Household Variables Research Articles

Using machine learning algorithms to identify predictors of social vulnerability in the event of a hazard: Istanbul case study

Abstract. To what extent an individual or group will be affected by the damage of a hazard depends not just on their exposure to the event but on their social vulnerability – that is, how well they are able to anticipate, cope with, resist, and recover from the impact of a hazard. Therefore, for mitigating disaster risk effectively and building a disaster-resilient society to natural hazards, it is essential that policy makers develop an understanding of social vulnerability. This study aims to propose an optimal predictive model that allows decision makers to identify households with high social vulnerability by using a number of easily accessible household variables. In order to develop such a model, we rely on a large dataset comprising a household survey (n = 41 093) that was conducted to generate a social vulnerability index (SoVI) in Istanbul, Türkiye. In this study, we assessed the predictive ability of socio-economic, socio-demographic, and housing conditions on the household-level social vulnerability through machine learning models. We used classification and regression tree (CART), random forest (RF), support vector machine (SVM), naïve Bayes (NB), artificial neural network (ANN), k-nearest neighbours (KNNs), and logistic regression to classify households with respect to their social vulnerability level, which was used as the outcome of these models. Due to the disparity of class size outcome variables, subsampling strategies were applied for dealing with imbalanced data. Among these models, ANN was found to have the optimal predictive performance for discriminating households with low and high social vulnerability when random-majority under sampling was applied (area under the curve (AUC): 0.813). The results from the ANN method indicated that lack of social security, living in a squatter house, and job insecurity were among the most important predictors of social vulnerability to hazards. Additionally, the level of education, the ratio of elderly persons in the household, owning a property, household size, ratio of income earners, and savings of the household were found to be associated with social vulnerability. An open-access R Shiny web application was developed to visually display the performance of machine learning (ML) methods, important variables for the classification of households with high and low social vulnerability, and the spatial distribution of the variables across Istanbul neighbourhoods. The machine learning methodology and the findings that we present in this paper can guide decision makers in identifying social vulnerability effectively and hence let them prioritise actions towards vulnerable groups in terms of needs prior to an event of a hazard.

청년들의 시간결핍, 누가 얼마나 경험하는가?

The purpose of this study is to analyze the time deficit experienced by the young generation. To this end, the factors affecting the time deficit of unmarried young people aged 19 to 34 were analyzed using the 2019 Time Use Survey data. Sleep, personal care, and leisure time were set as sub-dimensions of time deprivation, and the criteria for time deficit were 75% and 90% of the sample median. Tobit regression analysis was performed because the left truncated problem appeared in the distribution of the dependent variables. As a result of the analysis, the degree of time deprivation of young women was more serious, and when the time burden for other essential activities such as paid work, unpaid work, and study was high, the degree of time deprivation of young adults increased. Life-related cognitive variables such as life satisfaction and lack of time were also variables that had a significant effect. In the case of young people living with their parents, the statistical significance of household head-related variables was also confirmed. These results raise the need for youth policies that can alleviate the time deficit of young people.

Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach.

Effortful control (EC) is a dimension of temperament that encompass individual differences in self-regulation and the control of reactivity. Much research suggests that EC has a strong foundation on the development of executive attention, but increasing evidence also shows a significant contribution of the rearing environment to individual differences in EC. The aim of the current study was to predict the development of EC at 36 months of age from early attentional and environmental measures taken in infancy using a machine learning approach. A sample of 78 infants participated in a longitudinal study running three waves of data collection at 6, 9, and 36 months of age. Attentional tasks were administered at 6 months of age, with two additional measures (i.e., one attentional measure and another self-restraint measure) being collected at 9 months of age. Parents reported household environment variables during wave 1, and their child's EC at 36 months. A machine-learning algorithm was implemented to identify children with low EC scores at 36 months of age. An "attention only" model showed greater predictive sensitivity than the "environmental only" model. However, a model including both attentional and environmental variables was able to classify the groups (Low-EC vs. Average-to-High EC) with 100% accuracy. Sensitivity analyses indicate that socio-economic variables together with attention control processes at 6 months, and self-restraint capacity at 9 months, are the most important predictors of EC. Results suggest a foundational role of executive attention processes in the development of EC in complex interactions with household environments and provide a new tool to identify early markers of socio-emotional regulation development.

Building climate resilience through crop residue utilization: Experiences of Ghanaian smallholder farmers

AbstractA major limiting factor affecting the uptake of conservation agriculture practices in smallholder farming systems in sub‐Saharan Africa is the limited availability of sufficient crop residues for use as surface mulch. This paper assesses the trade‐offs in crop residue utilization among smallholder farmers and its implications for soil management in the face of climate change risks in northern Ghana. The paper triangulated data from 350 household surveys with participatory key informant interviews from seven selected communities in three districts of northern Ghana. The problem confrontation index (PCI) was adopted to identify and rank the challenges associated with farmers’ decision to use crop residues, while a multivariate probit model was used to analyse and predict the factors that influence farmers’ choice of crop residue management practices. Results showed that crop residues were used as cooking fuel in households (21%), livestock feed (21%), left on the farm to decompose as mulch (34%) or burned to clear the land (24%). Key challenges identified included high labour cost (PCI = 404), high labour intensity (PCI = 388), the cost and transport for collection and storage of externally sourced crop residue (PCI = 383) and the low benefit from crop residue to farm output/soil fertility (PCI = 339). Results from the multivariate probit model revealed that household and farm variables, institutional and socio‐psychological factors, and experience of some climate shocks all influence farmers’ choice of crop residue management practices. Crop residue use and management practices adopted were determined by factors including the crops being grown, challenges faced by farmers and the management options available. The study recommends the need for the Government of Ghana to empower farmers through the provision of technical knowledge and machinery for the sustainable utilization of crop residues due to the high labour intensity and cost associated with such practices.

Association of alcohol use and multimorbidity among adults aged 40 years and above in rural South Africa

We assessed the prevalence of reported alcohol use and its association with multimorbidity among adults aged 40 years and above in a rural, transitioning South African setting. Findings could potentially inform alcohol interventions integration in the prevention and treatment of chronic conditions. We analysed data from the first wave of The Health and Ageing in Africa—a longitudinal Study in an INDEPTH community (HAALSI) nested within the Agincourt Health and Demographic Surveillance Systems, conducted between November 2014 and November 2015 (n = 5059). We computed descriptive statistics and performed univariate analysis to determine factors independently associated with multimorbidity. Age, Body Mass Index, education, sex, and household wealth status and variables with a p-value < 0.20 in univariate analysis were included in multivariable Modified Poisson regression models. Any factors with a p-value of < 0.05 in the final models were considered statistically significant. The first wave of HAALSI was completed by 5059 participants aged 40 years and above and included 2714 (53.6%) females. The prevalence of reported ever alcohol use was 44.6% (n = 2253) and of these 51.9% (n = 1171) reported alcohol use in the last 30 days. The prevalence of HIV multimorbidity was 59.6% (3014/5059) and for multimorbidity without HIV 52.5% (2657/5059). Alcohol use was associated with HIV multimorbidity among all participants (RR: 1.05, 95% CI: 1.02–1.08), and separately for males (RR: 1.05, 95% CI: 1.00–1.10) and females (RR: 1.06, 95%CI: 1.02–1.11). Similarly, alcohol use was associated with multimorbidity without HIV among all participants (RR: 1.05, 95% CI: 1.02–1.09), and separately for males (RR: 1.06, 95% CI: 1.00–1.12) and females (RR: 1.06, 95% CI: 1.01–1.11). Reported alcohol use was common and associated with HIV multimorbidity and multimorbidity without HIV among older adults in rural northeast South Africa. There is a need to integrate Screening, Brief Interventions, and Referral for alcohol Treatment in the existing prevention and treatment of multimorbidity in South Africa.

Indoor resting behavior of Aedes aegypti (Diptera: Culicidae) in northeastern Thailand

BackgroundAedes aegypti is a vector of several arboviruses, notably dengue virus (DENV), which causes dengue fever and is often found resting indoors. Culex spp. are largely nuisance mosquitoes but can include species that are vectors of zoonotic pathogens. Vector control is currently the main method to control dengue outbreaks. Indoor residual spraying can be part of an effective vector control strategy but requires an understanding of the resting behavior. Here we focus on the indoor-resting behavior of Ae. aegypti and Culex spp. in northeastern Thailand.MethodsMosquitoes were collected in 240 houses in rural and urban settings from May to August 2019 at two collection times (morning/afternoon), in four room types (bedroom, bathroom, living room and kitchen) in each house and at three wall heights (< 0.75 m, 0.75–1.5 m, > 1.5 m) using a battery-driven aspirator and sticky traps. Household characteristics were ascertained. Mosquitoes were identified as Ae. aegypti, Aedes albopictus and Culex spp. Dengue virus was detected in Ae. aegypti. Association analyses between urban/rural and within-house location (wall height, room), household variables, geckos and mosquito abundance were performed.ResultsA total of 2874 mosquitoes were collected using aspirators and 1830 using sticky traps. Aedes aegypti and Culex spp. accounted for 44.78% and 53.17% of the specimens, respectively. Only 2.05% were Ae. albopictus. Aedes aegypti and Culex spp. rested most abundantly at intermediate and low heights in bedrooms or bathrooms (96.6% and 85.2% for each taxon of the total, respectively). Clothes hanging at intermediate heights were associated with higher mean numbers of Ae. aegypti in rural settings (0.81 [SEM: 0.08] vs. low: 0.61 [0.08] and high: 0.32 [0.09]). Use of larval control was associated with lower numbers of Ae. aegypti (yes: 0.61 [0.08]; no: 0.70 [0.07]). All DENV-positive Ae. aegypti (1.7%, 5 of 422) were collected in the rural areas and included specimens with single, double and even triple serotype infections.ConclusionsKnowledge of the indoor resting behavior of adult mosquitoes and associated environmental factors can guide the choice of the most appropriate and effective vector control method. Our work suggests that vector control using targeted indoor residual spraying and/or potentially spatial repellents focusing on walls at heights lower than 1.5 m in bedrooms and bathrooms could be part of an integrated effective strategy for dengue vector control.Graphical

Modelling seasonal household variation in harvested rainwater availability: a case study in Siaya County, Kenya

Rainwater harvesting reliability, the proportion of days annually when rainwater demand is fully met, is challenging to estimate from cross-sectional household surveys that underpin international monitoring. This study investigated the use of a modelling approach that integrates household surveys with gridded precipitation data to evaluate rainwater harvesting reliability, using two local-scale household surveys in rural Siaya County, Kenya as an illustrative case study. We interviewed 234 households, administering a standard questionnaire that also identified the source of household stored drinking water. Logistic mixed effects models estimated stored rainwater availability from household and climatological variables, with random effects accounting for unobserved heterogeneity. Household rainwater availability was significantly associated with seasonality, storage capacity, and access to alternative improved water sources. Most households (95.1%) that consumed rainwater faced insufficient supply of rainwater available for potable needs throughout the year, with intermittencies during the short rains for most households with alternative improved sources. Although not significant, stored rainwater lasts longer for households whose only improved water source was rainwater (301.8 ± 40.2 days) compared to those having multiple improved sources (144.4 ± 63.7 days). Such modelling analysis could enable rainwater harvesting reliability estimation, and thereby national/international monitoring and targeted follow-up fieldwork to support rainwater harvesting.

Payment for environmental services to reduce deforestation: Do the positive effects last?

Are the forest conservation practices sustained after Payment for Environmental Services (PES) programmes end? Using a sample of 268 (former) PES recipients and non-recipients from the Budongo-Bugoma PES programme in Western Uganda, we employ the before-after-control–intervention (difference-in-difference) approach to estimate the PES programme outcome on their privately owned forests. PES is associated with less deforestation during the operational period, but the link has vanished four years after programme termination. We label this weak permanence, i.e., PES recipients abandon the induced practices soon after the programme ended, but the gains (relatively higher forest cover) during the operational period are maintained. We also find that household and contextual variables are important in explaining PES permanence: low levels of agricultural land and remoteness from agricultural markets weaken PES permanence. Therefore monetary payments alone may be insufficient for lasting forest conservation. Integrating PES with other approaches, such as climate-smart agriculture may result in more permanent reductions in deforestation.

Identification and Model Application of Household Income Poverty and Asset Poverty in China

In order to measure the poverty status of Chinese households, the households were first identified under the income and asset criteria, then the incidence and gap rate of income poverty and asset poverty were analyzed by using the information of grouping variables. Finally, the prediction effect was analyzed by comparing the traditional model and the decision tree model, and the generated decision tree structure was used to reflect the internal poverty-causing variables of households. It provides some reference for displaying the poverty status of Chinese families and predicting the poverty status in the future.

Socio-Demographic Status among Rural Municipalities in Humla District, Nepal: A Comparative Study

This study compares the status of rural municipalities in the Humla district of Nepal regarding various household and demographic variables. This paper aims to compare rural municipalities of the Humla district in terms of population, area, sex ratio, literacy ratio, dependency ratio, fuel used, drinking water, internet user, etc. It is based on secondary data from the central bureau of statistics (CBS). The descriptive and comparative design is used to explore the result. It analyzes the data from the perspective of rural development. Rural development is the process of creating wellbeing and ensuring the quality of life among rural people. This study revealed that there are unequal situations in rural municipalities. In some rural municipalities, there is a situation of demographic loss. There is a lack of well-being among people. Deficiencies and deprivation of socio-economic requirements are common for them. Rural areas in the northern belt have acute problems compared to the south-eastern belt of the district. Three tiers of government along with market and cooperative sectors must focus to launch their policy and program to uplift the miserable situation of rural municipalities in Humla district.

Abstract 04: Neighborhood Social Vulnerability Associated With Increased Hypertension and Cardiovascular Disease in Port-au-Prince: Findings From the Haiti Cardiovascular Disease Cohort Study

Introduction: Cardiovascular disease (CVD) is the leading cause of mortality in low-income countries (LICs). Neighborhood social vulnerability has been associated with increased CVD risk in high-income countries, but the relationship is unknown in LICs. Our objective was to determine the association between neighborhood social vulnerability and prevalent CVD in urban Haiti. Hypothesis: We hypothesize that adults living in neighborhoods with increased social vulnerability will have a greater prevalence of hypertension (HTN) and CVD. Methods: We used cross-sectional enrollment data from the Haiti CVD Cohort study, a population-based cohort of adults ≥18 years living in Port-au-Prince distributed across 97 census blocks. To quantify vulnerability, we created a Haiti-specific Neighborhood Social Vulnerability Index (NSVI) based on the US CDC Social Vulnerability Index. The NSVI included three socioeconomic variables (income, sex, education), two household variables (single parent household, household with child &lt;18 years), and six neighborhood stress and well-being variables (neighborhood cohesion, neighborhood violence, perceived stress, food insecurity, depression, social support). Each variable was determined using validated questionnaires. NSVI was calculated for each census block, which were stratified into quartiles. HTN was defined as SBP ≥140 mmHg, DBP ≥90, or on antihypertensive medications; CVD included heart failure, stroke, myocardial infarction, and angina, defined using adjudicated criteria. HTN and CVD prevalence were calculated for each NSVI quartile. Associations between NSVI quartile and HTN or CVD were quantified using individual-level multivariate logistic regression to adjust for confounders. Results: Among 2932 participants, median age was 40 years (IQR 28-55) and 58% were female. Age-standardized prevalence of HTN and CVD was 32.3% and 13.8%, respectively. The age-standardized prevalence of HTN and CVD was 35.7% and 17.3% across 4 th NSVI quartile blocks, compared to 29.1% and 14.1% across 1 st quartile NSVI blocks (p &lt; 0.001). After adjusting for age, BMI, smoking, and alcohol use, participants living in 4 th NSVI quartile blocks had a greater risk of HTN (Adjusted Odds Ratio (aOR) 1.42; 95% CI 1.07-1.88; p-value 0.01) and CVD (aOR 1.36; CI: 0.99-1.88; p-value 0.06) compared to participants in the 1 st NSVI quartile blocks. NSVI was significantly associated with HTN in males but not females; conversely, NSVI was significantly associated with CVD in females and not males. Conclusions: Individuals living in neighborhoods with the greatest social vulnerability had a higher prevalence of HTN and CVD. Even in a setting of severe poverty such as urban Haiti, gradients in social vulnerabilities are associated with CVD disparities. Screening for social vulnerabilities should be incorporated into CVD prevention and treatment interventions in LICs.

Prenatal household size and composition are associated with infant fecal bacterial diversity in Cebu, Philippines.

The gut microbiome (GM) connects physical and social environments to infant health. Since the infant GM affects immune system development, there is interest in understanding how infants acquire microbes from mothers and other household members. As a part of the Cebu Longitudinal Health and Nutrition Survey (CLHNS), we paired fecal samples (proxy for the GM) collected from infants living in Metro Cebu, Philippines at 2 weeks (N=39) and 6 months (N=36) with maternal interviews about prenatal household composition. We hypothesized that relationships between prenatal household size and composition and infant GM bacterial diversity (as measured in fecal samples) would vary by infant age, as well as by household member age and sex. We also hypothesized that infant GM bacterial abundances would differ by prenatal household size and composition. Data from 16 S rRNA bacterial gene sequencing show that prenatal household size was the most precise estimator of infant GM bacterial diversity, and that the direction of the association between this variable and infant GM bacterial diversity changed between the two time points. The abundances of bacterial families in the infant GM varied by prenatal household variables. Results highlight the contributions of various household sources to the bacterial diversity of the infant GM, and suggest that prenatal household size is a useful measure for estimating infant GM bacterial diversity in this cohort. Future research should measure the effect of specific sources of household bacterial exposures, including social interactions with caregivers, on the infant GM.

Analisis Pangsa Pengeluaran Pangan Rumah Tangga Miskin Petani Kelapa Sawit Di Kecamatan Sinunukan Kabupaten Mandailing Natal

The purpose of this study was to determine the share of food expenditure of poor households and to analyze the effect of income, education of the head of the family, number of household members, length of household, amount of subsidized Raskin, non-food expenditure on the share of food expenditure of poor households in Sinunukan District, Mandailing Natal Regency. This research was conducted from September 10 to October 10, 2021. The location of the research was determined purposively with a total sample of 79 people. The data used are primary and secondary data. The method used in this research is descriptive analysis method using multiple linear regression analysis. Research results: 1. The share of food expenditure for poor households in Sinunukan District is 63% (food insecure households), : 2. The variables that partially have a significant effect on the share of food expenditure of poor households of oil palm farmers, Sinunukan Subdistrict, Mandailing Natal Regency, are income and non-food expenditure variables. Meanwhile, together (simultaneously) are household income variables, head of household education, number of family members, length of household, number of poor people received and non-food expenditure with sig 0.000 &lt; 0.05, the value of the coefficient of determination is 0.883.

Determinants of exposure to Aedes mosquitoes: A comprehensive geospatial analysis in peri-urban Cambodia

Aedes mosquitoes are some of the most important and globally expansive vectors of disease. Public health efforts are largely focused on prevention of human-vector contact. A range of entomological indices are used to measure risk of disease, though with conflicting results (i.e. larval or adult abundance does not always predict risk of disease). There is a growing interest in the development and use of biomarkers for exposure to mosquito saliva, including for Aedes spp, as a proxy for disease risk. In this study, we conduct a comprehensive geostatistical analysis of exposure to Aedes mosquito bites among a pediatric cohort in a peri‑urban setting endemic to dengue, Zika, and chikungunya viruses. We use demographic, household, and environmental variables (the flooding index (NFI), land type, and proximity to a river) in a Bayesian geostatistical model to predict areas of exposure to Aedes aegypti bites. We found that hotspots of exposure to Ae. aegypti salivary gland extract (SGE) were relatively small (< 500 m and sometimes < 250 m) and stable across the two-year study period. Age was negatively associated with antibody responses to Ae. aegypti SGE. Those living in agricultural settings had lower antibody responses than those living in urban settings, whereas those living near recent surface water accumulation were more likely to have higher antibody responses. Finally, we incorporated measures of larval and adult density in our geostatistical models and found that they did not show associations with antibody responses to Ae. aegypti SGE after controlling for other covariates in the model. Our results indicate that targeted house- or neighborhood-focused interventions may be appropriate for vector control in this setting. Further, demographic and environmental factors more capably predicted exposure to Ae. aegypti mosquitoes than commonly used entomological indices.

Socioeconomic Evaluation of Common Bean (Phaseolus vulgaris L.) Cultivation in Providing Sustainable Livelihood to the Mountain Populations of Kashmir Himalayas.

Phaseolus vulgaris L. is the major pulse cultivated and culturally inculcated in the food habits of the locals in the Himalayan mountainous region of Azad Jammu and Kashmir (AJK), Pakistan. The current study was designed to investigate the role of P. vulgaris cultivation in providing livelihood support and to evaluate its production and consumption patterns correlated with the household variables in the state of AJK. The socio-economic data was collected from nine bean cultivated areas in six districts of AJK. The data was acquired by administrating a total of 522 detailed semi structured questionnaires from a diverse array of the respondents following the snowball technique focusing on yield, consumption, revenue generation and livelihood support provided by bean cultivation. The results revealed that common bean cultivation provided significant livelihood support to the local mountainous populations with an average annual income of 50.80 $/family. Subsequently, bean production contributed an average annual per capita income of 6.81 $ in the area, which was attributed to the large family size. Local populations showed an average bean production of 33.93 kg/family, whereas the average annual bean consumption was recorded as 31.99 kg/family in the region. Bean crops were recorded to have an average price of $1.49/kg, with significant variations in the study area correlated with local yield. A data analysis indicated a strong correlation in bean production and consumption patterns. Common bean farmers had a very small farm size, averaging 0.24 ha, where 100% of farmers cultivated common beans as an intercrop with Maize as the primary crop. A Pearson’s test (p value < 0.05) revealed significant correlations between land holding and bean production as well as consumption, and bean production with annual per capita income. Small farm size, declining soil fertility, low bean pricing and the unavailability of market mechanisms were identified as the major challenges faced by the common bean farmers. It is recommended to employ an integrated bean farming approach to enhance the economic impact of common bean cultivation in the socioeconomic appraisal of the local populations.

Factors Associated with Early Child Development in Nepal – A Further Analysis of Multiple Indicator Cluster Survey 2019

Introduction: Information on child development is limited in Nepal, where a large number of children suffer from the negative consequences of poverty, nutritional deficiencies, and inadequate learning opportunities. The study aims to determine the proportion of children developmentally on track and its associated factors among 2870 children under 5 years using data from Multiple Indicator Cluster Survey. Methods: We used bivariate analysis and multivariate logistic regression to determine the association among household and maternal variables and child characteristics with the Child Development Index. Results: The multivariate analysis showed that age, province, educational status, wealth index quintile, health insurance, stunting, and functional disability were associated with the Child Development Index (P &lt; 0.05). The odds of child being developmentally on track at 4 years was more than two times (adjusted odds ratio [AOR] = 2; 95% confidence interval [CI]: 1.7–2.37) than of 3 years. Literate mothers had higher odds (AOR = 1.56 [95% CI: 1.29–1.89]) of child being developmentally on track compared to illiterate mothers. The odds of child being developmentally on track in the richest quintile was more than three times (AOR = 3.43 [95% CI: 2.39–4.91]) than child of the poorest quintile. With regard to stunting, there were higher odds of children who were not stunted (AOR = 1.35 [95% CI: 1.12–1.64]) being developmentally on track. Children who lack functional difficulty were more than six times (AOR = 6.7 [95% CI: 3.2–14.02]) on being developmentally on track. Children having health insurance were higher odds (AOR = 1.81 [95% CI: 1.04–3.15] P = 0.034) on being developmentally on track. Conclusion: The study found different factors such as age, province, educational status, wealth index quintile, health insurance, stunting, and functional disability associated with the Child Development Index providing insights to establish specific interventions based on socioeconomic barriers of household and health outcomes of children for ensuring child development.

Spatial epidemiology of acute respiratory infections in children under 5 years and associated risk factors in India: District-level analysis of health, household, and environmental datasets.

In India, acute respiratory infections (ARIs) are a leading cause of mortality in children under 5 years. Mapping the hotspots of ARIs and the associated risk factors can help understand their association at the district level across India. Data on ARIs in children under 5 years and household variables (unclean fuel, improved sanitation, mean maternal BMI, mean household size, mean number of children, median months of breastfeeding the children, percentage of poor households, diarrhea in children, low birth weight, tobacco use, and immunization status of children) were obtained from the National Family Health Survey-4. Surface and ground-monitored PM2.5 and PM10 datasets were collected from the Global Estimates and National Ambient Air Quality Monitoring Programme. Population density and illiteracy data were extracted from the Census of India. The geographic information system was used for mapping, and ARI hotspots were identified using the Getis-Ord Gi* spatial statistic. The quasi-Poisson regression model was used to estimate the association between ARI and household, children, maternal, environmental, and demographic factors. Acute respiratory infections hotspots were predominantly seen in the north Indian states/UTs of Uttar Pradesh, Bihar, Delhi, Haryana, Punjab, and Chandigarh, and also in the border districts of Uttarakhand, Himachal Pradesh, and Jammu and Kashmir. There is a substantial overlap among PM2.5, PM10, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM2.5, illiteracy levels, diarrhea in children, and maternal body mass index were associated with ARI. To decrease ARI in children, urgent interventions are required to reduce the levels of PM2.5 and PM10 (major environmental pollutants) in the hotspot districts. Furthermore, improving sanitation, literacy levels, using clean cooking fuel, and curbing indoor smoking may minimize the risk of ARI in children.

Factors associated with stunting among children 0 to 59 months of age in Angola: A cross-sectional study using the 2015-2016 Demographic and Health Survey.

Stunting among children under five years of age is a serious public health problem globally, with life-long consequences to health, well-being, and productivity. Stunted growth has complex and multifactorial causes, reflecting the interaction of a broad range of conditions that determine child health. The Angola 2015-2016 Demographic and Health Survey (DHS) collected nationally representative anthropometry for 6,359 children 0 to 59 months of age in Angola, and ascertained exposure to a wide range of child, parental, socio-economic, and geographic variables. This study used a cross-sectional design to identify exposures associated with stunting among children 0 to 59 months of age in Angola, while considering the multifactorial and multi-level causes of stunting. Main outcome was prevalence of stunting, defined as proportion of children with height-for-age Z-score (HAZ) two or more standard deviations below the median. Prevalence of stunting was associated with individual, household, and area-level exposure variables, including child age and sex, birth order, birthweight, diarrhea, maternal and paternal age and education, source of water, sanitary system, and province. In conclusion, prevalence of stunting in Angola is associated with several factors previously described in the literature. Stunting is associated with exposures at the distal, intermediate, and proximal levels, in line with the framework on the causes of childhood malnutrition. This study identifies opportunities for interventions at multiple levels to decrease prevalence of stunting among children in Angola. Main limitations of this study are the potential for survival bias and residual confounding.

Hypertension and undiagnosed hypertension among Bangladeshi adults: Identifying prevalence and associated factors using a nationwide survey.

Although undiagnosed hypertension (HTN) is a serious concern worldwide, it is less of an importance in Bangladesh, where there is a dearth of research on the subject. So, we aimed to identify the prevalence and associated factors for diagnosed and undiagnosed HTN. We analyzed the recent 2017-2018 Bangladesh Demographic and Health Survey data. We included 11,981 participants aged 18 years and above for the analysis. The prevalence rates of both diagnosed and undiagnosed hypertension were computed for all individuals and subgroups. The influence of socio-demographic, household, and community-related variables on HTN and undiagnosed HTN was investigated using multinomial regression analysis. The study finds 1,464 (12.2%) of the 11,981 respondents [6,815 females [56.9 %]; mean age 39.4 years] had diagnosed HTN, whereas 1 898 (15.8%) had undiagnosed HTN. The HTN and undiagnosed HTN were significantly prevalent in the elderly, type 2 diabetic (T2DM), and overweight and obese individuals. In terms of residential regions, people from coastal region had a significantly higher prevalence of both HTN (RRR: 1.37; 95% CI: 1.17-1.62) and undiagnosed HTN (RRR: 1.35; 95% CI: 1.17-1.56) compared to those from the central region of Bangladesh. The high prevalence of undetected hypertension in Bangladesh suggests that screening procedures for the current chronic illness may be inadequate in routine clinical practice. All populations should have access to hypertension screening, but it is especially crucial for the elderly, those with diabetes, those who are overweight or obese, and those from coastal and northern regions of Bangladesh.

Analyzing the Association of Objective Burden Measures to Perceived Burden with Regression Trees

Abstract Higher levels of perceived burden by respondents can lead to ambiguous responses to a questionnaire, item nonresponse, or refusals to continue participation in the survey which can introduce bias and downgrade the quality of the data. Therefore, it is important to understand what might influence the perception of burden in respondents. In this article, we demonstrate, using U.S. Consumer Expenditure Survey data, how regression tree models can be used to analyze the associations between perceived burden and objective burden measures conditioning on household demographics and other explanatory variables. The structure of the tree models allows these associations to easily be explored. Our analysis shows a relationship between perceived burden and some of the objective measures after conditioning on different demographic and household variables and that these relationships are quite affected by different respondent characteristics and the mode of the survey. Since the tree models were constructed using an algorithm that accounts for the sample design, inferences from the analysis can be made about the population. Therefore, any insights could be used to help guide future decisions about survey design and data collection to help reduce respondent burden.