Spatial clustering of malaria and associated risk factors during an epidemic in a highland area of western Kenya

Brooker, Simon; Clarke, Siân E.; Njagi, J Kiambo; Polack, Sarah; Mugo, Benbolt; Estambale, Benson B.; Muchiri, Eric M.; Magnussen, Pascal; Cox, Jonathan

doi:10.1111/j.1365-3156.2004.01272.x

Cited by 194 publications

(188 citation statements)

References 36 publications

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“…Multiple regression modeling confirmed that proximity to this site strongly influenced malaria risk over distances as small as 100 m. The finding that malaria transmission is highly focal in low transmission areas is consistent with other local scale spatial studies of malaria transmission carried out under both epidemic and non-epidemic conditions. [21][22][23] In this study, increases in maximum and minimum daily temperatures were both associated with moderate increases in malaria incidence after a 1-month delay. The IRRs reported here for weather factors are independent of larvae/pupae factors, because the latter were also included in the model.…”

Section: Discussionmentioning

confidence: 96%

A Temporal-Spatial Analysis of Malaria Transmission in Adama, Ethiopia

Peterson¹,

Borrell²,

El‐Sadr³

et al. 2009

The American Journal of Tropical Medicine and Hygiene

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Abstract. Urban malaria is a growing problem in Africa. Small-scale spatial studies are useful in identifying foci of malaria transmission in urban communities. A population-based cohort study comprising 8,088 individuals was conducted in Adama, Ethiopia. During a single malaria season, the Kulldorff scan statistic identified one temporally stable spatial malaria cluster within 350 m of a major Anopheles breeding site. Factors associated with malaria incidence were residential proximity to vector breeding site, poor house condition (incidence rate ratio [IRR] = 2.0, 95% confidence interval [CI] = 1.4, 2.9), and a high level of vegetation (IRR = 1.8, 95% CI = 1.0, 3.3). Maximum (IRR = 1.4, 95% CI = 1.1, 1.9) and minimum daily temperatures (°C; IRR = 1.3, 95% CI = 1.2, 1.5) were positively associated with malaria incidence after a 1-month delay. Rainfall was positively associated with malaria incidence after a 10-day delay. Findings support the use of small scale mapping and targeted vector control in urban malaria control programs in Africa.

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Section: Discussionmentioning

confidence: 96%

A Temporal-Spatial Analysis of Malaria Transmission in Adama, Ethiopia

Peterson¹,

Borrell²,

El‐Sadr³

et al. 2009

The American Journal of Tropical Medicine and Hygiene

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“…The abundance of water bodies and favorable temperatures, maize plantings, extensive deforestation, or farmland have been associated with increased larval or mosquito abundance and thus increased risk for malaria transmission in human populations. [31][32][33][34] Other studies have used geographic information systems and satellite imagery to investigate environmental factors that potentially drive the dynamics of malaria vector populations [34][35][36] and other vector-borne and zoonotic diseases such as dengue fever or hantavirus. [37][38][39] It has been shown that the efficacy of control measures such as intermittent preventive treatment (IPT) can be strongly dependent on the present malaria incidence, [40][41][42] and it can be assumed that direct and contextual effects increase with malaria risk after an intervention.…”

Section: Discussionmentioning

confidence: 99%

Modeling the Relationship between Precipitation and Malaria Incidence in Children from a Holoendemic Area in Ghana

Krefis¹,

Schwarz²,

Krüger³

et al. 2011

The American Society of Tropical Medicine and Hygiene

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Climatic factors influence the incidence of vector-borne diseases such as malaria. They modify the abundance of mosquito populations, the length of the extrinsic parasite cycle in the mosquito, the malarial dynamics, and the emergence of epidemics in areas of low endemicity. The objective of this study was to investigate temporal associations between weekly malaria incidence in 1,993 children < 15 years of age and weekly rainfall. A time series analysis was conducted by using cross-correlation function and autoregressive modeling. The regression model showed that the level of rainfall predicted the malaria incidence after a time lag of 9 weeks (mean = 60 days) and after a time lag between one and two weeks. The analyses provide evidence that high-resolution precipitation data can directly predict malaria incidence in a highly endemic area. Such models might enable the development of early warning systems and support intervention measures.

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“…The clusters identified in using this analytical approach are areas having a higher risk than the expected risk for the underlying at-risk population (8) . This approach assists in appropriate geographical targeting and determining the scale of planned interventions through identifying and delineating the physical locations, sizes and intensity of the clusters (high-risk areas).With an ultimate aim of optimizing interventions, spatial approaches have been used extensively in dealing with public health problems such as infectious diseases, malaria, intestinal nematodes and diarrhoea (9)(10)(11)(12)(13) . For example, spatial approaches through an identification of the local clustering of malaria cases have been used to identify 'hot spots' of malaria cases in an area (14) and to guide malaria control and response efforts (15) .…”

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confidence: 99%

Local spatial clustering of stunting and wasting among children under the age of 5 years: implications for intervention strategies

Gebreyesus

Mariam

Woldehanna

et al. 2015

Public Health Nutr.

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Objective: The present study aimed to evaluate the clustering of undernutrition indicators of children under the age of 5 years in relation to different scales. Design: A community-based cross-sectional study design was employed. We collected anthropometric data, geographic locations/elevations of households and other data from visited households. We used a retrospective purely spatial Poisson probability model to identify and locate clusters (high rates) of stunting and wasting using the software SaTScan™ version 9·1·1. We ran a logistic regression model to help evaluate the causes of clustering. Settings: Six villages in the Meskane Mareko District (38·45763°E, 8·042144°N) of southern Ethiopia. Subjects: We surveyed 2371 children aged <5 years, who were found in 1744 households. Results: We found a micro-level variation in the risk of stunting and wasting within the studied district. We found the most likely significant clusters for wasting and severe wasting in two of the six villages. For stunting, a single large cluster size of 390 cases (304·19 expected) in 756 households was identified (relative risk = 1·48, P < 0·01). For severe stunting, a single cluster size of 106 cases (69·39 expected) in 364 households was identified (relative risk = 1·69, P = 0·035). Conclusions: We conclude that the distribution of wasting and stunting was partly spatially structured. We identified distinct areas within and between villages that have a higher risk than the underlying at-risk population. Our analysis identified the spatial locations of high-risk areas for stunting that could be an input for geographically targeting and optimizing nutritional interventions. KeywordsClustering Undernutrition Stunting Wasting EthiopiaThe burden of undernutrition has declined progressively in developing countries, with the rate of reduction varying by region. The largest reductions have been in Asia, whereas a modest reduction in prevalence is documented in Africa (1) . Ethiopia has documented a significant decline in the prevalence of both stunting and underweight over the past 14 years (between 2000 and 2014). For example, the prevalence of stunting decreased by 31 % (from 58 % to 40 %), while the prevalence of underweight decreased by 39 % (from 41 % to 25 %), between 2000 and 2014 (2-5) .Although this is significant progress in the reduction of stunting and underweight prevalence, the current prevalence levels are still considerable. Based on the WHO recommended criteria for assessing the severity of stunting and underweight at the population level, the country is classified under a medium to high grade (prevalence range of 20-40 % for stunting and 10-30 % for underweight). To help ameliorate the current unacceptably high burden of undernutrition among children under the age of 5 years, the Government of Ethiopia has launched a national nutrition programme (June 2013-June 2015 prioritizing nutrition as a viable strategy for the country's growth and development plans (6) . Among other things, a reduction in the burden of ch...

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Spatial clustering of malaria and associated risk factors during an epidemic in a highland area of western Kenya

Cited by 194 publications

References 36 publications

A Temporal-Spatial Analysis of Malaria Transmission in Adama, Ethiopia

A Temporal-Spatial Analysis of Malaria Transmission in Adama, Ethiopia

Modeling the Relationship between Precipitation and Malaria Incidence in Children from a Holoendemic Area in Ghana

Local spatial clustering of stunting and wasting among children under the age of 5 years: implications for intervention strategies

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