Dataset Information

Quantifying excess mortality during the COVID-19 pandemic in 2020 in The Gambia: a time-series analysis of three health and demographic surveillance systems.

ABSTRACT:

Objectives

Estimates for COVID-19-related excess mortality for African populations using local data are needed to design and implement effective control policies.

Methods

We applied time-series analysis using data from three health and demographic surveillance systems in The Gambia (Basse, Farafenni, and Keneba) to examine pandemic-related excess mortality during 2020, when the first SARS-CoV-2 wave was observed, compared to the pre-pandemic period (2016-2019).

Results

Across the three sites, average mortality during the pre-pandemic period and the total deaths during 2020 were 1512 and 1634, respectively (Basse: 1099 vs 1179, Farafenni: 316 vs 351, Keneba: 98 vs 104). The overall annual crude mortality rates per 100,000 (95% CI) were 589 (559, 619) and 599 (571, 629) for the pre-pandemic and 2020 periods, respectively. The adjusted excess mortality rate was 8.8 (-34.3, 67.6) per 100,000 person-month with the adjusted rate ratio (aRR) = 1.01 (0.94,1.11). The age-stratified analysis showed excess mortality in Basse for infants (aRR = 1.22 [1.04, 1.46]) and in Farafenni for the 65+ years age group (aRR = 1.19 [1, 1.44]).

Conclusion

We did not find significant excess overall mortality in 2020 in The Gambia. However, some age groups may have been at risk of excess death. Public health response in countries with weak health systems needs to consider vulnerable age groups and the potential for collateral damage.

SUBMITTER: Mohammed NI

PROVIDER: S-EPMC9780021 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Quantifying excess mortality during the COVID-19 pandemic in 2020 in The Gambia: a time-series analysis of three health and demographic surveillance systems.

Mohammed Nuredin I NI Mackenzie Grant G Ezeani Esu E Sidibeh Mamadi M Jammeh Lamin L Sarwar Golam G Saine Aji Kumba Folawiyo AKF Sonko Bakary B Gomez Pierre P Dondeh Bai Lamin BL Hossain M Jahangir MJ Jasseh Momodou M Usuf Effua E Prentice Andrew M AM Jeffries David D Dalessandro Umberto U Roca Anna A

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 20221223

<h4>Objectives</h4>Estimates for COVID-19-related excess mortality for African populations using local data are needed to design and implement effective control policies.<h4>Methods</h4>We applied time-series analysis using data from three health and demographic surveillance systems in The Gambia (Basse, Farafenni, and Keneba) to examine pandemic-related excess mortality during 2020, when the first SARS-CoV-2 wave was observed, compared to the pre-pandemic period (2016-2019).<h4>Results</h4>Acro ...[more]

PMID: 36566776

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Project description:BackgroundHome delivery is defined as is an even of pregnant women getting giving birth in a woman her home or other homes without an unskilled health professional assistance. It is continuing as public health problem since its responsible for death of women and newborn. In Gambia there is a high maternal mortality rate, which may be related to home delivery. Therefore, this study aimed to assess the trend of home delivery and identify predictors using Gambia Demographic and Health Survey (GDHS) 2013 and 2019-2020 data sets.MethodsA Cross-Section survey was conducted based on GDHS 2013 and 2019-2020 among reproductive age group women. A total of 8607 women participated in this study. A bivariate decomposition model was fitted, and variables that had a p-value > 0.25 were dropped. Finally, variables that got a p-value of < 0.05 with 95% confidence interval (CI) in the multivariate decomposition analysis were considered as statistical significance variables in the overall decomposition.ResultsThere has been a dramatic decrement in maternal home delivery in Gambia. It was 36.18% (95% CI:34.78, 37.58) in 2013 GDHS and 14.39% (95% CI:13.31,15.47) in 2019-2020 GDHS. This reduction is real because there was a change in the characteristics effect of the population and the coefficient effect some variables in the home delivery. Changes in characteristics effect of husband education, women education, rural residents, more than three antenatal cares follow up, and no problem reaching health facilities played a significant role in the reduction of home delivery. Being urban resident and women who had occupation were variables that had a positive effect on coefficient effect change.ConclusionIn this study, the home delivery rate had steeply declined in the Gambia during the study period of the two surveys. Just above nine-tenths decrement in home delivery rate resulted because there was a change in the characteristics effect of the study participants. Enhancing more citizens to attend high school and above, narrowing the gap between rural and urban in terms of accessing health facilities, and improving the availability of infrastructure should be done.

Project description:BackgroundSurveillance systems are important in detecting changes in disease patterns and can act as early warning systems for emerging disease outbreaks. We hypothesized that analysis of data from existing global influenza surveillance networks early in the COVID-19 pandemic could identify outliers in influenza-negative influenza-like illness (ILI). We used data-driven methods to detect outliers in ILI that preceded the first reported peaks of COVID-19.Methods and findingsWe used data from the World Health Organization's Global Influenza Surveillance and Response System to evaluate time series outliers in influenza-negative ILI. Using automated autoregressive integrated moving average (ARIMA) time series outlier detection models and baseline influenza-negative ILI training data from 2015-2019, we analyzed 8,792 country-weeks across 28 countries to identify the first week in 2020 with a positive outlier in influenza-negative ILI. We present the difference in weeks between identified outliers and the first reported COVID-19 peaks in these 28 countries with high levels of data completeness for influenza surveillance data and the highest number of reported COVID-19 cases globally in 2020. To account for missing data, we also performed a sensitivity analysis using linear interpolation for missing observations of influenza-negative ILI. In 16 of the 28 countries (57%) included in this study, we identified positive outliers in cases of influenza-negative ILI that predated the first reported COVID-19 peak in each country; the average lag between the first positive ILI outlier and the reported COVID-19 peak was 13.3 weeks (standard deviation 6.8). In our primary analysis, the earliest outliers occurred during the week of January 13, 2020, in Peru, the Philippines, Poland, and Spain. Using linear interpolation for missing data, the earliest outliers were detected during the weeks beginning December 30, 2019, and January 20, 2020, in Poland and Peru, respectively. This contrasts with the reported COVID-19 peaks, which occurred on April 6 in Poland and June 1 in Peru. In many low- and middle-income countries in particular, the lag between detected outliers and COVID-19 peaks exceeded 12 weeks. These outliers may represent undetected spread of SARS-CoV-2, although a limitation of this study is that we could not evaluate SARS-CoV-2 positivity.ConclusionsUsing an automated system of influenza-negative ILI outlier monitoring may have informed countries of the spread of COVID-19 more than 13 weeks before the first reported COVID-19 peaks. This proof-of-concept paper suggests that a system of influenza-negative ILI outlier monitoring could have informed national and global responses to SARS-CoV-2 during the rapid spread of this novel pathogen in early 2020.

Project description:ObjectiveTo estimate the direct and indirect effects of the covid-19 pandemic on mortality in 2020 in 29 high income countries with reliable and complete age and sex disaggregated mortality data.DesignTime series study of high income countries.SettingAustria, Belgium, Czech Republic, Denmark, England and Wales, Estonia, Finland, France, Germany, Greece, Hungary, Israel, Italy, Latvia, Lithuania, the Netherlands, New Zealand, Northern Ireland, Norway, Poland, Portugal, Scotland, Slovakia, Slovenia, South Korea, Spain, Sweden, Switzerland, and United States.ParticipantsMortality data from the Short-term Mortality Fluctuations data series of the Human Mortality Database for 2016-20, harmonised and disaggregated by age and sex.InterventionsCovid-19 pandemic and associated policy measures.Main outcome measuresWeekly excess deaths (observed deaths versus expected deaths predicted by model) in 2020, by sex and age (0-14, 15-64, 65-74, 75-84, and ≥85 years), estimated using an over-dispersed Poisson regression model that accounts for temporal trends and seasonal variability in mortality.ResultsAn estimated 979 000 (95% confidence interval 954 000 to 1 001 000) excess deaths occurred in 2020 in the 29 high income countries analysed. All countries had excess deaths in 2020, except New Zealand, Norway, and Denmark. The five countries with the highest absolute number of excess deaths were the US (458 000, 454 000 to 461 000), Italy (89 100, 87 500 to 90 700), England and Wales (85 400, 83 900 to 86 800), Spain (84 100, 82 800 to 85 300), and Poland (60 100, 58 800 to 61 300). New Zealand had lower overall mortality than expected (-2500, -2900 to -2100). In many countries, the estimated number of excess deaths substantially exceeded the number of reported deaths from covid-19. The highest excess death rates (per 100 000) in men were in Lithuania (285, 259 to 311), Poland (191, 184 to 197), Spain (179, 174 to 184), Hungary (174, 161 to 188), and Italy (168, 163 to 173); the highest rates in women were in Lithuania (210, 185 to 234), Spain (180, 175 to 185), Hungary (169, 156 to 182), Slovenia (158, 132 to 184), and Belgium (151, 141 to 162). Little evidence was found of subsequent compensatory reductions following excess mortality.ConclusionApproximately one million excess deaths occurred in 2020 in these 29 high income countries. Age standardised excess death rates were higher in men than women in almost all countries. Excess deaths substantially exceeded reported deaths from covid-19 in many countries, indicating that determining the full impact of the pandemic on mortality requires assessment of excess deaths. Many countries had lower deaths than expected in children <15 years. Sex inequality in mortality widened further in most countries in 2020.

Project description:BackgroundThe number of COVID-19 deaths reported in Zambia (N = 4069) is most likely an underestimate due to limited testing, incomplete death registration and inability to account for indirect deaths due to socioeconomic disruption during the pandemic. We sought to assess excess mortality during the COVID-19 pandemic in Zambia.MethodsWe conducted a retrospective analysis of monthly-death-counts (2017-2022) and individual-daily-deaths (2020-2022) of all reported health facility and community deaths at district referral health facility mortuaries in 12 districts in Zambia. We defined COVID-19 wave periods based on a sustained nationally reported SARS-CoV-2 test positivity of greater than 5%. Excess mortality was calculated as the difference between observed monthly death counts during the pandemic (2020-2022) and the median monthly death counts from the pre-pandemic period (2017-2019), which served as the expected number of deaths. This calculation was conducted using a Microsoft Excel-based tool. We compared median daily death counts, median age at death, and the proportion of deaths by place of death (health facility vs. community) by wave period using the Mann-Whitney-U test and chi-square test respectively in R.ResultsA total of 112,768 deaths were reported in the 12 districts between 2020 and 2022, of which 17,111 (15.2%) were excess. Wave periods had higher median daily death counts than non-wave periods (median [IQR], 107 [95-126] versus 96 [85-107], p < 0.001). The median age at death during wave periods was older than non-wave periods (44.0 [25.0-67.0] versus 41.0 [22.0-63.0] years, p < 0.001). Approximately half of all reported deaths occurred in the community, with an even greater proportion during wave periods (50.6% versus 53.1%, p < 0.001), respectively.ConclusionThere was excess mortality during the COVID-19 pandemic in Zambia, with more deaths occurring within the community during wave periods. This analysis suggests more COVID-19 deaths likely occurred in Zambia than suggested by officially reported numbers. Mortality surveillance can provide important information to monitor population health and inform public health programming during pandemics.

Project description:ObjectivesTo assess problems with accessing healthcare and its associated factors among reproductived-age women in the Gambia using the Gambia Demographic and Health Survey 2019/2020.DesignA community-based cross-sectional study.SettingThis study was based on a large, community-based cross-sectional survey, conducted from 2019 to 2020 in the Gambia. The survey employed a stratified two-stage cluster sampling technique to recruit study participants. Descriptive statistics and logistic regression models were used to summarise descriptive data and identify factors associated with problems of accessing healthcare, respectively. A p value of less than 0.05 and 95% CI were used to determine statistical significance.ParticipantsA total of 11 865 reproductive-aged women (15-49 years) were included in the study. The mean age of the women was 28.21 with an SD of 9.33 and ranges 15-49 years. Regarding marital status, 7526 (63.43%) were married.ResultsThe magnitude of problems with accessing healthcare among reproductive-aged women in the Gambia was 45.5% (95% CI: 44.6%, 46.4%). Age (adjusted OR (AOR)=1.48, 95% CI: 1.12, 1.95), being from rural residence (AOR=1.28, 95% CI: 1.07, 1.53), parity (AOR=1.07, 95% CI: 1.03, 1.12), middle (AOR=0.54, 95% CI: 0.46, 0.64) and greater wealth (AOR=0.30, 95% CI: 0.25, 0.37), giving birth at health institutions (AOR=0.69, 95% CI: 0.58, 0.80), currently working (AOR=0.77, 95% CI: 0.68, 0.86) and geographical region were significantly associated with problems of accessing healthcare.ConclusionProblems of accessing healthcare among women of reproductive age were high in the Gambia. Age, region, working status, parity, residence, wealth and place of delivery were the identified factors associated with problems of accessing healthcare. Policymakers and public health experts should consider those factors while designing maternal healthcare programmes. Affordable healthcare service programmes should be designed to increase healthcare access in the Gambia.

Dataset Information

Quantifying excess mortality during the COVID-19 pandemic in 2020 in The Gambia: a time-series analysis of three health and demographic surveillance systems.

Objectives

Methods

Results

Conclusion

Publications

Quantifying excess mortality during the COVID-19 pandemic in 2020 in The Gambia: a time-series analysis of three health and demographic surveillance systems.

Similar Datasets

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