CoChat
Sign Up
Cited by 119Climate Change and Health ImpactsAir Quality and Health ImpactsThermoregulation and physiological responses

The DOI matches its Crossref record; 47 of the 50 references it cites were found in a registry or an index.

Time-series analysis of weather and mortality patterns in Nairobi's informal settlements

Open access
Egondi, Thaddaeus, Kyobutungi, Catherine, Kovats, Sari, Muindi, Kanyiva, Ettarh, Remare, Rocklöv, Joacim·2012·Global Health Action
DOI 10.3402/gha.v5i0.19065PMC PMC3509073
Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: Many studies have established a link between weather (primarily temperature) and daily mortality in developed countries. However, little is known about this relationship in urban populations in sub-Saharan Africa. OBJECTIVES: The objective of this study was to describe the relationship between daily weather and mortality in Nairobi, Kenya, and to evaluate this relationship with regard to cause of death, age, and sex. METHODS: We utilized mortality data from the Nairobi Urban Health and Demographic Surveillance System and applied time-series models to study the relationship between daily weather and mortality for a population of approximately 60,000 during the period 2003-2008. We used a distributed lag approach to model the delayed effect of weather on mortality, stratified by cause of death, age, and sex. RESULTS: Increasing temperatures (above 75th percentile) were significantly associated with mortality in children and non-communicable disease (NCD) deaths. We found all-cause mortality of shorter lag of same day and previous day to increase by 3.0% for a 1 degree decrease from the 25th percentile of 18°C (not statistically significant). Mortality among people aged 50+ and children aged below 5 years appeared most susceptible to cold compared to other age groups. Rainfall, in the lag period of 0-29 days, increased all-cause mortality in general, but was found strongest related to mortality among females. Low temperatures were associated with deaths due to acute infections, whereas rainfall was associated with all-cause pneumonia and NCD deaths. CONCLUSIONS: Increases in mortality were associated with both hot and cold weather as well as rainfall in Nairobi, but the relationship differed with regard to age, sex, and cause of death. Our findings indicate that weather-related mortality is a public health concern for the population in the informal settlements of Nairobi, Kenya, especially if current trends in climate change continue.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3402/gha.v5i0.19065

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Time series of all-cause (weekly) mortality and temperature (°C).
Fig. 1 Time series of all-cause (weekly) mortality and temperature (°C).CC BY · PubMed Central
Fig. 2 Annual seasonal variation plots for all-age and under-five mortality. The vertical axes show the log (relative risk) and the horizontal axis show the month starting with January. Confidence intervals (95%) are shown as dotted lines.
Fig. 2 Annual seasonal variation plots for all-age and under-five mortality. The vertical axes show the log (relative risk) and the horizontal axis show the month starting with January. Confidence intervals (95%) are shown as dotted lines.CC BY · PubMed Central
Fig. 3 Smooth functions of temperature for all and under-five mortality, and rainfall for all ages allowing lags of 0–13 days and 14–29 days. The vertical axes show the log (relative risk) and the horizontal axis show the scale of the explanatory variable. Confidence intervals (95%) are shown as dotted lines.
Fig. 3 Smooth functions of temperature for all and under-five mortality, and rainfall for all ages allowing lags of 0–13 days and 14–29 days. The vertical axes show the log (relative risk) and the horizontal axis show the scale of the explanatory variable. Confidence intervals (95%) are shown as dotted lines.CC BY · PubMed Central
Fig. 4 Sensitivity analyses, with increasing degrees of freedom, of the percentage increase in mortality for an increase in temperature of 1°C at lag 0–1 days for both cold and heat effects. The left vertical axis shows the heat effect and right vertical axis shows cold effect in percentages.
Fig. 4 Sensitivity analyses, with increasing degrees of freedom, of the percentage increase in mortality for an increase in temperature of 1°C at lag 0–1 days for both cold and heat effects. The left vertical axis shows the heat effect and right vertical axis shows cold effect in percentages.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 5

  1. Kinney PL, 2012, Bull Epidémiol Hebd, 12, 145
  2. McMichael AJ, 2000, Bull World Health Organ, 78, 1117
  3. Ferreira M, 2006, African Popul Studies, 21, 19
  4. Kynast-Wolf G, 2010, Trop Med Int Health, 15, 1082
  5. The Eurowinter Group, 1997, The Eurowinter Group. Lancet, 349, 1341

Cited by

Sorted by citations

See all 119

Related

See all

Metadata

Authors
Egondi, T., Kyobutungi, C., Kovats, S., Muindi, K., Ettarh, R., & Rocklöv, J.
Affiliations
  • African Population and Health Research Center (KE)
  • London School of Hygiene & Tropical Medicine (GB)
  • Umeå University (SE)
Corresponding author
Egondi, Thaddaeus
ORCID
Venue
Global Health Action
Publisher
Informa UK Limited
Published
2012-11-23
Volume
5
Issue
1
Pages
19065
ISSN
  • 1654-9716 (ISSN-L)
  • 1654-9880
DOI
10.3402/gha.v5i0.19065
PMC
PMC3509073
OpenAlex
W2120434156
Cited by
119
References
50
Citations by year
2026: 6 · 2025: 10 · 2024: 4 · 2023: 8 · 2022: 10 · 2021: 11 · 2020: 7 · 2019: 22 · 2018: 8 · 2017: 7 · 2016: 5 · 2015: 10 · 2014: 6 · 2013: 3 · 2012: 2
FWCI
4.66
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Global Health Action (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • DiVA at Umeå University (Umeå University) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
  • Health Sciences › Medicine › Physiology (medical) › Thermoregulation and physiological responses
Keywords
weather and mortality, temperature, cold exposure, heat exposure, rainfall, informal settlements, Nairobi, non-communicable disease mortality, child mortality, pneumonia, acute infection, sex differences, age differences, climate change, urban health
MeSH terms
Adolescent, Adult, Age Factors, Cause of Death, Child, Child, Preschool, Cities, Female, Humans, Infant, Kenya, Male, Middle Aged, Mortality, Population Surveillance, Rain, Seasons, Sex Factors, Temperature, Weather, Poisson Distribution, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review