This is the current Medical Service Study Area. California Medical Service Study Areas are created by the California Department of Health Care Access and Information (HCAI).Check the Data Dictionary for field descriptions.Search for the Medical Service Study Area data on the CHHS Open Data Portal.Checkout the California Healthcare Atlas for more Medical Service Study Area information.This is an update to the MSSA geometries and demographics to reflect the new 2020 Census tract data. The Medical Service Study Area (MSSA) polygon layer represents the best fit mapping of all new 2020 California census tract boundaries to the original 2010 census tract boundaries used in the construction of the original 2010 MSSA file. Each of the state's new 9,129 census tracts was assigned to one of the previously established medical service study areas (excluding tracts with no land area), as identified in this data layer. The MSSA Census tract data is aggregated by HCAI, to create this MSSA data layer. This represents the final re-mapping of 2020 Census tracts to the original 2010 MSSA geometries. The 2010 MSSA were based on U.S. Census 2010 data and public meetings held throughout California.Source of update: American Community Survey 5-year 2006-2010 data for poverty. For source tables refer to InfoUSA update procedural documentation. The 2010 MSSA Detail layer was developed to update fields affected by population change. The American Community Survey 5-year 2006-2010 population data pertaining to total, in households, race, ethnicity, age, and poverty was used in the update. The 2010 MSSA Census Tract Detail map layer was developed to support geographic information systems (GIS) applications, representing 2010 census tract geography that is the foundation of 2010 medical service study area (MSSA) boundaries. ***This version is the finalized MSSA reconfiguration boundaries based on the US Census Bureau 2010 Census. In 1976 Garamendi Rural Health Services Act, required the development of a geographic framework for determining which parts of the state were rural and which were urban, and for determining which parts of counties and cities had adequate health care resources and which were "medically underserved". Thus, sub-city and sub-county geographic units called "medical service study areas [MSSAs]" were developed, using combinations of census-defined geographic units, established following General Rules promulgated by a statutory commission. After each subsequent census the MSSAs were revised. In the scheduled revisions that followed the 1990 census, community meetings of stakeholders (including county officials, and representatives of hospitals and community health centers) were held in larger metropolitan areas. The meetings were designed to develop consensus as how to draw the sub-city units so as to best display health care disparities. The importance of involving stakeholders was heightened in 1992 when the United States Department of Health and Human Services' Health and Resources Administration entered a formal agreement to recognize the state-determined MSSAs as "rational service areas" for federal recognition of "health professional shortage areas" and "medically underserved areas". After the 2000 census, two innovations transformed the process, and set the stage for GIS to emerge as a major factor in health care resource planning in California. First, the Office of Statewide Health Planning and Development [OSHPD], which organizes the community stakeholder meetings and provides the staff to administer the MSSAs, entered into an Enterprise GIS contract. Second, OSHPD authorized at least one community meeting to be held in each of the 58 counties, a significant number of which were wholly rural or frontier counties. For populous Los Angeles County, 11 community meetings were held. As a result, health resource data in California are collected and organized by 541 geographic units. The boundaries of these units were established by community healthcare experts, with the objective of maximizing their usefulness for needs assessment purposes. The most dramatic consequence was introducing a data simultaneously displayed in a GIS format. A two-person team, incorporating healthcare policy and GIS expertise, conducted the series of meetings, and supervised the development of the 2000-census configuration of the MSSAs.MSSA Configuration Guidelines (General Rules):- Each MSSA is composed of one or more complete census tracts.- As a general rule, MSSAs are deemed to be "rational service areas [RSAs]" for purposes of designating health professional shortage areas [HPSAs], medically underserved areas [MUAs] or medically underserved populations [MUPs].- MSSAs will not cross county lines.- To the extent practicable, all census-defined places within the MSSA are within 30 minutes travel time to the largest population center within the MSSA, except in those circumstances where meeting this criterion would require splitting a census tract.- To the extent practicable, areas that, standing alone, would meet both the definition of an MSSA and a Rural MSSA, should not be a part of an Urban MSSA.- Any Urban MSSA whose population exceeds 200,000 shall be divided into two or more Urban MSSA Subdivisions.- Urban MSSA Subdivisions should be within a population range of 75,000 to 125,000, but may not be smaller than five square miles in area. If removing any census tract on the perimeter of the Urban MSSA Subdivision would cause the area to fall below five square miles in area, then the population of the Urban MSSA may exceed 125,000. - To the extent practicable, Urban MSSA Subdivisions should reflect recognized community and neighborhood boundaries and take into account such demographic information as income level and ethnicity. Rural Definitions: A rural MSSA is an MSSA adopted by the Commission, which has a population density of less than 250 persons per square mile, and which has no census defined place within the area with a population in excess of 50,000. Only the population that is located within the MSSA is counted in determining the population of the census defined place. A frontier MSSA is a rural MSSA adopted by the Commission which has a population density of less than 11 persons per square mile. Any MSSA which is not a rural or frontier MSSA is an urban MSSA. Last updated December 6th 2024.

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

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

The GERAS Study-US was a prospective, multicenter, observational study that aimed to assess societal costs and resource use associated with AD among patients and their primary caregivers across 76 sites in the United States. Data includes demographics/clinical characteristics; current medication; patient cognitive, functional, and behavioral assessments; patient and caregiver health-related quality of life; and patient and caregiver resource use. The data are available via the ADDI AD Workbench.

Please note: This is a Synthetic data file, also known as a Dummy file - it is not real data. This synthetic file should not be used for purposes other than to develop an test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical package 'code' (e.g. SPSS syntax, SAS programs, etc.) in preperation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Resource Data Centres. In the fall of 1991, the National Health Information Council recommended that an ongoing national survey of population health be conducted. This recommendation was based on consideration of the economic and fiscal pressures on the health care systems and the requirement for information with which to improve the health status of the population in Canada. Commencing in April 1992, Statistics Canada received funding for development of a National Population Health Survey (NPHS). The NPHS collects information related to the health of the Canadian population and related socio-demographic information to: aid in the development of public policy by providing measures of the level, trend and distribution of the health status of the population, provide data for analytic studies that will assist in understanding the determinants of health, and collect data on the economic, social, demographic, occupational and environmental correlates of health. In addition the NPHS seeks to increase the understanding of the relationship between health status and health care utilization, including alternative as well as traditional services, and also to allow the possibility of linking survey data to routinely collected administrative data such as vital statistics, environmental measures, community variables, and health services utilization. The NPHS collects information related to the health of the Canadian population and related socio-demographic information. It is composed of three components: the Households, the Health Institutions, and the North components. The Household component started in 1994/1995 and is conducted every two years. The first two cycles (1994/1995, 1996/1997) were both cross-sectional and longitudinal. The NPHS longitudinal sample includes 17,276 persons from all ages in 1994/1995 and these same persons are to be interviewed every two years. Each cycle, a common set of health questions is asked to the respondents. This allows for the analysis of changes in the health of the respondents over time. In addition to the common set of questions, the questionnaire does include focus content and supplements that change from cycle to cycle. Health Canada, Public Health Agency of Canada and provincial ministries of health use NPHS longitudinal data to plan, implement and evaluate programs and health policies to improve health and the efficiency of health services. Non-profit health organizations and researchers in the academic fields use the information to move research ahead and to improve health.

Snapshot img

US Population Health Management Market Size 2025-2029

The US population health management (PHM) market size is forecast to increase by USD 6.04 billion, at a CAGR of 7.4% between 2024 and 2029.

Population Health Management (PHM) is a critical aspect of healthcare delivery In the modern era, focusing on improving the health outcomes of large populations. The market is experiencing significant growth, driven by several key trends. One of the primary factors fueling this growth is the increasing adoption of healthcare IT solutions. These technologies enable healthcare providers to collect, manage, and analyze large amounts of patient data, facilitating personalized care and population health improvement. Another trend is the growing adoption of analytics in PHM. Analytics tools help identify patterns and insights from data, enabling early intervention and prevention of diseases. However, the high perceived costs associated with PHM solutions remain a challenge for market growth. Despite this, the benefits of PHM, including improved patient outcomes and reduced healthcare costs, make it a worthwhile investment for healthcare organizations.

What will be the Size of the market During the Forecast Period?

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Population Health Management (PHM) is a proactive healthcare approach focusing on improving the wider determinants of health and addressing health inequalities in various physical, economic, and social contexts. The market reflects the growing recognition of the importance of system-wide outcome focus, local intelligence, and data-driven decision-making in addressing ill health and managing chronic conditions such as cardiovascular disease. PHM integrates qualitative and quantitative data to identify and address the unique needs of populations, enabling personalized interventions and care models. Infrastructure, leadership, and information governance are crucial elements in implementing effective PHM strategies. 
Payment reform and incentives are driving the transformation of healthcare systems towards a more integrated care model, reducing hospitalization and improving overall population health. The market is experiencing significant growth due to the increasing awareness of the importance of addressing the root causes of ill health and the need for a more holistic approach to healthcare. This shift towards PHM is influenced by the economic, social, and demographic changes In the global population, emphasizing the need for a more resource-efficient and sustainable healthcare system.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Services


Deployment

  Cloud
  On-premises


End-user

  Healthcare providers
  Healthcare payers
  Employers and government bodies


Geography

  US

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

Population Health Management (PHM) software is a crucial tool In the US healthcare sector, collecting and analyzing patient data from various healthcare systems to predict health conditions and improve overall patient care. Advanced data analytics, including data visualizations and business intelligence, enable PHM software to identify health risks within communities and promote value-based care. The adoption of PHM software is on the rise due to the increasing prevalence of chronic conditions and the demand for efficient, cost-effective healthcare. PHM software also facilitates system-wide outcome focus, integrating qualitative and quantitative data, local intelligence, and decision-making to redesign care services for at-risk groups.

The US healthcare transformation prioritizes PHM, with NHS England, NHS trusts, Public health, VCSE organizations, and Integrated Care Systems (ICSs) utilizing PHM software to address health inequalities and improve health outcomes. PHM software's infrastructure, leadership, information governance, and digital infrastructure support the integration of interventions, care models, hospitalization incentives, payment reforms, and integrated care systems. PHM software plays a vital role in addressing health issues such as cardiovascular disease (CVD) and improving overall population health.

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Market Dynamics

Our US Population Health Management (PHM) Market researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in adopti

This dataset contains model-based census tract-level estimates for the PLACES project 2020 release. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 27 measures: 5 chronic disease-related unhealthy behaviors, 13 health outcomes, and 9 on use of preventive services. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.

These data represent the predicted (modeled) prevalence of adults (Age 18+) that Delayed Medical Care Because of Cost among all adults for each census tract in Colorado. Delayed medical care is defined as needing to see a doctor within the past 12 months but not able to do so because of cost(s).The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

The PLACES (Population Level Analysis and Community Estimates) is an expansion of the original 500 Cities project and is a collaboration between the CDC, the Robert Wood Johnson Foundation (RWJF), and the CDC Foundation (CDCF). The original 500 Cities Project provided city- and census tract-level estimates for chronic disease risk factors (5), health outcomes (13), and clinical preventive services use (9) for the 500 largest US cities. The PLACES Project extends these estimates to all counties, places (incorporated and census designated places), census tracts and ZIP Code Tabulation Areas (ZCTA) across the United States. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Data sources used to generate these measures include BRFSS data (2018 or 2017), Census Bureau 2010 census population data or annual population estimates for county vintage 2018 or 2017, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates.The health outcomes include arthritis, current asthma, high blood pressure, cancer (excluding skin cancer), high cholesterol, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary heart disease, diagnosed diabetes, mental health not good for >=14 days, physical health not good for >=14 days, all teeth lost and stroke.The preventive services uses include lack of health insurance, visits to doctor for routine checkup, visits to dentist, taking medicine for high blood pressure control, cholesterol screening, mammography use for women, cervical cancer screening for women, colon cancer screening, and core preventive services use for older adults (men and women).The unhealthy behaviors include binge drinking, current smoking, obesity, physical inactivity, and sleeping less than 7 hours.For more information about the methodology, visit https://www.cdc.gov/places or contact places@cdc.gov.CDC's source webpage.CDC's feature service.

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

Background: Suboptimal quality of care and disparities in services by healthcare providers are often reported in Nepal. Experience and perceptions about quality of care may differ according to women's socio-cultural background, individual characteristics, their exposure and expectations. This study aimed to compare perceptions of the quality of maternal healthcare services between two groups that are consistently considered vulnerable, women with disabilities from both the non-Dalit population and Dalit population and their peers without disabilities from both non-Dalit and Dalit communities.

Methods: A cross-sectional survey was conducted among 343 total women that included women with disabilities, Dalits and non-Dalits. Women were recruited for interview, who were aged 15–49 years, had been pregnant within the last five years and who had used maternal care services in one of the public health facilities of Rupandehi district. A 20-item, Likert-type scale with four sub-scales or dimensions: 'Health Facility', 'Healthcare Delivery', 'Inter-personal' and 'Access to Care' was used to measure women's perceptions of quality of care. Chi-square test and t test were used to compare groups and to assess differences in perceptions; and linear regression was applied to assess confounding effects of socio-demographic factors. The mean score was compared for each item and separately for each dimension.

Results: All groups, women with disabilities and women without disabilities, Dalit and non-Dalit rated their perceptions and experiences of quality of care lowly in a number of items. While perceived quality of care between women with disabilities and without disabilities in the 'Health Facility' dimension and associated items, was found to differ (p<0.05), this difference was linked to disability status, but was not linked to caste differences. For example, differences in mean scores relating to 'Cleanliness and Facilities', 'Open and Friendliness' and 'Compassion and Kindness' were highly significant (p<0.001), with women with disabilities rating these as better than women without disabilities. On the other hand, women without disabilities rated the 'Availability of cash Incentives' more highly (p<0.01). No significant differences were found between Dalit and non-Dalit women in perceived quality of care, except in relation to 'Cleanliness and facilities', which Dalit women rated lower than non-Dalits (p<0.05).

Conclusions: Perceptions about the quality of care differed significantly by disability status but not by caste. All groups rated the quality of healthcare delivery, interpersonal and personal factors as well as access to services 'low.' Poor service user experiences and perceptions of quality of care undermine opportunities to translate increased healthcare coverage into improved access and outcomes. Greater attention is required by policy makers, health planners and providers to the improvement of quality of care in health facilities.

The National Health and Nutrition Examination Survey I Epidemiologic Followup Study (NHEFS) is a longitudinal study that follows participants from the NHANES I who were aged 25-74 in 1971-1975. The NHEFS surveys were designed to investigate the association between factors measured at the baseline and the development of specific health conditions and functional limitations. Follow-up data were collected in 1982-1984 (ICPSR 8900), 1986 (ICPSR 9466), 1987 (ICPSR 9854), and 1992. The 1992 NHEFS collected information on changes in the health and functional status of the NHEFS cohort since the last contact period. The Vital and Tracing Status file (Part 1) provides summary information about the status of the NHEFS cohort. The Interview Data file (Part 2) covers selected aspects of the respondent's health history, including injuries, activities of daily living, vision and hearing, medical conditions, exercise, weight, family history of cancer, surgeries, smoking, alcohol use, and medical care utilization. The Health Care Facility Stay files (Parts 3 and 4) supply information about stays in hospitals, nursing homes, and mental health care facilities, as well as information abstracted from facility medical records. The Mortality Data file (Part 5) contains data abstracted from the death certificates for NHEFS decedents.

Abstract

The 1992 Namibia Demographic and Health Survey (NDHS) is a nationally representative survey conducted by the Ministry of Health and Social Services, assisted by the Central Statistical Office, with the aim of gathering reliable information on fertility, family planning, infant and child mortality, maternal mortality, maternal and child health and nutrition. Interviewers collected information on the reproductive histories of 5,421 women 15-49 years and on the health of 3,562 children under the age of five years.

The Namibia Demographic and Health Survey (NDHS) is a national sample survey of women of reproductive age designed to collect data on mortality and fertility, socioeconomic characteristics, marriage patterns, breastfeeding, use of contraception, immunisation of children, accessibility to health and family planning services, treatment of children during episodes of illness, and the nutritional status of women and children. More specifically, the objectives of NDHS are: - To collect data at the national level which will allow the calculation of demographic rates, particularly fertility rates and child mortality rates, and maternal mortality rates; To analyse the direct and indirect factors which determine levels and trends in fertility and childhood mortality, Indicators of fertility and mortality are important in planning for social and economic development; - To measure the level of contraceptive knowledge and practice by method, region, and urban/rural residence; - To collect reliable data on family health: immunisations, prevalence and treatment of diarrhoea and other diseases among children under five, antenatal visits, assistance at delivery and breastfeeding; - To measure the nutritional status of children under five and of their mothers using anthropometric measurements (principally height and weight).

Geographic coverage

The sample for the NDHS was designed to be nationally representative. The design involved a two- stage stratified sample which is self-weighting within each of the three health regions for which estimates of fertility and mortality were required--Northwest, Northeast, and the combined Central/South region. In order to have a sufficient number of cases for analysis, oversampling was necessary for the Northeast region, which has only 14.8 percent of the population. Therefore, the sample was not allocated proportionally across regions and is not completely self-weighting.

Analysis unit

• Household
• Women age 15-49

Universe

All women age 15-49 years who were either usual residents of the households in the sample or visitors present in the household on the night before the survey were eligible to be interviewed in the survey.

Kind of data

Sample survey data

Sampling procedure

The sample for the Namibia Demographic and Health Survey (NDHS) was designed to yield a nationally representative probability sample of 5000 completed interviews with women between the ages of 15 and 49, regardless of their marital status, selected from 175 area units throughout the country. The design involved a two-stage stratified sample, which is self-weighting in each of the three main reporting domains: the Northwest region, the Northeast region, and the combined Central and South region.

AREA SAMPLING FRAME

The Republic of Namibia undertook a population and housing census in 1991 (the census dates were from 21 to 30 October). For this purpose, the country was divided into 27 census districts. Each district was in turn demarcated into enumeration areas (EAs). A list of 2177 EAs, together with their measure of size, which is the EA population as recorded manually from the Enumerator's Record Books, was compiled and used to select the area units for the NDHS.

SAMPLE DESIGN

Within each of the three domains (Northwest, Northeast, and Central/South), the sampling frame for the NDHS was stratified by urban and rural, and then by census district. The sample was then selected in two stages: at the first stage, 175 primary sampling units (PSU) were selected from the frame with probability proportional to size, the size being the population in the PSU. In general, a PSU corresponds to an EA as defined for the 1991 population and housing census. For each selected PSU, the Enumerator's Record Books obtained from the census was used as the frame for selecting the households to be included in the survey.

SAMPLING PARAMETERS

The objective of the sample design was to obtain 5000 completed individual interviews with women between the ages of 15 and 49 regardless of their marital status. To allow for nonresponse and other losses, an appropriate number of households was selected so as to obtain 5500 eligible women. A proportional allocation of the 5500 women to the three domains would have yielded approximately 2400, 800, and 2300 to the Northwest, Northeast and Central/South regions, respectively. While the samples for the Northwest and Central/South regions would have been sufficiently large for providing reliable estimates, it was not the case for the Northeast region. For this reason, it was necessary to double the sampling rate for the Northeast region relative to the other two regions. Table B.1 shows the allocation of the sample to the three regions as well as the implied number of households and PSUs to be selected in each region.

Mode of data collection

Face-to-face

Research instrument

Two types of questionnaires were used in the NDHS: the Household Questionnaire and the Individual Questionnaire. The content of these questionnaires were based on the DHS model B questionnaire, which was designed for use in countries with low contraceptive prevalence. Additions and modifications to the model questionnaire were made in order to collect information particularly relevant to Namibia. Verbal autopsy and maternal mortality modules were added. The questionnaires were developed in English whereafter it was translated by experienced translators into six languages (Oshiwambo, Herere, Afrikaans, Lozi, Kwangali and Damara/Nama). The translation in the indigenous languages was necessary as it makes interviewing much less susceptible to interviewers interpretations. The prepared translation in the Damara/Nama language was not printed since the translated version would be required only in a small number of households, of which the majority speaks Afrikaans. All teams, however, carried a master copy of this questionnaire to serve as a reference should need arise.

a) The Household Questionnaire was used to enumerate all usual members of and visitors to the selected households and to obtain information on each individual's age, sex, relationship to the head of the household, and educational attainment. In addition, questions were asked about indicators of the socioeconomic position of the household, such as the source of water, sanitation facilities, and the availability of electricity and durable goods. Information recorded on the Household Questionnaire was used to identify respondents eligible for the individual interview.

b) The individual questionnaire was administered to women age 15-49 who spent the night preceding the household interview in the selected household. Information in the following areas was obtained during the individual interview: 1. Background characteristics of the respondent 2. Health services utilisation and availability 3. Reproductive behaviour and intentions 4. Knowledge and use of contraception 5. Breastfeeding, health, and vaccination status of children 6. Marriage 7. Fertility preferences 8. Husband's background and woman's work 9. Height and weight of children under five and their mothers 10. Causes of death in childhood 11. Maternal mortality

Cleaning operations

Data processing staff for the NDHS consisted of five data entry clerks of which one was used to control all incoming completed EAs from the field, and one supervisor (the head of data processing) from the Epidemiology Section. Periodic assistance was given by the Macro International staff. Four microcomputers were installed in the project office, Epidemiology Section, MOHSS, and were used to process the data utilizing ISSA software for processing. All data entry occurred in the project office in Windhoek.

Before questionnaires were passed for data entry, office editing was conducted. This entailed checking for intemal consistency of responses recorded in the questionnaire, that skip instructions were properly followed, that there were no omissions, and that all entries were legible. This secured completeness of the questionnaires and speeded up the work of data entry staff.

Data entry started in July and was completed in the second week of December 1992. As data entry continued, editing was carried out every second week by running the ISSA program to check for inconsistencies, and corrections were made (when possible) by referencing the original questionnaire. A standard set of data quality tables were run every second week. These tables provided data on the performance of each team and were taken into the field to discuss the results with the supervisors to improve data collection. The staff from the Epidemiology Section visited the teams in the field every second week.

The staff from the Epidemiology section with assistance from the Macro International staff completed the final editing in December 1992, and secondary editing was done by Macro International staff. Preparation and presentation of the Preliminary report was conducted in November and December 1992. The preliminary report was published in December 1992.

Response rate

A total of 5,006

This dataset contains census tract-level social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning. To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

Graph and download economic data for Business Applications from Corporations: Health Care and Social Assistance in the United States (BACBANAICS62SAUS) from Jul 2004 to Feb 2025 about business applications, healthcare, social assistance, health, corporate, business, and USA.

In early February 2024, we will be retiring the Mpox Vaccinations Given to SF Residents by Demographics dataset. This dataset will be archived and no longer update. A historic record of this data will remain available.

A. SUMMARY This dataset represents doses of mpox vaccine (JYNNEOS) administered in California to residents of San Francisco ages 18 years or older. This dataset only includes doses of the JYNNEOS vaccine given on or after 5/1/2022. All vaccines given to people who live in San Francisco are included, no matter where the vaccination took place. The data are broken down by multiple demographic stratifications.

B. HOW THE DATASET IS CREATED Information on doses administered to those who live in San Francisco is from the California Immunization Registry (CAIR2), run by the California Department of Public Health (CDPH). Information on individuals’ city of residence, age, race, ethnicity, and sex are recorded in CAIR2 and are self-reported at the time of vaccine administration. Because CAIR2 does not include information on sexual orientation, we pull information from the San Francisco Department of Public Health’s Epic Electronic Health Record (EHR). The populations represented in our Epic data and the CAIR2 data are different. Epic data only include vaccinations administered at SFDPH managed sites to SF residents.

Data notes for population characteristic types are listed below.

Age * Data only include individuals who are 18 years of age or older.

Race/ethnicity * The response option "Other Race" is categorized by the data source system, and the response option "Unknown" refers to a lack of data.

Sex * The response option "Other" is categorized by the source system, and the response option "Unknown" refers to a lack of data.

Sexual orientation * The response option “Unknown/Declined” refers to a lack of data or individuals who reported multiple different sexual orientations during their most recent interaction with SFDPH.

For convenience, we provide the 2020 5-year American Community Survey population estimates.

C. UPDATE PROCESS Updated daily via automated process.

D. HOW TO USE THIS DATASET This dataset includes many different types of demographic groups. Filter the “demographic_group” column to explore a topic area. Then, the “demographic_subgroup” column shows each group or category within that topic area and the total count of doses administered to that population subgroup.

E. CHANGE LOG

• UPDATE 1/3/2023: Due to low case numbers, this page will no longer include vaccinations after 12/31/2022.

Abstract

The Bangladesh Demographic and Health Survey (BDHS) is the first of this kind of study conducted in Bangladesh. It provides rapid feedback on key demographic and programmatic indicators to monitor the strength and weaknesses of the national family planning/MCH program. The wealth of information collected through the 1993-94 BDHS will be of immense value to the policymakers and program managers in order to strengthen future program policies and strategies.

The BDHS is intended to serve as a source of population and health data for policymakers and the research community. In general, the objectives of the BDHS are to: - asses the overall demographic situation in Bangladesh, - assist in the evaluation of the population and health programs in Bangladesh, and - advance survey methodology.

More specifically, the BDHS was designed to: - provide data on the family planning and fertility behavior of the Bangladesh population to evaluate the national family planning programs, - measure changes in fertility and contraceptive prevalence and, at the same time, study the factors which affect these changes, such as marriage patterns, urban/rural residence, availability of contraception, breastfeeding patterns, and other socioeconomic factors, and - examine the basic indicators of maternal and child health in Bangladesh.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 10-49
• Men

Kind of data

Sample survey data

Sampling procedure

Bangladesh is divided into five administrative divisions, 64 districts (zillas), and 489 thanas. In rural areas, thanas are divided into unions and then mauzas, an administrative land unit. Urban areas are divided into wards and then mahallas. The 1993-94 BDHS employed a nationally-representative, two-stage sample. It was selected from the Integrated Multi-Purpose Master Sample (IMPS), newly created by the Bangladesh Bureau of Statistics. The IMPS is based on 1991 census data. Each of the five divisions was stratified into three groups: 1) statistical metropolitan areas (SMAs) 2) municipalities (other urban areas), and 3) rural areas. In rural areas, the primary sampling unit was the mauza, while in urban areas, it was the mahalla. Because the primary sampling units in the IMPS were selected with probability proportional to size from the 1991 census frame, the units for the BDHS were sub-selected from the IMPS with equal probability to make the BDHS selection equivalent to selection with probability proportional to size. A total of 304 primary sampling units were selected for the BDHS (30 in SMAs, 40 in municipalities, and 234 in rural areas), out of the 372 in the IMPS. Fieldwork in three sample points was not possible, so a total of 301 points were covered in the survey.

Since one objective of the BDHS is to provide separate survey estimates for each division as well as for urban and rural areas separately, it was necessary to increase the sampling rate for Barisal Division und for municipalities relative to the other divisions, SMAs, and rural areas. Thus, the BDHS sample is not self-weighting and weighting factors have been applied to the data in this report.

After the selection of the BDHS sample points, field staffs were trained by Mitra and Associates and conducted a household listing operation in September and October 1993. A systematic sample of households was then selected from these lists, with an average "take" of 25 households in the urban clusters and 37 households in rural clusters. Every second household was identified as selected for the husband's survey, meaning that, in addition to interviewing all ever-married women age 10-49, interviewers also interviewed the husband of any woman who was successfully interviewed. It was expected that the sample would yield interviews with approximately 10,000 ever-married women age 10-49 and 4,200 of their husbands.

Note: See detailed in APPENDIX A of the survey final report.

Sampling deviation

Data collected for women 10-49, indicators calculated for women 15-49. A total of 304 primary sampling units were selected, but fieldwork in 3 sample points was not possible.

Mode of data collection

Face-to-face

Research instrument

Four types of questionnaires were used for the BDHS: a Household Questionnaire, a Women's Questionnaire, a Husbands' Questionnaire, and a Service Availability Questionnaire. The contents of these questionnaires were based on the DHS Model A Questionnaire, which is designed for use in countries with relatively high levels of contraceptive use. Additions and modifications to the model questionnaires were made during a series of meetings with representatives of various organizations, including the Asia Foundation, the Bangladesh Bureau of Statistics, the Cambridge Consulting Corporation, the Family Planning Association of Bangladesh, GTZ, the International Centre for Diarrhoeal Disease Research (ICDDR,B), Pathfinder International, Population Communications Services, the Population Council, the Social Marketing Company, UNFPA, UNICEF, University Research Corporation/Bangladesh, and the World Bank. The questionnaires were developed in English and then translated into and printed in Bangla.

The Household Questionnaire was used to list all the usual members and visitors of selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for individual interview. In addition, information was collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, and ownership of various consumer goods.

The Women's Questionnaire was used to collect information from ever-married women age 10-49. These women were asked questions on the following topics: - Background characteristics (age, education, religion, etc.), - Reproductive history, - Knowledge and use of family planning methods, - Antenatal and delivery care, - Breastfeeding and weaning practices, - Vaccinations and health of children under age three, - Marriage, - Fertility preferences, and - Husband's background and respondent's work.

The Husbands' Questionnaire was used to interview the husbands of a subsample of women who were interviewed. The questionnaire included many of the same questions as the Women's Questionnaire, except that it omitted the detailed birth history, as well as the sections on maternal care, breastfeeding and child health.

The Service Availability Questionnaire was used to collect information on the family planning and health services available in and near the sampled areas. It consisted of a set of three questionnaires: one to collect data on characteristics of the community, one for interviewing family welfare visitors and one for interviewing family planning field workers, whether government or non-governent supported. One set of service availability questionnaires was to be completed in each cluster (sample point).

Cleaning operations

All questionnaires for the BDHS were returned to Dhaka for data processing at Mitra and Associates. The processing operation consisted of office editing, coding of open-ended questions, data entry, and editing inconsistencies found by the computer programs. One senior staff member, 1 data processing supervisor, questionnaire administrator, 2 office editors, and 5 data entry operators were responsible for the data processing operation. The data were processed on five microcomputers. The DHS data entry and editing programs were written in ISSA (Integrated System for Survey Analysis). Data processing commenced in early February and was completed by late April 1994.

Response rate

A total of 9,681 households were selected for the sample, of which 9,174 were successfully interviewed. The shortfall is primarily due to dwellings that were vacant, or in which the inhabitants had left for an extended period at the time they were visited by the interviewing teams. Of the 9,255 households that were occupied, 99 percent were successfully interviewed. In these households, 9,900 women were identified as eligible for the individual interview and interviews were completed for 9,640 or 97 percent of these. In one-half of the households that were selected for inclusion in the husbands' survey, 3,874 eligible husbands were identified, of which 3,284 or 85 percent were interviewed.

The principal reason for non-response among eligible women and men was failure to find them at home despite repeated visits to the household. The refusal rate was very low (less than one-tenth of one percent among women and husbands). Since the main reason for interviewing husbands was to match the information with that from their wives, survey procedures called for interviewers not to interview husbands of women who were not interviewed. Such cases account for about one-third of the non-response among husbands. Where husbands and wives were both interviewed, they were interviewed simultaneously but separately.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the survey final report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling 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

Evidence suggests that reductions in healthcare utilization, including forgone care, during the COVID-19 pandemic may be contributing towards excess morbidity and mortality. The objective of this study was to describe individual and community-level correlates of forgone care during the COVID-19 pandemic. We conducted a cross-sectional, secondary data analysis of participants (n = 2,003) who reported needing healthcare in two population-representative surveys conducted in Baltimore, MD in 2021 and 2021–2022. Abstracted data included the experience of forgone care, socio-demographic data, comorbidities, financial strain, and community of residence. Participant’s community of residence were linked with data acquired from the Baltimore Neighborhood Indicators Alliance relevant to healthcare access and utilization, including walkability and internet access, among others. The data were analyzed using weighted random effects logistic regression. Individual-level factors found to be associated with increased odds for forgone care included individuals age 35–49 (compared to 18–34), female sex, experiencing housing insecurity during the pandemic, and the presence of functional limitations and mental illness. Black/African American individuals were found to have reduced odds of forgone care, compared to any other race. No community-level factors were significant in the multilevel analyses. Moving forward, it will be critical that health systems identify ways to address any barriers to care that populations might be experiencing, such as the use of mobile health services or telemedicine platforms. Additionally, public health emergency preparedness planning efforts must account for the unique needs of communities during future crises, to ensure that their health needs can continue to be met. Finally, additional research is needed to better understand how healthcare access and utilization practices have changed during versus before the pandemic.

Abstract

The Ministry of Health and Social Welfare (MOHSW) initiated the 2004 Lesotho Demographic and Health Survey (LDHS) to collect population-based data to inform the Health Sector Reform Programme (2000-2009). The 2004 LDHS will assist in monitoring and evaluating the performance of the Health Sector Reform Programme since 2000 by providing data to be compared with data from the first baseline survey, which was conducted when the reform programme began. The LDHS survey will also provide crucial information to help define the targets for Phase II of the Health Sector Reform Programme (2005-2008). Additionally, the 2004 LDHS results will serve as the main source of key demographic indicators in Lesotho until the 2006 population census results are available.

The LDHS was conducted using a representative sample of women and men of reproductive age.

The specific objectives were to: - Provide data at national and district levels that allow the determination of demographic indicators, particularly fertility and childhood mortality rates; - Measure changes in fertility and contraceptive use and at the same time analyse the factors that affect these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding patterns, and important social and economic factors; - Examine the basic indicators of maternal and child health in Lesotho, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, and immunisation coverage for children; - Describe the patterns of knowledge and behaviour related to the transmission of HIV/AIDS, other sexually transmitted infections, and tuberculosis; - Estimate adult and maternal mortality ratios at the national level; - Estimate the prevalence of anaemia among children, women and men, and the prevalence of HIV among women and men at the national and district levels.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The sample for the 2004 LDHS covered the household population. A representative probability sample of more than 9,000 households was selected for the 2004 LDHS sample. This sample was constructed to allow for separate estimates for key indicators in each of the ten districts in Lesotho, as well as for urban and rural areas separately.

The survey utilized a two-stage sample design. In the first stage, 405 clusters (109 in the urban and 296 in the rural areas) were selected from a list of enumeration areas from the 1996 Population Census frame. In the second stage, a complete listing of households was carried out in each selected cluster. Households were then systematically selected for participation in the survey.

All women age 15-49 who were either permanent household residents in the 2004 LDHS sample or visitors present in the household on the night before the survey were eligible to be interviewed. In addition, in every second household selected for the survey, all men age 15-59 years were eligible to be interviewed if they were either permanent residents or visitors present in the household on the night before the survey. In the households selected for the men's survey, height and weight measurements were taken for eligible women and children under five years of age. Additionally, eligible women, men, and children under age five were tested in the field for anaemia, and eligible women and men were asked for an additional blood sample for anonymous testing for HIV.

Note: See detailed sample implementation in the APPENDIX A of the final 2004 Lesotho Demographic and Health Survey Final Report.

Mode of data collection

Face-to-face

Research instrument

Three questionnaires were used for the 2004 LDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. To reflect relevant issues in population and health in Lesotho, the questionnaires were adapted during a series of technical meetings with various stakeholders from government ministries and agencies, nongovernmental organizations and international donors. The final draft of the questionnaire was discussed at a large meeting of the LDHS Technical Committee organized by the MOHSW and BOS. The adapted questionnaires were translated from English into Sesotho and pretested during June 2004.

The Household Questionnaire was used to list all of the usual members and visitors in the selected households. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. Some basic information was also collected on the characteristics of each person listed, including age, sex, education, residence and emigration status, and relationship to the head of the household. For children under 18, survival status of the parents was determined. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, ownership of various durable goods, and access to health facilities. For households selected for the male survey subsample, the questionnaire was used to record height, weight, and haemoglobin measurements of women, men and children, and the respondents’ decision about whether to volunteer to give blood samples for HIV.

The Women’s Questionnaire was used to collect information from all women age 15-49. The women were asked questions on the following topics: - Background characteristics (education, residential history, media exposure, etc.) - Birth history and childhood mortality - Knowledge and use of family planning methods - Fertility preferences - Antenatal and delivery care - Breastfeeding and infant feeding practices - Vaccinations and childhood illnesses - Marriage and sexual activity - Woman’s work and husband’s background characteristics - Awareness and behaviour regarding AIDS, other sexually transmitted infections (STIs), and tuberculosis (TB) - Maternal mortality

The Men’s Questionnaire was administered to all men age 15-59 living in every other household in the 2004-05 LDHS sample. The Men’s Questionnaire collected much of the same information found in the Women’s Questionnaire, but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health, nutrition, and maternal mortality.

Geographic coordinates were collected for each EA in the 2004 LDHS.

Cleaning operations

The processing of the 2004 LDHS results began shortly after the fieldwork commenced. Completed questionnaires were returned periodically from the field to BOS headquarters, where they were entered and edited by data processing personnel who were specially trained for this task. The data processing personnel included two supervisors, two questionnaire administrators/office editors-who ensured that the expected number of questionnaires from each cluster was received-16 data entry operators, and two secondary editors. The concurrent processing of the data was an advantage because BOS was able to advise field teams of problems detected during the data entry. In particular, tables were generated to check various data quality parameters. As a result, specific feedback was given to the teams to improve performance. The data entry and editing phase of the survey was completed in May 2005.

Response rate

Response rates are important because high non-response may affect the reliability of the results. A total of 9,903 households were selected for the sample, of which 9,025 were found to be occupied during data collection. Of the 9,025 existing households, 8,592 were successfully interviewed, yielding a household response rate of 95 percent.

In these households, 7,522 women were identified as eligible for the individual interview. Interviews were completed with 94 percent of these women. Of the 3,305 eligible men identified, 85 percent were successfully interviewed. The response rate for urban women and men is somewhat higher than for rural respondents (96 percent compared with 94 percent for women and 88 percent compared with 84 percent for men). The principal reason for non-response among eligible women and men was the failure to find individuals at home despite repeated visits to the household. The lower response rate for men reflects the more frequent and longer absences of men from the household, principally because of employment and life style.

Response rates for the HIV testing component were lower than those for the interviews.

See summarized response rates in Table 1.2 of the Final Report.

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 2004 Lesotho Demographic and Health Survey (LSDHS) 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 2004 LSDHS 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