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Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India.

ABSTRACT: BACKGROUND:Spatial analysis has been vital in mapping the spread of diseases and assisting in policy making. Targeting diarrhea transmission hotspots is one of the potential strategies for reducing diarrhea cases. This study aimed to examine the spatial-temporal variations and to identify the modifiable determinants of diarrhea while controlling for the spatial dependence in the data. METHODS:An ecological study on diarrhea data from DLHS-3 and NFHS- 4 in India. Moran's I and LISA were used to detect the spatial clustering of diarrhea cases and to test for clustering in the data. Spatial regression was used to identify the modifiable factors associated with the prevalence of diarrhea. The study comprised of the prevalence of diarrhea among the children below the age of five years (U-5?s) across different states in India. The determinants of diarrhea were obtained using spatial lag models. The software used were GeoDa 1.6.6 and QGIS 2.0. RESULTS:The presence of spatial autocorrelation in DLHS-3 and NFHS-4 (Moron's I?=?0.577 and 0.369 respectively) enforces the usage of geographical properties while modeling the diarrhea data. The geographic clustering of high-prevalence districts was observed in the state of UP consistently. The spatial pattern of the percentage of children with diarrhea was persistently associated with the household with a sanitation facility (%) (p?=?0.023 and p?=?0.011). Compared to the diarrhea cases in the period 2007-2008, no much reduction was observed in the period 2015-2016. The prevalence of diarrhea and percentage of household with sanitation were ranging between 0.1-33.8% and 1.3-96.1% in the period 2007-2008 and 0.6-29.1% and 10.4-92.0% in the period 2015-2016 respectively. The least and highest prevalence of diarrhea being consistently from Assam and UP respectively. CONCLUSION:Despite improvements in controlling spread of diarrheal disease, the burden remains high. Focus on widespread diarrheal disease control strategy by addressing the social determinants of health like basic sanitation is crucial to reduce the burden of diarrhea among U-5?s in India. The identification of hotspots will aid in the planning of control strategies for goal setting in the targeted regions.

SUBMITTER: Nilima

PROVIDER: S-EPMC6251155 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India.

Nilima Kamath Asha A Shetty Karthik K Unnikrishnan B B Kaushik Siddharth S Rai Shesh N SN

BMC public health 20181123 1

<h4>Background</h4>Spatial analysis has been vital in mapping the spread of diseases and assisting in policy making. Targeting diarrhea transmission hotspots is one of the potential strategies for reducing diarrhea cases. This study aimed to examine the spatial-temporal variations and to identify the modifiable determinants of diarrhea while controlling for the spatial dependence in the data.<h4>Methods</h4>An ecological study on diarrhea data from DLHS-3 and NFHS- 4 in India. Moran's I and LISA ...[more]

PMID: 30470208

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Project description:Decades after the establishment of clear guidelines for management, mostly due to irrational approach, diarrhea is still a major concern in the developing world, including India. The scenario is even worse in urban slums owing to poor health-seeking and socio-environmental vulnerability. Determining the distribution of rational diarrhea management by practitioners and identification of its important predictors seemed urgent to minimize the potential for antibiotic resistance, diarrhea-related mortality and morbidity in these areas.Between May 2011 and January 2012, 264 consenting, randomly selected qualified and non-qualified practitioners (including pharmacists) were interviewed in the slums of Kolkata, a populous city in eastern India, regarding their characteristics, diarrhea-related knowledge (overall and in six separate domains: signs/symptoms, occurrence/spread, management, prevention/control, cholera and ORS), prescribed antibiotics, intravenous fluid (IVF) and laboratory investigations. Rationality was established based on standard textbooks.Among participants, 53.03% had no medical qualifications, 6.06% were attached to Governmental hospitals, 19.32% had best knowledge regarding diarrhea. While treating diarrhea, 7.20%, 17.80% and 20.08% respectively advised antibiotics, IVF and laboratory tests rationally. Logistic regression revealed that qualified and Governmental-sector practitioners managed diarrhea more rationally. Having best diarrhea-related knowledge regarding signs/symptoms (OR=5.49, p value=0.020), occurrence/spread (OR=3.26, p value=0.035) and overall (OR=6.82, p value=0.006) were associated with rational antibiotic prescription. Rational IVF administration was associated with best knowledge regarding diarrheal signs/symptoms (OR=3.00, p value=0.017), occurrence/spread (OR=3.57, p value=0.004), prevention/control (OR=4.89, p value=0.037), ORS (OR=2.55, p value=0.029) and overall (OR=4.57, p value<0.001). Best overall (OR=2.68, p value=0.020) and cholera-related knowledge (OR=2.34, p value=0.019) were associated with rational laboratory testing strategy.Diarrheal management practices were unsatisfactory in urban slums where practitioners' knowledge was a strong predictor for rational management. Interventions targeting non-qualified, independent practitioners to improve their diarrhea-related knowledge seemed to be required urgently to ensure efficient management of diarrhea in these endemic settings.

Project description:BackgroundDespite declining incidence over the past decade, malaria remains an important health burden in India. This study aimed to assess the village-level temporal patterns of Plasmodium infection in two districts of the north-eastern state of Meghalaya and evaluate risk factors that might explain these patterns.MethodsPrimary Health Centre passive malaria case data from 2014 to 2018 were analysed to characterize village-specific annual incidence and temporal trends. Active malaria case detection was undertaken in 2018 and 2019 to detect Plasmodium infections using PCR. A questionnaire collected socio-demographic, environmental, and behavioural data, and households were spatially mapped via GPS. Adult mosquitoes were sampled at a subset of subjects' houses, and Anopheles were identified by PCR and sequencing. Risk factors for Plasmodium infection were evaluated using bivariate and multivariate logistic regression analysis, and spatial cluster analysis was undertaken.ResultsThe annual malaria incidence from PHC-based passive surveillance datasets in 2014-2018 was heterogenous but declining across villages in both districts. Active surveillance in 2018 enrolled 1468 individuals from 468 households (West Jaintia Hills) and 1274 individuals from 359 households (West Khasi Hills). Plasmodium falciparum prevalence per 100 people varied from 0 to 4.1% in the nine villages of West Jaintia Hills, and from 0 to 10.6% in the 12 villages of West Khasi Hills. Significant clustering of P. falciparum infections [observed = 11, expected = 2.15, Relative Risk (RR) = 12.65; p < 0.001] was observed in West Khasi Hills. A total of 13 Anopheles species were found at 53 houses in five villages, with Anopheles jeyporiensis being the most abundant. Risk of infection increased with presence of mosquitoes and electricity in the households [Odds Ratio (OR) = 1.19 and 1.11], respectively. Households with reported animals had reduced infection risk (OR = 0.91).ConclusionMalaria incidence during 2014-2018 declined in all study villages covered by the passive surveillance data, a period that includes the first widespread insecticide-treated net campaign. The survey data from 2018 revealed a significant association between Plasmodium infection and certain household characteristics. Since species of Plasmodium-competent mosquito vectors continue to be abundant, malaria resurgence remains a threat, and control efforts should continue.

Project description:BackgroundDespite improvements in prevention efforts, childhood diarrhea remains a public health concern. However, there may be substantial variation influenced by place, time, and season. Description of diarrheal clusters in time and space and understanding seasonal patterns can improve surveillance and management. The present study investigated the spatial and seasonal distribution and purely spatial, purely temporal, and space-time clusters of childhood diarrhea in Southern Ethiopia.MethodsThe study was a retrospective analysis of data from the Health Management Information System (HMIS) under-five diarrheal morbidity reports from July 2011 to June 2017 in Sidama Zone. Annual diarrhea incidence at district level was calculated. Incidence rate calculation and seasonal trend analysis were performed. The Kulldorff SaTScan software with a discrete Poisson model was used to identify statistically significant special, temporal, and space-time diarrhea clusters. ArcGIS 10.1 was used to plot the maps.ResultsA total of 202,406 under-five diarrheal cases with an annual case of 5822 per 100,000 under-five population were reported. An increasing trend of diarrhea incidence was observed over the 6 years with seasonal variation picking between February and May. The highest incidence rate (135.8/1000) was observed in the year 2016/17 in Boricha district. One statistically significant most likely spatial cluster (Boricha district) and six secondary clusters (Malga, Hulla, Aleta Wondo, Shebedino, Loka Abaya, Dale, and Wondogenet) were identified. One statistically significant temporal cluster (LLR = 2109.93, p < 0.001) during December 2013 to May 2015 was observed in all districts. Statistically significant spatiotemporal primary hotspot was observed in December 2012 to January 2015 in Malga district with a likelihood ratio of 1214.67 and a relative risk of 2.03. First, second, third, and fourth secondary hotspots occurred from January 2012 to May 2012 in Loka Abaya, December 2011 in Bursa, from March to April 2014 in Gorchie, and March 2012 in Wonsho districts.ConclusionChildhood diarrhea was not distributed randomly over space and time and showed an overall increasing trend of seasonal variation peaking between February and May. The health department and other stakeholders at various levels need to plan targeted interventional activities at hotspot seasons and areas to reduce morbidity and mortality.

Project description:BackgroundThe objective of this study is to analyze the spatial and temporal patterns of malaria incidence as to determine the means by which climatic factors such as temperature, rainfall and humidity affect its distribution in Maputo province, Mozambique.MethodsThis study presents a model of malaria that evolves in space and time in Maputo province-Mozambique, over a ten years period (1999-2008). The model incorporates malaria cases and their relation to environmental variables. Due to incompleteness of climatic data, a multiple imputation technique is employed. Additionally, the whole province is interpolated through a Gaussian process. This method overcomes the misalignment problem of environmental variables (available at meteorological stations--points) and malaria cases (available as aggregates for every district--area). Markov Chain Monte Carlo (MCMC) methods are used to obtain posterior inference and Deviance Information Criteria (DIC) to perform model comparison.ResultsA Bayesian model with interaction terms was found to be the best fitted model. Malaria incidence was associated to humidity and maximum temperature. Malaria risk increased with maximum temperature over 28 °C (relative risk (RR) of 0.0060 and 95% Bayesian credible interval (CI) of 0.00033-0.0095) and humidity (relative risk (RR) of 0.00741 and 95% Bayesian CI 0.005141-0.0093). The results would suggest that additional non-climatic factors including socio-economic status, elevation, etc. also influence malaria transmission in Mozambique.ConclusionsThese results demonstrate the potential of climate predictors particularly, humidity and maximum temperature in explaining malaria incidence risk for the studied period in Maputo province. Smoothed maps obtained as monthly average of malaria incidence allowed to visualize months of initial and peak transmission. They also illustrate a variation on malaria incidence risk that might not be related to climatic factors. However, these factors are still determinant for malaria transmission and intensity in the region.

Dataset Information

Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India.

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Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India.

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