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

Public perception regarding diagnostic sample types as well as personal experiences can influence willingness to test. As such, public preferences for specific sample type(s) should be used to inform diagnostic and surveillance testing programs to improve public health response efforts. To understand where preferences lie, we conducted an international survey regarding the sample types used for SARS-CoV-2 tests. A Qualtrics survey regarding SARS-CoV-2 testing preferences was distributed via social media and email. The survey collected preferences regarding sample methods and key demographic data. Python was used to analyze survey responses. From March 30th to June 15th, 2022, 2,094 responses were collected from 125 countries. Participants were 55% female and predominantly aged 25–34 years (27%). Education and employment were skewed: 51% had graduate degrees, 26% had bachelor’s degrees, 27% were scientists/researchers, and 29% were healthcare workers. By rank sum analysis, the most preferred sample type globally was the oral swab, followed by saliva, with parents/guardians preferring saliva-based testing for children. Respondents indicated a higher degree of trust in PCR testing (84%) vs. rapid antigen testing (36%). Preferences for self- or healthcare worker-collected sampling varied across regions. This international survey identified a preference for oral swabs and saliva when testing for SARS-CoV-2. Notably, respondents indicated that if they could be assured that all sample types performed equally, then saliva was preferred. Overall, survey responses reflected the region-specific testing experiences during the COVID-19. Public preferences should be considered when designing future response efforts to increase utilization, with oral sample types (either swabs or saliva) providing a practical option for large-scale, accessible diagnostic testing.

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.

Population Health Management Systems Market Outlook

The global population health management systems market has been witnessing significant growth, with a market size valued at approximately USD 34.8 billion in 2023. Projections indicate that this market is expected to experience a robust CAGR of 13.5% from 2024 to 2032, reaching an estimated market size of USD 104.5 billion by 2032. The primary growth factors driving this optimistic forecast include the increasing demand for efficient healthcare delivery systems, the need for cost reduction in healthcare services, and the growing emphasis on patient-centered care. As the global healthcare sector transitions towards value-based care models, population health management systems are becoming instrumental in facilitating the shift by enabling healthcare providers to manage, analyze, and optimize the health of entire populations.

One of the major growth drivers in the population health management systems market is the rising prevalence of chronic diseases and the aging population worldwide. The increasing incidence of conditions such as diabetes, cardiovascular diseases, and respiratory disorders necessitates comprehensive health management strategies that can effectively track and manage patient health data. Population health management systems enable healthcare providers to integrate and analyze this data, leading to improved patient outcomes and more efficient use of healthcare resources. Additionally, the aging population presents a unique challenge as older adults generally require more frequent and intensive healthcare services, further driving the demand for robust health management solutions.

Another significant growth factor is the ongoing advancements in healthcare IT and data analytics technologies, which are critical enablers of population health management systems. The integration of advanced analytics, artificial intelligence, and machine learning technologies into these systems allows for more precise and predictive insights, enabling healthcare providers to proactively manage patient health and identify potential health risks before they escalate into severe conditions. The adoption of electronic health records (EHRs) and interoperability standards is also contributing to the seamless exchange of health data across various healthcare settings, enhancing the effectiveness of population health management initiatives.

The push towards value-based healthcare models is also fueling the growth of the population health management systems market. As healthcare systems worldwide shift from fee-for-service to value-based care, there is an increased need for solutions that can help healthcare providers meet quality metrics while controlling costs. Population health management systems offer the tools necessary to align healthcare delivery with these new reimbursement models by facilitating the coordination of care, improving patient engagement, and ensuring compliance with regulatory requirements. Moreover, government initiatives aimed at improving healthcare access and quality, particularly in developing regions, are expected to further boost the adoption of these systems.

In terms of regional outlook, North America currently dominates the market, largely due to the presence of a well-established healthcare infrastructure, high adoption of advanced healthcare technologies, and favorable government initiatives promoting value-based care. However, other regions, particularly the Asia Pacific, are expected to witness significant growth during the forecast period. Factors such as the increasing healthcare expenditure, rising awareness about population health management, and the burgeoning demand for healthcare IT solutions in countries like China and India are driving this growth. Additionally, Europe and Latin America are also anticipated to contribute to market expansion owing to the increasing focus on improving healthcare delivery and the rising prevalence of chronic diseases.

Component Analysis

The population health management systems market is segmented by component into software and services, each playing a crucial role in the overall functioning and effectiveness of these systems. The software segment encompasses a wide range of applications, including data analytics, care management, and patient engagement platforms, which are essential for collecting, analyzing, and utilizing healthcare data to improve patient outcomes. These software solutions are being constantly upgraded with advanced features such as predictive analytics and artificial intelligence to provide deeper insights into patient health trends and facilitate proactive interventions.

Abstract

The Liberia Demographic and Health Survey (LDHS) was conducted as part of the worldwide Demographic and Health Surveys (DHS) program, in which surveys are being carried out in countries in Africa, Asia, Latin America, and the Middle East. Liberia was the second country to conduct a DHS and the first country in Africa to do so. THe LDHS was a national-level survey conducted from February to July 1986, covering a sample of 5,239 women aged 15 to 49.

The major objective of the LDHS was to provide data on fertility, family planning and maternal and child health to planners and policymakers in Liberia for use in designing and evaluating programs. Although a fair amount of demographic data was available from censuses and surveys, almost no information existed concerning family planning, health, or the determinants of fertility, and the data that did exist were drawn from small-scale, sub-national studies. Thus, there was a need for data to make informed policy choices for family planning and health projects.

A more specific objective was to provide baseline data for the Southeast Region Primary Health Care Project. In order to effectively plan strategies and to eventually evaluate the progress of the project in meeting its goals, there was need for data to indicate the health situation in the two target counties prior to the implementation of the project. Many of the desired topics, such as immunizations, family planning use, and prenatal care, were already incorporated into the model DHS questionnaire; nevertheless, the LDHS was able to better accommodate the needs of this project by adding several questions and by oversampling women living in Sinoe and Grand Gedeh Counties.

Another important goal of the LDHS was to enhance tile skills of those participating in the project for conducting high-quality surveys in the future. Finally, the contribution of Liberian data to an expanding international dataset was also an objective of the LDHS.

Geographic coverage

National

Analysis unit

• Households
• Children age 0-5
• Women age 15 to 49
• Men

Kind of data

Sample survey data

Sampling procedure

The sample for the Liberia Demographic and Health Survey was based on the sampling frame of about 4,500 censal enumeration areas (EAs) that were created for the 1984 Population Census. It was decided to eliminate very remote EAs prior to selecting the sample. The definition of remoteness used was "any EA in which the largest village was estimated to be more than 3-4 hours' walk from a road." According to the 1984 census, the excluded areas represent less than 3 percent of the total number of households in the country. Since the major analytic objective of the LDHS was to adequately estimate basic demographic and health indicators including fertility, mortality, and contraceptive prevalence for the whole country and the two sub-universes (Since and Grand Gedeh Counties), it was decided to oversample these two counties. Consequently, three explicit sub-universes of EAs were created: (1) Since County, (2) Grand Gedeh County, and (3) the rest of the country.

The design provided a self-weighted sample within each sub-universe, but, because of the oversampling in Sinoe and Grand Gedeh Counties, the sample is not self-weighting at the national level. Eligible respondents for the survey were women aged 15-49 years who were present the night before the interview in any of the households included in the sample selected for the LDHS.

The total sample size was expected to be about 6,000 women aged 15-49 with a target by sub-universe of 1,000 each in Sinoe and Grand Gedeh Counties and 4,000 in the rest of the country. It was decided that a sample of approximately 5,500 households selected through a two-stage procedure would be appropriate to reach those objectives. Sampling was carried out independently in each sub-universe. In the rest of the country sub-universe, counties were arranged for selection in serpentine order from the northwest (Cape Mount County) to the southeast (Maryland County). In the first stage EAs were selected systematically with probability proportional to size (size = number of households in 1984). Twenty-four EAs were selected in each of Sinoe and Grand Gedeh Counties and 108 EAs in the rest of the country.

See full sample procedure in the survey final report.

Mode of data collection

Face-to-face

Research instrument

The Liberia Demographic and Health Survey (LDHS) utilized two questionnaires: One to list members of the selected households (Household Questionnaire) and the other to record information from all women aged 15-49 who were present in the selected households the night before the interview (Individual Questionnaire).

Both questionnaires were produced in Liberian English and were pretested in September 1985. The Individual Questionnaire was an early version of the DHS model questionnaire. It covered three main topics: (1) fertility, including a birth history and questions concerning desires for future childbearing, (2) family planning knowledge and use, and (3) family health, including prevalence of childhood diseases, immunizations for children under age five, and breasffeeding and weaning practices.

Cleaning operations

Data from the questionnaires were entered onto microcomputers at the Bureau of Statistics office in Monrovia. The data were then subjected to extensive checks for consistency and accuracy.

Errors detected during this operation were resolved either by referring to the original questionnaire, or, in some cases, by logical inference from other information given in the record. Finally, dates were imputed for the small number of cases where complete dates of important events were not given.

Response rate

Out of the total of 6,1306 households selected, 14.5 percent were found not to be valid households in the field, either because the dwelling had been vacated or destroyed, or the household could not be located or did not exist. Of the 5,609 households that were found to exist, 90 percent were successfully interviewed. In the households that were interviewed, a total of 5,340 women were identified as being eligible for individual interview (that is, they were aged 15-49 and had spent the night before the interview in the selected household). This represents an average of slightly over one eligible woman per household.

The response rate for eligible women was 98 percent. The main reason for nonresponse was the absence of the woman. Similar data are presented by sample subuniverse.

Sampling error estimates

The results from sample surveys are affected by two types of errors: (1) nonsampling error and (2) sampling error. Nonsampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way questions are asked, misunderstanding of the questions on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the Liberia Demographic and Health Survey to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

The sample of women selected in the LDHS is only one of many samples of the same size that could have been selected from the same population, using the same design. Each one would have yielded results that differed somewhat from the actual sample selected. The variability observed between all possible samples constitutes sampling error, which, although it is not known exactly, can be estimated from the survey results. Sampling error is usually measured in terms of the "standard error" of a particular statistic (mean, percentage, etc.), which is the square root of the variance of the statistic across all possible samples of equal size and design.

The standard error can be used to calculate confidence intervals within which one can be reasonably assured the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples of identical size and design will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the LDHS sample design depended on stratification, stages, and clusters and consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS was used to assist in computing the sampling errors with the proper statistical methodology.

Data appraisal

Information on the completeness of date reporting is of interest in assessing data quality. With regard to dates of birth of individual women, 42 percent of respondents reported both a month and year of birth, 21 percent gave a year of birth in addition to current age, and 37 percent gave only their ages. With regard to children's dates of birth in the birth history, 85 percent of births had both month and year reported, 12 percent had year and age reported, 1 percent had only age reported, and 2 percent had no date information.

Rate of Emergency Department visits per 1,000 populationThis data describes emergency department visits indicated by ambulatory visit records from Ontario care centres. Data are based on all ambulatory visits classified as an unplanned emergency visit. Geographic information is based on patient’s place of residence, not where the visit occurred. Data from ICD-10 chapter 20 through 23 and unknown diagnosis are excluded as they do not represent diseases or conditions or cannot be a main problem diagnosis (MPDx).Source: IntelliHealth – NACRS (2022) Rate of frequent users of Emergency Department (less than or equal to 4 visits per year)This data describes emergency department visits indicated by ambulatory visit records from Ontario care centres. Data are based on all ambulatory visits classified as an unplanned emergency visit. Geographic information is based on patient’s place of residence, not where the visit occurred. Data from ICD-10 chapter 20 through 23 and unknown diagnosis are excluded as they do not represent diseases or conditions or cannot be a main problem diagnosis (MPDx). Frequent emergency department users are individuals who have accessed the emergency department four or more times per calendar year.Source: IntelliHealth – NACRS (2022)Rate of hospitalizations (all causes) per 1,000 population This data describes hospitalizations indicated by inpatient discharge records from Ontario care centres. Data are based on all inpatient discharges from acute care hospitals, excluding patients admitted as newborn or stillborn infants, and excluding ICD-10 chapters 20 through 23 and with unknown diagnosis. Geographic information is based on patient’s place of residence, not where the hospitalization occurred. Source: IntelliHealth – DAD (2022)

Explore one of the most extensive health surveys in the world, capturing essential data on the lifestyle, health behaviors, and preventive care habits of adults across the United States. Conducted by the Behavioral Risk Factor Surveillance System (BRFSS), this dataset compiles information from over 400,000 annual interviews, revealing how everyday choices impact chronic health conditions and overall well-being. From smoking and alcohol use to exercise frequency and mental health, this data provides crucial insights into the health status of U.S. residents and the factors that contribute to their longevity and quality of life.

Dataset Features:

• Respondent ID: Unique identifier for each individual in the survey.
• Demographics: Age, gender, marital status, income, education, and employment status.
• Health Status: Self-rated health, days impacted by mental or physical health issues, chronic conditions (e.g., diabetes, cardiovascular diseases).
• Health Behaviors: Information on tobacco and alcohol use, dietary habits like fruit and vegetable consumption, and physical activity levels.
• Preventive Health Services: Data on the utilization of preventive health services, including blood pressure checks, cholesterol screenings, flu vaccinations, and HIV testing.
• Mental Health and Support Systems: Insight into reported stress, depression, life satisfaction, and the availability of social support networks.

Usage:

This dataset offers a powerful foundation for: - Public health research: Analyze trends in health behaviors, investigate health disparities, and examine their impact on chronic conditions. - Policy-making: Guide the creation of targeted public health policies at both state and national levels, helping to address specific health risks within communities. - Predictive modeling: Develop predictive models to assess risk factors for chronic diseases based on demographic and lifestyle data.

Coverage:

Collected from adults across all 50 states, the District of Columbia, and three U.S. territories, this dataset provides a comprehensive snapshot of the nation’s health landscape, allowing comparisons and analyses across different geographic and demographic groups.

License:

Public Domain

Who Can Benefit:

• Researchers and Data Scientists: For in-depth analysis of health trends and risk factors associated with lifestyle choices.
• Public Health Agencies and Policymakers: To inform strategies aimed at reducing health disparities and promoting wellness.
• Healthcare Providers: To identify population-level health risks and improve preventive care programs.

How to Use It:

• Trend Analysis: Examine shifts in health behaviors and prevalence of risk factors across years and regions.
• Predictive Modeling: Create algorithms to predict chronic disease likelihood based on a person’s lifestyle and preventive care habits.
• Program Evaluation: Assess the effectiveness of public health programs and interventions over time and tailor new initiatives to meet emerging needs.

Dataset Information:

• License: Public Domain
• Region: United States
• Type: Survey Data
• Version: 1.0

Collection Information:

• Access: TEXT/CSV
• Formats: CSV
• Records: Over 400,000 per year
• Last Updated: 2015
• Size: Large (several GBs, depending on data structure)

US Healthcare Readmissions and Mortality

Evaluating Hospital Performance

By Health [source]

About this dataset

This dataset contains detailed information about 30-day readmission and mortality rates of U.S. hospitals. It is an essential tool for stakeholders aiming to identify opportunities for improving healthcare quality and performance across the country. Providers benefit by having access to comprehensive data regarding readmission, mortality rate, score, measure start/end dates, compared average to national as well as other pertinent metrics like zip codes, phone numbers and county names. Use this data set to conduct evaluations of how hospitals are meeting industry standards from a quality and outcomes perspective in order to make more informed decisions when designing patient care strategies and policies

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How to use the dataset

This dataset provides data on 30-day readmission and mortality rates of U.S. hospitals, useful in understanding the quality of healthcare being provided. This data can provide insight into the effectiveness of treatments, patient care, and staff performance at different healthcare facilities throughout the country.

In order to use this dataset effectively, it is important to understand each column and how best to interpret them. The ‘Hospital Name’ column displays the name of the facility; ‘Address’ lists a street address for the hospital; ‘City’ indicates its geographic location; ‘State’ specifies a two-letter abbreviation for that state; ‘ZIP Code’ provides each facility's 5 digit zip code address; 'County Name' specifies what county that particular hospital resides in; 'Phone number' lists a phone contact for any given facility ;'Measure Name' identifies which measure is being recorded (for instance: Elective Delivery Before 39 Weeks); 'Score' value reflects an average score based on patient feedback surveys taken over time frame listed under ' Measure Start Date.' Then there are also columns tracking both lower estimates ('Lower Estimate') as well as higher estimates ('Higher Estimate'); these create variability that can be tracked by researchers seeking further answers or formulating future studies on this topic or field.; Lastly there is one more measure oissociated with this set: ' Footnote,' which may highlight any addional important details pertinent to analysis such as numbers outlying National averages etc..

This data set can be used by hospitals, research facilities and other interested parties in providing inciteful information when making decisions about patient care standards throughout America . It can help find patterns about readmitis/mortality along county lines or answer questions about preformance fluctuations between different hospital locations over an extended amount of time. So if you are ever curious about 30 days readmitted within US Hospitals don't hesitate to dive into this insightful dataset!

Research Ideas

• Comparing hospitals on a regional or national basis to measure the quality of care provided for readmission and mortality rates.
• Analyzing the effects of technological advancements such as telemedicine, virtual visits, and AI on readmission and mortality rates at different hospitals.
• Using measures such as Lower Estimate Higher Estimate scores to identify systematic problems in readmissions or mortality rate management at hospitals and informing public health care policy

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Readmissions_and_Deaths_-_Hospital.csv | Column name | Description | |:-------------------------|:---------------------------------------------------------------------------------------------------| | Hospital Name ...

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.

Relationships of demographic, socioeconomic, geographic, benefits scheme, and economic status factors with mammograms and Pap smear using Multiple Logistic Regression, Backward stepwise.

The National Medical Expenditure Survey (NMES) series provides information on health expenditures by or on behalf of families and individuals, the financing of these expenditures, and each person's use of services. The Household Survey was fielded over four rounds of personal and telephone interviews at four-month intervals. Baseline data on household composition, employment, and insurance characteristics were updated each quarter, and information on all uses of and expenditures for health care services and sources of payment was obtained. In addition to the core data, Public Use Tape 18 provides supplemental information on income, assets, and taxes. Income-related variables distinguish among 26 types of income. Also included are demographic characteristics of respondents (age, race/ethnicity, sex), tax filing status, home ownership, type of occupation, medical deductions, type of payment for health care, day care arrangements for children, pregnancies during 1987, related prenatal care, veteran status, and loss of a close relative or friend.

In 2022, children and teens are over-represented as health center patients compared to their proportion in the population. This statistic depicts the age distribution of health center patients compared to overall U.S. population as of 2022.

Abstract

The 2005 Armenia Demographic and Health Survey (2005 ADHS) is the second in a series of nationally representative sample surveys designed to provide information on population and health issues in Armenia. As in the 2000 ADHS, the primary goal of the 2005 survey was to develop a single integrated set of demographic and health data pertaining to the population of the Republic of Armenia. In addition to integrating measures of reproductive, child, and adult health, another feature of the 2005 ADHS survey is that the majority of data are presented at the marz (region) level.

The 2005 ADHS was conducted by the National Statistical Service (NSS) and the MOH of the Republic of Armenia from September through December 2005. ORC Macro provided technical support for the survey through the MEASURE DHS project. MEASURE DHS is a worldwide project, sponsored by the United States Agency for International Development (USAID), with a mandate to assist countries in obtaining information on key population and health indicators. USAID/Armenia provided funding for the survey, while the United Nations Children’s Fund (UNICEF)/Armenia and the United Nations Population Fund (UNFPA)/Armenia supported the survey through in-kind contributions.

The 2005 ADHS collected national- and regional-level data on fertility and contraceptive use, maternal and child health, adult health, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. Data are presented by marz wherever sample size permits.

The 2005 ADHS results are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of and health services for the people of Armenia. The 2005 ADHS also contributes to the growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-49

Kind of data

Sample survey data

Sampling procedure

The sample was designed to permit detailed analysis-including the estimation of rates of fertility, infant/child mortality, and abortion-for the national level, for Yerevan, and for total urban and total rural areas separately. Many indicators can also be estimated at the regional (marz) level.

A representative probability sample of 7,565 households was selected for the 2005 ADHS sample. The sample was selected in two stages. In the first stage, 308 clusters were selected from a list of enumeration areas in a subsample from a master sample that was designed from the 2001 Population Census. 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 residents of the households in the 2005 ADHS sample or visitors present in the household on the night before the survey were eligible to be interviewed. Interviews were completed with 6,566 women. In addition, in a subsample of one-third of all the households selected for the survey, all men age 15-49 were eligible to be interviewed if they were either permanent residents or visitors present in the household on the night before the survey. Interviews were completed with 1,447 men.

Note: See detailed summarized sample implementation tables in APPENDIX A of the report which is presented in this documentation.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2005 ADHS: a Household Questionnaire, a Women’s Questionnaire, and a Men’s questionnaire. The Household and Individual Questionnaires were based on model survey instruments developed in the MEASURE DHS program and on questionnaires used in the 2000 ADHS. The model questionnaires were adapted for use by experts from the NSS and MOH. Input was also sought from a number of non-governmental organizations. The questionnaires were developed in English and translated into Armenian. The Household and Individual Questionnaires were pretested in June 2005.

The Household Questionnaire was used to list all usual members of and visitors to the selected households and to collect information on the socioeconomic status of the household. The first part of the Household Questionnaire collected information on the age, sex, educational attainment, and relationship to the household head of each household member or visitor. This information provides basic demographic data for Armenian households. It also was used to identify the women and men who were eligible for the individual interview (i.e., women and men age 15-49). In the second part of the Household Questionnaire, there were questions on housing characteristics (e.g., flooring material, source of water, type of toilet facilities), on ownership of a variety of consumer goods, and other questions relating to the socioeconomic status of the household. In addition, the Household Questionnaire was used to record height and weight measurements of women, men, and children under age five; hemoglobin measurement of women and children under age five; and blood pressure measurement of women and men.

The Women’s Questionnaire obtained data from women age 15-49 on the following topics: • Background characteristics • Pregnancy history • Antenatal, delivery, and postnatal care • Knowledge, attitudes, and use of contraception • Reproductive and adult health • Health care utilization • Vaccinations, birth registration, and health of children under age five • Episodes of diarrhea and respiratory illness of children under age five • Breastfeeding and weaning practices • Marriage and recent sexual activity • Fertility preferences • Knowledge of and attitude toward HIV/AIDS and other sexually transmitted infections

The Men’s Questionnaire, administered to men age 15-49, focused on the following topics: • Background characteristics • Health and health care utilization • Marriage and recent sexual activity • Attitudes toward and use of condoms • Knowledge of and attitude toward HIV/AIDS and other sexually transmitted infections • Attitudes toward women’s status

Response rate

A total of 7,565 households were selected for the sample, of which 7,003 were occupied at the time of fieldwork. The main reason for the difference is that some of the dwelling units that were occupied during the household listing operation were either vacant or the household was away for an extended period at the time of interviewing. Of the occupied households, 96 percent were successfully interviewed.

In these households, 6,773 women were identified as eligible for the individual interview, and interviews were completed with 97 percent of them. Of the 1,630 eligible men identified, 89 percent were successfully interviewed. Response rates are almost identical in urban and rural areas.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the report which is presented this documentation.

Sampling error estimates

Estimates derived from a sample survey are affected by two types of errors: 1) non-sampling errors, and 2) 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 on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2005 Armenia DHS (2005 ADHS) to minimize this type of error, non-sampling 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 2005 ADHS 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 within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2005 ADHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use a more complex formula. The computer software used to calculate sampling errors for the 2005 ADHS is the sampling error module in ISSA (Integrated System for Survey Analysis). This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. Another approach, the Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: See detailed

The 1987 NMES provides information on health expenditures by or on behalf of families and individuals, the financing of these expenditures, and each person's use of services. Public Use Tape 14.5 provides three data files containing information on the use of and expenditures for ambulatory medical services reported in the Household Survey. The Household Survey is one of the three major components of the 1987 National Medical Expenditure Survey (NMES). (The other two components are the Survey of American Indians and Alaska Natives [SAIAN] and the Institutional Population Component.) The Household Survey was fielded over four rounds of personal and telephone interviews at four-month intervals. Baseline data on household composition, employment, and insurance characteristics were updated each quarter, and information on all uses of and expenditures for health care services and sources of payment was obtained. An ambulatory visit is defined as a single contact with a medical provider for one or more services in either a hospital outpatient department or emergency room, a setting other than an inpatient hospital (such as a physician's office, a clinic, or a lab), a nursing home, or a person's home. The first file includes visits and telephone calls to physicians' offices (including HMOs and health departments) in settings other than a hospital or at home, and to providers of care (e.g., chiropractors and psychologists). The second file includes visits to hospital outpatient departments, and the third file covers visits to hospital emergency rooms, both regardless of provider type. A record on any of these data files represents a unique ambulatory visit. In addition, each file contains demographic information such as age, sex, and race, dates of visits, medical conditions associated with the visit, and variables such as types of procedures performed and the main reason for the visit.

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Find Massachusetts health data by community, county, and region, including population demographics. Build custom data reports with over 100 health and social determinants of health data indicators and explore over 28,000 current and historical data layers in the map room.

The proportion of births attended by skilled healthcare personnel is sourced from the United Nations Statistics website under data related to Sustainable Development Goals. This data is related to Goal 3, “Ensure healthy lives and promote well-being for all at all ages”, and falls under target 3.1, “By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births.” Gathering data regarding the proportion of births attended by skilled healthcare personnel can help to achieve these goals by identifying where there are care gaps and what conditions are risk factors for an increased rate of maternal mortality. National-level household surveys are the main data sources used to collect data for skilled health personnel. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS) and other national surveys based on similar methodologies. Surveys are undertaken every 3 to 5 years. Data sources also include routine service statistics Population-based surveys is the preferred data source in countries with a low utilization of childbirth services, where private sector data are excluded from routine data collection, and/or with weak health information systems. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS) and other national surveys based on similar methodologies. In MICS, DHS and similar surveys, the respondent is asked about the last live birth and who helped during delivery for a period up to five years before the interview. The surveys are generally undertaken every 3 to 5 years. Routine service/facility records is a more common data source in countries where a high proportion of births occur in health facilities and are therefore recorded. These data can be used to track the indicator on an annual basis.This data set is just one of the many datasets on the Global Midwives Hub, a digital resource with open data, maps, and mapping applications (among other things), to support advocacy for improved maternal and newborn services, supported by the International Confederation of Midwives (ICM), UNFPA, WHO, and Direct Relief.

Analysis of ‘MSSA Detail 2010c1 public’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/493330c8-eda1-4c32-b839-09b08e144875 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

Medical Service Study Areas - Census Detail, 2010

Medical Service Study Areas (MSSAs) are sub-city and sub-county geographical units used to organize and display population, demographic and physician data. MSSAs were developed in 1976 by the California Healthcare Workforce Policy Commission (formerly California Health Manpower Policy Commission) to respond to legislative mandates requiring it to determine "areas of unmet priority need for primary care family physicians" (Song-Brown Act of 1973) and "geographical rural areas where unmet priority need for medical services exist" (Garamendi Rural Health Services Act of 1976).

MSSAs are recognized by the U.S. Health Resources and Services Administration, Bureau of Health Professions' Office of Shortage Designation as rational service areas for purposes of designating Health Professional Shortage Areas (HPSAs), and Medically Underserved Areas and Medically Underserved Populations (MUAs/MUPs).

The MSSAs incorporate the U.S. Census total population, socioeconomic and demographic data and are updated with each decadal census. Office of Statewide Health Planning and Development provides updated data for each County's MSSAs to the County and Communities, and will schedule meetings for areas of significant population change. Community meetings will be scheduled throughout the State as needed.

Adopted by the California Healthcare Workforce Policy Commission on May 15, 2002.

Each MSSA is composed of one or more complete census tracts. MSSAs will not cross county lines. All population centers within the MSSA are within 30 minutes travel time to the largest population center.

Urban MSSA - Population range 75,000 to 125,000. Reflect recognized community and neighborhood boundaries. Similar demographic and socio-economic characteristics.

Rural MSSA - Population density of less than 250 persons per square mile. No population center exceeds 50,000.

Frontier MSSA - Population density of less than 11 persons per square mile.

--- Original source retains full ownership of the source dataset ---

This dataset covers Healthcare coverage status of adults in New York state, Health insurance minorities and low English level by census tract, Health insurance single parent and disability by census tracts, Health insurance and socioeconomic status by census tracts, Medicare Part B, Part D drug cost and utilization data, Medicare Part D plan reconciliation contract, Part D plan reconciliation parent organization, Medicare plan payment Part C county & plan level, Medicare plan payment Part D.

Percentage of the population with unmet healthcare need due to cost for Statistical Area 2 (2018) units. Original data sourced from Census 2018 and New Zealand Health Survey 2017/18 and 2018/19. Data provided are synthetic data produced from spatial microsimulation modelling.