Household Characteristics Research Articles

Household’s Characteristics and Learning Poverty: An Empirical Study

‘Learning poverty’ among school students is a cause of concern. Studies of family effects on students’ learning outcomes in the context of North-eastern region of India and Meghalaya are significantly lacking. The present study is about ‘learning poverty’ and its relationship with household characteristics such as household-income, parents’ educational level, parents’ occupation, nature of residence and number of siblings in the context of the Khasi–Jantia tribes of Meghalaya, which follows a matrilineal system. From the field experiment conducted on 407 students of classes 9 and 10 from 15 randomly selected schools from Shillong and Jowai towns, the findings indicate that ‘learning poverty’ is significantly higher in proportion for students belonging to the lower household-income groups. Besides low household-income, low level of parents’ education, not having and staying in one’s own house, higher number of siblings and parents’ occupation in low-end wage employment have emerged as critical factors responsible for the presence of ‘learning poverty’. The study recommends that policymakers and school management consider family effects seriously and work with parents from poor households to improve learning outcomes, mitigating the menace of ‘learning poverty’ on the society. However, the results of the study need to be examined with one caveat that it has not taken into consideration the school effects, which could too impact learning outcomes. JEL Codes: I240, I250, I210

Disparities in access to water, sanitation, and hygiene (WASH) services and the status of SDG-6 implementation across districts and states in India

IntroductionAccess to affordable and improved Water, Sanitation, and Hygiene (WASH) facilities is essential for people's daily lives, and it is the primary goal of Sustainable Development Goal 6 (SDG-6). However, achieving this goal is a significant challenge for many countries, including India. The aim of this study is to assess the progress made towards achieving SDG-6 targets in Indian districts, states, and Union Territories (UTs) and to identify clusters by measuring spatial inequality of WASH coverage in India. Aim and objectiveThe primary objective of this study is to measure the progress made towards achieving the SDG-6 targets for WASH facilities in Indian districts, states, and UTs. To fulfill this objective, the study used the household data of the National Family Health Survey-5 (NFHS-5) conducted from 2019 to 21. Data and methodsThe study used the household data of NFHS-5, which is a nationally representative survey that provides information on household and individual-level characteristics related to health and nutrition. The study identified the variables associated with WASH and created a composite index to measure WASH coverage separately and combined. The study used Gini coefficient to show WASH inequality, and Moran's statistics were used to show spatial dependency. ResultThe study found that the inequality of improved water coverage sources in Indian districts was high. Western and northeastern districts need to catch up in terms of achieving the SDG-6 targets. The value of the Gini coefficient (0.29) indicates that inequality in sanitation coverage is also high. All states have reached close to SDG-6 achievement in hygiene indicators. Goa, Sikkim, Andaman & Nicobar Islands, and Lakshadweep are close to the overall WASH coverage achievements of SDG-6. However, Jharkhand, Orissa, Tripura, Assam, and Rajasthan are behind in meeting the goal of SDG-6. ConclusionThe study suggests that more government initiatives and investments are needed to increase the availability, accessibility, and affordability of WASH facilities to improve WASH conditions in western and northeastern Indian districts. The localization or bottom-up approach by giving responsibility to rural and urban local bodies can also help enforce the achievement of SDG-6. The findings of this study can be used to guide policymakers in developing targeted interventions to improve WASH conditions and reduce inequality in India.

The Impact of Housing Price Volatility on Household Consumption in Northeast China Across Different Demographic Profiles

Amidst the backdrop of an economic downturn, urban housing prices in China have exhibited substantial volatility, significantly impacting domestic demand and household consumption. Demographic characteristics of households have played a pivotal moderating role in this dynamic. Due to economic stagnation and population outflow, Northeast China has garnered heightened attention. This study employs microdata from households in Liaoning and Heilongjiang provinces, spanning from 1989 to 2015, to conduct an empirical examination. The findings indicate that rising housing prices exert a pronounced positive influence on household consumption, particularly among households with children and those headed by elderly individuals. Conversely, the educational attainment of the household head does not exhibit a statistically significant impact on the real estate wealth effect. The study concludes with policy recommendations aimed at stimulating domestic demand and stabilizing housing prices in Northeast China.

Determinants Of Water Access Among Households In Setegn Meda, Addis Ababa, Ethiopia

Water and sanitation access are fundamental to human health and well-being. Globally, inadequate access to clean water and proper sanitation facilities has been linked to the spread of waterborne diseases such as cholera, typhoid, diarrhea, and dysentery. Despite efforts by governments, NGOs, and development affiliates to meet this need, there is still a gap in identifying the determinant factors that affect water accessibility in Setegn Meda informal settlement in Ethiopia. To promote sustainable water access for all, It is vital to understand and address factors that come into play. Therefore, this study was designed to explore the determinants influencing water access in regions like Setegn Meda in Addis Ababa, Ethiopia. The target population was 600 households, with a sample study size of 200 households obtained from Setegn Meda. The study employed a cross-sectional research design and data was collected through semi-structured questionnaires. Data was analyzed using descriptive and inferential statistical methods, where: percentages and frequencies analysis tests were computed. The study observed that education level, employment status, and monthly household income significantly influenced water access. The results revealed significant disparities in the socioeconomic characteristics of households, including income variability. The median household monthly income was 3,500 Ethiopian Birr, with a range from 0 to 30,000 Birr. Educational attainment levels varied, with 23% of respondents without formal education. Among the 77% with formal education, 33% completed primary education, 34% completed secondary education, and only 10% attended tertiary education. Employment status also varied, with 23% employed in the formal sector, 30% in informal employment, 25% unemployed, and 22% in subsistence farming or household duties. Despite the fact that over 90% of the respondents reported using tap water, water accessibility in Setegn Meda was found to be inadequate according to WHO and UNICEF guidelines, with challenges in both quality and quantity. There were significant associations between water treatment practices and the prevalence of water-related diseases (χ2= 9.773, p = 0.021), and between primary water sources and disease prevalence (χ2 = 3.494, p = 0.010). Typhoid fever was the most commonly reported water-related disease, followed by cholera affecting all age groups. The rank correlation value for these diseases was 0.68 (p < 0.01), indicating a strong relationship between inadequate water quality and the prevalence of these diseases. I recommend targeted interventions addressing the creation of awareness on water conservation and appropriate household water storage, economic empowerment, and water supply infrastructural improvements to enhance water quality and public health in the Setegn Meda settlement

Assessing production, processing and utilization of sorghum in West Pokot County, Kenya

Sorghum is one of the most important and under-utilized cereal crops in the Arid and Semi-Arid land (ASAL) regions of Kenya. The crop is grown in drought-prone marginal agricultural areas of Western Central and Eastern and coastal regions of Kenya. The diverse biotic and abiotic constraints including climate changes reduce sorghum productivity. Sorghum technology generators continue promoting suitable management practices and innovations to cushion and improve the resilience of smallholder farmers against the adverse effects of climate change. These interventions contribute to the attainment of not only household and national food security but also enhance incomes. Despite the increasing sorghum production and utilization in West Pokot County, there is limited knowledge of the status of production, processing, and utilization technologies. The aim of this study was to assess the current sorghum production, processing, utilization technology, innovations, and management practices among smallholder farmers. A household survey was undertaken using a jointly developed structured questionnaire by a multi- and inter-disciplinary team of researchers. Using a multi-stage sampling technique, 599 households were randomly selected and interviewed. Data collected were on household and farm characteristics including technologies. The study revealed that compared to maize and pasture, land allocated to sorghum enterprise was relatively low ranging from 0.32 acres to 0.80 acres with an average of 0.73 acres. The adoption of various sorghum technological components was also low. Adoption of improved sorghum varieties was less than 7% while fertilizer use was less than 20%. These contribute to low crop yields of about 140kg per acre. In addition, value addition was also low. Sorghum use was limited to ugali (56%) and porridge (39%) against the diverse value-added products including bread baking. and industrial processing of pure and blended products. More farmer and stakeholders training need to be done. In order to fully upgrade the sorghum value chain; there is the need to have targeted sorghum policies focusing on a range of activities along the value chain. These interventions could be integrated into the West Pokot County Integrated Development Plan. Key words: Sorghum, Production, Marketing, Utilization, Challenges, Opportunities, West Pokot

Microbial communities in rural and urban homes and their relationship to surrounding land use, household characteristics, and asthma status

The relationships between the microbial communities that develop within homes and local outdoor features, household conditions and respiratory health outcomes are not well understood. In this study, high-throughput bacterial 16S and fungal ITS rDNA sequencing were coupled with a quantitative, GIS-based land use approach in a sample of 95 homes in adjacent urban and rural areas to investigate how local land use features surrounding homes relate to the home microbiome. Bacterial richness was higher in homes surrounded by less development while fungal richness was higher in homes surrounded by more development. Home mycobiomes in less developed areas were enriched with crop-associated taxa whereas in more developed areas human-associated bacterial taxa and wood decaying fungal taxa predominated. Not all vegetation had the same effect on the home microbiome and distinct differences were observed between bacterial and fungal communities, and the distance between home and vegetation. Crops and tree coverage were strongly related to indoor mycobiomes due to the abundance of crop-related taxa and wood decaying taxa, respectively. Regarding asthma, the microbiomes in homes with asthmatic children had greater bacterial diversity and lower fungal diversity compared to homes without asthmatic children. In addition, ‘protective’ taxa were enriched in non-asthmatic homes. The buffer-based approach used for quantitatively categorizing land use and development levels around homes identified specific outdoor environmental factors that affect the indoor microbiomes in urban and rural homes. The study also supports the growing body of scientific evidence that the indoor microbiome can affect health outcomes such as childhood asthma.

Characterization of a Covid-fired urban bike delivery system: The Montreal experience

The Covid-19 pandemic led to a rapid change in consumers' demand for home deliveries and traditional delivery modes were unable to deliver consumers and shops due to the sudden increase in demand. This study investigates the results of a local cargo bike delivery initiative in Montreal, Canada, during the forced closure of non-essential businesses from March to May 2020. The objective of this study is to identify the variables which interacted with the performance of a cargo bike delivery initiative while considering the specific context of the Covid-19 pandemic. To assess such changing conditions, a multilevel linear regression analysis is carried out. This analysis allows to quantify the number of home deliveries while considering the impact of the number of active Covid-19 cases and sociodemographic predictors on the cargo bike initiative. The dataset covers 6,700 deliveries made by cargo bikes in the city of Montreal over 16 weeks. The results show that the presence of advertising and the characteristics of consumers' households, such as a smaller household size and a younger average age are amongst the strongest factors for the success of such initiative. The specific context of the Covid-19 pandemic allowed a better penetration of cargo bikes within the last-mile ecosystem.

Flood damage models and flood damage factors in a data-scarce river basin, Nigeria

ABSTRACT To evaluate the effectiveness of various adaptation strategies, it is critical to develop a flood damage function that considers building characteristics for future risk evaluation. This study analysed the contributions of some important flood and household characteristics in flood damage processes and developed a new, more accurate, multi-variable flood loss model for flood damage assessment for residential buildings and contents, considering different house types in Ogun River Basin, Nigeria. A detailed household questionnaire survey was conducted to collect data (149 samples). Depth-damage functions were developed to estimate direct flood damage to building structures and contents using the nonlinear logistic and power (concave) models, and the model parameters were estimated using the Gauss–Newton algorithm. The performance of all flood damage regression analysis functions for building structures and building contents under different construction materials and basement floors was evaluated by comparing predicted damages and after-flood survey damages, which demonstrated acceptable comparativeness. In the event of a flood disaster in the area, the models can be used to forecast future damages and devise risk-mitigation strategies. The study’s findings will aid in developing flood protection adaptation measures.

Single-Person Households: Insights from a Household Survey of Fruit and Vegetable Purchases.

Despite the efforts made to promote consumption, some countries are not increasing their fruit and vegetable intake, while household structures are undergoing relevant changes. Fruit and vegetable consumption is necessary but not sufficient for a healthy diet. Previous research has linked adequate fruit and vegetable consumption to a lower risk of cardiovascular diseases, type 2 diabetes, and some mental health conditions. Furthermore, millions of deaths are reported annually worldwide due to diets low in fruit and vegetables, highlighting their critical public health importance. This study aims to separately analyze the purchases of fruit and vegetables in single-person households. We used three waves of the Family Budget Survey, Encuesta de Presupuestos Familiares, in Chile, which is nationally representative of urban areas and includes over 10,000 households in each wave. We employed descriptive statistics to examine the characteristics of the head of household and the food shopper as well as the structure, composition, and overall characteristics of households. Additionally, we performed separate analyses for fruit and vegetable purchases, using these variables to determine the marginal effect on the probability of purchasing fruit or vegetables through probit models. Results show that, from 2011-2012 to 2021-2022, the share of households not purchasing fruit and vegetables increased from 5.0% to 8.4% and that, in single-person households, it rose from 11.2% to 19.1%. Male-headed, single-person households with low education and income were more likely not to purchase fruit, and these households also have decreasing vegetable purchases. Additionally, household income significantly impacts fruit purchases but does not significantly affect vegetable purchases. Our findings highlight the importance of considering single-person households as a target population segment for future public policies to promote fruit and vegetable consumption.

Why Survey-Based Subjective Expectations Are Meaningful and Important

For decades, households’ subjective expectations elicited via surveys have been considered meaningless because they often differ substantially from the forecasts of professionals and ex-post realizations. In sharp contrast, the literature we review shows that household characteristics and the ways in which households collect and process economic information help us understand previously considered puzzling facts about their subjective expectations. In turn, subjective expectations contribute to explain heterogeneous consumption, saving, investment, and debt choices as well as different reactions by similar households to the same monetary and fiscal policy measures. Matching microdata on households’ characteristics with the price signals the same households observe, their subjective expectations, and their real-world economic decisions is crucial to establishing these facts. Our growing understanding of households’ subjective expectations inspires several theoretical and empirical research directions and begets the design of innovative and more effective policy instruments.

What predicts hurricane evacuation decisions? The importance of efficacy beliefs, risk perceptions, and other factors

Risk theories and empirical research indicate that a variety of factors can influence people’s protective decisions for natural hazards. Using data from an online survey that presented coastal U.S. residents with a hypothetical hurricane scenario, this study investigates the relative importance of cognitive risk perceptions, negative affect, efficacy beliefs, and other factors in explaining people’s anticipated evacuation decisions. The analysis finds that multiple factors, including individual and household characteristics, previous experiences, cognitive and affective risk perceptions, and efficacy beliefs, can help predict hurricane evacuation intentions. However, the largest amount of variance in survey participants’ evacuation intentions is explained by their evacuation-related response efficacy (coping appraisals) and their perceived likelihood of getting hurt if they stay home during the storm. Additional analysis explores how risk perceptions and efficacy beliefs interact to influence people’s responses to risk information. Although further investigation in additional situations is needed, these results suggest that persuading people at high risk that evacuating is likely to reduce harm can serve as an important risk communication lever for motivating hurricane evacuation.

His unemployment, her response, and the moderating role of welfare policies in European countries. Results from a preregistered study.

Recent changes in labour markets have increased employment instability. Under these conditions, in male breadwinner families women might increase their labour supply when their male partners become unemployed. Previous studies have extensively investigated the role played by household and individual characteristics in explaining such increases in the labour supply of women. However, studies which examine the moderating role of specific welfare policies are missing. Our study contributes to the literature by investigating the moderating effect of childcare and tax-benefit policies for the labour supply response of women following the unemployment of their partner. We focus on a sample of 24 EU member states and the UK, during the period 2009-2019, combining longitudinal microdata from EU-SILC with country-period specific policy indicators generated with the use of the tax-benefit simulation model EUROMOD, UKMOD and country-period specific indicators of childcare use. We find that women indeed respond to men's unemployment by increasing their labour supply though the response is fairly weak. We also find the response is not restricted by having children at home or availability of public childcare and does not depend on the generosity of unemployment benefits. It is, however, negatively affected by marginal tax rates.

Poverty Impacts of the Pakistan Flood 2022

AbstractIn the summer of 2022, devastating floods hit Pakistan with a profound negative impact on lives and livelihoods. Apart from the irreconcilable loss of life, household welfare was negatively impacted through the loss of household income, loss of assets, and rising food prices. Yet, in the absence of just-in-time household survey data, evaluating the welfare impact is challenging, particularly in terms of estimating monetary poverty. This paper demonstrates how real-time satellite data, in combination with administrative data and historical household survey data, can be used to model the impact of floods on Pakistani households. In the context of data and time constraints, we present a practical methodology to simulate the expected impact of a natural disaster on monetary poverty from an ex-ante perspective. Estimates show that, as a direct consequence of the floods, the national poverty rate would increase by 4.0 to 4.3 percentage points, pushing around 9 million people below the national poverty line. Moreover, a spatially disaggregated analysis showed that the magnitude of shocks varied across locations and households depending on the intensity of the flooding and household characteristics. These estimates, which informed the Post Disaster Needs Assessment and were later used to frame the Donor Conference for Pakistan, provide an example of rapid modeling in the absence of real-time household survey data.

Inability to face unexpected expenses and monetary poverty in Poland: Are these two faces on the same coin?

Research background: The economic literature often states that monetary poverty does not coincide with other types of poverty. The paper examines monetary poverty and financial distress, which refer to distinct aspects of poverty. It addresses the issue by explaining how the same household characteristics affect these different types of poverty. Purpose of the article: The paper aims to identify socioeconomic variables influencing financial distress and monetary poverty in Poland. In addition, the relative contribution of household-level variables in explaining McFadden’s R2 for the financial dimensions under consideration is assessed. Methods: The study relies on data from the EU Statistics on Income and Living Conditions (EU-SILC) survey in 2022. Logistic regression analysis empirically tests the impact of socioeconomic variables on financial distress and income poverty. Moreover, the relative importance of regressors is determined using the Shapley-Owen decomposition analysis. Findings & value added: The results have revealed that the smallest group consisted of only monetary poor households, followed by both monetary poor and financially distressed. The largest group was made up of households that experienced only financial distress. Such an incomplete overlap in experiencing the examined types of poverty implies the importance of studying financial distress alongside traditional income indicator. The study indicated a statistically significant role for characteristics such as disability, unemployment, education, the burden of the repayment of debts, household type, and tenure status in experiencing all the types of poverty considered. Furthermore, it was observed that the explanatory power of the models varied depending on the types of poverty under consideration. The results also revealed a substantial relative contribution of education to McFadden’s R2 in all models, indicating that education level substantially explains vulnerability to financial fragility. The contribution of other regressors varied among the models describing the types of poverty analyzed. These findings should stimulate policymakers, as effective policies are needed to alleviate different types of poverty.

Adolescents’ behavioral changes and preventive practices against COVID-19 in South Africa: The influence of household characteristics

The South African government instituted a countermeasure against COVID-19 in March 2020, which had a significant impact on many individuals, particularly the youth. However, there is a limited understanding of how household characteristics influenced adolescents’ behavioral changes and their adoption of preventive measures during the pandemic. This study aimed to explore the relationship between the role of household characteristics and adolescents’ behavioral responses to COVID-19 in South Africa. Using the latest data from the South African National Income Dynamics-Coronavirus Rapid Mobile Survey (Wave 5), which includes responses from 5581 (99.9%) youths aged 15 – 24, we analyzed changes in their behaviors and the preventive measures they adopted. The study controlled for various household, demographic, and socioeconomic characteristics of the respondents. We utilized cross-tabulations and an ordinal logistics regression model to determine the relationship between the covariates and the outcome of behavioral changes and preventive measures adopted. Our findings revealed that none of the respondents adopted all recommended precautionary measures for COVID-19, while 55.7% exhibited low behavioral changes, and only 2.7% exhibited higher behavioral changes throughout the pandemic. The results indicate that, at both the individual and household levels, factors such as gender, educational attainment, source of household income, and access to electricity increase the odds of behavioral changes against COVID-19 in South Africa. In addition, among household-level factors, adolescents without access to water were more likely to adopt one to three preventive measures compared to those with access to water (52.2% vs. 47.9%, p < 0.001). Therefore, to effectively prevent the spread of infection in South Africa, it is pertinent to address these household characteristics and encourage preventive measures tailored to these factors.

Econometric analysis of factors influencing electricity consumption in Spain: Implications for policy and pricing strategies

This study employs a comprehensive database of Spanish homes and econometric modeling to assess the factors influencing electricity consumption in Spain. Using panel data covering the period from 1998 to 2023, we analyzed the average yearly electricity usage across the 18 Spanish regions. Our findings reveal that fluctuations in electricity prices have remained the primary driver of consumption over this period. The price elasticity of demand has been exceptionally high, exceeding levels observed in earlier research and periods. Additionally, factors such as income, hours of sunlight, and temperature fluctuations positively influence electricity usage, albeit to a lesser extent than household and family characteristics (e.g., whether a single parent heads the household or if it includes foreign residents). There is no significant correlation between demand and factors such as the age or employment status of family members. Based on these findings, policy actions that target power pricing (e.g., price-based instruments) are likely to be the most effective in reducing electricity consumption.

Beyond grassland degradation: Pathways to resilience for pastoralist households in alpine grassland ecosystems

Sustaining the development of rural and pastoral communities' hinges on livelihood resilience. Pastoralist household resilience relies on resource availability and decision-making abilities. Despite extensive studies on pastoralist livelihoods, a significant knowledge gap remains in understanding the nuanced adaptive capacities of diverse households, particularly amid grassland degradation. Thus, this study investigates the household-based livelihood resilience of pastoralists in China's Three-River Headwater Region, offering policy recommendations for resilient livelihoods. Using stratified random sampling, 758 pastoralist household heads underwent semi-structured interviews to collect data. Five household characteristics, encompassing age, gender, energy use, well-being perception, and multi-household grazing participation, were evaluated. Looking ot the nature of data, descriptive statistics and non-parametric tests were performed in this study to draw the valid inferences. The results revealed a positive correlation (p < 0.05) between household head age and livelihood resilience, with divergent resilience across age groups. Varied energy usage yielded distinct impacts; households employing solar or mixed energy sources exhibited heightened resilience (p < 0.05). Household well-being emerged as an invariant variable concerning resilience. Furthermore, engagement in multi-household grazing (an informal institution) significantly (p < 0.05) influenced pastoralist livelihood resilience. These insights advocate targeted support for young household heads and the adoption of clean energy. Exploring the deeper strategies and mechanisms of multi-household grazing can enhance understanding and policy integration, guiding eco-friendly progress within rustic landscapes for pastoral communities.

Civilians under missile attack: post-traumatic stress disorder among the Jewish and Bedouin population of Southern Israel

BackgroundOver the past 20 years, Jewish and Bedouin civilians in southern Israel have faced the ongoing threat of missile attacks from Gaza, with possible mental health consequences. This study aimed to assess the prevalence of post-traumatic stress disorder (PTSD) among Jewish and Bedouin adult civilians in southern Israel in a period with few missile attacks from Gaza, and no military operations.MethodsThe study population included 389 participants (246 Jews, 143 Bedouins) living within 40 km/25 mi from Gaza for at least 2 years and interviewed between January and March 2023 (before the ongoing war that started on October 7th, 2023). The PTSD Checklist (PCL-5) was used, with a score of 33 as a cutoff point for the presence of PTSD.ResultsCompared to Jews, a significantly lower proportion of Bedouins reported accessibility to bomb shelters and siren warning systems. Overall, 20.3% of the respondents exhibited PTSD. Multivariate analysis revealed that after adjustment for demographic and household characteristics, Bedouins had a six-fold significantly higher probability of PTSD in comparison to Jews (OR 5.6, 95%CI 2.8–10.8). Compared to participants with high socioeconomic status (SES), participants with low SES had a six-fold significantly higher probability of PTSD (OR 6.0, 95%CI 2.2–16.5). Participants who did not have an alarm system had more than two-fold odds for PTSD (OR 2.3, 95%CI 1.1–5.5). Being single, living in urban areas, or having a disability significantly increased the probability of PTSD.ConclusionsThe findings of this study demonstrate a significantly higher prevalence of PTSD among the Bedouin population of Southern Israel. Several sociodemographic characteristics were associated with the increased prevalence of PTSD, the most prominent of which was low SES. Healthcare professionals and authorities should be proactive in screening for PTSD, and provide tailored treatment and support, taking into account ethnical and cultural background. Authorities should address the disparity in bomb shelter access and siren warning coverage between Bedouin and Jewish communities.

Understanding Diagnosis Delay in Children With Cancer: Evidence From a Single Institution in Jordan.

This study aimed to evaluate the factors associated with diagnosis delay in children with cancer who are treated at a single institution, which caters to most children with cancer in Jordan. This was a cross-sectional study with a retrospective chart review of selected patients who were diagnosed from August 2018 to December 2021. Data on patient and household characteristics, medical history, and diagnostic delay were collected through structured interviews. Univariable and multivariable linear and logistic regression models were used to identify predictors of delay. The study included a cohort of 202 patient-caregiver pairs, with a median total delay from symptom onset to treatment initiation of 47 days (interquartile range [IQR], 21 to 114d). Notably, 86% of families pursued medical consultation within a month of recognizing symptoms. A regression model revealed CNS tumors as a significant independent predictor of increased total delay ( P =0.002), with affected patients experiencing a median delay markedly longer than those with other cancer types. In addition, older patient age predicted longer total delay ( P =0.025). Symptomatology played a pivotal role in the timeliness of the diagnosis; specifically, visible symptoms such as pallor, bruises, and jaundice were associated with more expedient medical attention, with significantly shorter delays ( P values: 0.011, <0.001, and 0.045, respectively). Furthermore, our investigation disclosed a notable variance in symptom prevalence across different cancer categories, elucidating the complex relationship between clinical presentation and diagnostic timelines. This study highlights the importance of the diagnosis of CNS tumors, patient age, and symptoms in predicting diagnosis delay in pediatric oncology patients. These findings can inform interventions to reduce delays in diagnosis and improve outcomes for these patients. These insights are crucial for developing targeted educational programs aimed at healthcare professionals and families to accelerate the recognition and referral of pediatric cancer cases.

Ownership of Cash Value Life Insurance among Rural Households: Utilization of Machine Learning Algorithms to Find Predictors

This study examines the determinants of life insurance ownership with a focus on rural areas and farming households in the United States. Utilizing data from online surveys conducted in 2019 and 2021, this paper explores how psychological factors, financial knowledge, and household characteristics influence life insurance ownership. Traditional indicators like wealth, income, and age were evaluated alongside less frequently discussed variables such as farm loans and rural residency. Machine learning techniques, including neural networks, Support Vector Machine modeling, Gradient Boosting, and logistic regression, were employed to identify the most robust predictors of life insurance demand. The findings reveal that farming-associated factors, particularly holding a farm loan and living in a farming household, significantly predict life insurance ownership. The study also highlights the complexity of life insurance demand, showing that financial education and management practices are critical determinants. This research underscores the need for tailored financial risk management strategies for rural and farming households and contributes to a nuanced understanding of life insurance demand in varying contexts.