Dynamic reflections of multidimensional health poverty in Pakistan
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ABSTRACT:
SUBMITTER: Mustafa K
PROVIDER: S-EPMC8570466 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesTo identify the chronic health conditions associated with multidimensional poverty.DesignCross-sectional study of the nationally representative Survey of Disability, Ageing and Carers, conducted by the Australian Bureau of Statistics.SettingAustralian population in 2003.Participants35 704 individuals randomly selected from the Australian population by the Australian Bureau of Statistics.Outcome measuresMultidimensional poverty status, costs of disability, short form 6D health utility score, income, education attainment.ResultsAmong those who were multidimensionally poor, 75% had a chronic health condition and the most common health conditions were back problems (11% of those in multidimensional poverty had back problems) and arthritis (11%). The conditions with the highest proportion of individuals in multidimensional poverty were depression/mood affecting disorders (26% in multidimensional poverty) and mental and behavioural disorders (22%). Those with depression/mood affecting disorders were nearly seven times (OR 6.60, 95% CI 5.09 to 8.55, p<0.0001) more likely to be multidimensionally poor than those with no health condition. Equivalising for the additional costs of disability increased the proportion of individuals in multidimensional poverty for all conditions and the conditions with the highest proportion of individuals in multidimensional poverty changed.ConclusionsOwing to the influence of certain health conditions on poverty status, health interventions have the potential to improve national living standards and poverty rates in a similar way that 'traditional' policy responses such as changes to welfare payment currently do. Using a multidimensional poverty measure reveals the health conditions that should be the focus of such efforts.
| S-EPMC3845066 | biostudies-literature
Project description:BackgroundThough the concept of multidimensional poverty has been acknowledged cutting across the disciplines (among economists, public health professionals, development thinkers, social scientists, policy makers and international organizations) and included in the development agenda, its measurement and application are still limited. OBJECTIVES AND METHODOLOGY: Using unit data from the National Family and Health Survey 3, India, this paper measures poverty in multidimensional space and examine the linkages of multidimensional poverty with child survival. The multidimensional poverty is measured in the dimension of knowledge, health and wealth and the child survival is measured with respect to infant mortality and under-five mortality. Descriptive statistics, principal component analyses and the life table methods are used in the analyses.ResultsThe estimates of multidimensional poverty are robust and the inter-state differentials are large. While infant mortality rate and under-five mortality rate are disproportionately higher among the abject poor compared to the non-poor, there are no significant differences in child survival among educationally, economically and health poor at the national level. State pattern in child survival among the education, economical and health poor are mixed.ConclusionUse of multidimensional poverty measures help to identify abject poor who are unlikely to come out of poverty trap. The child survival is significantly lower among abject poor compared to moderate poor and non-poor. We urge to popularize the concept of multiple deprivations in research and program so as to reduce poverty and inequality in the population.
| S-EPMC3203176 | biostudies-literature
Project description:BackgroundMultidimensional poverty is associated with dementia, but no evidence is available for countries in conflict.MethodsA cross-sectional study was conducted in two provinces of Afghanistan between February 15th 2022 and April 20th 2022 among adults age 50 and older. Multidimensional poverty included six dimensions of well-being and 16 indicators of deprivation. The Rowland Universal Dementia Assessment Scale measured dementia. Poverty between adults with and without dementia was examined, adjusting for sex. Associations between dementia and poverty were investigated using multivariate regression model.FindingsOf the 478 adults included, 89 (52.7%) had mild, and 25 (14.8%) had moderate to severe dementia. More women than men had mild (52.7% vs 33.3%) and moderate-to-severe dementia (14.8% vs 5.8%). Approximately 33.9% adults with mild and 51.2% adults with moderate-to-severe dementia were found to be deprived in four or more dimensions compared to 21.8% without dementia. The difference in four dimensions of multidimensional poverty between adults with mild and moderate-to-severe dementia and adults without dementia was respectively 59.5% and 152.88%. Education, employment, health, and living conditions were the main contributors to the adjusted poverty head count ratio. Multidimensional poverty in four or five dimensions was strongly associated with dementia among older adults particularly over 70 years old (odds ratio [OR], 17.38; 95% CI, 2.22-135.63), with greater odds for older women overall (OR, 2.69; 95% CI, 1.76-4.11).InterpretationOur findings suggest that early improvement in social determinants of health through targeted structural policies may lower dementia risk later in life. Specifically, better access to free, quality education, healthcare, and basic living standard together with employment opportunities could reduce risk of dementia.FundingThe present study was funded by a grant from the Alzheimer Association (AARG-NTF-21-851241).
| S-EPMC10030911 | biostudies-literature
Project description:Young children in low- and middle-income countries (LMICs) are vulnerable to adverse effects of household microenvironments. The UN Sustainable Development Goals (SDGs)-specifically SDG 3 through 7-urge for a comprehensive multi-sector approach to achieve the 2030 goals. This study addresses gaps in understanding the health effects of household microenvironments in resource-poor settings. It studies associations of household microenvironment variables with episodes of acute respiratory infection (ARI) and diarrhoea as well as with stunting among under-fives using logistic regression. Comprehensive data from a nationally representative, cross-sectional demographic and health survey (DHS) in Uganda were analysed. We constructed and applied the multidimensional energy poverty index (MEPI) and the three-dimensional women empowerment index in multi-variate regressions. The multidimensional energy poverty was associated with higher risk of ARI (OR = 1.32, 95% CI 1.10 to 1.58). Social independence of women was associated with lower risk of ARI (OR= 0.91, 95% CI 0.84 to 0.98), diarrhoea (OR = 0.93, 95% CI 0.88 to 0.99), and stunting (OR = 0.83, 95% CI 0.75 to 0.92). Women's attitude against domestic violence was also significantly associated with episodes of ARI (OR = 0.88, 95% CI 0.82 to 0.93) and diarrhoea (OR = 0.89, 95% CI 0.84 to 0.93) in children. Access to sanitation facilities was associated with lower risk of ARI (OR = 0.55, 95% CI 0.45 to 0.68), diarrhoea (OR = 0.83, 95% CI 0.71 to 0.96), and stunting (OR = 0.64, 95% CI 0.49 to 0.86). Investments targeting synergies in integrated energy and water, sanitation and hygiene, and women empowerment programmes are likely to contribute to the reduction of the burden from early childhood illnesses. Research and development actions in LMICs should address and include multi-sector synergies.
| S-EPMC9180902 | biostudies-literature
Project description:Poverty is a big threat to prosperity in developing countries like Pakistan. Alleviating poverty needs concerted efforts including how to measure and analyze poverty. Therefore, this paper employs synthetic panel technique and uses repeated cross-sections household survey dataset (Household Integrated and Economic Survey (HIES)) of Pakistan for 2010-11 and 2015-16, to derive poverty bounds for Pakistan. The findings of the paper suggest that 17% of population still remains in poverty in 2015-16 as they were in 2010-11. They don't move in or out of poverty. In the same periods 19% population affected by poverty. The 2.5% poor's of 2010-11 moves out of poverty in 2015-16. This constitutes the first attempt to provide an insight into poverty dynamics in Pakistan using the available survey data.
| S-EPMC10035873 | biostudies-literature
Project description:The Multidimensional Poverty Index is used increasingly to measure poverty in developing countries. The index is constructed using selected indicators that cover health, education, and living standards dimensions. The accuracy of this tool, however, depends on how each indicator is measured. This study explores the effect of accounting for water quality in multidimensional poverty measurement. Access to drinking water is traditionally measured by water source types. The study uses a more comprehensive measure, access to safely managed drinking water services, which are free from E. coli contamination, available when needed and accessible on premises in line with Sustainable Development Goal target 6.1. The study finds that the new measure increases national multidimensional headcount poverty by 5-13 percentage points, which would mean that 5-13 million more people are multidimensionally poor. It also increases the poverty level in urban areas to a greater extent than in rural areas. The finding is robust to changes in water contamination risk levels and Multidimensional Poverty Index aggregation approaches and weighting structures.
| S-EPMC7737968 | biostudies-literature
Project description:High resolution poverty mapping supports evidence-based policy and research, yet about half of all countries lack the survey data needed to generate useful poverty maps. To overcome this challenge, new non-traditional data sources and deep learning techniques are increasingly used to create small-area estimates of poverty in low- and middle-income countries (LMICs). Convolutional Neural Networks (CNN) trained on satellite imagery are emerging as one of the most popular and effective approaches. However, the spatial resolution of poverty estimates has remained relatively coarse, particularly in rural areas. To address this problem, we use a transfer learning approach to train three CNN models and use them in an ensemble to predict chronic poverty at 1 km2 scale in rural Sindh, Pakistan. The models are trained with spatially noisy georeferenced household survey containing poverty scores for 1.67 million anonymized households in Sindh Province and publicly available inputs, including daytime and nighttime satellite imagery and accessibility data. Results from both hold-out and k-fold validation exercises show that the ensemble provides the most reliable spatial predictions in both arid and non-arid regions, outperforming previous studies in key accuracy metrics. A third validation exercise, which involved ground-truthing of predictions from the ensemble model with original survey data of 7000 households further confirms the relative accuracy of the ensemble model predictions. This inexpensive and scalable approach could be used to improve poverty targeting in Pakistan and other low- and middle-income countries.
| S-EPMC10072451 | biostudies-literature
Project description:One in three Cambodian children under 5 years of age are stunted. This study adopted a multidimensional approach to understand the influence of a series of contextual factors on the nutritional status of Cambodian children. In this study, we use a multidimensional poverty framework to identify a combination of interventions likely to achieve reductions in child stunting, in Cambodia. We used the UNICEF Multiple Overlapping Deprivation Analysis tool and data from the Cambodia Demographic and Health Survey 2014 to assess children's multidimensional poverty across nutrition, health, early childhood development, water, sanitation, and housing dimensions. We found that 80% of children under age five were deprived in at least two poverty dimensions. Multivariate logistic regression analysis revealed that reducing the likelihood of being deprived in three or more poverty dimensions was associated with a significant reduction of the probability of being stunted, after controlling for potential confounders. The combined probability of nondeprivation in nutrition, sanitation, and health dimensions had the largest effect on reducing the probability of stunting. The results of this study highlight the multiple, intersecting needs of children and their associated explanatory factors. Targeted and integrated cross-sectoral policies that reinforce comprehensive early childhood interventions are needed for improving nutritional status as part of a wider set of child poverty reduction measures in Cambodia.
| S-EPMC7591312 | biostudies-literature
Project description:IntroductionNarrow, unidimensional measures of poverty often fail to measure true poverty and inadequately capture its drivers. Multidimensional indices of poverty more accurately capture the diversity of poverty. There is little research regarding the association between multidimensional poverty and depression.MethodsA cross-sectional survey was administered in five sub-locations in Migori County, Kenya. A total of 4,765 heads of household were surveyed. Multidimensional poverty indices were used to determine the association of poverty with depression using the Patient Health Questionnaire (PHQ-8) depression screening tool.ResultsAcross the geographic areas surveyed, the overall prevalence of household poverty (deprivation headcount) was 19.4%, ranging from a low of 13.6% in Central Kamagambo to a high of 24.6% in North Kamagambo. Overall multidimensional poverty index varied from 0.053 in Central Kamagambo to 0.098 in North Kamagambo. Of the 3,939 participants with depression data available, 481 (12.2%) met the criteria for depression based on a PHQ-8 depression score ≥10. Poverty showed a dose-response association with depression.ConclusionsMultidimensional poverty indices can be used to accurately capture poverty in rural Kenya and to characterize differences in poverty across areas. There is a clear association between multidimensional poverty and depressive symptoms, including a dose effect with increasing poverty intensity. This supports the importance of multifaceted poverty policies and interventions to improve wellbeing and reduce depression.
| S-EPMC8594838 | biostudies-literature
Project description:According to the global Multidimensional Poverty Index (MPI), an internationally comparable measure, poverty in developing countries has fallen substantially over the last 15 years. The COVID-19 pandemic and associated economic contraction are negatively impacting multiple dimensions of poverty and jeopardising this progress. This paper uses recent assessments of food insecurity and school closures made by UN agencies to inform microsimulations of potential short-term impacts of the pandemic under alternative scenarios. These simulations use the nationally representative datasets underlying the 2020 update of the global MPI. Because these datasets were collected in various years before the pandemic, we develop models to translate the simulated impacts to 2020. Our approach accounts for the country-specific joint distribution of deprivations in the simulations, recent poverty reduction trends, and resulting differences in the responsiveness of the global MPI to the scenarios. Aggregating results across 70 countries that account for 89% of the global poor according to the 2020 global MPI, we find that the potential setback to multidimensional poverty reduction is between 3.6 and 9.9 years under the alternative scenarios. We argue that the extent to which such disruptions result in persistent increases of poverty and deprivations may be attenuated by appropriate policy responses.
| S-EPMC8500841 | biostudies-literature