Dataset Information

Survey-based socio-economic data from slums in Bangalore, India.

ABSTRACT: In 2010, an estimated 860 million people were living in slums worldwide, with around 60 million added to the slum population between 2000 and 2010. In 2011, 200 million people in urban Indian households were considered to live in slums. In order to address and create slum development programmes and poverty alleviation methods, it is necessary to understand the needs of these communities. Therefore, we require data with high granularity in the Indian context. Unfortunately, there is a paucity of highly granular data at the level of individual slums. We collected the data presented in this paper in partnership with the slum dwellers in order to overcome the challenges such as validity and efficacy of self reported data. Our survey of Bangalore covered 36 slums across the city. The slums were chosen based on stratification criteria, which included geographical location of the slum, whether the slum was resettled or rehabilitated, notification status of the slum, the size of the slum and the religious profile. This paper describes the relational model of the slum dataset, the variables in the dataset, the variables constructed for analysis and the issues identified with the dataset. The data collected includes around 267,894 data points spread over 242 questions for 1,107 households. The dataset can facilitate interdisciplinary research on spatial and temporal dynamics of urban poverty and well-being in the context of rapid urbanization of cities in developing countries.

SUBMITTER: Roy D

PROVIDER: S-EPMC5759370 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Survey-based socio-economic data from slums in Bangalore, India.

Roy Debraj D Palavalli Bharath B Menon Niveditha N King Robin R Pfeffer Karin K Lees Michael M Sloot Peter M A PMA

Scientific data 20180109

In 2010, an estimated 860 million people were living in slums worldwide, with around 60 million added to the slum population between 2000 and 2010. In 2011, 200 million people in urban Indian households were considered to live in slums. In order to address and create slum development programmes and poverty alleviation methods, it is necessary to understand the needs of these communities. Therefore, we require data with high granularity in the Indian context. Unfortunately, there is a paucity of ...[more]

PMID: 29313840

Similar Datasets

Project description:BackgroundUrban slums are characterised by unique challenging living conditions, which increase their inhabitants' vulnerability to specific health conditions. The identification and prioritization of the key health issues occurring in these settings is essential for the development of programmes that aim to enhance the health of local slum communities effectively. As such, the present study sought to identify and prioritise the key health issues occurring in urban slums, with a focus on the perceptions of health professionals and community workers, in the rapidly growing city of Bangalore, India.MethodsThe study followed a two-phased mixed methods design. During Phase I of the study, a total of 60 health conditions belonging to four major categories: - 1) non-communicable diseases; 2) infectious diseases; 3) maternal and women's reproductive health; and 4) child health - were identified through a systematic literature review and semi-structured interviews conducted with health professionals and other relevant stakeholders with experience working with urban slum communities in Bangalore. In Phase II, the health issues were prioritised based on four criteria through a consensus workshop conducted in Bangalore.ResultsThe top health issues prioritized during the workshop were: diabetes and hypertension (non-communicable diseases category), dengue fever (infectious diseases category), malnutrition and anaemia (child health, and maternal and women's reproductive health categories). Diarrhoea was also selected as a top priority in children. These health issues were in line with national and international reports that listed them as top causes of mortality and major contributors to the burden of diseases in India.ConclusionsThe results of this study will be used to inform the development of technologies and the design of interventions to improve the health outcomes of local communities. Identification of priority health issues in the slums of other regions of India, and in other low and lower middle-income countries, is recommended.

Project description:BackgroundHousehold food and nutrition insecurity continued to be a development and policy agenda in Ethiopia. Assessing the patterns and determinants of household dietary diversity is an important area of research given its importance for policy uptake in the country. This study is, therefore, initiated to identify the dominant food groups consumed by households and to investigate the determinants of household dietary diversity in the country.MethodWe used data from the 4th wave of the Ethiopian socioeconomic survey. The survey data for this study included 3,115 households living in rural areas (hereafter called 'rural households'). Household Dietary Diversity Score (HDDS) was calculated and categorized as per the FAO's recommendation: low HDDS category for those who consume three or less food groups, medium HDDS for those who consume four to six, and high HDDS for those who consume seven and more food groups during the past seven days. Ordinal logistic regression model was employed to estimate the determinants of rural household's dietary diversity.ResultsCereals were the most dominant food group consumed by 96.4% of the households followed by pulses, which was consumed by 82% of the households Nutrition-dense food commodities such as lean meat, vegetables and fruits were the least consumed food groups by households in Ethiopia. In terms of determinants of dietary diversity, female headed households have 38% more chance of consuming diverse foods compared to their male-headed counterparts (AOR = 1.38, 95% CI: 1.10, 1.73). Household heads who completed secondary education and above have 62% more chance of consuming diverse foods compared to uneducated household heads (AOR = 1.62, 95% CI: 1.2, 2.30). Household heads who are single have 37% less chance of consuming diverse foods compared to those household heads who are married (AOR = 0.63, 95% CI: 0.50, 0.80). Those households located in Harari regional state and in the rural surroundings of Diredawa town have 6.56 times more chance of consuming diverse foods compared to those households living in Tigray and Amhara regional states (AOR = 6.56, 95% CI: 4.60, 9.37). The results also highlighted that households who are in the upper wealth category have 9 times more chance of consuming diverse foods compared to those households who are the lower wealth category (AOR = 8.54, 95% CI: 6.79, 11.98).

Project description:BackgroundThe incidence of preterm birth and subsequent low birth weight (LBW) are vital global public health issues. It contributes to high infant and child mortality in the early stages of life and later on in adult life; it increases the risk for non-communicable diseases. The study aims to understand the socio-economic status-related inequality for LBW among children in India. It hypothesises that there is no association between the socio-economic status of the household and the newborn's LBW in India.MethodsThe study utilised data from the fourth round of the National Family Health Survey, a national representative cross-sectional survey conducted in 2015-16 (N = 127,141). The concentration index (CCI) and the concentration curve (CC) measured socio-economic inequality in low birth status among newborns. Wagstaff decomposition further analysed key contributors in CCI by segregating significant covariates.ResultsAbout 18.2% of children had low birth weight status. The value of concentration was - 0.05 representing that low birth weight status is concentrated among children from lower socio-economic status. Further, the wealth quintile explained 76.6% of the SES related inequality followed by regions of India (- 44%) and the educational status of mothers (43.4%) for LBW among children in India. Additionally, the body mass index of the women (28.4%), ante-natal care (20.8%) and residential status (- 15.7%) explained SES related inequality for LBW among children in India.ConclusionAdequate attention should be given to the mother's nutritional status. Awareness of education and usage of health services during pregnancy should be promoted. Further, there is a need to improve the coverage and awareness of the ante-natal care (ANC) program. In such cases, the role of the health workers is of utmost importance. Programs on maternal health services can be merged with maternal nutrition to bring about an overall decline in the LBW of children in India.

Dataset Information

Survey-based socio-economic data from slums in Bangalore, India.

Publications

Survey-based socio-economic data from slums in Bangalore, India.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets