Changes in Household Size and Depression: A Temporal Analysis

This study examines the relationship between changes in household size and depression through a temporal analysis using the Korean Welfare Panel Study. The number of household members at both t-1 and t year was measured and a generalized estimating equation was used. Households that increased in size after a year showed a lower prevalence of depression than the corresponding reference groups. On the contrary, when individuals from multi-person households inhabited single-person households after a year, their probability of experiencing depression increased by more than 70% in comparison to those who remained in single-person households throughout.

Prevalence of SARS-Cov-2 antibodies and living conditions: the French national random population-based EPICOV cohort

Background We aimed to estimate the seroprevalence of SARS-CoV-2 infection in France and to identify the populations most exposed during the first epidemic wave. Methods Random selection of individuals aged 15 years or over, from the national tax register (96% coverage). Socio-economic data, migration history, and living conditions were collected via self-computer-assisted-web or computer-assisted-telephone interviews. Home self-sampling was performed for a random subsample, to detect IgG antibodies against spike protein (Euroimmun), and neutralizing antibodies with in-house assays, in dried blood spots (DBS). Results The questionnaire was completed by 134,391 participants from May 2nd to June 2st, 2020, including 17,441 eligible for DBS 12,114 of whom were tested. ELISA-S seroprevalence was 4.5% [95% CI 3.9–5.0] overall, reaching up to 10% in the two most affected areas. High-density residences, larger household size, having reported a suspected COVID-19 case in the household, working in healthcare, being of intermediate age and non-daily tobacco smoking were independently associated with seropositivity, whereas living with children or adolescents did not remain associated after adjustment for household size. Adjustment for both residential density and household size accounted for much of the higher seroprevalence in immigrants born outside Europe, twice that in French natives in univariate analysis. Conclusion The EPICOV cohort is one of the largest national representative population-based seroprevalence surveys for COVID-19. It shows the major role of contextual living conditions in the initial spread of COVID-19 in France, during which the availability of masks and virological tests was limited.

Large household reduces dementia mortality.

Background Large households/families create more positive psychological well-being which may offer a life course protection against dementia development and deliver more comprehensive healthcare to dementia patients. Methods Dementia specific mortality rates of the 183 member states of World Health Organization were calculated and matched with the respective country data on household size, Gross Domestic Product (GDP), urban and ageing. Scatter plots were produced to explore and visualize the correlations between household size and dementia mortality rates. Pearsons and nonparametric correlations were used to evaluate the strength and direction of the associations between household size and all other variables. Partial correlation of Pearsons moment-product approach was used to identify that household size protects against dementia regardless of the competing effects from ageing, GDP and urbanization. Multiple regression identified large household was a significant predictor of dementia mortality. Results Household size was in a negative and moderately strong correlation (r = -0.6034, p < 0.001) with dementia mortality. This relationship was confirmed in both Pearson r (r= - 0.524, p<0.001) and nonparametric (rho = -0.579, p < 0.001) analyses. Regardless of the contribution of ageing, SES and urban lifestyle to dementia mortality, large household was an independent predictor of dementia mortality (r = −0.331, p <0.001) in partial correlation analysis. Stepwise multiple regression analysis selected large household as the variable having the greatest contribution to dementia mortality with R2 = 0.263 while ageing was placed second increasing R2 to 0.259. GDP and urbanization were removed as having no statistically significant influence on dementia mortality. Conclusions Independent of ageing, urbanization and GDP, large household protects against dementia mortality. As part of dementia prevention, healthcare practitioners should encourage people to increase their positive interactions with persons from their neighbourhood or other fields where large household/family size is hard to achieve.

How to manage the Value of Drinking water by using Extension on the Contingent Valuation Method

This paper examines an explanatory policy in the management of drinking water in Tunisia. Indeed, the price variable, while is considered, in general way, as a confident indicator in the regulation of drinking water consumption, it seems to be insufficient since the management of drinking water is influenced by other set of variables such as: the revenue level of households, the consumption of bottled water, the alternative sources, the household size and, above all, the quality variable, which presents an enormous variability inter- and intra-regions. Through our study, we have shown, that the variation in willingness to pay (WTP) is essentially due to psychological and subjective consideration (the degree of importance of these variables in the scale of preferences of consumers).

Analysis on Resource Use Efficiency of Smallholder's Rice Production Farmers in Federal Capital Territory of Nigeria: Probit Model Approach

This study examined the resource-use efficiency of smallholder rice production farmers in Federal Capital Territory, Nigeria. The problem of resource use among small-scale rice production farmers is preponderance in the country. Hence, the study investigated the drivers of the problem in the Federal Capital Territory of Nigeria. Specifically, the study was designed to determine the factors influencing the resource-use efficiency of the respondents. A multi-stage sampling technique was used to select a total sample size of one hundred and seventy-five (175) rice farmers in Federal Capital Territory, Nigeria. Seven estimators such as age, household size, farming experience, educational level, extension services, access to credit, and off-farm income in the Probit model were found statistically significant. Results show that the probability of resource use efficiency of inputs used by the farmers increases with age, farm size, household size, educational level, extension services, experiences in farming, access to credits, but decreases where they have off-farm income. Mc Fadden Pseudo-R2 gives 0.6772, and the Probit model explains a significant proportion of the variations in smallholder farmers' resource use. The study concluded that the socio-economic variables in the model play an important role in influencing resource use efficiency. The study recommends that government agencies and donors should provide simplified, accessible and obtainable credits and grants to existing and prospective rice farmers in order to sustain the current giant stride in rice production in the country.

Decomposition Analysis of Poverty among Rural Farming Household in Oyo State, Nigeria

The incidence of poverty in Nigeria is worrisome, and it has constituted a national menace. The occurrence of poverty incidence had been found to be more pronounced among Rural farming households in the country. However, paucity of data exists in terms of decomposition of household poverty into relevant subgroups using their socio-economic characteristics. Therefore, this paper assessed the decomposition analysis of poverty among rural farming Households in Oyo State, Nigeria using the data collected through a well-structured interview schedule from 170 respondents who were selected through a multi-stage sampling procedure. Data collected were described using frequency counts and percentage while poverty Indices was analyzed using Foster-Greer Thorbecke model and Decomposition analysis. The findings revealed that higher proportion (73.5%) of the respondents were above 40 years, 65.3% were male, 78.2% were married and 27.6% had secondary education, while 62.4% had household size of between 5 and 9 persons. Majority (79.4%) had farm size of more than 1.5 hectares and 62.9% had no access to remittance. Poverty incidence (P0) was 40.59%, Poverty depth/gap (P1) was 16.11% and Poverty severity (P2) was 0.09%, among the respondents using income-poverty line measure. Decomposition analysis showed that Poverty was high among households that were headed by male, young with low literacy level, and large household size. The severity of poverty was higher among households headed by labour of other farms. Effective poverty reduction strategies should therefore focus on education, livelihood diversification and control of household size.

Household Characteristics and Food Security in Low-Income Urban Areas of South Africa

In South Africa, food security at the household level is an issue still needing empirical attention. This study conducts an empirical analysis on the effect of household size, income of household head, gender of household head, location of the household, and distance from food markets on food security in the Gauteng Province of South Africa. A binary logistic regression model is estimated to analyze determinants of food security from 900 households randomly selected from three townships of Gauteng Province, South Africa. Data was collected using a survey questionnaire, with food security measured by the Household Food Insecurity Access Scale. This study shows that the income of the household head promotes food security while household size and location of the household contribute to food insecurity. Our results also show that the gender of the household head is essential in maintaining sustainable levels of food security. Furthermore, it was found that the distance from food markets neither makes households food secure nor insecure. This study validates that matriarchal households exhibited comparatively greater food security than male-headed households. The findings of this study are essential in formulating policy on food security in low-income areas.

Investigations of socioeconomic factors associated with follow-up compliance with malaria treatment in Haiti

Objective. To identify factors affecting compliance with follow-up during treatment in confirmed malaria patients at two health centers in Haiti. Methods. A prospective observational study of malaria patients undergoing treatment over a six-week period. Patients’ return visits (follow-up visits) to the health centers for consultation in accordance with the physicians’ requests were recorded and used to determine compliance. Socioeconomic data were obtained from patient enrollment questionnaires and through post-treatment interviews. The management practices and procedures at the health centers to retain patients were also reviewed. Descriptive statistics and Spearman’s rank correlation were used to identify significant factors, which were used as variables in a logistic regression model. Results. Sixty-eight percent of the malaria patients completed follow-up, with higher compliance being recorded in the larger, more established health center of Leogane (67%) than Cite Soleil (33%). The patient socioeconomic profiles differed between the two health center locations by level of education, religious diversity, household size, and percentage of married individuals. Crude logistic regression analyses identified health center location (OR = 0.179 [95% CI 0.064, 0.504]) and household size (OR = 1.374 [95% CI 1.056, 1.787]) to be associated with compliance. The adjusted model only identified health center location (OR = 0.226 [95% CI 0.056, 0.918]) as significantly associated with compliance. Conclusion. Although patients’ household size may be important according to the crude logistic regression analysis, in the adjusted analysis the site location of the health center where patients receive treatment was identified as the only important factor associated with follow-up compliance in malaria patients during treatment in Haiti. This information might be helpful to improve treatment outcomes and contribute to the monitoring of antimalarial resistance in Haiti.

Uneven economic burden of non-communicable diseases among Indian households: A comparative analysis

Background Non-communicable diseases (NCDs) are the leading global cause of death and disproportionately concentrate among those living in low-income and middle-income countries. However, its economic impact on households remains less well known in the Indian context. This study aims to assess the economic impact of NCDs in terms of out-of-pocket expenditure (OOPE) and its catastrophic impact on NCDs affected households in India. Materials and methods Data were collected from the 75th round of the National Sample Survey Office, Government of India, conducted in the year 2017–18. This is the latest round of data available on health, which constitutes a sample of 113,823 households. The collection of data is based on a stratified multi-stage sampling method. Generalised Linear Regression model was employed to identify the socio-economic covariates associated with the catastrophic health expenditure (CHE) on hospitalisation. Results The result shows a higher burden of OOPE on NCDs affected households. The mean expenditure by NCDs households in public hospitals is INR 13,170 which is more than twice as compared to the non-NCDs households INR 6,245. Particularly, the proportion of total medical expenditure incurred on medicines (0.39) and diagnostics (0.15) is troublesome for households with NCDs, treated in public hospitals. Moreover, results from the generalised linear regression model confirm the significant relationship between CHE with residence, caste, religion, household size, and economic status of households. The intensity of CHE is more for the households who are poor, drinking unsafe water, using firewood as cooking fuel, and household size of 1–5 members. Conclusion Therefore, an urgent need for a prevention strategy should be made by the government to protect households from the economic burden of NCDs. Specifically, to reduce the burden of CHE associated with NCDs, a customised disease-specific health insurance package should be introduced by the government of India in both public and private facilities.

Acute respiratory infection symptoms and COVID-19 testing behaviour: results based on South Australian health surveys

Background Effective syndromic surveillance alongside COVID-19 testing behaviours in the population including in higher risk and hard to reach subgroups is vital to detect re-emergence of COVID-19 transmission in the community. The aim of this paper was to identify the prevalence of acute respiratory infection symptoms and coronavirus testing behaviour among South Australians using data from a population based survey. Methods We used cross-sectional data from the 2020 state-wide population level health survey on 6857 respondents aged 18 years and above. Descriptive statistics were used to explore the risk factors and multivariable logistic regression models were used to assess the factors associated with the acute respiratory infection symptoms and coronavirus testing behaviour after adjusting for gender, age, household size, household income, Aboriginal and/or Torres Strait Islander status, SEIFA, Country of birth, number of chronic diseases, wellbeing, psychological distress, and mental health. Results We found that 19.3% of respondents reported having symptoms of acute respiratory infection and the most commonly reported symptoms were a runny nose (11.2%), coughing (9.9%) and sore throat (6.2%). Fever and cough were reported by 0.8% of participants. Of the symptomatic respondents, 32.6% reported seeking health advice from a nurse, doctor or healthcare provider. Around 18% (n = 130) of symptomatic respondents had sought testing and a further 4.3% (n = 31) reported they intended to get tested. The regression results suggest that older age, larger household size, a higher number of chronic disease, mental health condition, poor wellbeing, and psychological distress were associated with higher odds of ARI symptoms. Higher household income was associated with lower odds of being tested or intending to be tested for coronavirus after adjusting for other explanatory variables. Conclusions There were relatively high rates of self-reported acute respiratory infection during a period of very low COVID-19 prevalence and low rate of coronavirus testing among symptomatic respondents. Ongoing monitoring of testing uptake, including in higher-risk groups, and possible interventions to improve testing uptake is key to early detection of disease.