In 2021, seven in ten physicians preferred to receive health and healthcare-related information by completing CME modules or activities. Attending conferences and lectures, and use of online medical information services ranked second on the list of most preferred health information sources. This statistic displays the share of sources physicians prefer to receive healthcare-related information in the U.S. in 2021.

AI in Healthcare - Quick Overview Statistics

Artificial Intelligence in healthcare refers to the use of advanced computer algorithms and machine learning techniques to analyze data in the healthcare sector to provide better healthcare services.

AI helps healthcare providers make more accurate and real-time diagnoses, personalize treatment plans, and improve patient safety by identifying health risks earlier.

Types of AI Applications in Healthcare Statistics

• Medical imaging analysis
• Natural language processing (NLP)
• Disease prediction and risk assessment
• Virtual Assistants and Chabot’s
• Drug discovery and development
• Robot-assisted surgery
• Patient engagement
• Diagnosis and treatment
• Machine learning

This dataset presents the age-adjusted death rates for the 10 leading causes of death in the United States beginning in 1999. Data are based on information from all resident death certificates filed in the 50 states and the District of Columbia using demographic and medical characteristics. Age-adjusted death rates (per 100,000 population) are based on the 2000 U.S. standard population. Populations used for computing death rates after 2010 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for non-census years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Causes of death classified by the International Classification of Diseases, Tenth Revision (ICD–10) are ranked according to the number of deaths assigned to rankable causes. Cause of death statistics are based on the underlying cause of death. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf.

Intervention coverage data for the regression analysis was extracted from LiST model.7LiST uses baseline data mainly from Demographic and Health Surveys (DHS),8Malaria Indicators Survey (MIS),9Multiple Indicator Cluster Surveys (MICS)10as shown in the table above.1http://stats.oecd.org/index.aspx?r=842905 (Accessed August 5, 2010).2http://cherg.org/projects/underlying_causes.html (Accessed August 5, 2010).3http://www.un.org/esa/population/ (Accessed August 5, 2010).4http://www.childinfo.org/mortality_igme.html (Accessed August 5, 2010).5http://www.healthmetricsandevaluation.org/resources/datasets/2010/mortality/results/child/child.html (Accessed August 5, ,2010).6http://www.jhsph.edu/dept/ih/IIP/list/manuals/AIMManual.pdf (Accessed August 5, 2010).7http://www.jhsph.edu/dept/ih/IIP/list/manuals.html for details intervention on coverage data sources (Accessed August 5, 2010).8http://www.measuredhs.com/start.cfm (Accessed August 5, 2010).9http://www.measuredhs.com/aboutsurveys/mis/start.cfm (Accessed August 5, 2010).10http://www.unicef.org/ceecis/resources_10594.html (Accessed August 5, 2010).

Editor’s Choice

• The Healthcare IT market size is expected to be worth around USD 1728 Bn by 2032
• According to a report by UNESCO, countries in South and South-West Asia have the highest number of illiterate adults in the world, estimated at 388 million.
• Approximately 36% of adult Americans possess only basic or below basic health literacy skills.
• Only 12% of Americans are considered proficient in their health literacy skills.
• Health literacy levels in China increased from 6.48% of the population in 2008 to 23.15% in 2020.
• A recent study analyzing global health literacy research from 1995 to 2020 identified the United States, Australia, and the United Kingdom as major contributors to the international collaboration network on health literacy.
• Mental health has been the most active research field in recent years in the context of health literacy.

Abstract

This study examines various dimensions of primary health care delivery in Uganda, using a baseline survey of public and private dispensaries, the most common lower level health facilities in the country.

The survey was designed and implemented by the World Bank in collaboration with the Makerere Institute for Social Research and the Ugandan Ministries of Health and of Finance, Planning and Economic Development. It was carried out in October - December 2000 and covered 155 local health facilities and seven district administrations in ten districts. In addition, 1617 patients exiting health facilities were interviewed. Three types of dispensaries (both with and without maternity units) were included: those run by the government, by private for-profit providers, and by private nonprofit providers, mainly religious.

This research is a Quantitative Service Delivery Survey (QSDS). It collected microlevel data on service provision and analyzed health service delivery from a public expenditure perspective with a view to informing expenditure and budget decision-making, as well as sector policy.

Objectives of the study included: 1) Measuring and explaining the variation in cost-efficiency across health units in Uganda, with a focus on the flow and use of resources at the facility level; 2) Diagnosing problems with facility performance, including the extent of drug leakage, as well as staff performance and availability;
3) Providing information on pricing and user fee policies and assessing the types of service actually provided; 4) Shedding light on the quality of service across the three categories of service provider - government, for-profit, and nonprofit; 5) Examining the patterns of remuneration, pay structure, and oversight and monitoring and their effects on health unit performance; 6) Assessing the private-public partnership, particularly the program of financial aid to nonprofits.

Geographic coverage

The study districts were Mpigi, Mukono, and Masaka in the central region; Mbale, Iganga, and Soroti in the east; Arua and Apac in the north; and Mbarara and Bushenyi in the west.

Analysis unit

• local dispensary with or without maternity unit

Universe

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

The sample design was governed by three principles. First, to ensure a degree of homogeneity across sampled facilities, attention was restricted to dispensaries, with and without maternity units (that is, to the health center III level). Second, subject to security constraints, the sample was intended to capture regional differences. Finally, the sample had to include facilities in the main ownership categories: government, private for-profit, and private nonprofit (religious organizations and NGOs). The sample of government and nonprofit facilities was based on the Ministry of Health facility register for 1999. Since no nationwide census of for-profit facilities was available, these facilities were chosen by asking sampled government facilities to identify the closest private dispensary.

Of the 155 health facilities surveyed, 81 were government facilities, 30 were private for-profit facilities, and 44 were nonprofit facilities. An exit poll of clients covered 1,617 individuals.

The final sample consisted of 155 primary health care facilities drawn from ten districts in the central, eastern, northern, and western regions of the country. It included government, private for-profit, and private nonprofit facilities. The nonprofit sector includes facilities owned and operated by religious organizations and NGOs. Approximately one third of the surveyed facilities were dispensaries without maternity units; the rest provided maternity care. The facilities varied considerably in size, from units run by a single individual to facilities with as many as 19 staff members.

Ministry of Health facility register for 1999 was used to design the sampling frame. Ten districts were randomly selected. From the selected districts, a sample of government and private nonprofit facilities and a reserve list of replacement facilities were randomly drawn. Because of the unreliability of the register for private for-profit facilities, it was decided that for-profit facilities would be identified on the basis of information from the government facilities sampled. The administrative records for facilities in the original sample were first reviewed at the district headquarters, where some facilities that did not meet selection criteria and data collection requirements were dropped from the sample. These were replaced by facilities from the reserve list. Overall, 30 facilities were replaced.

The sample was designed in such a way that the proportion of facilities drawn from different regions and ownership categories broadly mirrors that of the universe of facilities. Because no nationwide census of for-profit health facilities is available, it is difficult to assess the extent to which the sample is representative of this category. A census of health care facilities in selected districts, carried out in the context of the Delivery of Improved Services for Health (DISH) project supported by the U.S. Agency for International Development (USAID), suggests that about 63 percent of all facilities operate on a for-profit basis, while government and nonprofit providers run 26 and 11 percent of facilities, respectively. This would suggest an undersampling of private providers in the survey. It is not clear, however, whether the DISH districts are representative of other districts in Uganda in terms of the market for health care.

For the exit poll, 10 interviews per facility were carried out in approximately 85 percent of the facilities. In the remaining facilities the target of 10 interviews was not met, as a result of low activity levels.

Sampling deviation

In the first stage in the sampling process, eight districts (out of 45) had to be dropped from the sample frame due to security concerns. These districts were Bundibugyo, Gulu, Kabarole, Kasese, Kibaale, Kitgum, Kotido, and Moroto.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

• District Health Team Questionnaire;
• District Facility Data Sheets;
• Uganda Health Facility Survey Questionnaire;
• Facility Data Sheets;
• Facility Patient Exit Poll Questionnaire.

The survey collected data at three levels: district administration, health facility, and client. In this way it was possible to capture central elements of the relationships between the provider organization, the frontline facility, and the user. In addition, comparison of data from different levels (triangulation) permitted cross-validation of information.

At the district level, a District Health Team Questionnaire was administered to the district director of health services (DDHS), who was interviewed on the role of the DDHS office in health service delivery. Specifically, the questionnaire collected data on health infrastructure, staff training, support and supervision arrangements, and sources of financing.

The District Facility Data Sheet was used at the district level to collect more detailed information on the sampled health units for fiscal 1999-2000, including data on staffing and the related salary structures, vaccine supplies and immunization activity, and basic and supplementary supplies of drugs to the facilities. In addition, patient data, including monthly returns from facilities on total numbers of outpatients, inpatients, immunizations, and deliveries, were reviewed for the period April-June 2000.

At the facility level, the Uganda Health Facility Survey Questionnaire collected a broad range of information related to the facility and its activities. The questionnaire, which was administered to the in-charge, covered characteristics of the facility (location, type, level, ownership, catchment area, organization, and services); inputs (staff, drugs, vaccines, medical and nonmedical consumables, and capital inputs); outputs (facility utilization and referrals); financing (user charges, cost of services by category, expenditures, and financial and in-kind support); and institutional support (supervision, reporting, performance assessment, and procurement). Each health facility questionnaire was supplemented by a Facility Data Sheet (FDS). The FDS was designed to obtain data from the health unit records on staffing and the related salary structure; daily patient records for fiscal 1999-2000; the type of patients using the facility; vaccinations offered; and drug supply and use at the facility.

Finally, at the facility level, an exit poll was used to interview about 10 patients per facility on the cost of treatment, drugs received, perceived quality of services, and reasons for using that unit instead of alternative sources of health care.

Cleaning operations

Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.

STATA cleaning do-files and the data quality reports on the datasets can also be found in external resources.

We present 10 tables with different, related data. Table 1 is the result of an extensive narrative literature review depicting published national secular suicide trends extending by at least a century. Table 2 pinpoints all reforms in the statistical national system by year, period and political regimen since 1886. In Table 3, we relate different consecutive versions of international classification of diseases and causes of death, by year of international approval and periodic implementation in the national statistical system, also by period of political regimen, depicting periods when different data was made accessible (sex, age), when categories of causes of external death begun to be collected (total external, suicide, accidents, undetermined), and types of dates were apt to be estimated (eg., crude death rates, age-standardised death rates, age-specific death rates); Table 3 also shows a cumulative index of years and attributes bibliographic primary sources for each line of data since 1886. Table 4 presents economic cycles – recession, stagnation, expansion –, in Portugal, by year, political regimen, with indicated sources, since 1886. Tables 5 to 9 present yearly raw numbers, crude death rates of suicide, accidents, and undetermined deaths, by sex, since 1886 for suicide and 1971 for accidents and undetermined deaths; and age-standardised death rates for the population aged more than 15 years old, by sex, since 1913 for suicide, and 1971 both for accidents and undetermined deaths. Table 10 lists the reference sources for mortality primary data and nosology changes by yearly periods. Finally, Figures shows structural changes and breakpoints, from 1913-2018, by sex and group of cause of death, taking general mortality as a gold standard.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Context

The CMS National Plan and Provider Enumeration System (NPPES) was developed as part of the Administrative Simplification provisions in the original HIPAA act. The primary purpose of NPPES was to develop a unique identifier for each physician that billed medicare and medicaid. This identifier is now known as the National Provider Identifier Standard (NPI) which is a required 10 digit number that is unique to an individual provider at the national level.

Once an NPI record is assigned to a healthcare provider, parts of the NPI record that have public relevance, including the provider’s name, speciality, and practice address are published in a searchable website as well as downloadable file of zipped data containing all of the FOIA disclosable health care provider data in NPPES and a separate PDF file of code values which documents and lists the descriptions for all of the codes found in the data file.

Content

The dataset contains the latest NPI downloadable file in an easy to query BigQuery table, npi_raw. In addition, there is a second table, npi_optimized which harnesses the power of Big Query’s next-generation columnar storage format to provide an analytical view of the NPI data containing description fields for the codes based on the mappings in Data Dissemination Public File - Code Values documentation as well as external lookups to the healthcare provider taxonomy codes . While this generates hundreds of columns, BigQuery makes it possible to process all this data effectively and have a convenient single lookup table for all provider information.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:nppes?_ga=2.117120578.-577194880.1523455401

https://console.cloud.google.com/marketplace/details/hhs/nppes?filter=category:science-research

Dataset Source: Center for Medicare and Medicaid Services. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @rawpixel from Unplash.

Inspiration

What are the top ten most common types of physicians in Mountain View?

What are the names and phone numbers of dentists in California who studied public health?

In 2023, Singapore ranked first with a health index score of ****, followed by Japan and South Korea. The health index measures the extent to which people are healthy and have access to the necessary services to maintain good health, including health outcomes, health systems, illness and risk factors, and mortality rates. The statistic shows the health and health systems ranking of countries worldwide in 2023, by their health index score.

Editor’s Choice

• The global biopharmaceuticals market size is expected to be worth around USD 566 billion by 2032 from USD 263 billion in 2022, growing at a CAGR of 8.2% during the forecast period from 2022 to 2032.
• The overall success rate for phase I trials is around 16%, while for phase II trials it is around 10%. Phase III trials, which involve larger patient populations, have a success rate of around 25%.
• The average cost of bringing a new drug to market is projected to be around USD 2.6 billion.
• The National Genome Institute estimates that the cost of sequencing a human genome has fallen from around USD 100 million in 2001 to less than USD 1,000 in recent years.
• In 2020, WIPO received over 23,500 biotechnology-related patent applications, demonstrating the growing innovation and research in the field.
• The US biotechnology industry employed over 2.1 million people in 2020, including direct and indirect jobs.

(Source: Journal of Clinical Oncology, Journal of Health Economics, World Intellectual Property Organization, Biotechnology Innovation Organization)

Table contains count of heat-related deaths among county residents. Heat-related deaths are summarized to include those occurred during May to September months of a year. Data are masked when the number of events is 1 to 10. Source: Santa Clara County Public Health Department, Vital Records Business Intelligence System, 2011-2020. Data as of 7/1/2021METADATA:notes (String): Lists table title, notes, sourcesyear (String): Year of deathcount (Numeric): Number of heat-related deaths

Abstract

The principal objective of the 2009-10 Timor-Leste Demographic and Health Survey (TLDHS) was to provide current and reliable data on fertility and family planning behavior, child mortality, adult and maternal mortality, child nutritional status, the utilization of maternal and child health services, and knowledge of HIV/AIDS.

The specific objectives of the survey were to: - collect data at the national level that will allow the calculation of key demographic rates; - analyze the direct and indirect factors that determine the levels and trends in fertility; - measure the level of contraceptive knowledge among women and men, and measure the level of practice among women by method, according to urban or rural residence; - collect quality data on family health, including immunization coverage among children, prevalence and treatment of diarrhea and other diseases among children under age 5, and maternity care indicators, including antenatal visits, assistance at delivery, and postnatal care; - collect data on infant and child mortality and on maternal and adult mortality; - obtain data on child feeding practices, including breastfeeding, and collect anthropometric measures to use in assessing the nutritional status of women and children; - collect information on knowledge of tuberculosis (TB), knowledge of the spread of TB, and attitudes towards people infected with TB among women and men; - collect data on use of treated and untreated mosquito nets, persons who sleep under the nets, use of drugs for malaria during pregnancy, and use of antimalarial drugs fortreatment of fever among children under age 5; - collect data on knowledge and attitudes of women and men about sexually transmitted infections and HIV/AIDS, and evaluate patterns of recent behavior regarding condom use; - collect information on the sexual practices of women and men; their number of sexual partners in the past 12 months, and over their lifetime; risky sexual behavior, including condom use at last sexual intercourse; and payment for sex; - conduct hemoglobin testing on women age 15-49 and children age 6-59 months in a subsample of households selected for the survey to provide information on the prevalence of anemia among women of reproductive age and young children; - collect information on domestic violence

This information is essential for informed policy decisions, planning, monitoring, and evaluation of programs on health in general, and on reproductive health in particular, at both the national and district levels. A long-term objective of the survey is to strengthen the technical capacity of government organizations to plan, conduct, process, and analyze data from complex national population and health surveys. Moreover, the 2009-10 TLDHS provides national and district-level estimates on population and health that are comparable to data collected in similar surveys in other developing countries. The first Demographic and Health Survey (DHS) in Timor-Leste was done in 2003. Unlike the 2003 DHS, however, the 2009-10 TLDHS was conducted under the worldwide MEASURE DHS program, funded by the United States Agency for International Development (USAID) and with technical assistance provided by ICF Macro. Data from the 2009-10 TLDHS allow for comparison of information gathered over a longer period of time and add to the vast and growing international database on demographic and health variables.

The 2009-10 TLDHS supplements and complements the information collected through the censuses, updates the available information on population and health issues, and provides guidance in planning, implementing, monitoring and evaluating Timor-Leste's health programs. Further, the results of the survey assist in monitoring the progress made towards meeting the Millennium Development Goals (MDGs) and other international initiatives.

The 2009-10 TLDHS includes topics related to fertility levels and determinants; family planning; fertility preferences; infant, child, adult and maternal mortality; maternal and child health; nutrition; malaria; domestic violence; knowledge of HIV/AIDS and women's empowerment. The 2009-10 TLDHS for the first time also includes anemia testing among women age 15-49 and children age 6-59 months. As well as providing national estimates, the survey also provides disaggregated data at the level of various domains such as administrative district, as well as for urban and rural areas. This being the third survey of its kind in the country (after the 2002 MICS and the 2003 DHS), there is considerable trend information on demographic and reproductive health indicators.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary focus of the 2009-10 TLDHS was to provide estimates of key population and health indicators, including fertility and mortality rates, for the country as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates of most key variables for the 13 districts.

Sampling Frame

The TLDHS used the sampling frame provided by the list of census enumeration areas (EAs) with population and household information from the 2004 Population and Housing Census (PHC). Administratively, Timor-Leste is divided into 13 districts. Stratification is achieved by separating each of the 13 districts into urban and rural areas. In total, 26 sampling strata were created. Samples were selected independently in every stratum, through a two-stage selection process. Implicit stratification was achieved at each of the lower administrative levels by sorting the sampling frame before sample selection, both according to administrative units and also by using a probability proportional-to-size selection at the first stage of sampling. The implicit stratification also allowed for the proportional allocation of sample points at each of the lower administrative levels.

Sample Selection

At the first stage of sampling, 455 enumeration areas (116 urban areas and 339 rural areas) were selected with probability proportional to the EA size, which is the number of households residing in the EA at the time of the census. A complete household listing operation in all of the selected EAs is the usual procedure to provide a sampling frame for the second-stage selection of households. However, a complete household listing was only carried out in select clusters in Dili, Ermera, and Viqueque, where more than 20 percent of the households had been destroyed. In all other clusters, a complete household listing was not possible because the country does not have written boundary maps for clusters. Instead, using the GPS coordinate locations for structures in each selected cluster as provided for by the 2004 PHC, households were randomly selected using their Geographic Information System (GIS) location identification in the central office. A map for each cluster was then generated, marking the households to be surveyed with their location identification. The maps also contained all the other households, roads, rivers, and major landmarks for easier location of selected households in the field. To provide statistically reliable estimates of key demographic and health variables and to cater for nonresponse, 27 households each were selected.

The survey was designed to cover a nationally representative sample of 12,285 residential households, taking into account nonresponse; to obtain completed interviews of 11,800 women age 15-49 in every selected household; and to obtain completed interviews of 3,800 men age 15-49 in every third selected household.

Note: See detailed description of the sample design in Appendix A of the report presented in this documentation.

Mode of data collection

Face-to-face

Research instrument

Three questionnaires were administered in the TLDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. These questionnaires were adapted from the standard MEASURE DHS core questionnaires to reflect the population and health issues relevant to Timor-Leste based on a series of meetings with various stakeholders from government ministries and agencies, NGOs, and international donors. The final draft of each questionnaire was discussed at a questionnaire design workshop organized by NSD on March 10, 2009, in Dili. These questionnaires were then translated and back translated from English into the two main local languages-Tetum and Bahasa—and pretested prior to the main fieldwork to ensure that the original meanings of the questions were not lost in translation.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. For children under age 18, survival status of the parents was determined. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. 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 ownership of mosquito nets. Additionally, the Household Questionnaire was used to record height and weight measurements for women age 15-49 and children under age 5, and to list hemoglobin measurements for women age 15-49 and children age 6-59 months.

The Woman’s Questionnaire was used to collect information from women age 15-49.

Abstract

The Bangladesh Demographic and Health Survey (BDHS) is part of the worldwide Demographic and Health Surveys program, which is designed to collect data on fertility, family planning, and maternal and child health.

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: - assess 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 objective of the BDHS is to provide up-to-date information on fertility and childhood mortality levels; nuptiality; fertility preferences; awareness, approval, and use of family planning methods; breastfeeding practices; nutrition levels; and maternal and child health. This information is intended to assist policymakers and administrators in evaluating and designing programs and strategies for improving health and family planning services in the country.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 10-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

Bangladesh is divided into six administrative divisions, 64 districts (zillas), and 490 thanas. In rural areas, thanas are divided into unions and then mauzas, a land administrative unit. Urban areas are divided into wards and then mahallas. The 1996-97 BDHS employed a nationally-representative, two-stage sample that was selected from the Integrated Multi-Purpose Master Sample (IMPS) maintained by the Bangladesh Bureau of Statistics. Each division was stratified into three groups: 1 ) statistical metropolitan areas (SMAs), 2) municipalities (other urban areas), and 3) rural areas. 3 In the 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 so as to retain the overall probability proportional to size. A total of 316 primary sampling units were utilized for the BDHS (30 in SMAs, 42 in municipalities, and 244 in rural areas). In order to highlight changes in survey indicators over time, the 1996-97 BDHS utilized the same sample points (though not necessarily the same households) that were selected for the 1993-94 BDHS, except for 12 additional sample points in the new division of Sylhet. Fieldwork in three sample points was not possible (one in Dhaka Cantonment and two in the Chittagong Hill Tracts), so a total of 313 points were covered.

Since one objective of the BDHS is to provide separate estimates for each division as well as for urban and rural areas separately, it was necessary to increase the sampling rate for Barisal and Sylhet Divisions and 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.

Mitra and Associates conducted a household listing operation in all the sample points from 15 September to 15 December 1996. A systematic sample of 9,099 households was then selected from these lists. Every second household was selected for the men's survey, meaning that, in addition to interviewing all ever-married women age 10-49, interviewers also interviewed all currently married men age 15-59. It was expected that the sample would yield interviews with approximately 10,000 ever-married women age 10-49 and 3,000 currently married men age 15-59.

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

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 Men' s Questionnaire and a Community 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. These model questionnaires were adapted for use in Bangladesh during a series of meetings with a small Technical Task Force that consisted of representatives from NIPORT, Mitra and Associates, USAID/Bangladesh, the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Population Council/Dhaka, and Macro International Inc (see Appendix D for a list of members). Draft questionnaires were then circulated to other interested groups and were reviewed by the BDHS Technical Review Committee (see Appendix D for list of members). The questionnaires were developed in English and then translated into and printed in Bangla (see Appendix E for final version in English).

The Household Questionnaire was used to list all the usual members and visitors in the 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 the 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 five, - Marriage, - Fertility preferences, - Husband's background and respondent's work, - Knowledge of AIDS, - Height and weight of children under age five and their mothers.

The Men's Questionnaire was used to interview currently married men age 15-59. It was similar to that for women except that it omitted the sections on reproductive history, antenatal and delivery care, breastfeeding, vaccinations, and height and weight. The Community Questionnaire was completed for each sample point and included questions about the existence in the community of income-generating activities and other development organizations and the availability of health and family planning services.

Response rate

A total of 9,099 households were selected for the sample, of which 8,682 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 8,762 households occupied, 99 percent were successfully interviewed. In these households, 9,335 women were identified as eligible for the individual interview (i.e., ever-married and age 10-49) and interviews were completed for 9,127 or 98 percent of them. In the half of the households that were selected for inclusion in the men's survey, 3,611 eligible ever-married men age 15-59 were identified, of whom 3,346 or 93 percent were interviewed.

The principal reason for non-response among eligible women and men was the failure to find them at home despite repeated visits to the household. The refusal rate was low.

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

Sampling error estimates

The estimates 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 BDHS 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 BDHS 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 BDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the BDHS is the ISSA Sampling Error Module. This module used the Taylor

Retrospectively collected medical data has the opportunity to improve patient care through knowledge discovery and algorithm development. Broad reuse of medical data is desirable for the greatest public good, but data sharing must be done in a manner which protects patient privacy. Here we present Medical Information Mart for Intensive Care (MIMIC)-IV, a large deidentified dataset of patients admitted to the emergency department or an intensive care unit at the Beth Israel Deaconess Medical Center in Boston, MA. MIMIC-IV contains data for over 65,000 patients admitted to an ICU and over 200,000 patients admitted to the emergency department. MIMIC-IV incorporates contemporary data and adopts a modular approach to data organization, highlighting data provenance and facilitating both individual and combined use of disparate data sources. MIMIC-IV is intended to carry on the success of MIMIC-III and support a broad set of applications within healthcare.

The spreadsheet is a comprehensive database of articles related to moral distress in nursing. It includes the following columns, which provide detailed information on each entry: 1. Authors: Lists the authors of the articles. 2. Year of Publication: The year the article was published. 3. Title of the Article: The full title of the research article. 4. Journal Title: The name of the journal where the article was published. 5. Country: Countries where the research was conducted. 6. Language: The language in which the article is written. 7. Institution: The leading institution or institutions responsible for the research. 8. Type of Publication: The category of publication, indicating the field, e.g., nursing publications. 9. Methodological Characteristics: A brief description of the methodology used in the study. 10. Type of Study: Specifies whether it was a literature review, qualitative, or quantitative research. 11. Method of Data Collection: How data were collected for the study. 12. Moral Distress Sources: Identifies the main sources of moral distress highlighted in the study. 13. Practice Context: The practical context where the study was conducted (e.g., hospitals, COVID-19 pandemic context). 14. Conclusion: Whether the conclusions were justified by the research results. 15. Recommendations: Suggestions and recommendations made by the authors. 16. Evidence Level: The level of evidence provided in the article (e.g., systematic reviews, single studies). 17. Limitations or Biases: Any limitations or potential biases identified in the study.

Abstract

This survey covering 252 primary health facilities and 30 local governments was carried out in the states of Kogi and Lagos in Nigeria in the latter part of 2002. Nigeria is one of the few countries in the developing world to systematically decentralize the delivery of basic health and education services to locally elected governments. Its health policy has also been guided by the Bamako Initiative to encourage and sustain community participation in primary health care services. The survey data provide systematic evidence on how these institutions of decentralization are functioning at the level local—governments and community based organizations—to deliver primary health service.

The evidence shows that locally elected governments indeed do assume responsibility for services provided in primary health care facilities. However, the service delivery environments between the two states are strikingly different. In largely urban Lagos, public delivery by local governments is influenced by the availability of private facilities and proximity to referral centers in the state. In largely rural Kogi, primary health services are predominantly provided in public facilities, but with extensive community participation in the maintenance of service delivery. The survey identified an issue which is highly relevant for decentralization policies—the non-payment of health staff salaries in Kogi—which is suggestive of problems with local accountability when local governments are heavily dependent on fiscal transfers from higher tiers of government.

Geographic coverage

Data were collected in 30 local governments, 252 health facilities, and from over 700 health workers, in Lagos and Kogi states.

Kind of data

Sample survey data [ssd]

Sampling procedure

A multi-stage sampling process was employed where first 15 local governments were randomly selected from each state; second, 100 facilities from Lagos and 152 facilities from Kogi were selected using a combination of random and purposive sampling from the list of all public primary health care facilities in the 30 selected LGAs that was provided by the state governments; third, the field data collectors were instructed to interview all staff present at the health facility at the time of the visit, if the total number of staff in a facility were less than or equal to 10. In cases where the total number of staff were greater than 10, the field staff were instructed to randomly select 10 staff, but making sure that one staff in each of the major ten categories of primary health care workers was included in the sample.

Health facilities were selected through a combination of random and purposive sampling. First, all facilities were randomly selected from the available list for 30 LGAs. This process resulted in no facility being selected from a few LGAs. Between 1-3 facilities were then randomly selected from these LGAs, and an equal number of facilities were randomly dropped from overrepresented LGAs, defined as those where the proportion of selected facility per LGA is higher than the average proportion of selected facilities for all sampled LGAs.

A list of replacement facilities was also randomly selected in the event of closure or non-functioning of any facility in the original sample. An inordinate amount of facilities were replaced in Kogi (27 in total), some due to inaccessibility given remote locations and hostile terrain, and some due to non-availability of any health staff. The local community volunteered in these cases that the reason there was no staff available was because of non-payment of salaries by the LGA. This characteristic of the functioning of health facilities in Kogi is a striking result that will be discussed in this report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The approach adopted to addressing these issues revolves around extensive and rigorous survey work, at the level of the primary health care facilities and the local governments. Two basic survey instruments of primary data collection were agreed upon, based on collecting information from government officials and public service delivery facilities: 1. Survey of primary health care facilities—including interviews of facility managers and workers, as well as direct collection of data on inputs and outputs from facility records. 2. Survey of local governments (under whose jurisdiction the health facilities reside)—including interviewers of local government treasurers for information on budgeted resources and investment activity, and interviews of primary health care coordinators for roles, responsibilities, and outcomes at the local government level.

Survey instruments at the health facility level

The facility level survey instruments were designed to collect data along the following lines: 1. Basic characteristics of the health facility: who built it; when was it built; what other facilities exist in the neighborhood; access to the facility; hours of service etc. 2. Type of services provided: focusing on ante-natal care; deliveries; outpatient services, with special emphasis on malaria and routine immunization 3. Availability of essential equipment to provide the above services 4. Availability of essential drugs to provide the above services 5. Utilization of the above services, referral practices 6. Tracking and use of epidemiological and public health data 7. Characteristics of health facility staff: professional qualifications; training; salary structure, and whether payments are received in a timely fashion; informal payments received; fringe benefits received; do they have their own private practice; time allocation across different services; residence; place of origin 8. Sources of financing-who finances the building infrastructure and its maintenance; who finances the purchase of basic equipment; who finances the purchase of drugs; what is the user fee policy; revenues from user fees; retention rate of these revenues; financing available from the community 9. Management structure and institutions of accountability: activities of and interaction with the local government and with the community development committees

Survey instrument at the local government level

The local government survey instruments were designed to collect data along the following lines: 1. Basic characteristics: when was the local government created, population, proportion urban and rural, presence of an urban center, presence of NGOs and international donors 2. Number of primary health care facilities by type (types 1 and 2) and ownership (public-local government, state, and federal government; private-for-profit; private-not-for-profit) 3. Supervisory responsibilities over the general functioning of the primary health care centers 4. Health staff: number of staff by type of professional training and civil service cadre; salary; 5. Monitoring the performance of health staff: how is staff performance monitored and by whom; are staff rewarded for good performance or sanctioned for poor performance, and how; instances when local government has received complaints; what disciplinary action was taken 6. Budget and financing: data on actual LGA revenues and expenditure from available budget documents; 7. Management structures: functioning of the Primary Health Care Management Committee (PHCMC), the Primary Health Care Technical Committee (PHCTC), and the community based organizations-the Village Development Committee (VDC) and the District Development Committee (DDC) 8. Health services outputs at the local government level: records of immunization, and environmental health activities

The focus of the study is thus public service delivery outcomes as measured at the level of frontline delivery agencies—the public primary health care facilities. We also originally planned to include interviews of patients present at the health facilities, to get the user’s perspective on public service delivery, but found that difficult to follow-through given local capacity constraints in implementing a survey of this kind.

The survey instruments were developed through an iterative process of discussions between the World Bank team, NPHCDA, and local consultants at the University of Ibadan, over the months of March-May 2002. During May 2002, four questionnaires were finalized through repeated field-testing—1) Health Facility Questionnaire: to be administered to the health facility manager, and to collect recorded data on inputs and outputs at the facility level; 2) Staff Questionnaire: to be administered to individual health workers; 3) Local Government Treasurer Questionnaire: to collect local government budgetary information; and 4) Primary Health Care Coordinator Questionnaire: to collect information on local government activities and policies in primary health care service service delivery.

Cleaning operations

Random Data Checking Procedure

Following the dual data entry of all records by Nigerian consultants and the merging and cleaning of the data files(as outlined below) by World Bank staff, the hard copies of the questionnaires were randomly checked against the entries in the data files (*) for errors by World Bank staff. Five LGAs were selected at random in both the Kogi and Lagos states. In each of these ten LGAs, the hard copy of the PHC Coordinator Questionnaire, the hard copy of the LGA Treasurer Questionnaire, and up to five hard copies of both the Staff Questionnaires and the Health Facility Questionnaires were randomly selected and checked against the entries in the data files. While in several instances parts of the alphanumeric entries were abbreviated or omitted, no substantive differences between the hard copies of the

Basketball Player Analysis - 2023/2024 Season

Introduction

This dataset provides a comprehensive overview of basketball players' performance during the 2023/2024 season. The following analysis highlights intriguing insights into individual statistics and players' impact on the games.

Data Used

• Source: Basketball Reference
• Key Variables:
  • Player Name
  • Points per Game
  • Assists
  • Rebounds
  • Other relevant statistics

Key Insights

1. Points per Game:

   • Average points of top players.
   • Distribution graph of scoring.

2. Assists and Rebounds:

   • Relationship between assists and rebounds.
   • Emphasis on versatile players.

3. Efficiency:

   • Shooting efficiency analysis.
   • Players with the best performance in crucial moments.

Code

Link to the code snippet on my GitHub: etl_nba_data

Feel free to explore the detailed code for extracting insights from the dataset.

Enjoy the read!

About this dataset

Context

The dataset tabulates the Medical Lake population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Medical Lake. The dataset can be utilized to understand the population distribution of Medical Lake by age. For example, using this dataset, we can identify the largest age group in Medical Lake.

Key observations

The largest age group in Medical Lake, WA was for the group of age 25-29 years with a population of 480 (9.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in Medical Lake, WA was the 80-84 years with a population of 35 (0.72%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Medical Lake is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Medical Lake total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Medical Lake Population by Age. You can refer the same here

Abstract

The 2017 Ghana Maternal Health Survey (2017 GMHS) was designed to produce representative estimates for maternal mortality indicators for the country as a whole, and for each of the three geographical zones, namely Coastal (Western, Central, Greater Accra and Volta), Middle (Eastern, Ashanti and Brong Ahafo) and Northern (Northern, Upper East and Upper West). For other indicators such as maternal care, fertility and child mortality, the survey was designed to produce representative results for the country as whole, for the urban and rural areas, and for each of the country’s 10 administrative regions.

The primary objectives of the 2017 GMHS were as follows: • To collect data at the national level that will allow an assessment of the level of maternal mortality in Ghana for the country as a whole and for three zones: Coastal (Western, Central, Greater Accra, and Volta regions), Middle (Eastern, Ashanti, and Brong Ahafo regions), and Northern (Northern, Upper East, and Upper West regions) • To identify specific causes of maternal and non-maternal deaths, in particular deaths due to abortionrelated causes, among adult women • To collect data on women’s perceptions of and experiences with antenatal, maternity, and emergency obstetrical care, especially with regard to care received before, during, and following the termination or abortion of a pregnancy • To measure indicators of the utilisation of maternal health services, especially post-abortion care services • To allow follow-on studies and surveys that will be used to observe possible reductions in maternal mortality as well as abortion-related mortality

The information collected through the 2017 GMHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2017 GMHS was designed to provide estimates of key reproductive health indicators for the country as a whole, for urban and rural areas separately, for three zonal levels (Coastal, Middle, and Northern), and for each of the 10 administrative regions in Ghana (Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East, and Upper West).

The sampling frame used for the 2017 GMHS is the frame of the 2010 Population and Housing Census (PHC) conducted in Ghana. The 2010 PHC frame is maintained by GSS and updated periodically as new information is received from various surveys. The frame is a complete list of all census enumeration areas (EAs) created for the PHC.

The 2017 GMHS sample was stratified and selected from the sampling frame in two stages. Each region was separated into urban and rural areas; this yielded 20 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before the sample selection, according to administrative units at different levels, and by using a probability proportional to size selection at the first stage of sampling.

In the first stage, 900 EAs (466 EAs in urban areas and 434 EAs in rural areas) were selected with probability proportional to EA size and with independent selection in each sampling stratum. A household listing operation was implemented from 25 January to 9 April 2017 in all of the selected EAs. The resulting lists of households then served as a sampling frame for the selection of households in the second stage. The household listing operation included inquiring of each household if there had been any deaths in that household since January 2012 and, if so, the name, sex, and age at time of death of the deceased person(s).

Some of the selected EAs were very large. To minimise the task of household listing, each large EA selected for the 2017 GMHS was segmented. Only one segment was selected for the survey with probability proportional to segment size. Household listing was conducted only in the selected segment. Thus, in the GMHS, a cluster is either an EA or a segment of an EA. As part of the listing, the field teams updated the necessary maps and recorded the geographic coordinates of each cluster. The listing was conducted by 20 teams that included a supervisor, three listers/mappers, and a driver.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2017 GMHS: the Household Questionnaire, the Woman’s Questionnaire, and the Verbal Autopsy Questionnaire.

Cleaning operations

All electronic data files for the 2017 GMHS were transferred via the IFSS to the GSS central office in Accra, where they were stored on a password-protected computer. The data processing operation included registering and checking for any inconsistencies and outliers. Data editing and cleaning included structure and consistency checks to ensure completeness of work in the field. The central office also conducted secondary editing, which required resolution of computer-identified inconsistencies and coding of openended questions. The data were processed by five GSS staff members. Data editing was accomplished using CSPro software. Secondary editing and data processing were initiated in June and completed in November 2017.

Response rate

A total of 27,001 households were selected for the sample, of which 26,500 were occupied at the time of fieldwork. Of the occupied households, 26,324 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 25,304 eligible women were identified for individual interviews; interviews were completed with 25,062 women, yielding a response rate of 99%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and 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 2017 Ghana Maternal Health Survey (2017 GMHS) 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 2017 GMHS is only one of many samples that could have been selected from the same population, using the same design and sample 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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 in. For example, for any given statistic calculated from a sample survey, the true value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017 GMHS sample is the result of a multi-stage stratified sampling, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed by SAS programs developed by ICF International. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Completeness of information on siblings - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends

See details of the data quality tables in Appendix C of the survey final report.