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Localised estimates and spatial mapping of poverty incidence in the state of Bihar in India-An application of small area estimation techniques.

ABSTRACT: Poverty affects many people, but the ramifications and impacts affect all aspects of society. Information about the incidence of poverty is therefore an important parameter of the population for policy analysis and decision making. In order to provide specific, targeted solutions when addressing poverty disadvantage small area statistics are needed. Surveys are typically designed and planned to produce reliable estimates of population characteristics of interest mainly at higher geographic area such as national and state level. Sample sizes are usually not large enough to provide reliable estimates for disaggregated analysis. In many instances estimates are required for areas of the population for which the survey providing the data was unplanned. Then, for areas with small sample sizes, direct survey estimation of population characteristics based only on the data available from the particular area tends to be unreliable. This paper describes an application of small area estimation (SAE) approach to improve the precision of estimates of poverty incidence at district level in the State of Bihar in India by linking data from the Household Consumer Expenditure Survey 2011-12 of NSSO and the Population Census 2011. The results show that the district level estimates generated by SAE method are more precise and representative. In contrast, the direct survey estimates based on survey data alone are less stable.

SUBMITTER: Chandra H

PROVIDER: S-EPMC5991681 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Localised estimates and spatial mapping of poverty incidence in the state of Bihar in India-An application of small area estimation techniques.

Chandra Hukum H Aditya Kaustav K Sud U C UC

PloS one 20180607 6

Poverty affects many people, but the ramifications and impacts affect all aspects of society. Information about the incidence of poverty is therefore an important parameter of the population for policy analysis and decision making. In order to provide specific, targeted solutions when addressing poverty disadvantage small area statistics are needed. Surveys are typically designed and planned to produce reliable estimates of population characteristics of interest mainly at higher geographic area ...[more]

PMID: 29879202

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Project description:IntroductionCommunity Acquired Pneumonia (CAP) is the leading cause of mortality in children younger than five years of age in developing countries, including India. Hence, this prospective study was performed to estimate the incidence of CAP in children (2-59 months)in four districts of Northern India.MethodsA cross-sectional survey in rural Lucknow was conducted using cluster sampling technique to assess the proportion of CAP cases that were hospitalized in last 12 months (hospitalization fraction). Another prospective study was done to assess number of hospitalized CAP cases in same districts in 2016. For this, a surveillance network of hospitals that admitted children was established. Cases with WHO-defined CAP with less than 14 days of illness were eligible for inclusion. Informed written parental consent was obtained. A mathematical model was developed to estimate the incidence of CAP in each district, taking into account number of cases hospitalized in one year, assuming it to be equal to hospitalization fraction and using Lucknow district as reference, correcting for child-population per hospital for each district. Population census data of 2011 was taken as denominator.ResultsIn cross-sectional survey (February to May 2016), 3351 children (2-59 months) from 240 villages were included. Of these 24.58% (824/3351) children suffered from CAP in last 12 months and out of these 4% (33/824) children were hospitalized. Computed incidence of CAP per 1000 child-year for Lucknow was 86.50 (95%CI: 85.72-87.29); Etawah 177.01(95%CI: 175.44-178.58); Patna 207.78 (95%CI: 207.20-208.37) and Darbhanga 221.18 (95%CI: 220.40-221.97). Infants (2-11 months)had almost five to ten times higher incidence of CAP than those in 12-59 months age category.ConclusionsIncidence of CAP in Uttar Pradesh and Bihar is high, being much higher in infants. Hence there is an urgent need for introduction of preventive strategies, improving health seeking behavior and quality of care for CAP.

Project description:BackgroundThe world is rapidly becoming urban with the global population living in cities projected to double by 2050. This increase in urbanization poses new challenges for the spread and control of communicable diseases such as malaria. In particular, urban environments create highly heterogeneous socio-economic and environmental conditions that can affect the transmission of vector-borne diseases dependent on human water storage and waste water management. Interestingly India, as opposed to Africa, harbors a mosquito vector, Anopheles stephensi, which thrives in the man-made environments of cities and acts as the vector for both Plasmodium vivax and Plasmodium falciparum, making the malaria problem a truly urban phenomenon. Here we address the role and determinants of within-city spatial heterogeneity in the incidence patterns of vivax malaria, and then draw comparisons with results for falciparum malaria.Methodology/principal findingsStatistical analyses and a phenomenological transmission model are applied to an extensive spatio-temporal dataset on cases of Plasmodium vivax in the city of Ahmedabad (Gujarat, India) that spans 12 years monthly at the level of wards. A spatial pattern in malaria incidence is described that is largely stationary in time for this parasite. Malaria risk is then shown to be associated with socioeconomic indicators and environmental parameters, temperature and humidity. In a more dynamical perspective, an Inhomogeneous Markov Chain Model is used to predict vivax malaria risk. Models that account for climate factors, socioeconomic level and population size show the highest predictive skill. A comparison to the transmission dynamics of falciparum malaria reinforces the conclusion that the spatio-temporal patterns of risk are strongly driven by extrinsic factors.Conclusion/significanceClimate forcing and socio-economic heterogeneity act synergistically at local scales on the population dynamics of urban malaria in this city. The stationarity of malaria risk patterns provides a basis for more targeted intervention, such as vector control, based on transmission 'hotspots'. This is especially relevant for P. vivax, a more resilient parasite than P. falciparum, due to its ability to relapse and the operational shortcomings of delivering a "radical cure".

Project description:BackgroundModel-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes are not large enough to support direct estimates of HIV prevalence with adequate precision. We computed district-level HIV prevalence estimates and their 95% confidence intervals for districts in Uganda.MethodsOur analysis used direct survey and model-based estimation methods, including Fay-Herriot (area-level) and Battese-Harter-Fuller (unit-level) small area models. We used regression analysis to assess for consistency in estimating HIV prevalence. We use a ratio analysis of the mean square error and the coefficient of variation of the estimates to evaluate precision. The models were applied to Uganda Population-Based HIV Impact Assessment 2016/2017 data with auxiliary information from the 2016 Lot Quality Assurance Sampling survey and antenatal care data from district health information system datasets for unit-level and area-level models, respectively.ResultsEstimates from the model-based and the direct survey methods were similar. However, direct survey estimates were unstable compared with the model-based estimates. Area-level model estimates were more stable than unit-level model estimates. The correlation between unit-level and direct survey estimates was (β1 = 0.66, r2 = 0.862), and correlation between area-level model and direct survey estimates was (β1 = 0.44, r2 = 0.698). The error associated with the estimates decreased by 37.5% and 33.1% for the unit-level and area-level models, respectively, compared to the direct survey estimates.ConclusionsAlthough the unit-level model estimates were less precise than the area-level model estimates, they were highly correlated with the direct survey estimates and had less standard error associated with estimates than the area-level model. Unit-level models provide more accurate and reliable data to support local decision-making when unit-level auxiliary information is available.

Dataset Information

Localised estimates and spatial mapping of poverty incidence in the state of Bihar in India-An application of small area estimation techniques.

Publications

Localised estimates and spatial mapping of poverty incidence in the state of Bihar in India-An application of small area estimation techniques.

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