Evidence map›Paper›PMID 37611028›Full record

ArticlePLOS global public health2023

Estimating excess mortality during the COVID-19 pandemic from a population-based infectious disease surveillance in two diverse populations in Kenya, March 2020-December 2021.

Clifford Oduor, Allan Audi, Samwel Kiplangat, Joshua Auko, Alice Ouma, George Aol, Carolyne Nasimiyu, George O Agogo, Terrence Lo, Peninah Munyua and 3 more

Open access · goldAbstract read
In one paragraph

Article in PLOS global public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Advance Warning and Response Systems in Kenya: A Scoping Review.medRxiv : the preprint server for health sciences · 2025
    Article
  5. Article
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 3 institutions in 2 countries.

Clifford OduorCenter for Global Health Research, Kenya Medical Research Institute (KEMRI), Kisumu, Kenya.ORCID https://orcid.org/0000-0002-8676-9122
Allan AudiCenter for Global Health Research, Kenya Medical Research Institute (KEMRI), Kisumu, Kenya.
Samwel KiplangatCenter for Global Health Research, Kenya Medical Research Institute (KEMRI), Kisumu, Kenya.ORCID https://orcid.org/0009-0009-8547-6457
Joshua AukoCenter for Global Health Research, Kenya Medical Research Institute (KEMRI), Kisumu, Kenya.
Alice OumaCenter for Global Health Research, Kenya Medical Research Institute (KEMRI), Kisumu, Kenya.ORCID https://orcid.org/0000-0001-9512-4791
George AolCenter for Global Health Research, Kenya Medical Research Institute (KEMRI), Kisumu, Kenya.
Carolyne NasimiyuWashington State University (WSU) Global Health Kenya, Nairobi, Kenya.ORCID https://orcid.org/0000-0002-7559-3109
George O AgogoDivision of Global Health Protection, Global Health Center, Centers for Disease Control and Prevention, Nairobi, Kenya.ORCID https://orcid.org/0000-0002-7685-7743
Terrence LoDivision of Global Health Protection, Global Health Center, Centers for Disease Control and Prevention, Nairobi, Kenya.ORCID https://orcid.org/0000-0002-3858-2275
Peninah MunyuaDivision of Global Health Protection, Global Health Center, Centers for Disease Control and Prevention, Nairobi, Kenya.ORCID https://orcid.org/0000-0003-1950-8616
Amy Herman-RoloffDivision of Global Health Protection, Global Health Center, Centers for Disease Control and Prevention, Nairobi, Kenya.ORCID https://orcid.org/0000-0002-3027-5940
Godfrey BigogoCenter for Global Health Research, Kenya Medical Research Institute (KEMRI), Kisumu, Kenya.ORCID https://orcid.org/0000-0002-5177-0676
Patrick K MunywokiDivision of Global Health Protection, Global Health Center, Centers for Disease Control and Prevention, Nairobi, Kenya.ORCID https://orcid.org/0000-0001-9419-7155
Kenya Medical Research Institute · KECenters for Disease Control and Prevention · KEWashington State Department of Health · US

Funding

CGH CDC HHS U01 GH000031
6 · The paper itself

Abstract

Robust data on the impact of the COVID-19 pandemic on mortality in Africa are relatively scarce. Using data from two well-characterized populations in Kenya we aimed to estimate excess mortality during the COVID-19 pandemic period. The mortality data arise from an ongoing population-based infectious disease surveillance (PBIDS) platform, which has been operational since 2006 in rural western Kenya (Asembo, Siaya County) and an urban informal settlement (Kibera, Nairobi County), Kenya. PBIDS participants were regularly visited at home (2-3 times a year) by field workers who collected demographic data, including deaths. In addition, verbal autopsy (VA) interviews for all identified deaths are conducted. We estimated all-cause and cause-specific mortality rates before and during the height of the COVID-19 pandemic, and we compared associated mortality rates between the periods using incidence rate ratios. Excess deaths during the COVID-19 period were also estimated by modelling expected deaths in the absence of COVID-19 by applying a negative binomial regression model on historical mortality data from January 2016. Overall and monthly excess deaths were determined using the P-score metric. Spearman correlation was used to assess whether there is a relationship between the generated P-score and COVID-19 positivity rate. The all-cause mortality rate was higher during the COVID-19 period compared to the pre-COVID-19 period in Asembo [9.1 (95% CI, 8.2-10.0) vs. 7.8 (95% CI, 7.3-8.3) per 1000 person-years of observation, pyo]. In Kibera, the all-cause mortality rate was slightly lower during the COVID-19 period compared to the pre-COVID-19 period [2.6 (95% CI, 2.2-3.2 per 1000 pyo) vs. 3.1; 95% CI, 2.7-3.4 per 1000 pyo)]. An increase in all-cause mortality was observed (incidence rate ratio, IRR, 1.16; 95% CI, 1.04-1.31) in Asembo, unlike in Kibera (IRR, 0.88; 95% CI, 0.71-1.09). The notable increase in mortality rate in Asembo was observed among persons aged 50 to 64 years (IRR, 2.62; 95% CI, 1.95-3.52), persons aged 65 years and above (5.47; 95% CI, 4.60-6.50) and among females (IRR, 1.25; 95% CI, 1.07-1.46). These age and gender differences were not observed in Kibera. We observed an increase in the mortality rate due to acute respiratory infection, including pneumonia (IRR, 1.45;95% CI, 1.03-2.04), and a reduction in the mortality rate due to pulmonary tuberculosis (IRR, 0.22; 95% CI, 0.05-0.87) among older children and adults in Asembo. There was no statistically significant change in mortality rates due to leading specific causes of death in Kibera. Overall, during the COVID-19 period observed deaths were higher than expected deaths in Asembo (P-score = 6.0%) and lower than expected in Kibera (P-score = -22.3%).Using well-characterized populations in the two diverse geographic locations, we demonstrate a heterogenous impact of the COVID-19 pandemic on all-cause and cause-specific mortality rates in Kenya. We observed more deaths than expected during the COVID-19 period in our rural site in western Kenya contrary to the urban site in Nairobi, the capital city in Kenya.

Identifiers

PMID37611028
PMCPMC10446178
OpenAlexW4386099518

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.