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The refreshment training was deemed necessary to ensure a correct identification of mother-infant pairs with child acute malnutrition given that untrained mothers or caretakers are unlikely to properly detect and self-report acute malnutrition in their children [ 22 ]. CHWs were also assigned to identify households in which adults with self-reported diabetes or hypertension lived. During the data collection phase, the data collection team was introduced to each household in which a person of interest was identified.

The purpose of the study was explained to the head of the household and permission to carry out the interview was asked. Mothers were selected if being a mother of a child presenting with severe acute malnutrition. If the targeted person was absent, the data collectors could proceed to the next targeted household on the list and come back the following day until the person was found. A written and signed consent to participate in the study was sought before the interview started in the same household, an informal caregiver was identified and asked to consent to the study.

For every household in which a patient was recruited, a community member in the nearest neighbourhood was randomly selected by spinning a pen and following the direction in which it pointed. At this stage, an adult with the closest age and ideally but not always with the same sex as the neighbour patient was approached and asked to participate in the study, after providing a written consent.

If in the selected neighbouring household there was no consenting adult, interviewers could move to a next household chosen through the same random process until they found a consenting adult.

Living Standards Analytics

People who refused to provide an informed consent or were severely ill, physically or mentally unable to withstand an interview were excluded. A simple identification form was used by the CHWs during the phase of identifying households in which patients with known morbidity lived, within the entire health area. This helped us generate a sampling frame with information on age, sex, village of residence and type of morbidity. A structured and pre-tested paper-based questionnaire designed to capture socio-demographic and health characteristics data was administered to a convenience sample all identified individuals living in villages nearest to health centres, their informal caregivers and randomly selected neighbours by trained research assistants who were all nurses.

To assess the functional and social disability related to health condition, we used the WHO Disability Assessment Schedule 2. It also is able to detect small changes over time [ 26 ]. A bilingual panel comprised of the principal investigator, key health professionals working in the health areas of study and community health workers leaders reviewed the translated version in order to address its potential cross-cultural inadequacies in terms of incomprehensibility or lack of clarity. Drawing on both frameworks, we examined social including social cohesion , demographic and economic status as possible explanatory parameters.

Socio-demographic characteristics included among other variables age measured on a continuous scale in completed years , gender male or female , education continuous variable measured as complete years of schooling or household size number of people sleeping in the same house and eating from the same cooking pot or health zone of residence. Some categorical variables needed to be recoded to obtain sufficient numbers in strata for ease of the comparisons.

This was, for example, the case for marital status, tribe or occupation. Social cohesion and networking were approximated by regularly attending church activities and being member of a local socio-economic or savings network. To define the socio-economic status, we ran a Multiple Correspondence Analysis on household assets and housing characteristics to create wealth indices [ 30 ] based on ownership of a television, a radio, a computer, a manufactured bed, small animals, cattle, land, a bicycle, a motorcycle and on housing characteristics including pavement and permanent, semi-permanent or temporary structure.

We then derived five socio-economic quintiles from wealth indices. We ended up with three socio-economic classes least poor, middle poor, poorest. The main dependant variable under study was functional and social disability defined as a three-level ordinal variable resulting from a Principal Component Analysis PCA with clustering performed on the six WHODAS domains scores see explanation here below. Data were entered in EpiInfo7 and exported to Stata 15 for exploratory analyses.

We first added up the recoded item scores within each domain. This algorithm was implemented in Stata The distribution of continuous variables was assessed graphically and statistically using the Shapiro-Wilk test. Extreme and implausible outlying values were checked for and set to missing. Qualitative variables were summarized in frequencies and proportions while continuous variables were described in terms of mean with standard deviation SD or median with interquartile range IQR depending on the shape of the distribution.

Development through the Lens of Household Survey Data

To define medico-psychosocial clusters, we first ran a principal component analysis on seven summary scores of the WHODAS domains. Three ordered clusters were created and termed cluster 1, cluster 2 and cluster 3. We used chi-squared and Kruskal-Wallis tests to compare the characteristics of the study participants by enrolment status or clustering.

To establish the factors associated with functional and social disability clustering, we did the inter-cluster comparison using a mixed-effects ordinal proportional odds logit regression model with cluster as a fixed effect and health area as a random effect. This strategy enabled us to take into account the inherent non-independence of socio-demographic factors at health area level, thus ensuring more accurate standards errors for the measures of association between within-health area characteristics and disability clusters. The proportional odds model was favoured over the other ordinal models since the former is most suited to studies under which the outcome is obtained from categorizing a certain underlying continuum.

In addition to its greater statistical power to detect differences in a relatively smaller sample [ 33 ], this model often generates much simpler interpretable coefficients, even when the order of the outcome is reversed in which case only the sign of the coefficient is changed [ 34 ]. Variables were hierarchically selected into the multivariable model in three stages, based either on a p -value equal to or below 0.

Socio-demographic factors were selected first. We then included household attributes before adding proximate factors reflecting physical health impairment. A VIF greater than 4 was suspected of collinearity. We used R 3. Respondents provided singed informed consent for participation in the study, either by written signature or by fingerprints, depending on literacy. Of the participants approached by data collectors in the field, provided valid information on functional and social disability.

The majority of the participants were female The mean SD age was Participants lived in bigger size households [median IQR : 6. Farming or petty trading were the main occupation for over half of the heads of households While The median IQR duration of schooling was 6 3—10 years. The hierarchical clustering of the principal components of seven WHODAS domains scores resulted in three ordered categories of functional, cognitive and social disabilities termed cluster 1, cluster 2 and cluster 3 Fig. Cluster 2 had Respondents in cluster 3 9.

The trend was consistent among all the WHODAS domains; the higher the cluster order, the more worrying the health status of the individuals. The age of the respondents and the proportion of women increased with cluster ordering. The majority of respondents in cluster 3 were female Only 1.

Data Collection-Surveys and Observation

Diabetes was more common in cluster 3 Clustering was independent of acute malnutrition status of the child, tribe, religion and church attendance, but dependent on occupation. The crude and adjusted odds ratios of health status clustering based on functional, cognitive and social disability are presented in Table 3. The factors associated with clustering were being a patient compared to a neighbour AOR: 4. This community-based study proposes a new way of stratifying population health in function of dependency or disability and social context rather than in function of specific diseases.

Similar approaches have been quite frequently studied in high-income countries but scantily tested in LMICs.

The implied hypothesis is that this way of stratifying population health may be a powerful lever for change in healthcare prioritization processes. The pyramidal distribution of the study population in three clusters with 9. The observed differences in the proportion of individuals in high healthcare needs clusters between our findings and those from high-income settings can partly be due to the heterogeneity in study design and outcome measurements; therefore, the comparison with our study can only be indirect.


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Both studies based their outcome measurements on health service utilization. Moreover, the former study used hospital data that may represent people with lower access to healthcare services or with tacit non-disease based healthcare needs, such as social support of social participation. Additionally, a higher life expectancy and aging of the population in high-income countries could explain the higher proportion of individuals with more healthcare needs in these studies compared to our study.

In our study sample, the participants in cluster 3 or 9. Furthermore, by changing the prioritization process, not all diseased people need the same level of support. For example, We also found that individuals in this high dependency cluster had a higher likelihood of presenting with both acute and chronic morbidities.

They were sustaining complex medico-psychosocial problems that would require targeted healthcare interventions, such as systematic home visits and care, multidisciplinary case discussion and management, involving psychologist and social assistants. Individuals in the middle disability cluster may benefit more from health coaching strategies aiming to empower people to self-manage their health conditions, in addition to primary prevention of acute and chronic conditions.

These strategies have proven useful and cost-effective in the management of chronic conditions and in averting or delaying disability [ 38 , 39 , 40 ]. This may be achieved through development of Kaiser-like integrated healthcare models and health promotion programmes enabling clients to take charge of their own health to lead an acceptable and good quality life [ 41 , 42 , 43 , 44 ].

Our study also identified socio-economic risk factors of cognitive, functional and social dependency. Indeed, the odds of being in higher disability clusters were significantly higher for individuals with poor socio-economic background and empowerment, such as being a woman, elderly, rural resident and with acute or chronic morbidity. We observed that vulnerability factors such as lower socio-economic status, older age, being a female or rural resident were significantly associated with higher odds of being in higher disability clusters than cluster 1. These findings are substantiated by results from studies from both high- and LMICs [ 45 , 46 , 47 , 48 , 49 ].

However, education had a significant effect on disability in the bi-variable analysis but was no longer significant after adjustment for potential confounders. A multi-country study on disability—measured by WHODAS in adults aged 50 and above—found no association between education and disability in Ghana whereas a protective effect of education was reported in Russia, China, India and South Africa [ 46 ].

Post-hoc analysis in individuals aged 50 and above did not change the pattern of association in our study. This difference may be related to the heterogeneity in socio-economic structure between low-income countries like DR Congo and Ghana and middle- or high-income countries.

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Health status approached through disability dimensions is more common among the poorer. Thus, in low-income countries like DR Congo and Ghana, confounding by socioeconomic background may underestimate the beneficial effects of education on cognitive, functional and social disability because individuals with higher disability scores will tend to be poorer. Though the likelihood of being in higher disability clusters was higher in rural areas in general compared to urban areas, there were clear disparities between health zones within rural areas.

In fact, participants form Walungu health zone were worse off in terms of functional, cognitive and social disability compared to those living in Katana and Miti-Murhesa health zone. This difference is substantiated by the fact that Walungu zone had experienced longer and more direct effects of armed conflicts than Katana and Miti-Murhesa.

It has been shown that the severity and gender dimensions of armed conflicts in Walungu has compromised family relationships and social interaction [ 50 , 51 , 52 ], resulting in long-lasting effects of war including post-traumatic disorders, depression, destruction of the social structure and economy of the region [ 53 ]. Future studies involving mothers of inpatient children with severe acute malnutrition and by including qualitative approaches may clarify such a link.

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This study had some limitations. Neither can our findings be generalisable to individuals severely physically or mentally impaired to the extent that they could not consent to the study or withstand the interview. However, we believe that by having extended the sampling to caregivers and randomly selected individuals in the neighborhood contributed to gaining insights in health status of individuals not presenting with the tracer conditions aforementioned and helped alleviating the effect of this potential bias.

The sampling was also confined to villages close to the health centre in each health area in order to be able to assess how change in the way healthcare services are being provided at the health centre may have impacted on the health status of the population, in the framework of the research for development project on which this study draws. The sample selection was based on the assumption that people in villages far away from the health centre were more likely to seek health services from health centres in neighboring health areas, therefore would have been hard to follow up with linkage to the research for development project on which this study is drawn.

This selection might have induced a selection bias whereby individuals living in remote villages relative to the health centre may have limited access to health services, which in turn may impact on their health outcomes.

http://gelatocottage.sg/includes/2020-09-22/4215.php Sixty three percent of our respondents were female. Both of these initiatives build on the strengths of the National Accounts, as a complete, integrated and coherent picture of the macro economy, and as describing the economic experience and living standards of Australians.