Skip to main content
Download PDF

An assessment of excess mortality during the COVID-19 pandemic, a retrospective post-mortem surveillance in 12 districts – Zambia, 2020–2022

BMC Public Health volume 24, Article number: 2625 (2024) Cite this article

Abstract

Background

The 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.

Methods

We 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.

Results

A 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.

Conclusion

There 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.

Peer Review reports

Background

Since the first cases of SARS-CoV-2 were reported in Zambia in March 2020 through to October 12, 2023, 349,287 cases and 4,069 COVID-19-related deaths were reported [1]. During this period, several phylogenetically distinct strains of SARS-CoV-2 were identified in Zambia, each associated with varying case fatality rates across different wave periods [2,3,4,5].

The number of cases and deaths reported in Zambia likely underestimates the true impact of the pandemic. A nationwide SARS-CoV-2 prevalence survey, using nasal specimens for PCR testing, revealed that for every reported SARS-CoV-2 infection in Zambia, approximately 92 infections went unreported [6]. A systematic postmortem prevalence study among deaths that occurred at a tertiary hospital in Lusaka showed a high SARS-CoV-2 PCR prevalence among deceased persons from January 2021 to June 2021, with greater per cent positivity during peak COVID-19 transmission periods [7]. However, it should be noted that while SARS-CoV-2 was detected among the deceased individuals in this study, it was not determined whether the virus directly caused their deaths or if its presence was merely incidental. Among those with a positive SARS-CoV-2 diagnosis postmortem, only a minority had a COVID-19 diagnosis antemortem. As such, officially reported COVID-19 deaths in Zambia may underestimate the total number of COVID-19 deaths that occurred during the pandemic.

During a public health emergency such as the COVID-19 pandemic, monitoring all-cause mortality trends may help assess the severity and impact of the emergency on the population affected [8]. Deaths among confirmed COVID-19 patients do not capture the full extent of the COVID-19 burden. In contrast, deaths from all causes can be used to estimate excess mortality and provide a more complete picture of the impact of the COVID-19 pandemic [9, 10]. For many countries, reporting statistics on COVID-19 mortality has been a challenge due to variations in access to testing, differential diagnostic capacity, the inconsistent and sub-optimal certification of COVID-19 as the immediate cause of death, and the nonspecific clinical presentation of COVID-19 (including in fatal cases) [11]. Additionally, the pandemic led to an increase in deaths due to other causes, because of disruptions to routine health services and loss of livelihoods due to the stringent non-pharmacological interventions put in place to mitigate the pandemic [12]. Therefore, the World Health Organization (WHO) recommends the surveillance of confirmed COVID-19 mortality and all-cause mortality during the COVID-19 pandemic [13].

A study of mortality trends among community deaths at the University Teaching Hospital (UTH) in Lusaka from April 2020 to December 2020 showed 1,139 excess deaths from all causes during the study period compared to the pre-pandemic baseline (2017–2019) [14]. Whilst excess mortality during the COVID-19 pandemic has been established in Lusaka [14, 15], the country’s predominantly urban capital, it remains unclear whether other regions within the country with different socio-demographic factors had comparable or worse outcomes. We, therefore, conducted this study to assess excess mortality across 12 districts of Zambia during the COVID-19 pandemic.

Methods

We conducted a mixed methods retrospective analysis of mortality records in Zambia by triangulating data from two different sources (health facility and community death mortuary records) between January 2017 and December 2022. These records were obtained from the mortuaries of the main district referral health facilities in the selected districts. Typically, the most life-threatening medical cases are referred to these referral health facilities from other health facilities within the district and deaths occurring within the communities outside health facilities are brought to these district hospital morgues.

We purposively selected 12 districts of Zambia (Chingola, Kabwe, Kitwe, Livingstone, Luanshya, Lusaka, Kasama, Mansa, Chipata, Mongu, Solwezi and Ndola) primarily based on the known availability of mortuary records over the study period, to ensure an adequate mix of urban and rural districts and to ensure adequate geographic representation from across the country. Combined, the selected districts are home to an estimated 6,882,437 (37.4% of the total projected 2021 population) Zambians [16].

Monthly counts of all health facility deaths and all reported community deaths between 2017 and 2019 were collected to estimate baseline all-cause mortality before the COVID-19 pandemic and monthly counts from 2020 to 2022 to estimate excess deaths during the COVID-19 pandemic. A COVID-19 wave period was defined as a sustained increase in the SARS-CoV-2 national test positivity of greater than five per cent, and between January 2020 and December 2022 days were dichotomized as wave or non-wave days. Additionally, wave days were further classified as the first wave (1 Jun 2020 to 1 Oct 2020), second wave (3rd Jan 2021 to 17th March 2021), third wave (29th May 2021 to 20th Aug 2021), fourth wave (12th Dec 2021 to 9th May 2022) and fifth wave (24th Dec 2022 to 31st Dec 2022), all other dates during the study period were non-wave days. Additionally, from January 2020 through to December 2022, daily individual records of all inpatient deaths and all reported community deaths by age and sex disaggregation were collected to analyse changes in the sex and age distribution of deaths between COVID-19 wave periods and non-COVID-19 wave periods. A categorical variable, age group, was recoded from the continuous variable age.

For the all-cause excess mortality analysis, the district-specific monthly counts of all deaths from 2017 to 2019 were used as the COVID-19 pre-pandemic baseline to compare with the period of the COVID-19 pandemic (2020–2022) using a Microsoft Excel-based tool developed by Resolve to Save Lives/Prevent Epidemics to calculate monthly medians and 95% confidence intervals for historic deaths [17]. This tool is recommended by an expert panel for Rapid Mortality Surveillance during COVID-19 [18]. We quantified the availability of death records by calculating the percentage of months with complete monthly count information over the total number of months within the study period (January 2017 to December 2022) for each district. To address missing monthly death counts at the district level, mean imputation was applied to the districts with missing data (Ndola, Solwezi, and Livingstone), as the monthly death counts at the district level were normally distributed.

Excess mortality was defined as the difference between the all-cause pandemic monthly death counts (2020–2022) (Observed events) and the median pre-pandemic all-cause monthly death counts and the 95% confidence interval (2017–2019) in the 12 districts (The previous step yields baseline mortality and a “normal” range of variability around that baseline). The 95% confidence interval for the median historical monthly count was calculated separately for each month using the sample standard deviations. Excess mortality was present if the observed count was greater than the 95% confidence limit of the historic number of deaths. District-specific excess mortality was calculated separately among community deaths and health facility deaths and then summed to get monthly and yearly excess mortality counts for each district.

To estimate overall excess mortality in Zambia, we assumed that there was sufficient heterogeneity in factors contributing to excess deaths in the 12 selected districts to be representative of the entire population of Zambia and that the excess mortality rate was only a function of the population. Based on these assumptions, we extrapolated the median excess mortality rate from the 12 districts to the entire country to estimate the total excess deaths in Zambia. We then compared this national estimate of excess deaths to the number of officially reported COVID-19 deaths in Zambia to calculate the ratio of reported COVID-19 deaths to excess deaths during the pandemic.

The individual-level death information from 2020 to 2022 was used to calculate daily death counts among community deaths and health facility deaths. Decedents with missing age or sex variables were dropped from subsequent analyses involving age or sex variables but included in the time series graphs of daily deaths and daily death count analysis. Shapiro-Wilk test was used to test the normality assumption for the distribution of the daily death counts and age variables. All reported daily deaths were plotted as a 14-day rolling average time series disaggregated by place of death (community versus facility), district and age groups. To detect any difference in the median number of reported daily deaths between wave periods and non-wave period days, we used the Mann-Whitney u test. We then used the Kruskal-Wallis rank sum test to determine whether there was a statistically significant difference between the median daily number of deaths across the six identified wave periods (non-wave period, first wave, second wave, third wave, fourth wave, fifth wave). To identify which median daily death counts varied significantly by wave period, pairwise comparisons of median daily death count by wave period were conducted using the Wilcoxon rank sum test with continuity correction. The Benjamini & Hochberg p-value adjustment method was employed for multiple comparisons.

Furthermore, we compared disparities in the distribution of the median age at death, the proportion of individuals above 65 at the time of death, as well as sex, place of death (facility or community), and district of death between wave periods and non-wave periods. These comparisons were conducted using the Wilcoxon rank sum test for median age and Pearson’s Chi-squared test for categorical variables. All statistical analyses were performed using R version 4.2.1 statistical software.

Results

Between 2017 and 2022, there were 217 140 deaths reported in the 12 districts (range 32786 [2018] to 41613 [2021]) (Fig. 1). In the baseline period (2017–2019), October had the highest average death count (3071.3, 95% CI: 2984.8-3157.8) while May had one of the lowest (2710.0, 95% CI: 2528.1-2891.9) (Table 1).

Table 1 Baseline all-cause mortality monthly death counts, 12 districts of Zambia, 2017 to 2019
Full size table
Fig. 1
figure 1

Monthly All-cause Mortality, Reported COVID-19 Cases and Excess Mortality in 12 Districts Zambia, 2017–2022

Full size image

Districts contributed from 2.4% (Mongu) to 41.0% (Lusaka) of the reported deaths. A total of 112,768 deaths were reported in the 12 districts between 2020 and 2022 of these 17,111 (15.2%) were excess (Fig. 1). The median district excess mortality rate was 237.5 (Interquartile range (IQR) [170.5-282.5]) per 100,000 population (Table 2). There was district-by-district variation in the number of excess deaths with the highest excess deaths reported in Chingola (449.5 per 100,000 population) and the lowest excess deaths reported in Mongu (101.1 per 100,000 population) (Fig. 2; Table 2). Most excess deaths were observed in 2021 (n = 8992, 52.6%). By extrapolation from these 12 districts, we estimate the median excess deaths across all three years of the COVID-19 pandemic in Zambia to be 43,701 (IQR, 31372-51,982). Compared to officially reported COVID-19 deaths [1], for every reported COVID-19 death, there was a median of 11 (IQR, 8–13) excess deaths during the COVID-19 pandemic in Zambia.

Table 2 Excess mortality in 12 districts of Zambia, 2020–2022
Full size table
Fig. 2
figure 2

Map of the Distribution of excess mortality in 12 districts of Zambia per 100,000 population, 2020–2022

Full size image

We analysed 112,768 individual death records across the 12 districts between January 2020 to December 2022. Overall, only 2.4% of all deaths had a variable missing (age or sex). Over the entire study period, on average, there were more deaths per day during wave periods than during non-wave periods (median [IQR]: 107 [95–126] versus, 96 [85–107]) respectively, < 0.001) (Table 3). The second (median [IQR]: 124 [113–138]), third (median IQR: 140 [116–168]) and fourth (median [IQR]: 102 [92–109]) waves had more deaths per day than non-wave periods (median [IQR]: 96 [85–107], p < 0.001), but no difference was seen between the first and fifth waves (Fig. 3; Table 4).

Table 3 Demographic characteristics of reported all-cause deaths, 12 districts of Zambia, 2020–2022
Full size table
Fig. 3
figure 3

Box and Violin plots of comparison of daily counts of all-cause deaths by wave period, 12 districts of Zambia 2020–2022

*Between waves = Non-COVID-19 Wave Period

Full size image
Table 4 Pairwise comparison of median deaths per wave period Zambia, 2020–2022
Full size table

During wave periods, there was an increase in the daily number of deaths across all districts with a return to baseline after the wave periods (Fig. 4). When disaggregated by place of death, more deaths during the third wave were reported as having occurred within the community than in health facilities (Fig. 5). During non-wave periods approximately half of all reported deaths across all 12 districts occurred in the community (50.6%), and this proportion increased during wave periods (53.1%, p < 0.001) (Table 3). There was district-by-district variation in the baseline number of deaths per day, with Lusaka accounting for the highest proportion of all deaths (33.49%) and Mongu the least (2.96%). During wave periods, there was an increase in the proportion of all deaths from Lusaka (45.39%, p < 0.001) (Table 3).

Fig. 4
figure 4

Time series trends of the 14-day rolling average of all-cause mortality (Facility and Community) in 12 districts, Zambia 2020–2022

Full size image
Fig. 5
figure 5

7-day rolling average of daily death counts disaggregated by place of death in 12 districts Zambia 2020–2022

Full size image

In both wave and non-wave periods, more males died (58.7%, non-wave period versus 58.6%), However, the difference in proportions of men who died between the two periods was not statistically significant (p = 0.860). There was an increase in the median age at death during wave periods with a return to baseline median age at death after the wave period (41.0 years, IQR [22.0–63.0] versus 44.0 years, IQR [25.0–67.0], p < 0.001) (Table 3, supplementary Figs. 1 and 2). There was a corresponding increase in the proportion of individuals aged 65 and over who died during wave periods than those that died during non-wave periods (12886 [23.4%] non-wave period versus 11721 [27.7%] wave periods, p < 0.001) (Table 3, supplementary Fig. 1). When the time series of daily deaths was plotted by age group, the most noticeable change from baseline rates was in the 60 and over age group, which showed an increased number of deaths in this age category across the first, second, third and fourth waves (Fig. 6).

Fig. 6
figure 6

Trendline of the 14-day rolling average daily count of all-cause mortality by age group in 12 districts, Zambia 2020–2022

NA = Missing age variable

Full size image

Discussion

There was excess mortality in all 12 districts included in the study between January 2020-December 2022, with the most excess deaths (52.6%) in 2021. These results suggest that the impact of the COVID-19 pandemic in Zambia was more widespread than official statistics indicate, and not only restricted to urban centres such as Lusaka [7, 15, 19, 20]. Similar findings of excess mortality in other sub-Saharan countries during the COVID-19 pandemic have been observed [21, 22]. Globally, the World Health Organisation estimates excess mortality is 2.74 times higher than reported COVID-19 deaths [22].

Modelled estimates of excess mortality during the COVID-19 pandemic in Zambia vary widely (74.3 credible interval (CI) [2.5-147.6] [23] vs. 228.2 [165.9-322.8] per 100,000 population [24]). Due to the unavailability of publicly available population-level mortality data, these current estimates of excess mortality in Zambia used statistical models to directly predict excess mortality for Zambia [24] or used mathematical models to generate historical and current mortality data and then calculated the excess mortality rate [23].

Our national estimate of the excess mortality rate (median 237.5 [(IQR)170.5-282.5] per 100,000 population), which lies within the range of modelled estimates for Zambia, is most likely an underestimate as not all deaths that occur within communities are reported at health facilities, and therefore were not included in this analysis. Our study demonstrates the value of applying mortality surveillance to understand the impact of a major public health event. If done in real-time, it could have also helped inform public health messaging and policymaking in response to the COVID-19 pandemic in Zambia. Further studies need to be conducted to understand the characteristics and explore surveillance strategies to detect and report these otherwise undocumented community deaths.

The observed variation in excess mortality rates across districts may be attributed to district-specific differences in geospatial factors influencing COVID-19 transmission dynamics, variations in healthcare quality and its utilization by the community, and the proportion of deaths occurring within the community that are reported to health facilities. Consequently, there is a degree of underreporting of community deaths at health facilities, the extent of which is unknown but tends to be higher in rural areas compared to urban areas. Evidence suggests that rural-urban residence significantly impacts the location of deaths in Zambia [25]. Additionally, differences in geographical factors between different regions have been shown to affect the spread of COVID-19 [26, 27]. Further studies are required to identify these factors and their impact on the spread of COVID-19 in Zambia.

Most of the observed excess deaths were likely due to COVID-19 or due to the socioeconomic disruption due to the pandemic. We observed an increase in the daily number of deaths during COVID-19 wave periods compared to non-COVID-19 wave periods. To our knowledge, there were no other reported widespread public health emergencies during the study period that could explain the abrupt increase in the number of deaths during the specific wave periods and across all 12 districts almost simultaneously [1]. Additionally, when we analysed the different wave periods, we noted that the relative increase in daily deaths during wave periods was associated with the relative case fatality of the predominant strain of SARS-CoV-2 associated with that wave. The predominant strain during Zambia’s third wave, which had the highest median daily death count, was the delta variant, a finding consistent with numerous other countries [7, 19, 28]. Further, after an increase in the daily number of deaths during a COVID-19 wave period, we observed a return to baseline pre-pandemic mortality rates between waves and this was consistent across all 12 districts visited and across the different age groups.

COVID-19 mortality has been shown to disproportionately affect the elderly [29,30,31,32]. We observed an increase in the overall median age at death (44 years vs. 41 years, p < 0.001) and in the proportion of deceased persons aged 65 years and older (27.7 per cent vs. 23.4 per cent, p < 0.001) during COVID-19 wave periods with a return to baseline between wave periods respectively. However, the exact proportion of these excess deaths that were directly attributable to COVID-19 remains unknown because of limitations in antemortem and postmortem SARS-CoV-2 testing and limited death registration and certification across the country.

There were more community deaths than facility deaths in both wave periods and non-wave periods. Community deaths increased during wave periods, suggesting potential gaps in health services brought on by the COVID-19 pandemic. As not all deaths that occur within the community are brought to health facilities before burial, the actual proportion of total deaths that occur within the community may even be higher. An analysis of places of death in Zambia among adults 15–59 years between 2010 and 2012 showed that slightly less than half of the adult deaths occurred in the home, factors associated with dying in a health facility included higher educational attainment, urban versus rural residence, and being of female gender [25]. The observed increase in the proportion of deaths in the community during COVID-19 waves could be due to barriers to access to health facilities as some facilities were repurposed to serve as specialised COVID-19 treatment centres whilst other facilities scaled down services offered by only offering essential health services or attending to only emergencies [33,34,35]. This could have compromised the quality of outpatient care that chronically ill patients received. Additionally, the myths and misconceptions around COVID-19 could have prevented those in most need of care from seeking health care [36]. We recommend risk communication and engagement strategies tailored to increasing demand within the community for seeking health services during public health emergencies. Additionally, surge capacity plans should be developed by the Ministry of Health and implemented during public health emergencies. These could help ensure the continued provision of essential health services as well as provide additional capacity to respond to the public health emergency.

Our study had several limitations. As we investigated excess deaths, we were unable to quantify the proportion of these excess deaths that were due to COVID-19, however, due to the timing and demographic composition of these deaths and the absence of other explanatory events, we believe that most of these deaths could have been due to COVID-19. Only community deaths that were reported at health facilities were analysed during this investigation, as such our findings underestimate the total number of deaths that occurred within these districts, as not all community deaths are reported at these facilities. While our study employed purposive sampling for site selection, which may have introduced selection bias and impacted the generalizability of our findings, the consistency observed across all selected districts—despite varying sociodemographic factors—suggests that our results are broadly reflective of the entire country. This consistent pattern across diverse districts supports the robustness of our findings and their applicability at a national level. Sampling additional districts was impossible because of resource limitations (data abstraction was time-consuming). Our method of determining excess mortality did not consider potential seasonal variations. However, due to the large size of the data set, the consistency of findings across the different districts, the consistency of our findings with current known epidemiological characteristics of COVID-19 and the consistency of our findings with other similar studies, we believe our findings are credible.

There was excess mortality in all 12 districts visited during the COVID-19 pandemic in Zambia with most of these deaths occurring within the community and among the elderly. These findings suggest the impact of the COVID-19 pandemic in Zambia was far greater than implied by reported COVID-19 deaths alone. Strengthening routine and continuous mortality surveillance systems with cause of death ascertainment especially among community deaths could help guide public health decision-making and strengthen risk communication and community engagement during public health emergencies.

Data availability

The data that support the findings of this study are available from the Permanent Secretary Technical Services of the Ministry of Health, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Permanent Secretary of Technical Services of the Ministry of Health.

References

  1. ZNPHI – A Centre of Excellence in. Public Health Security for a Healthy Zambia. [cited 2023 Jan 23]. http://znphi.co.zm/

  2. Liu J, Wei H, He D. Differences in case-fatality-rate of emerging SARS-CoV-2 variants. Public Health Pract. 2023;5:100350.

    Article  Google Scholar 

  3. Kim K, Cho K, Song J, Rahmati M, Koyanagi A, Lee SW et al. The case fatality rate of COVID-19 during the Delta and the Omicron epidemic phase: A meta-analysis. J Med Virol. 2023 Feb 1 [cited 2023 Oct 18];95(2):e28522. https://onlinelibrary.wiley.com/doi/full/https://doi.org/10.1002/jmv.28522

  4. Wang C, Liu B, Zhang S, Huang N, Zhao T, Lu Q, Bin et al. Differences in incidence and fatality of COVID-19 by SARS-CoV-2 Omicron variant versus Delta variant in relation to vaccine coverage: A world-wide review. J Med Virol. 2023 Jan 1 [cited 2023 Oct 18];95(1):e28118. https://onlinelibrary.wiley.com/doi/full/https://doi.org/10.1002/jmv.28118

  5. Simulundu E, Mupeta F, Chanda-Kapata P, Saasa N, Changula K, Muleya W et al. First COVID-19 case in Zambia — Comparative phylogenomic analyses of SARS-CoV-2 detected in African countries. International Journal of Infectious Diseases. 2021 Jan 1 [cited 2021 Apr 18];102:455–9. https://doi.org/10.1016/j.ijid.2020.09.1480

  6. Mulenga LB, Hines JZ, Fwoloshi S et al. Prevalence of severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in six districts in Zambia—July 2020: a cross-sectional multi-stage cluster-sampled household survey. Lancet Glob Health 2021 Publication Pending. 2021.

  7. Gill CJ, Mwananyanda L, Macleod WB, Kwenda G, Pieciak RC, Etter L et al. What is the prevalence of COVID-19 detection by PCR among deceased individuals in Lusaka, Zambia? A postmortem surveillance study. BMJ Open. 2022 Dec 6 [cited 2023 Apr 20];12(12). https://pubmed.ncbi.nlm.nih.gov/36600354/

  8. Estimating Excess Mortality from COVID-. 19 - Prevent Epidemics. [cited 2023 Oct 15]. https://preventepidemics.org/covid19/resources/excess-mortality/

  9. Rossen LM, Branum AM, Ahmad FB, Sutton P, Anderson RN. Excess Deaths Associated with COVID-19, by Age and Race and Ethnicity — United States, January 26–October 3, 2020. MMWR Morb Mortal Wkly Rep. 2020 Oct 23 [cited 2021 Dec 13];69(42):1522–7. https://www.cdc.gov/mmwr/volumes/69/wr/mm6942e2.htm

  10. Revealing the Toll of COVID-19. A Technical Package for Rapid Mortality Surveillance and Epidemic Response A Technical Package for Rapid Mortality Surveillance and Epidemic Response. 2020 [cited 2021 Dec 28]; Available from: www.Bloomberg.org.

  11. Msemburi W, Karlinsky A, Knutson V, Aleshin-Guendel S, Chatterji S, Wakefield J. The WHO estimates of excess mortality associated with the COVID-19 pandemic. Nature 2022 613:7942. 2022 Dec 14 [cited 2023 Jun 24];613(7942):130–7. https://www.nature.com/articles/s41586-022-05522-2

  12. Global excess deaths associated with the COVID. -19 pandemic. [cited 2023 Jun 24]. https://www.who.int/news-room/questions-and-answers/item/global-excess-deaths-associated-with-the-COVID-19-pandemic

  13. Public health surveillance for COVID. -19: interim guidance. [cited 2021 Dec 13]. https://www.who.int/publications/i/item/who-2019-nCoV-surveillanceguidance-2020.7

  14. Hamukale A, Hines JZ, Sinyange N, Fwoloshi S, Malambo W, Sivile S et al. SARS-CoV-2 Mortality Surveillance among Community Deaths brought to University Teaching Hospital Mortuary in Lusaka, Zambia, 2020. medRxiv. 2021 Nov 15 [cited 2021 Dec 5];2021.11.14.21266330. https://www.medrxiv.org/content/https://doi.org/10.1101/2021.11.14.21266330v1

  15. Sheppard RJ, Watson OJ, Pieciak R, Lungu J, Kwenda G, Moyo C et al. Using mortuary and burial data to place COVID-19 in Lusaka, Zambia within a global context. Nature Communications 2023 14:1. 2023 Jun 29 [cited 2023 Oct 15];14(1):1–15. https://www.nature.com/articles/s41467-023-39288-6

  16. Population, Demographic, Projections. 2011–2035 - Zambia Data Portal. [cited 2023 Oct 16]. https://zambia.opendataforafrica.org/ZMPHC2015/population-and-demographic-projections-2011-2035?country=1000000-zambia

  17. Estimating Excess Mortality from COVID-. 19 - Prevent Epidemics. [cited 2023 Jun 10]. https://preventepidemics.org/covid19/resources/excess-mortality/

  18. Revealing the Toll of COVID-19. A Technical Package for Rapid Mortality Surveillance and Epidemic Response A Technical Package for Rapid Mortality Surveillance and Epidemic Response. 2020 [cited 2024 Mar 24]; Available from: www.Bloomberg.org.

  19. Mwananyanda L, Gill CJ, Macleod W, Kwenda G, Pieciak R, Mupila Z et al. Title: COVID-19 deaths detected in a systematic post-mortem surveillance study in Africa. [cited 2021 Feb 15]; https://doi.org/10.1101/2020.12.22.20248327

  20. Mudenda V, Mumba C, Pieciak RC, Mwananyanda L, Chimoga C, Ngoma B, et al. Histopathological evaluation of deceased persons in Lusaka, Zambia with or without Coronavirus Disease 2019 (COVID-19) infection: results obtained from minimally invasive tissue sampling. Clin Infect Dis. 2021;73:S465–71.

    Article  CAS  PubMed  Google Scholar 

  21. Mohammed NI, Mackenzie G, Ezeani E, Sidibeh M, Jammeh L, Sarwar G et al. Quantifying excess mortality during the COVID-19 pandemic in 2020 in The Gambia: a time-series analysis of three health and demographic surveillance systems. International Journal of Infectious Diseases. 2023 Mar 1 [cited 2023 Apr 20];128:61–8. http://www.ijidonline.com/article/S1201971222006543/fulltext

  22. Warsame A, Bashiir F, Freemantle T, Williams C, Vazquez Y, Reeve C et al. Excess mortality during the COVID-19 pandemic: a geospatial and statistical analysis in Mogadishu, Somalia. International Journal of Infectious Diseases. 2021 Dec 1 [cited 2023 Jun 24];113:190–9. http://www.ijidonline.com/article/S120197122100758X/fulltext

  23. 14.9 million excess deaths associated with the COVID-19 pandemic. in 2020 and 2021. [cited 2023 Jun 24]. https://www.who.int/news/item/05-05-2022-14.9-million-excess-deaths-were-associated-with-the-covid-19-pandemic-in-2020-and-2021

  24. Wang H, Paulson KR, Pease SA, Watson S, Comfort H, Zheng P et al. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020-21. Lancet. 2022 Apr 16 [cited 2023 Jun 24];399(10334):1513–36. http://www.thelancet.com/article/S0140673621027963/fulltext

  25. Chisumpa VH, Odimegwu CO, De Wet N. Adult mortality in sub-saharan Africa, Zambia: where do adults die? SSM Popul Health. 2017;3:227–35.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Juhn YJ, Wheeler P, Wi CI, Bublitz J, Ryu E, Ristagno EH et al. Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis. Mayo Clin Proc Innov Qual Outcomes. 2021 Oct [cited 2024 Aug 16];5(5):916. /pmc/articles/PMC8272975/.

  27. Xie Z, Zhao R, Ding M, Zhang Z. A Review of Influencing Factors on Spatial Spread of COVID-19 Based on Geographical Perspective. Int J Environ Res Public Health. 2021 Nov 1 [cited 2024 Aug 16];18(22). https://pubmed.ncbi.nlm.nih.gov/34831938/

  28. Ministry of Health. » Press Releases. [cited 2022 Jan 4]. https://www.moh.gov.zm/?page_id=5399

  29. Dessie ZG, Zewotir T. Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients. BMC Infect Dis. 2021 Dec 1 [cited 2023 Apr 20];21(1):1–28. https://bmcinfectdis.biomedcentral.com/articles/https://doi.org/10.1186/s12879-021-06536-3

  30. Wang H, Paulson KR, Pease SA, Watson S, Comfort H, Zheng P et al. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21. The Lancet. 2022 Apr 16 [cited 2022 Apr 20];399(10334):1513–36. http://www.thelancet.com/article/S0140673621027963/fulltext

  31. Chang D, Chang X, He Y, Tan KJK. The determinants of covid-19 morbidity and mortality across countries. Sci Rep. 2022;12(1):5888.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Owoyemi A, Balogun T, Okoro J, Ndoro T, Fasominu O, Atanda A, et al. An Assessment of systemic factors and COVID-19 mortality in Africa. Int J Public Health. 2022;67:210.

    Article  Google Scholar 

  33. Nshimyiryo A, Barnhart DA, Cubaka VK, Dusengimana JMV, Dusabeyezu S, Ndagijimana D et al. Barriers and coping mechanisms to accessing healthcare during the COVID-19 lockdown: a cross-sectional survey among patients with chronic diseases in rural Rwanda. BMC Public Health. 2021 Dec 1 [cited 2023 Oct 18];21(1):1–11. https://bmcpublichealth.biomedcentral.com/articles/https://doi.org/10.1186/s12889-021-10783-z

  34. Fekadu G, Bekele F, Tolossa T, Fetensa G, Turi E, Getachew M et al. Impact of COVID-19 pandemic on chronic diseases care follow-up and current perspectives in low resource settings: a narrative review. Int J Physiol Pathophysiol Pharmacol. 2021 [cited 2023 Oct 18];13(3):86. /pmc/articles/PMC8310882/.

  35. Núñez A, Sreeganga SD, Ramaprasad A. Access to Healthcare during COVID-19. Int J Environ Res Public Health. 2021 Mar 2 [cited 2023 Oct 18];18(6):1–12. Available from: /pmc/articles/PMC7999346/.

  36. Schmidt T, Cloete A, Davids A, Makola L, Zondi N, Jantjies M. Myths, misconceptions, othering and stigmatizing responses to Covid-19 in South Africa: A rapid qualitative assessment. PLoS One. 2020 Dec 1 [cited 2023 Oct 18];15(12):e0244420. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244420

Download references

Acknowledgements

Francis Swaba, Jane Nalwimba, Paul Linde, Micheal Mwamba, Patrick Kamfwa.

Funding

This work has been partly financially supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) through the US Centers for Disease Control and Prevention (CDC) and the CDC Emergency Response to the COVID-19 pandemic through a cooperative agreement with the Zambia Ministry of Health (CoAg ID number: GH002234; CoAg funding period: 9/30/2020-9/29/2025).

Author information

Authors and Affiliations

  1. Surveillance and Disease Intelligence Cluster, Zambia National Public Health Institute, Stand No. 1186 Corner of Chaholi & Addis Ababa Roads, Lusaka, Zambia

    Stephen Longa Chanda, Amos Hamukale, Nathan Kapata, Nyambe Sinyange, Muzala Kapina & Roma Chilengi

  2. United States Centers for Disease Control and Prevention, Lusaka, Zambia

    Jonas Z. Hines & Warren Malambo

  3. Office of the State Forensic Pathologist, Government of the Republic of Zambia, Ministry of Home Affairs and Internal Security, Lusaka, Zambia

    Luchenga Adam Mucheleng’anga

Authors
  1. Stephen Longa Chanda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonas Z. Hines
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Warren Malambo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amos Hamukale
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nathan Kapata
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nyambe Sinyange
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Muzala Kapina
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Luchenga Adam Mucheleng’anga
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Roma Chilengi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Stephen Longa Chanda, MBChB, MSc: Project conception, Protocol development and implementation, monitoring, data collection, management and analysis, manuscript writting. Nathan Kapata, MBChB, PhD, Nyambe Sinyange, MBChB, MSc, Muzala Kapina, MBChB, MPH, Amos Hamukale, MSc: Protocol development and implementation, data collection and manuscript writting. Luchenga Adam Mucheleng’anga, Fellow FP, MMed Path, MBChB, BScHB: Protocol development, data collection and management. Jonas Hines, MD Surveillance Team Lead: Protocol development, data collection and management manuscript writing. Warren Malambo, MPH Public Health Specialist: Project conception, Protocol development, monitoring, data analysis, manuscript writing. Roma Chilengi, MBChB, MSc, Director General Zambia National Public Health Institute: Protocol development, monitoring, data management and analysis and manuscript writing.

Corresponding author

Correspondence to Stephen Longa Chanda.

Ethics declarations

Ethics approval and consent to participate

Informed consent was waived by Excellence Research Ethics and Science (ERES) converge (Ref. No 2022-April-018) (IRB and Federal Wide Assurance (FWA) numbers, 00005948 and 00011697 respectively) and authority to conduct the study from the National Health Research Authority (NHR0000005/31/10/2022). The activity was reviewed by the US Centers for Disease Control and Prevention and was conducted consistent with applicable US federal law and CDC policy.

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.

CDC disclaimer

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention (CDC).

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Figure 1: Population pyramid of deaths by wave period, 12 districts of Zambia, 2020–2022

12889_2024_20045_MOESM2_ESM.png

Supplementary Figure 2: Trendline of median age at death and median daily count of deaths in 12 districts, Zambia 2020–2022

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chanda, S.L., Hines, J.Z., Malambo, W. et al. An assessment of excess mortality during the COVID-19 pandemic, a retrospective post-mortem surveillance in 12 districts – Zambia, 2020–2022. BMC Public Health 24, 2625 (2024). https://doi.org/10.1186/s12889-024-20045-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-024-20045-3

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us