Importance Of Socioeconomic Variables Research Articles

Who continues to work after retirement age?

BackgroundDemographic changes in all industrialized countries have led to a keen interest in extending working lives for older workers. To achieve this goal, it is essential to understand the patterns of retirement and specifically what characterizes individuals who continue to work beyond retirement age. Thus, the aim of this paper was to contribute to the international body of empirical knowledge about individuals who continue in the workforce after retirement age. We present evidence from Denmark and examine what characterizes individuals who continue in the workforce after retirement age and investigate the likelihood of continued work after retirement age while controlling for a set of socio-economic and lifestyle factors.MethodsThe study population consisted of 5,474 respondents to the Copenhagen Aging and Midlife Biobank (CAMB) 2021 survey, divided into two groups. The first group included subjects (n = 1,293) who stayed longer in the workforce even though they had the possibility to retire. The second group consisted of subjects who had retired full-time at the time of the survey (n = 4,181). Survey data was linked to register data to provide a broader dataset. In order to investigate the heterogeneity between the two groups in terms of important socio-economic, work-related and health-related variables, t-test, Mann-Whitney U (Wilcoxon Rank) test, and chi-square tests were employed. Further, to examine the probability of an individual working after retirement age a logit model with step-wise inclusion was utilized.ResultsOverall, individuals who continue to work even though they could retire tend to be wealthier, healthier, and males compared to individuals who are retired full-time. Further, there are more older workers who have partners and are co-habitants than retirees. The likelihood of continuing in the workforce past retirement age is affected by several work-related factors as well as life-style factors. The likelihood of working past retirement age decreases by years spent in the workforce (marginal effect of -0.003), if you have a partner (-0.080) and if your partner is outside of the workforce (marginal effect of -0.106). The likelihood increases by health (marginal effect of -0.044 of moving from excellent/very good health to good health or to fair/poor health, physical working capability (marginal effect of -0.083 of moving from no/some problems to severe problems or cannot work at all) and income (marginal effect of 0.083 from moving from the lowest income-quantile to higher quantiles).ConclusionThese results are in line with the previous literature and suggest the importance of designing retirement policies that tailor the transition toward retirement according to specific characteristics of both the individual and the segment of occupation.

Determinants of dietary patterns of Ghanaian mother-child dyads: A Demographic and Health Survey.

Having a comprehensive understanding of a population's dietary patterns is a key component of any effective strategy for preventing malnutrition, planning, and putting nutrition interventions and policies into place. It's interesting to note that information on dietary patterns of Ghana's vulnerable subpopulations of women and children is lacking. The purpose of this study is to characterize the dietary patterns of women (15-49 years old) and their young children (0-3 years old), as well as to investigate into the socioeconomic and demographic factors influencing the characterized dietary patterns. The sociodemographic information and food consumption of mother-child dyads (n = 1,548) were collected for this nationally representative cross-sectional study. Principal component analysis and multiple variable logistic regression were used, respectively, to determine the dietary patterns of dyads and the determinants of the identified dietary patterns. For women and children, respectively, four dietary patterns ('Beverage & sugary based', 'Meat based', 'Indigenous- tuber based' and 'Indigenous- grain based') and two ('Indigenous' and 'Milk, Meat, & cereal based') emerged. Ethnicity, wealth quintiles, parity, seasonality, dyad's age, body mass index, education, residency, marital status, and household size were the socioeconomic / demographic determinants of the dietary patterns. To sum up for women and children, meat based and indigenous staple based dietary patterns were identified, with several important socioeconomic and demographic variables acting as predictors of the dietary patterns. The identified dietary patterns and their determinants may serve as a basis for nutrition intervention and policies for women and children in Ghana.

Analyzing the Health Status of Slum Dwellers: An Evidence of Lahore, Pakistan

Background: Miserable status of health and public health is one of the major problems of more than 1 billion people living in slums across the world. Most of the governments, do not pay due attention. Purpose: Current study is especially devised to highlight the major problems of slum population in relation with health and public health so that better policy measures might be suggested. The second most populous city of Pakistan is Lahore and it is the home of about 27 million people, slums area of Lahore were chosen as population. Methodology: Through stratified sampling technique 250 households from ten slums were chosen as a sample. Inferential and descriptive statistical techniques have been used study in hand. The logistic regression model has been used to analyze the impact of important socio-economic variables on the health status of the family. Results: From descriptive analysis, we know that about 73 percent of the slum residents have no access to even basic health facilities, 32 percent of the slum children are not immunized, 72 percent of the slum women give birth to children without any proper assistance and 56 percent do not receive antenatal check – ups during pregnancy. Further, about 66 percent of the dwellers have no access to safe drinking water while majority face unhygienic and poor solid waste management status. Conclusion: The results of regression model shows that per capita income, education level of the family head, availability of safe drinking water and employment status of the family have positive while smoking and drinking leave negative impact on the health status of the dwellers. The findings address the authorities concerned to take holistic and practical measures for betterment of human development. Keywords: Slums, Antenatal Care, Basic Health Facilities, Safe Water, Immunization, Solid Waste Management

Knowledge and self-reported food safety practices among meat consumers in Ilorin, Nigeria.

As an imperative source of protein to man, meat could also serve as a source of infections when processed poorly. This research studied consumers' knowledge and self-reported food safety practices among 869 meat consumers of different socioeconomic statuses. We summarized the data obtained using descriptive and inferential statistics. Two outcome variables were developed to determine satisfactory knowledge and practices. The knowledge and practice scores were determined through a numeric scoring system based on the respondents' correct responses to knowledge and practices questions. These outcome variables were further categorized into binary variables based on a cut-off point (mean + 1 SD of the scores) with scores greater and lower than the cut-off points considered satisfactory (acceptable/appropriate) and unsatisfactory, respectively. The respondents are predominantly female (54.9%) within the age range of 19-25 years (54.2%) and were unmarried (71.1%). Less than half (46.4% and 40.0%) of the respondents have adequate knowledge and practice levels of food safety. Most meat consumers have identified various challenges to food safety and hazards related to unsafe food safety practices. Important socioeconomic variables of the meat consumers such as occupation, age, and marital status, were significantly (p < 0.05) associated with food safety knowledge. Meat consumers with higher age categories and educational levels were more likely to have good food safety knowledge than those of the 15-18 years age category and primary educational level, respectively. Respondents with higher levels of education were more likely to demonstrate satisfactory food safety practices than those of primary education status, while males (OR = 1.34; 95% CI: 1.02, 1.76; p = 0.043) were significantly more likely to report satisfactory food safety practices. In conclusion, this study emphasized the need for an improvement in consumers' food safety practices.

Exploring the Impact of Socio-economic Indicators on Economic Growth of Pakistan

Since independence, Pakistan has faced many economic problems. Social and administrative issues have been an obstacle to its economic growth. Economic, social, and administrative problems have amalgamated and engulfed the whole nation like an epidemic. Being the most critical issue, bad governance is destroying the economy in many ways. Poor governance negatively contributes to the empirics of economic growth through increased poverty directly and through increased social evils, such as crimes, indirectly. The present study has used the Three Stages Least Square (3SLS) estimation technique to examine the role of governance in stimulating economic growth by considering important socio-economic variables like poverty and crime. The study results suggest that poor governance is contributing to increasing poverty in Pakistan, which is, in turn, raising crime rates drastically. Since high crime rates slow down the economic pace of the economy, improved governance, increased employment opportunities, mobilization of private investment, and diversion of public investment to rural areas are essential for promoting economic growth.

A Correlation of Socio-economic Determinants and Food Security Status in Pakistan

This study gives details of the interrelationship among important socioeconomic variables and food security status in Pakistan. The major socio-economic explanatory variables; education, livestock, poverty, receipts of foreign remittances, and female house-head status were analyzed against food security. The data was derived from Pakistan Social and Living Standard Measurement survey dataset 2019-20. A binary logistic model has been applied for the estimation of poverty and food security indices. The study results showed that education, livestock, foreign remittances, and female house-head have a positive significant impact on food security while poverty has a negative and significant impact on food security. The study recommendations are that government must focus to increase agriculture growth, increased dependence on livestock, foreign remittances, and education.

Strata-based quantification of distributional uncertainty in socio-economic indicators: A comparative study of Indian states

This paper reports a comprehensive study of distributional uncertainty in a few socio-economic indicators across the various states of India over the years 2001–2011. We show that the discrete generalized beta (DGB) distribution, a typical rank order distribution, provide excellent fits to the district-wise empirical data for the population size, literacy rate (LR) and work participation rate (WPR) within every states in India, through its two distributional parameters. Moreover, taking resort to the entropy formulation of the DGB distribution, a proposed uncertainty percentage (UP) unveils the dynamics of the uncertainty of LR and WPR in all states of India. We have also commented on the changes in the estimated parameters and the UP values from the years 2001–2011. Additionally, a gender based analysis of the distribution of these important socio-economic variables within different states of India has also been discussed. Interestingly, it has been observed that, although the distributions of the numbers of literate and working people has a direct (linear) correspondence with that of the population size, the literacy and work-participation rates are distributed independently of the population distributions. This detailed study of distributional uncertainty or inequalities of different socio-economic indicators within and between the various strata along with their relations with the population may help policy makers to frame better policies for a sustainable and equitable development of the country.

Towards Use of Cleaner Fuels in Urban and Rural Households in Colombia: Empirical Evidence from 2010 to 2016

Natural Gas (NG) and Liquefied Petroleum Gas (LPG) are considered as modern fuels particularly in the urban and rural areas in developing countries. In Colombia, approximately 6.5 million people, around 13% of the population in Colombia use traditional solid fuels for cooking. Using the Colombia Living Standard Survey (CLSS) data for 2010 to 2016 in this study is investigated the mainly socioeconomic factors that influence the choice of cooking fuel in households. A Multinomial Logit Regression (MLR) model is used for empirical analysis. Results reveal several important socioeconomic variables that determine the use of fuels such as type of household, education level and household income. Moreover, results allow to show the successful implementation of the Colombian substitution energy policy, from solid to cleaner fuels such as LPG and NG along national territory. Additionally, we provide several maps to show the evolution of cooking fuel usage in rural and urban areas of Colombia from 2010 to 2016.

The impacts of the built environment on the incidence rate of COVID-19: A case study of King County, Washington

With COVID-19 prevalent worldwide, current studies have focused on the factors influencing the epidemic. In particular, the built environment deserves immediate attention to produce place-specific strategies to prevent the further spread of coronavirus. This research assessed the impact of the built environment on the incidence rate in King County, US and explored methods of researching infectious diseases in urban areas. Using principal component analysis and the Pearson correlation coefficient to process the data, we built multiple linear regression and geographically weighted regression models at the ZIP code scale. Results indicated that although socioeconomic indicators were the primary factors influencing COVID-19, the built environment affected COVID-19 cases from different aspects. Built environment density was positively associated with incidence rates. Specifically, increased open space was conducive to reducing incidence rates. Within each community, overcrowded households led to an increase in incidence rates. This study confirmed previous research into the importance of socioeconomic variables and extended the discussion on spatial and temporal variation in the impacts of urban density on the spread of COVID, effectively guiding sustainable urban development.

Socio-economic features of mandarin (Citrus reticulata blanco) growers: A case study in Nagaland, India

Socio-economic upliftment is the basis and prerequisite for all-round and sustainable development of a community. This study is an attempt to explore the socio-economic condition of the mandarin growers in Nagaland, India. In this study, two districts of Nagaland were included. An interview schedule was prepared for data collection. Altogether, 120 experienced mandarin growers were included as the study community. All important socio-economic variables were included in the study. The average age of the respondents was above 52 years. About 91.00 per cent of the respondents were staying in the kutcha house. The average landholding was 2.88 ha, and land allocation for mandarin cultivation was 0.57 ha. The farmers were experienced enough, but the average production of mandarin was 841.50 kg. The average annual income from mandarin cultivation was ₹27,219, and it was contributing about 23.10 per cent to the yearly average income of 17,799. In respect of information dissemination and technology transfer, mandarin growers were not getting minimum extension services. The public sector extension system's performance was relatively inadequate, and NGOs were somewhat supporting the study community. The overall socio-economic condition of the study community was relatively less satisfactory. An immense opportunity was there to up-scale the socio-economic situation by mobilizing and involving the young generation to take agriculture as an enterprise. Productivity and income from mandarin cultivation can be up-scaled to improve the socio-economic condition. Therefore, concerned authorities of the state government may take the initiative.

Positive association between COVID-19 deaths and influenza vaccination rates in elderly people worldwide

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global health crisis, directly and indirectly impacting all spheres of human life. Some pharmacological measures have been proposed to prevent COVID-19 or reduce its severity, such as vaccinations. Previous reports indicate that influenza vaccination appears to be negatively correlated with COVID-19-associated mortality, perhaps as a result of heterologous immunity or changes in innate immunity. The understanding of such trends in correlations could prevent deaths from COVID-19 in the future. The aim of this study was therefore to analyze the association between COVID-19 related deaths and influenza vaccination rate (IVR) in elderly people worldwide.MethodsTo determine the association between COVID-19 deaths and influenza vaccination, available data sets from countries with more than 0.5 million inhabitants were analyzed (in total 39 countries). To accurately estimate the influence of IVR on COVID-19 deaths and mitigate effects of confounding variables, a sophisticated ranking of the importance of different variables was performed, including as predictor variables IVR and some potentially important geographical and socioeconomic variables as well as variables related to non-pharmaceutical intervention. The associations were measured by non-parametric Spearman rank correlation coefficients and random forest functions.ResultsThe results showed a positive association between COVID-19 deaths and IVR of people ≥65 years-old. There is a significant increase in COVID-19 deaths from eastern to western regions in the world. Further exploration is needed to explain these findings, and additional work on this line of research may lead to prevention of deaths associated with COVID-19.

Spatial Variability of Health Inequalities of Older People in China and Related Health Factors.

China is facing serious population aging issues because of many unintended consequences of the economic reforms that began in the 1980s and with social policies such as the “one child” policy. Understanding the spatial distribution of the health status of older people has attracted more and more attention in many countries, including China. By employing descriptive analysis, this study uses data from the Chinese Population Censusand Statistical Year Bookto explore the health inequalities of older people at the national level. Based on the Getis-Ord Gi*, this study finds that the uneven spatial distribution of socio-economic status results in health inequalities for older people at the national level. The geographic distribution of life expectancy was correlated with a number of important demographic, socio-economic, and environmental variables. For further research, investigations should be conducted among individuals at micro-geographic scales.

Exploring Decision Rules for Election Results by Classification Trees

This study explores the most important socio-economic variables determining the voting decisions of the provinces in Municipality Elections by using classification trees. We collected data on many potential variables that may affect voting decisions in favor of a political party. Each province’s economic, geographic and demographic data is taken into consideration as independent variables. The dependent variable is the winner party in 2014 Municipality Elections. Data set consists of 81 provinces’ data on 69 variables. The aim of the study is to find which variables affect voting decision the most and try to find a pattern that may lead political campaigns. Amongst many classification algorithms, we used C5.0 algorithm coded in R. It helps us explore the structure of a set of data, while developing easy to visualize decision rules for predicting a categorical (classification tree) or continuous (regression tree) outcome. The C5.0 algorithm determines the separation criterion with the greatest information gain in each decision node and performs optimal separation.&#x0D; Since our data size is small, we used k=1000 trials (estimations) and then summarized them to provide more robust results. By choosing C5.0 algorithm’s sub-trial size as 5, 5000 trees are formed and the mean of all importance scores of all trees formed are calculated and interpreted. The most important independent variables discriminating the voting decision are found to be the result of the previous elections, mean household population, proportion of population between ages 15 and 19, electricity consumption per person, and proportion of population between ages 55 and 64.&#x0D; Keywords: classification trees, voting decision, C5.0 algorithm, decision trees

Spatiotemporal assessment of drought hazard, vulnerability and risk in the Krishna River basin, India

Spatial and temporal assessment of drought hazard over the Krishna River basin of India has been performed using long-term (January 1901–December 2002) precipitation and temperature data. Various meteorological drought indices such as the Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Standardized Effective Precipitation Evapotranspiration Index and Reconnaissance Drought Index (RDI) have been evaluated on a 12-month timescale for assessment of the drought hazard. Various physical drought characteristics such as the maximum drought magnitude, maximum drought duration and probability of occurrence of droughts, i.e., drought frequency, are also analyzed for the Krishna River basin. Analysis led to identification of major dry periods from the evaluated drought characteristics and generation of spatial maps of magnitude, duration and intensity for each index for each of the dry periods. The socioeconomic aspects of drought have also been explored by analyzing drought hazard, vulnerability and risk which are mapped spatially to evaluate drought susceptibility of various regions in the basin. The study revealed a positive correlation between the maximum drought magnitude, drought duration and drought risk, and an indirect proportionality between drought intensities and drought frequency. The analysis of physical drought characteristics revealed that the RDI deviated significantly from the remainder indices. The importance of socioeconomic variables is also highlighted as districts having normal meteorological conditions became the hotspots of drought risk because of sensitive demographics in the study basin.

Mapping Socioeconomic Exposure for Flood Risk Assessment in Italy

Detailed spatial representation of socioeconomic exposure and the related vulnerability to natural hazards has the potential to improve the quality and reliability of risk assessment outputs. We apply a spatially weighted dasymetric approach based on multiple ancillary data to downscale important socioeconomic variables and produce a grid data set for Italy that contains multilayered information about physical exposure, population, gross domestic product, and social vulnerability. We test the performances of our dasymetric approach compared to other spatial interpolation methods. Next, we combine the grid data set with flood hazard estimates to exemplify an application for the purpose of risk assessment.

Estimating the impacts of urban form on CO2 emission efficiency in the Pearl River Delta, China

The improvement of CO2 emission efficiency is of great significance to realizing energy-saving and emission reduction targets and achieving low-carbon development. While it is increasingly recognized that urban form could significantly influence the CO2 emissions of urban areas, few studies have been able to quantify the implications of urban form in relation to CO2 emission efficiency. The purpose of this paper is thus to contribute to existing literature by empirically quantifying how urban form influences CO2 emission efficiency. CO2 emission efficiency in this study is presented in terms of CO2 economic efficiency (CEE) and CO2 social efficiency (CSE). Firstly, we calculated the CEE and CSE of nine cities in the Pearl River Delta (Guangzhou, Shenzhen, Zhuhai, Foshan, Jiangmen, Zhaoqing, Huizhou, Dongguan, and Zhongshan) using locally important socioeconomic variables over the period 1990–2013. Then, seven landscape metrics were selected in order to quantify three dimensions of urban form (extension, irregularity, and compactness) using remote sensing data. Finally, panel data models were utilized to estimate the associations between urban form and CO2 emission efficiency. We identified a negative correlation between urban sprawl and CEE as well as CSE, a finding that indicates that urban growth decreases CO2 economic efficiency. Further, increasing irregularity in the form of cities was found to decrease both CEE and CSE—a larger degree of irregularity, in other words, results in lower CO2 emission efficiency. Conversely, urban compactness was identified as having a significant positive influence on both CEE and CSE, indicating that the compact development of cities can actually help to improve CO2 emission efficiency. The findings of this study hold important implications for building low-carbon cities in China.

Effect of Area-Level Deprivation on Cancer Survival Time: A Register-Based Follow-up Study of 145 585 Korean Subjects.

Our goal is to evaluate the strengths of the associations between area-level deprivation and survival time for a variety of cancers. A total of 145 585 subjects were diagnosed with various cancers in Busan, Korea, and an area-level deprivation index constructed from several important socioeconomic variables. A multilevel Cox model was used in the analysis to investigate the effects of multiple risk factors. After adjusting for gender and age, an increased area-level deprivation index was found to be significantly associated with a higher hazard rate for major cancers. Estimated hazard ratios (95% confidence interval) were 1.08 (1.06, 1.11), 1.15 (1.13, 1.18), and 1.22 (1.18, 1.25) for the second, third, and fourth quartiles of deprivation index groups, respectively, when compared with the least deprived group. When compared with the least deprived group, the more deprived group showed significant decrease in survival time for major cancers. This finding highlights the importance of preventive and care services incorporating socioeconomic characteristics of areas.

MEASURING SOCIO-ECONOMIC INDICATORS FOR CLIMATIC ANALYSIS

Socio-economic indicators are important indexes to measure the effect of human activities towards climate. This paper aims to study on the two main socioeconomic indicators for the urban area, namely population and GDP, to determine the relationship between these variables and climate condition. The State of Selangor, Malaysia was chosen as the study area as this state is among the highest GDP contributors to the country. Secondary data was used for this study by utilising datasets from Statistical Department and Department of Meteorology. The model derived shows that climate condition is moderately dependence of population and GDP. Further analysis can focus on more important socioeconomic variables which may contributed to the climate condition in the urban area.

MEASURING SOCIO-ECONOMIC INDICATORS FOR CLIMATIC ANALYSIS

Socio-economic indicators are important indexes to measure the effect of human activities towards climate. This paper aims to study on the two main socioeconomic indicators for the urban area, namely population and GDP, to determine the relationship between these variables and climate condition. The State of Selangor, Malaysia was chosen as the study area as this state is among the highest GDP contributors to the country. Secondary data was used for this study by utilising datasets from Statistical Department and Department of Meteorology. The model derived shows that climate condition is moderately dependence of population and GDP. Further analysis can focus on more important socioeconomic variables which may contributed to the climate condition in the urban area.

MEASURING SOCIO-ECONOMIC INDICATORS FOR CLIMATIC ANALYSIS

Socio-economic indicators are important indexes to measure the effect of human activities towards climate. This paper aims to study on the two main socioeconomic indicators for the urban area, namely population and GDP, to determine the relationship between these variables and climate condition. The State of Selangor, Malaysia was chosen as the study area as this state is among the highest GDP contributors to the country. Secondary data was used for this study by utilising datasets from Statistical Department and Department of Meteorology. The model derived shows that climate condition is moderately dependence of population and GDP. Further analysis can focus on more important socioeconomic variables which may contributed to the climate condition in the urban area.