Dataset Information

Malaria incidence and mortality in Zimbabwe during the COVID-19 pandemic: analysis of routine surveillance data.

ABSTRACT:

Background

The coronavirus disease 2019 (COVID-19) pandemic has posed a unique challenge to health care systems globally. To curb COVID-19 transmission, mitigation measures such as travel restrictions, border closures, curfews, lockdowns, and social distancing have been implemented. However, these measures may directly and indirectly affect the delivery and utilization of essential health services, including malaria services. The suspension of indoor residual spraying (IRS) and insecticide-treated net (ITN) distribution, shortages of malaria commodities, and reduced demand for health services have hindered the continued delivery of malaria services. The overall goal of this analysis was to describe the trends in malaria incidence and mortality in Zimbabwe prior to and during the pandemic to understand the consequences of COVID-19-related changes in the delivery and utilization of malaria services.

Methods

Monthly data on the number of malaria cases and deaths by district for the period January 2017 to June 2020 were obtained from the national health management information system (HMIS). District-level population data were obtained from the 2012 Census. Malaria incidence per 1000 population and malaria deaths per 100,000 population were calculated for 2017, 2018, 2019, and 2020 and mapped to describe the spatial and temporal variation of malaria at the district level.

Results

Compared to the same period in 2017, 2018 and 2019, there was an excess of over 30,000 malaria cases from January to June 2020. The number of malaria deaths recorded in January to June 2020 exceeded the annual totals for 2018 and 2019. District level maps indicated that areas outside high malaria burden provinces experienced higher than expected malaria incidence and mortality, suggesting potential outbreaks.

Conclusions

The observed surge in malaria cases and deaths in January to June 2020 coincided with the onset of COVID-19 in Zimbabwe. While further research is needed to explore possible explanations for the observed trends, prioritizing the continuity of essential malaria services amid the COVID-19 pandemic remains crucial.

SUBMITTER: Gavi S

PROVIDER: S-EPMC8142064 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundAccurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study's aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility.MethodsOutpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011-2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts.ResultsA total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the 3 years in Kihihi (0.5 cases per person-year (PPY) vs 1.7 cases PPY) and Nagongera (0.3 cases PPY vs 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera).ConclusionsAlthough malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

Project description:ObjectiveTo examine whether epilepsy-related deaths increased during the COVID-19 pandemic and if the proportion with COVID-19 listed as the underlying cause is different between people experiencing epilepsy-related deaths and those experiencing deaths unrelated to epilepsy.MethodsThis was a Scotland-wide, population-based, cross-sectional study of routinely-collected mortality data pertaining to March-August of 2020 (COVID-19 pandemic peak) compared to the corresponding periods in 2015-2019. ICD-10-coded causes of death of deceased people of any age were obtained from a national mortality registry of death certificates in order to identify those experiencing epilepsy-related deaths (coded G40-41), deaths with COVID-19 listed as a cause (coded U07.1-07.2), and deaths unrelated to epilepsy (death without G40-41 coded). The number of epilepsy-related deaths in 2020 were compared to the mean observed through 2015-2019 on an autoregressive integrated moving average (ARIMA) model (overall, men, women). Proportionate mortality and odds ratios (OR) for deaths with COVID-19 listed as the underlying cause were determined for the epilepsy-related deaths compared to deaths unrelated to epilepsy, reporting 95% confidence intervals (CIs).ResultsA mean number of 164 epilepsy-related deaths occurred through March-August of 2015-2019 (of which a mean of 71 were in women and 93 in men). There were subsequently 189 epilepsy-related deaths during the pandemic March-August 2020 (89 women, 100 men). This was 25 more epilepsy-related deaths (18 women, 7 men) compared to the mean through 2015-2019. The increase in women was beyond the mean year-to-year variation seen in 2015-2019. Proportionate mortality with COVID-19 listed as the underlying cause was similar between people experiencing epilepsy-related deaths (21/189, 11.1%, CI 7.0-16.5%) and deaths unrelated to epilepsy (3,879/27,428, 14.1%, CI 13.7-14.6%), OR 0.76 (CI 0.48-1.20). Ten of 18 excess epilepsy-related deaths in women had COVID-19 listed as an additional cause.ConclusionsThere is little evidence to suggest there have been any major increases in epilepsy-related deaths in Scotland during the COVID-19 pandemic. COVID-19 is a common underlying cause of both epilepsy-related and unrelated deaths.

Project description:ObjectivesReporting of COVID-19 cases, deaths and testing has often lacked context for appropriate assessment of disease burden within risk groups. The research considers how routine surveillance data might provide initial insights and identify risk factors, setting COVID-19 deaths early in the pandemic into context. This will facilitate the understanding of wider consequences of a pandemic from the earliest stage, reducing fear, aiding in accurately assessing disease burden and ensuring appropriate disease mitigation.SettingUK, 2020.ParticipantsThe study is a secondary analysis of routine, public domain, surveillance data and information from Office for National Statistics (ONS), National Health Service (NHS) 111 and Public Health England (PHE) on deaths and disease.Primary and secondary outcome measuresOur principal focus is ONS data on deaths mentioning COVID-19 on the death certificate. We also consider information provided in NHS 111 and PHE data summaries.ResultsDeaths with COVID-19 significantly contributed to, yet do not entirely explain, abnormally elevated all-cause mortality in the UK from weeks 12-18 of 2020. Early in the UK epidemic, COVID-19 was the greatest threat to those with underlying illness, rarely endangering people aged under 40 years. COVID-19-related death rates differed by region, possibly reflecting underlying population structure. Risk of COVID-19-related death was greater for healthcare and social care staff and black, Asian and minority ethnic individuals, having allowed for documented risk factors.ConclusionEarly contextualisation of public health data is critical to recognising who gets sick, when and why. Understanding at-risk groups facilitates a targeted response considering indirect consequences of society's reaction to a pandemic alongside disease-related impacts. COVID-19-related deaths mainly mirror historical patterns, and excess non-COVID-19-related deaths partly reflect reduced access to and uptake of healthcare during lockdown. Future outbreak response will improve through better understanding of connectivity between disease monitoring systems to aid interpretation of disease risk patterns, facilitating nuanced mitigation measures.

Project description:Since the beginning of the COVID-19 pandemic, the Epidemiological surveillance program of the Lebanese Ministry of Public Health has launched a rapid surveillance system for collecting COVID-19-related mortality data. In this study, we document the Lebanese experience of COVID-19 mortality surveillance and provide an analysis of the epidemiological characteristics of confirmed deaths. The implementation of the rapid COVID-19 mortality surveillance system, data sources, and data collection were described. A retrospective descriptive analysis of the epidemiological characteristics of confirmed cases occurring in Lebanon between February 20, 2020, and September 15, 2021, was performed. Epidemiological curves of Covid-19 confirmed cases and deaths as well as the geographic distribution map of mortality rates were generated. Between February 21, 2020, and September 15, 2021, a total of 8163 COVID-19-related deaths were reported with a predominance of males (60.4%). More than 60% were aged 70 years or above. Of all deaths, 84% occurred at hospitals and 16% at home. The overall cumulative mortality rate was 119.6 per 100,000. The overall case fatality ratio (CRF) was 1.3%. Of the total deaths, 82.2% had at least one underlying medical condition. The top reported COVID-19 comorbidities associated with COVID-19-related deaths are cardiovascular diseases including hypertension (59.1%), diabetes (37.2%), kidney diseases including dialysis (11%), cancer (6.7%), and lung diseases (6.3%). The CFR was 30.9% for kidney diseases, 20.2% for cancer, 20.2% for lung diseases, 18.1% for liver diseases, 14% for diabetes, and 12.2% for cardiovascular diseases. Considering the limited human and financial resources in Lebanon due to the economic and political crisis, the rapid mortality surveillance system can be considered successful. Improving this system is important and would contribute to better detection of deaths from emerging and re-emerging diseases during health crises.

Project description:ImportanceSchool closures because of COVID-19 have left 1.6 billion students around the world without in-person classes for a prolonged period. To our knowledge, no study has documented whether reopening schools in low- and middle-income countries during the pandemic was associated with increased aggregate COVID-19 incidence and mortality with appropriate counterfactuals.ObjectiveTo test whether reopening schools under appropriate protocols during the COVID-19 pandemic was associated with increased municipal-level COVID-19 cases and deaths in São Paulo State, Brazil.Design setting and participantsThis observational study of municipalities in São Paulo State, Brazil, uses a difference-in-differences analysis to examine the association between municipal decisions to reopen schools during the COVID-19 pandemic and municipal-level COVID-19 case and death rates between October and December 2020. The study compared 129 municipalities that reopened schools in 2020 with 514 that did not and excluded data for 2 municipalities that reopened schools and closed then again.Main outcomes and measuresNew COVID-19 cases and deaths per 10 000 inhabitants up to 12 weeks after school reopenings and municipal-level aggregate mobility for a subset of municipalities.ResultsThere were 8764 schools in the 129 municipalities that reopened schools compared with 9997 in the control group of 514 municipalities that did not reopen schools. The municipalities that reopened schools had a cumulative COVID-19 incidence of 20 cases per 1000 inhabitants and mortality of 0.5 deaths per 1000 inhabitants in September 2020 (the baseline period) compared with an incidence of 18 cases per 1000 inhabitants and mortality of 0.45 deaths per 1000 inhabitants during the baseline period in the comparison group. The findings indicated that there were no statistically significant differences between municipalities that authorized schools to reopen and those that did not for (1) weekly new cases (difference-in-differences, -0.03; 95% CI, -0.09 to 0.03) and (2) weekly new deaths (difference-in-differences, -0.003; 95% CI, -0.011 to 0.004) before and after October 2020. Reopening schools was not associated with higher disease activity, even in relatively vulnerable municipalities, nor aggregate mobility.Conclusions and relevanceThe findings from this study suggest that keeping schools open during the COVID-19 pandemic did not contribute to the aggregate disease activity.