| dc.creator |
Mrema, Sigilbert |
|
| dc.creator |
Shamte, Amri |
|
| dc.creator |
Selemani, Majige |
|
| dc.creator |
Masanja, Honorati |
|
| dc.date |
2016-07-08T12:21:20Z |
|
| dc.date |
2016-07-08T12:21:20Z |
|
| dc.date |
2012-11 |
|
| dc.date.accessioned |
2018-03-27T09:13:24Z |
|
| dc.date.available |
2018-03-27T09:13:24Z |
|
| dc.identifier |
Mrema, S., Shamte, A., Selemani, M. and Masanja, H., 2012. The influence of weather on mortality in rural Tanzania: a time-series analysis 1999–2010. Glob Health Action, 5, pp.33-43. |
|
| dc.identifier |
http://hdl.handle.net/20.500.11810/2915 |
|
| dc.identifier |
10.3402/gha.v5i0.19068 · Source: PubMed |
|
| dc.identifier.uri |
http://hdl.handle.net/20.500.11810/2915 |
|
| dc.description |
Background: Weather and climate changes are associated with a number of immediate and long-term impacts on human health that occur directly or indirectly, through mediating variables. Few studies to date have established the empirical relationship between monthly weather and mortality in sub-Saharan Africa. Objectives: The objectives of this study were to assess the association between monthly weather (temperature and rainfall) on all-cause mortality by age in Rufiji, Tanzania, and to determine the differential susceptibility by age groups. Methods: We used mortality data from Rufiji Health and Demographic Surveillance System (RHDSS) for the period 1999 to 2010. Time-series Poisson regression models were used to estimate the association between monthly weather and mortality adjusted for long-term trends. We used a distributed lag model to estimate the delayed association of monthly weather on mortality. We stratified the analyses per age group to assess susceptibility. Results: In general, rainfall was found to have a stronger association in the age group 0-4 years (RR=1.001, 95% CI=0.961-1.041) in both short and long lag times, with an overall increase of 1.4% in mortality risk for a 10 mm rise in rainfall. On the other hand, monthly average temperature had a stronger association with death in all ages while mortality increased with falling monthly temperature. The association per age group was estimated as: age group 0-4 (RR=0.934, 95% CI=0.894-0.974), age group 5-59 (RR=0.956, 95% CI=0.928-0.985) and age group over 60 (RR=0.946, 95% CI=0.912-0.979). The age group 5-59 experienced more delayed lag associations. This suggests that children and older adults are most sensitive to weather related mortality. Conclusion: These results suggest that an early alert system based on monthly weather information may be useful for disease control management, to reduce and prevent fatal effects related to weather and monthly weather. |
|
| dc.language |
en |
|
| dc.subject |
Time-series |
|
| dc.subject |
Monthly weather |
|
| dc.subject |
All-cause mortality |
|
| dc.subject |
Monthly temperature |
|
| dc.subject |
Monthly average temperature |
|
| dc.subject |
Climate |
|
| dc.title |
The Influence of Weather on Mortality in Rural Tanzania: A Time-Series Analysis 1999–2010 |
|
| dc.type |
Journal Article, Peer Reviewed |
|