Background The aim of the present work was to investigate the relative importance of socio-demographic and physical health status factors for subjective functioning, as well as to examine the role of social support. Methods A cross-sectional health survey was carried out in a Greek municipality. 1356 adults of the general population were included in the study. Personal interviews were conducted with house-to-house visits. The response rate was 91.2%. Functioning has been measured by five indexes: 'The Social Roles and Mobility' scale (SORM), 'The Self-Care Restrictions' scale (SCR), 'The Serious Limitations' scale (SL), 'The Minor Self-care Limitations' scale (MSCR) and 'The Minor Limitations in Social Roles and Mobility' scale (MSORM). Results Among the two sets of independent variables, the socio-demographic ones had significant influence on the functional status, except for MSORM. Allowing for these variables, the physical health status indicators had also significant effects on all functioning scales. Living arrangements and marital status had significant effects on four out of five indexes, while arthritis, Parkinson's disease, past stroke and kidney stones had significant effects on the SCR and SL scales. Conclusions These results suggest that socio-demographic factors are as important as physical health variables in affecting a person's ability to function normally in their everyday life. Social support appears to play a significant role in explaining differences in subjective functioning: people living alone or only with the spouse, particularly the elderly, seem to be in greater risk for disability problems and should be targeted by preventive programs in the community.

BackgroundOur aim was to assess the level and socio-demographic correlates of knowledge about rights to healthcare services among children in post-communist Albania in order to inform targeted interventions and policies to promote equitable healthcare access for all children.MethodsAn online survey conducted in Albania in September 2022 included a nationwide representative sample of 7,831 schoolchildren (≈54% girls) aged 12–15 years. A structured and anonymous questionnaire was administered inquiring about children’s knowledge on their rights to healthcare services. Binary logistic regression was used to assess the association of children’s knowledge about their rights to healthcare services with socio-demographic characteristics.ResultsOverall, about 78% of the children had knowledge about their rights to healthcare services. In multivariable adjusted logistic regression models, independent “predictors” of lack of knowledge about rights to healthcare services included male gender (OR = 1.2, 95% CI = 1.1–1.3), younger age (OR = 1.3, 95% CI = 1.1–1.4), pertinence to Roma/Egyptian community (OR = 1.6, 95% CI = 1.1–2.2), and a poor/very poor economic situation (OR = 1.3, 95% CI = 1.0–1.6).ConclusionOur findings indicate a significantly lower level of knowledge about rights to healthcare services among children from low socioeconomic families and especially those pertinent to ethnic minorities such as Roma/Egyptian communities, which can result in limited access to essential health services, increased vulnerability to health disparities, and barriers to receiving appropriate care and advocacy for their health and well-being. Seemingly, gender, ethnicity, and economic status are crucial for children’s knowledge of their healthcare rights because these factors shape their access to information, influence their experiences with healthcare systems, and can drive policy and practice to address disparities and ensure equitable access to health services. Health professionals and policymakers in Albania and elsewhere should be aware of the unmet needs for healthcare services due to lack of awareness to navigate the system particularly among disadvantaged population groups.

An information system based on data from the healthcare sector and related areas. The online portal gives researchers the opportunity to research various health topics including population, socio-economic factors, health insurance, health laws.

• Laws

• Population

  • Births
  • Deaths
  • Population Numbers
  • other

• Pregnancy, Births

• Terminations of Pregnancy

• Households, Families, Living Communities

• Social Conditions

  • Education
  • Habitation
  • Employment
  • Unemployment
  • Poverty and Disparity
  • Single parent
  • Income
  • Social Welfare
  • other

• Training of the Health Personnel

• Economy

• Research

• Private Health Insurance

• Statutory Health Insurance

  • Statutory Health Insurance: Income, Expenditure
  • Statutory Health Insurance: Members, Insured Persons
  • Statutory Health Insurance: Services
  • Statutory Health Insurance: Services (Pharmaceuticals)
  • Statutory Health Insurance: other

• Nursing Care Insurance

• Pension Insurance

  • Insured Persons
  • Rehabilitation Services
  • Pensions because of old age
  • Ppensions because of diminution of ability to wrk
  • Pensions because of death (widow/widower pensions)
  • other

• Accident Insurance, employers' liability insurance association

Sample distribution by Demographic Factors (N = 384).

Background The United States has experienced high surge in COVID-19 cases since the dawn of 2020. Identifying the types of diagnoses that pose a risk in leading COVID-19 death casualties will enable our community to obtain a better perspective in identifying the most vulnerable populations and enable these populations to implement better precautionary measures. Objective To identify demographic factors and health diagnosis codes that pose a high or a low risk to COVID-19 death from individual health record data sourced from the United States. Methods We used logistic regression models to analyze the top 500 health diagnosis codes and demographics that have been identified as being associated with COVID-19 death. Results Among 223,286 patients tested positive at least once, 218,831 (98%) patients were alive and 4,455 (2%) patients died during the duration of the study period. Through our logistic regression analysis, four demographic characteristics of patients; age, gender, race and region, were deemed to be associated with COVID-19 mortality. Patients from the West region of the United States: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming had the highest odds ratio of COVID-19 mortality across the United States. In terms of diagnoses, Complications mainly related to pregnancy (Adjusted Odds Ratio, OR:2.95; 95% Confidence Interval, CI:1.4 - 6.23) hold the highest odds ratio in influencing COVID-19 death followed by Other diseases of the respiratory system (OR:2.0; CI:1.84 – 2.18), Renal failure (OR:1.76; CI:1.61 – 1.93), Influenza and pneumonia (OR:1.53; CI:1.41 – 1.67), Other bacterial diseases (OR:1.45; CI:1.31 – 1.61), Coagulation defects, purpura and other hemorrhagic conditions(OR:1.37; CI:1.22 – 1.54), Injuries to the head (OR:1.27; CI:1.1 - 1.46), Mood [affective] disorders (OR:1.24; CI:1.12 – 1.36), Aplastic and other anemias (OR:1.22; CI:1.12 – 1.34), Chronic obstructive pulmonary disease and allied conditions (OR:1.18; CI:1.06 – 1.32), Other forms of heart disease (OR:1.18; CI:1.09 – 1.28), Infections of the skin and subcutaneous tissue (OR: 1.15; CI:1.04 – 1.27), Diabetes mellitus (OR:1.14; CI:1.03 – 1.26), and Other diseases of the urinary system (OR:1.12; CI:1.03 – 1.21). Conclusion We found demographic factors and medical conditions, including some novel ones which are associated with COVID-19 death. These findings can be used for clinical and public awareness and for future research purposes.

Data in this layer represents demographic data from the American Community Survey 5 yr estimates, 2015-2019 for Age, Disability, Female Population, Limited English Proficiency, Low Income, Place of Birth, Race, and Zero Vehicle Households. Each layer contains a number of attributes pertaining to the specific topic. For additional information about the data, definitions, and source please contact NJTPA (gfausel@njtpa.org).

Study 2 demographic factors predicting average RAW-SAW differences.

This statistic shows the demographic and socio-economic factors most likely to shape global industries according to executive respondents from large companies worldwide, as of July 2015. 44% of executives believe that the changing nature of work or flexible work will cause major change in their industry by 2020.

aMissing data, which accounted for less than 2% of participants for any question, was omitted from analysis. P-value for all comparisons except gender

Correlation analysis between the demographic factors and AHI or RDI.

The associations between physical activity and demographic factors.

Abstract Objective: the present study aims to investigate the association between population ageing in municipal regions in the state of Rio Grande do Norte, and socioeconomic, demographic and regional factors. Method: an ecological study that used municipal regions of the state of Rio Grande do Norte as a unit of analysis was carried out. Data collection was conducted through databases from the Brazilian Institute of Geography and Statistics, the Institute of Applied Economic Research and the Atlas of Human Development. The factor of Increased Age was created based on factor analysis, which was related to socioeconomic, demographic and regional variables. The chi-squared test with a significance level of 5% was used in addition to the Hosmer and Lemeshow technique for logistic regression. Result: it was found that municipal regions in the Central mesoregion have an older/ageing population, while those with intermediate populations have the oldest individuals. Furthermore, it was found that municipal regions with unequal income distribution and higher levels of education have an older population. Conclusion: it can be concluded that municipal regions classified as older/more aged were associated with the mesoregion to which the municipal region belongs; and those with intermediate population size were associated with favorable educational levels and unequal income distribution.

This dataset tracks the updates made on the dataset "Socio-demographic factors and self-reported funtional status: the significance of social support" as a repository for previous versions of the data and metadata.

The data is an extract from the 2018 Nigerian Demographic and Health Survey (NDHS). The NDHS allows researchers to use the data for reseach work.

Relationship between Oxford happiness scores, health status and demographic variables.

*Demographic information collected for 35 interviewees and one carer.

This is the replication data for manuscript titled "Unpacking drivers of online censorship endorsement: Psychological and demographic factors" submittted to review. The abstract of the manuscript is as follows. Abstract: This study explores the complex dynamics of online censorship endorsements within a national context. We examined the impact of some of the influential psychological and demographic factors contributing to online censorship endorsement of Iranian Telegram users. Through the analysis of 517 responses to an online questionnaire, we investigated the influence of variables such as age, education level, gender, the use of state-controlled media, political interests, personal trust, religiosity, perceived similarity, and motivated resistance to censorship on individuals' attitudes toward censorship. Our findings reveal that education level, state-controlled media usage, religiosity, perceived similarity, and motivated resistance to censorship significantly shape censorship endorsements in the Iranian Telegram users. In the discussion section, we highlighted the implications of these findings and offered avenues for further research.

The United States remains the epicenter for the COVID-19 pandemic. Having accurate data reporting and analysis at the local, state, and national levels would help steer containment efforts, build a more targeted response strategy, and foster learnings across cities and states as new hotspots arise. Throughout the course of the pandemic, Population Council researchers have been tracking how COVID-19 data are reported and analyzed using a comprehensive analysis of 62 COVID-19 data sources from the Centers for Disease Control and Prevention (CDC) and health departments across 50 states, Washington D.C., and ten major cities. We assessed data completeness for COVID-19 testing and four outcomes (cases, hospitalizations, recoveries, and deaths), and examined disaggregation of COVID-19 testing and outcomes by a core set of demographic indicators, including age, race/ethnicity, sex/gender, geography, and underlying health conditions. This analysis also investigated how social and community level data were reported and analyzed, variations in data reporting, and changes over the course of the pandemic. Having this information can equip national and local health officials to deploy a more targeted response effort such as testing, contact tracing, treatment, and containment strategy.

American Community Survey 5-year Estimates 2016-2020. Includes Age, Disability, Education, Female Population, LEP, Low Income, Place of Birth (Foreign Born), Race (Minority) and Zero Vehicle Households for MCDs.

Abstract

The 2013 Turkey Demographic and Health Survey (TDHS-2013) is a nationally representative sample survey. The primary objective of the TDHS-2013 is to provide data on socioeconomic characteristics of households and women between ages 15-49, fertility, childhood mortality, marriage patterns, family planning, maternal and child health, nutritional status of women and children, and reproductive health. The survey obtained detailed information on these issues from a sample of women of reproductive age (15-49). The TDHS-2013 was designed to produce information in the field of demography and health that to a large extent cannot be obtained from other sources.

Specifically, the objectives of the TDHS-2013 included: - Collecting data at the national level that allows the calculation of some demographic and health indicators, particularly fertility rates and childhood mortality rates, - Obtaining information on direct and indirect factors that determine levels and trends in fertility and childhood mortality, - Measuring the level of contraceptive knowledge and practice by contraceptive method and some background characteristics, i.e., region and urban-rural residence, - Collecting data relative to maternal and child health, including immunizations, antenatal care, and postnatal care, assistance at delivery, and breastfeeding, - Measuring the nutritional status of children under five and women in the reproductive ages, - Collecting data on reproductive-age women about marriage, employment status, and social status

The TDHS-2013 information is intended to provide data to assist policy makers and administrators to evaluate existing programs and to design new strategies for improving demographic, social and health policies in Turkey. Another important purpose of the TDHS-2013 is to sustain the flow of information for the interested organizations in Turkey and abroad on the Turkish population structure in the absence of a reliable and sufficient vital registration system. Additionally, like the TDHS-2008, TDHS-2013 is accepted as a part of the Official Statistic Program.

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49
• Children under age of five

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years and women age 15-49 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design and sample size for the TDHS-2013 makes it possible to perform analyses for Turkey as a whole, for urban and rural areas, and for the five demographic regions of the country (West, South, Central, North, and East). The TDHS-2013 sample is of sufficient size to allow for analysis on some of the survey topics at the level of the 12 geographical regions (NUTS 1) which were adopted at the second half of the year 2002 within the context of Turkey’s move to join the European Union.

In the selection of the TDHS-2013 sample, a weighted, multi-stage, stratified cluster sampling approach was used. Sample selection for the TDHS-2013 was undertaken in two stages. The first stage of selection included the selection of blocks as primary sampling units from each strata and this task was requested from the TURKSTAT. The frame for the block selection was prepared using information on the population sizes of settlements obtained from the 2012 Address Based Population Registration System. Settlements with a population of 10,000 and more were defined as “urban”, while settlements with populations less than 10,000 were considered “rural” for purposes of the TDHS-2013 sample design. Systematic selection was used for selecting the blocks; thus settlements were given selection probabilities proportional to their sizes. Therefore more blocks were sampled from larger settlements.

The second stage of sample selection involved the systematic selection of a fixed number of households from each block, after block lists were obtained from TURKSTAT and were updated through a field operation; namely the listing and mapping fieldwork. Twentyfive households were selected as a cluster from urban blocks, and 18 were selected as a cluster from rural blocks. The total number of households selected in TDHS-2013 is 14,490.

The total number of clusters in the TDHS-2013 was set at 642. Block level household lists, each including approximately 100 households, were provided by TURKSTAT, using the National Address Database prepared for municipalities. The block lists provided by TURKSTAT were updated during the listing and mapping activities.

All women at ages 15-49 who usually live in the selected households and/or were present in the household the night before the interview were regarded as eligible for the Women’s Questionnaire and were interviewed. All analysis in this report is based on de facto women.

Note: A more technical and detailed description of the TDHS-2013 sample design, selection and implementation is presented in Appendix B of the final report of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two main types of questionnaires were used to collect the TDHS-2013 data: the Household Questionnaire and the Individual Questionnaire for all women of reproductive age. The contents of these questionnaires were based on the DHS core questionnaire. Additions, deletions and modifications were made to the DHS model questionnaire in order to collect information particularly relevant to Turkey. Attention also was paid to ensuring the comparability of the TDHS-2013 findings with previous demographic surveys carried out by the Hacettepe Institute of Population Studies. In the process of designing the TDHS-2013 questionnaires, national and international population and health agencies were consulted for their comments.

The questionnaires were developed in Turkish and translated into English.

Cleaning operations

TDHS-2013 questionnaires were returned to the Hacettepe University Institute of Population Studies by the fieldwork teams for data processing as soon as interviews were completed in a province. The office editing staff checked that the questionnaires for all selected households and eligible respondents were returned from the field. A total of 29 data entry staff were trained for data entry activities of the TDHS-2013. The data entry of the TDHS-2013 began in late September 2013 and was completed at the end of January 2014.

The data were entered and edited on microcomputers using the Census and Survey Processing System (CSPro) software. CSPro is designed to fulfill the census and survey data processing needs of data-producing organizations worldwide. CSPro is developed by MEASURE partners, the U.S. Bureau of the Census, ICF International’s DHS Program, and SerPro S.A. CSPro allows range, skip, and consistency errors to be detected and corrected at the data entry stage. During the data entry process, 100% verification was performed by entering each questionnaire twice using different data entry operators and comparing the entered data.

Response rate

In all, 14,490 households were selected for the TDHS-2013. At the time of the listing phase of the survey, 12,640 households were considered occupied and, thus, eligible for interview. Of the eligible households, 93 percent (11,794) households were successfully interviewed. The main reasons the field teams were unable to interview some households were because some dwelling units that had been listed were found to be vacant at the time of the interview or the household was away for an extended period.

In the interviewed 11,794 households, 10,840 women were identified as eligible for the individual interview, aged 15-49 and were present in the household on the night before the interview. Interviews were successfully completed with 9,746 of these women (90 percent). Among the eligible women not interviewed in the survey, the principal reason for nonresponse was the failure to find the women at home after repeated visits to the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TDHS-2013 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TDHS-2013 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall