Seasonal Pattern of Pneumonia Mortality among Under-Five Children in Nairobi’s Informal Settlements

Yé, Yazoumé; Zulu, Eliya M.; Mutisya, Maurice; Orindi, Benedict; Emina, Jacques; Kyobutungi, Catherine

doi:10.4269/ajtmh.2009.09-0070

Cited by 37 publications

(28 citation statements)

References 32 publications

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“…In addition, time-series studies on respiratory mortality have suggested that temperature has lag effects up to 3 weeks after exposure 14. As one of the major respiratory diseases, pneumonia has been reported to be influenced by meteorological factors 11 15 – 17. However, the relationships varied across areas with different weather patterns and latitudes.…”

Section: Introductionmentioning

confidence: 99%

Temporal relationship between hospital admissions for pneumonia and weather conditions in Shanghai, China: a time-series analysis

Liu

Kan

et al. 2014

BMJ Open

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ObjectivesTo explore the association between weather conditions and hospital admissions for pneumonia in Shanghai.DesignA time-series analysis was performed for a period of 4 years (January 2008–December 2011). A generalised additive model was used to calculate the relative risks.SettingShanghai, China.ParticipantsAll daily hospital admissions for pneumonia were obtained from the Shanghai health insurance system between 1 January 2008 and 31 December 2011 (n=99 403).ResultsThe relationship between the mean temperature and pneumonia hospital admissions followed a V-shaped curve, with an optimum temperature (OT) at 13°C. When the mean temperature was below the OT, a 1°C decrease corresponded to a 4.88% (95% CI 2.71% to 7.09%) and 5.34% (95% CI 2.04% to 8.74%) increase in pneumonia hospital admissions in lag 4 using a single-day lag structure and lag 0–7 using a multiday lag structure. When the mean temperature ≥OT, no adverse effects from the temperature on pneumonia hospital admissions were found. The magnitude of the effects of temperature varied across gender and age groups. Hospitalisations for pneumonia increased by 15.99% (95% CI 0.06% to 34.46%) in the cold period.ConclusionsCold temperature may be one of the important risk factors for pneumonia hospitalisations. Prevention programmes are needed to reduce the impact of cold temperature on pneumonia hospitalisations such as developing a weather warning system within a wide public health context.

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Section: Introductionmentioning

confidence: 99%

Temporal relationship between hospital admissions for pneumonia and weather conditions in Shanghai, China: a time-series analysis

Liu

Kan

et al. 2014

BMJ Open

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“…Most of these deaths occur in the tropics, where pneumonia in children occurs most commonly in the rainy season [2][3][4] . The mechanisms underlying this seasonality are unknown but possibilities include increased pathogen survival or stability in aerosols (possibly due to variations in temperature, humidity or sunlight), reduced host immunity (possibly due to variations in nutrition, sunlight, or co-infections) and increased host mixing due to seasonal variations in behavioural patterns.…”

Section: Introductionmentioning

confidence: 99%

Sunshine, rainfall, humidity and child pneumonia in the tropics: time-series analyses

et al. 2012

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3 SummaryFew studies have formally examined the relationship between meteorological factors and the incidence of child pneumonia in the tropics, despite the fact that most child pneumonia deaths occur here. We examined the association between four meteorological exposures (rainy days, sunshine, relative humidity and temperature) and the incidence of clinical pneumonia in young children in the Philippines using three time series methods: correlation of seasonal patterns, distributed lag regression, and case-crossover. Lack of sunshine was most strongly associated with pneumonia in both lagged regression (RR for a 1 hour increase in sunshine per day was 0.67 (95%CI 0.51 to 0.87)) and case-crossover analysis (OR for a 1 hour increase in mean daily sunshine 8 to 14 days earlier was 0.95 (95%CI 0.91 to 1.00)). This association is well known in temperate settings but has not been noted previously in the tropics. Further research to assess causality is needed.4

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“…The approach has been used to determine CODs among people of all ages in a wide range of settings (2, 7, 9–16); however, in Kenya, although VAs have been widely used as a tool for documenting CODs (7, 12–14, 17–21) , no study has yet been published in which the WHO-compliant InterVA-4 has been applied across deaths of all ages. The InterVA-4 model uses posterior probabilities for CODs, given an a priori distribution of CODs in the population and conditional probabilities for circumstances leading to death.…”

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confidence: 99%

Causes of death among persons of all ages within the Kilifi Health and Demographic Surveillance System, Kenya, determined from verbal autopsies interpreted using the InterVA-4 model

Ndila

Bauni

Mochamah

et al. 2014

Global Health Action

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BackgroundThe vast majority of deaths in the Kilifi study area are not recorded through official systems of vital registration. As a result, few data are available regarding causes of death in this population.ObjectiveTo describe the causes of death (CODs) among residents of all ages within the Kilifi Health and Demographic Surveillance System (KHDSS) on the coast of Kenya.DesignVerbal autopsies (VAs) were conducted using the 2007 World Health Organization (WHO) standard VA questionnaires, and VA data further transformed to align with the 2012 WHO VA instrument. CODs were then determined using the InterVA-4 computer-based probabilistic model.ResultsFive thousand one hundred and eighty seven deaths were recorded between January 2008 and December 2011. VA interviews were completed for 4,460 (86%) deaths. Neonatal pneumonia and birth asphyxia were the main CODs in neonates; pneumonia and malaria were the main CODs among infants and children aged 1–4, respectively, while HIV/AIDS was the main COD for adult women of reproductive age. Road traffic accidents were more commonly observed among men than women. Stroke and neoplasms were common CODs among the elderly over the age of 65.ConclusionsWe have established the main CODs among people of all ages within the area served by the KHDSS on the coast of Kenya using the 2007 WHO VA questionnaire coded using InterVA-4. We hope that our data will allow local health planners to estimate the burden of various diseases and to allocate their limited resources more appropriately.

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Seasonal Pattern of Pneumonia Mortality among Under-Five Children in Nairobi’s Informal Settlements

Cited by 37 publications

References 32 publications

Temporal relationship between hospital admissions for pneumonia and weather conditions in Shanghai, China: a time-series analysis

Temporal relationship between hospital admissions for pneumonia and weather conditions in Shanghai, China: a time-series analysis

Sunshine, rainfall, humidity and child pneumonia in the tropics: time-series analyses

Causes of death among persons of all ages within the Kilifi Health and Demographic Surveillance System, Kenya, determined from verbal autopsies interpreted using the InterVA-4 model

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