in Japan in April 2021. However, little is known about the indirect effects of COVID-19 on the number of non-COVID-19-related deaths. We then estimated the excess deaths from non-COVID-19-related causes in Japan and its 47 prefectures from January 2020 through May 2021 by place of death. Vital statistical data on deaths were obtained from the Ministry of Health, Labour and Welfare. Using quasi-Poisson regression models, we estimated the expected weekly number of deaths due to all-causes excluding COVID-19 (non-COVID-19) and due to respiratory disease, circulatory disease, malignant neoplasms, and senility. Estimates were made separately for deaths in all locations, as well as for deaths in hospitals and clinics, in nursing homes and elderly care facilities, and at home. We defined a week with excess deaths as one in which the observed number of deaths exceeded the upper bound of the two-sided 95% prediction interval. Excess death was expressed as a range of differences between the observed and expected number of deaths and the 95% upper bound of the two-sided predictive interval. The excess percentage was calculated as the number of excess deaths divided by the expected number of deaths. At the national level, excess deaths from non-COVID-19-related all-causes were observed during April 19 to May 16, 2021. The largest excess percentage was 2.73-8.58% (excess deaths 689-2161) in the week of May 3-9. Similar trends were observed for all four cause categories. The cause-of-death categories which contributed to the excesses showed heterogeneity among prefectures. When stratified by place of death, excess mortality tended to be observed in nursing homes and elderly care facilities for all categories, in hospitals and clinics for circulatory disease, and at home for respiratory disease, malignant neoplasms, and senility. A caution is necessary that for the lastest three months (March-May 2021), adjusted data were used to account for possible reporting delays.

[Temporal variations in excess mortality during phase 1 and phase 2 of the COVID-19 epidemic in Italy]. / Variazioni temporali della mortalità totale e nei decessi per COVID-19 durante la fase 1 e la fase 2 dell'epidemia in Italia.

OBJECTIVES: to assess the temporal variation in excess total mortality and the portion of excess explained by COVID-19 deaths by geographical area, gender, and age during the COVID-19 epidemic. DESIGN: descriptive analysis of temporal variations of total excess deaths and COVID-19 deaths in the phase 1 and phase 2 of the epidemic in Italy. SETTING AND PARTICIPANTS: 12 Northern cities and 20 Central-Southern cities from December 2019 to June 2020: daily mortality from the National Surveillance System of Daily Mortality (SiSMG) and COVID-19 deaths from the integrated COVID-19 surveillance system. MAIN OUTCOME MEASURES: total mortality excess and COVID-19 deaths, defined as deaths in microbiologically confirmed cases of SARS-CoV-2, by gender and age groups. RESULTS: the largest excess mortality was observed in the North and during the first phase of the epidemic. The portion of excess mortality explained by COVID-19 decreases with age, decreasing to 51% among the very old (>=85 years). In phase 2 (until June 2020), the impact was more contained and totally attributable to COVID-19 deaths and this suggests an effectiveness of social distancing measures. CONCLUSIONS: mortality surveillance is a sensible information basis for the monitoring of health impact of the different phases of the epidemic and supporting decision making at the local and national level on containment measures to put in place in coming months.