The high cost of diarrhoeal illness for urban slum households–a cost-recovery approach: a cohort study

Objectives: Rapid urbanisation has often meant that public infrastructure has not kept pace with growth leading to urban slums with poor access to water and sanitation and high rates of diarrhoea with greater household costs due to illness. This study sought to determine the monetary cost of diarrhoea to urban slum households in Kaula Bandar slum in Mumbai, India. The study also tested the hypotheses that the cost of water and sanitation infrastructure may be surpassed by the cumulative costs of diarrhoea for households in an urban slum community. Design: A cohort study using a baseline survey of a random sample followed by a systematic longitudinal household survey. The baseline survey was administered to a random sample of households. The systematic longitudinal survey was administered to every available household in the community with a case of diarrhoea for a period of 5 weeks. Participants: Every household in Kaula Bandar was approached for the longitudinal survey and all available and consenting adults were included. Results: The direct cost of medical care for having at least one person in the household with diarrhoea was 205 rupees. Other direct costs brought total expenses to 291 rupees. Adding an average loss of 55 rupees per household from lost wages and monetising lost productivity from homemakers gave a total loss of 409 rupees per household. During the 5-week study period, this community lost an estimated 163 600 rupees or 3635 US dollars due to diarrhoeal illness. Conclusions: The lack of basic water and sanitation infrastructure is expensive for urban slum households in this community. Financing approaches that transfer that cost to infrastructure development to prevent illness may be feasible. These findings along with the myriad of unmeasured benefits of preventing diarrhoeal illness add to pressing arguments for investment in basic water and sanitation infrastructure

The psychological toll of slum living in Mumbai, India: A mixed methods study

In India, “non-notified” slums are not officially recognized by city governments; they suffer from insecure tenure and poorer access to basic services than “notified” (government-recognized) slums. We conducted a study in a non-notified slum of about 12,000 people in Mumbai to determine the prevalence of individuals at high risk for having a common mental disorder (i.e., depression and anxiety), to ascertain the impact of mental health on the burden of functional impairment, and to assess the influence of the slum environment on mental health. We gathered qualitative data (six focus group discussions and 40 individual interviews in July-November 2011), with purposively sampled participants, and quantitative data (521 structured surveys in February 2012), with respondents selected using community-level random sampling. For the surveys, we administered the General Health Questionnaire-12 (GHQ) to screen for common mental disorders (CMDs), the WHO Disability Assessment Schedule 2.0 (WHO DAS) to screen for functional impairment, and a slum adversity questionnaire, which we used to create a composite Slum Adversity Index (SAI) score. Twenty-three percent of individuals have a GHQ score ≥5, suggesting they are at high risk for having a CMD. Psychological distress is a major contributor to the slum’s overall burden of functional impairment. In a multivariable logistic regression model, household income, poverty-related factors, and the SAI score all have strong independent associations with CMD risk. The qualitative findings suggest that non-notified status plays a central role in creating psychological distress—by creating and exacerbating deprivations that serve as sources of stress, by placing slum residents in an inherently antagonistic relationship with the government through the criminalization of basic needs, and by shaping a community identity built on a feeling of social exclusion from the rest of the city

Multidimensional Measurement of Household Water Poverty in a Mumbai Slum: Looking Beyond Water Quality

Objective: A focus on bacterial contamination has limited many studies of water service delivery in slums, with diarrheal illness being the presumed outcome of interest. We conducted a mixed methods study in a slum of 12,000 people in Mumbai, India to measure deficiencies in a broader array of water service delivery indicators and their adverse life impacts on the slum’s residents. Methods: Six focus group discussions and 40 individual qualitative interviews were conducted using purposeful sampling. Quantitative data on water indicators—quantity, access, price, reliability, and equity—were collected via a structured survey of 521 households selected using population-based random sampling. Results: In addition to negatively affecting health, the qualitative findings reveal that water service delivery failures have a constellation of other adverse life impacts—on household economy, employment, education, quality of life, social cohesion, and people’s sense of political inclusion. In a multivariate logistic regression analysis, price of water is the factor most strongly associated with use of inadequate water quantity (≤20 liters per capita per day). Water service delivery failures and their adverse impacts vary based on whether households fetch water or have informal water vendors deliver it to their homes. Conclusions: Deficiencies in water service delivery are associated with many non-health-related adverse impacts on slum households. Failure to evaluate non-health outcomes may underestimate the deprivation resulting from inadequate water service delivery. Based on these findings, we outline a multidimensional definition of household “water poverty” that encourages policymakers and researchers to look beyond evaluation of water quality and health. Use of multidimensional water metrics by governments, slum communities, and researchers may help to ensure that water supplies are designed to advance a broad array of health, economic, and social outcomes for the urban poor

Predictors of inadequate water quantity in a multivariate logistic regression model.

<p>^aINR = Indian rupees</p><p>Predictors of inadequate water quantity in a multivariate logistic regression model.</p

The relationship between price of water and quantity of water consumed.

<p>The relationship between price of water and quantity of water consumed.</p

The distribution of water-related indicators in Kaula Bandar.

<p>(A) Household income per capita in the last month in Indian rupees (Gini coefficient = 0.31); (B) quantity of water consumed in liters per capita per day (Gini coefficient = 0.42); (C) price of water in Indian rupees per 1,000 liters of water (Gini coefficient = 0.41); and (D) water spending as a percentage of household income in the last month (Gini coefficient = 0.47).</p

Adverse life impacts of deficiencies in water service delivery in Kaula Bandar based on analysis of the qualitative data.

<p>Adverse life impacts of deficiencies in water service delivery in Kaula Bandar based on analysis of the qualitative data.</p

The relationships among water service delivery failures and adverse life impacts based on the qualitative findings.

<p>This diagram may also serve as a multidimensional framework for defining and evaluating household-level “water poverty” in slums.</p

The psychological toll of slum living—an assessment of mental health, disability, and slum-related adversities in Mumbai, India

Background: Few studies have examined mental health in developing country slums. We ascertain the prevalence of common mental disorders in adults and identify slum-related stressors associated with risk of common mental disorders in a slum of 14 000 people in Mumbai, India. Methods: Participants were selected with random sampling. We completed 521 interviews during February, 2012; the non-response rate was 9%. We administered the General Health Questionnaire-12 (GHQ) to screen for common mental disorders, the WHO Disability Assesment Schedule 2.0 to screen for disability, and a slum stressor questionnaire. Logistic regression was used to identify predictors of having a common mental disorder. Linear regression was used to assess the contribution of GHQ score and physical impairments to disability (ie, WHO Disability Assesment Schedule 2.0). Findings: 121 (23%) of 521 individuals had a GHQ score of 5 or more (ie, high risk of common mental disorder). Factors associated with a GHQ score of 5 or more in the multivariate logistic regression model (R2=0·32, n=502) include age older than 45 years, having a loan, food insecurity, sleeping sitting up or outside because of overcrowding, being affected by rats, and paying a high price for water. 5–9 years of education and an income greater than 3000 rupees per month are protective against common mental disorders. In linear regression analyses, 22% of variation in the WHO Disability Assesment Schedule score is explained by GHQ score; only 19% is explained by physical impairments. Interpretation: This slum's burden of common mental disorders exceeds that for all other population-based Indian studies. Psychological distress contributes greatly to the disability burden of the slum. Interventions to address slum-related stressors and poverty might help to alleviate the high burden of mental illness and disability in slums. Funding: The Fogarty International Clinical Research Fellows Program at Vanderbilt (R24 TW007988), Harvard T32 post-doctoral clinical research fellowship (NIAID AI 007433), the Eunice Kennedy Shriver National Institute for Child Health and Human Development (5R24HD047879), the National Institutes of Health (5T32HD007163), the Rockefeller Foundation, and the Weatherhead Center for International Affairs at Harvard University