Socio-economic determinants for malaria transmission risk in an endemic primary health centre in Assam, India

Yadav, Kavita; Dhiman, Sunil; Rabha, Bipul; Saikia, PK; Veer, Vijay

doi:10.1186/2049-9957-3-19

Cited by 71 publications

(89 citation statements)

References 29 publications

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“…The odds of developing malaria among people who used ITN occasionally was nearly nine times (AOR; 8.7; 95%CI: 1.5-49.6) compared to who use ITNs frequently. Similar finding was also reported from study in Ethiopia [11] and India [17]. Respondents who lived in households with inadequate number of ITNs were 13 times more likely to develop malaria disease (AOR: 13.4: 95%CI: 1.9-93).…”

Section: Discussionsupporting

confidence: 88%

Malaria Outbreak Investigation in Pastoral Communities of BenaTsemay District, Southern Ethiopia: A Case Control Study

Wondimu¹

2019

AJHR

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Over 60% (60 million) of Ethiopian populations live in areas at risk for malaria. On June 17 /2017, surveillance data from BenaTsemay district showed that the district was experiencing an unusual high number of malaria cases in three kebeles (lowest administrative unit). We investigated the outbreak to describe malaria epidemiology in the district, identify population risk factors, and guide intervention measures for the community. Malaria case records from the District Health Office were reviewed to describe the outbreak. Unmatched case-control study was conducted with 60 randomly selected cases and 119 controls using a semi-structured questionnaire. Malaria cases were confirmed by either microscopy or malaria rapid diagnostic tests (RDTs) from June 5 to July 23/2017. Controls were persons with no diagnosis of malaria, and lives in similar kebele. Environmental assessment, observation of ownership and utilization of insecticide treated bed-nets (ITNs) were carried out. Multivariable regression model analysis was conducted to identify independent malaria risk factors. There were 648 malaria cases (50.3%-males) from three kebeles with no deaths recorded. The overall attack rate (AR) was 114/1000 populations, and it was highest in Sille kebele (167/1000 populations) and among 5-14 years old (179/1000 populations). Age <5 years (AOR=8.5; 95%CI: 1.2-59), living near mosquito breeding sites (AOR=6.5; 95%CI: 1.15-36.5), irregular use of ITNs (AOR=8.7; 95%CI: 1.5-49.6), inadequate ITNs per family size (AOR=13.4; 95%CI: 1.9-93) predicted having of malaria. Wearing long sleeved clothes was a protective factor (AOR=0.6; 95%C.I: 0.004-0.96). Several unmanaged stagnated water sources with Anopheles mosquito larvae, and the use of ITNs for other purposes were observed. The outbreak was associated with presence of nearby vector-breeding sites, and poor access and utilization of ITNs. Adequate information should be given along ITNs for the community in addition to early management of nearby vectorbreeding sites to prevent similar outbreak in future.

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Section: Discussionsupporting

confidence: 88%

Malaria Outbreak Investigation in Pastoral Communities of BenaTsemay District, Southern Ethiopia: A Case Control Study

Wondimu¹

2019

AJHR

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“…Malaria currently has a higher inequality in distribution than any other disease of public health importance, as up to 58.0% of malaria deaths occur among the poorest 20.0% of the world's population (Yadav, Dhiman, Rabha, Saikia, & Veer, 2014) and continues to present a public health burden in the tropical and sub-tropical regions of the world (Dako-Gyeke & Kofie, 2006;World Health Organisation [WHO], 2011). In Ghana, for example, outpatient department attendance due to malaria alone increased from 37.1% in 1985 to 44.7% in 2003 and the disease accounts for 24.6% of total hospital admission, 19.5% total bed days and 17.1% of total deaths in facilities (Adams, Darko, & Accorsi, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the global malaria control strategy has largely emphasized malaria control by insecticide treated bed nets (ITNs), indoor residual spraying (IRS), increased case detection and subsequent treatment, as well as other vector control interventions while neglecting the socio-economic deprivation from which people have difficulty in adequately handling malaria (Yadav et al, 2014). This study thus seeks to add to the literature on the subject matter by investigating the linkages between poverty and malaria morbidity in the Jirapa District of the Upper West Region of Ghana.…”

Section: Introductionmentioning

confidence: 99%

Poverty and malaria morbidity in the Jirapa District of Ghana: A count regression approach

Nkegbe

Kuunibe

Sekyi

2017

Cogent Economics & Finance

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Malaria potentially affects everyone in the tropics and sub-tropics, however, the poor and vulnerable are worse affected mainly due to the socio-economic constraints that confront them. In Ghana, the Upper West Region, which is the poorest, is one of the worse affected in terms of malaria burden. Given social and economic factors directly relate to malaria morbidity, global malaria control strategy unfortunately has not particularly targeted the effects of socio-economic deprivation on the disease morbidity and control. This study investigates the linkages between poverty and malaria morbidity using count data models, with Jirapa District in the Upper West Region of Ghana as the study area. Empirical results confirm the presence of poverty in the study area as more than half of households depend on heads whose incomes are below the poverty line of US$1 per day and that significant relationships exist between poverty and education on the one hand and malaria morbidity on the other, since gender and level of education of household head, and household poverty situation are significant determinants of malaria morbidity. The study thus recommends that policies aimed at reducing and/or eradicating malaria should include measures to increase income earning capacity of households in the study area.

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“…In addition to climate, socio-economic factors play a critical role in malaria transmission [9,10]. Therefore, addressing malaria through the design of optimal interventions can benefit from a clear understanding of the impact of both climate and non-climate factors.…”

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confidence: 99%

Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors

Chirombo

Ceccato²,

Lowe

et al. 2020

Malar J

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Background: Malaria transmission is influenced by a complex interplay of factors including climate, socioeconomic , environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions. Methods: Routine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socioeconomic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with districtlevel variation in clinical malaria rates in Malawi, and visualized using maps. Results: Between 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi. Conclusion: The modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

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Socio-economic determinants for malaria transmission risk in an endemic primary health centre in Assam, India

Cited by 71 publications

References 29 publications

Malaria Outbreak Investigation in Pastoral Communities of BenaTsemay District, Southern Ethiopia: A Case Control Study

Malaria Outbreak Investigation in Pastoral Communities of BenaTsemay District, Southern Ethiopia: A Case Control Study

Poverty and malaria morbidity in the Jirapa District of Ghana: A count regression approach

Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors

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