The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS) which is part of the Centers for Disease Control and Prevention (CDC). The National Health Survey Act of 1956 provided for a continuing survey and special studies to secure accurate and current statistical information on the amount, distribution, and effects of illness and disability in the United States and the services rendered for or because of such conditions. The survey referred to in the Act, now called the National Health Interview Survey, was initiated in July 1957. Since 1960, the survey has been conducted by NCHS, which was formed when the National Health Survey and the National Vital Statistics Division were combined. NHIS data are used widely throughout the Department of Health and Human Services (DHHS) to monitor trends in illness and disability and to track progress toward achieving national health objectives. The data are also used by the public health research community for epidemiologic and policy analysis of such timely issues as characterizing those with various health problems, determining barriers to accessing and using appropriate health care, and evaluating Federal health programs. The NHIS also has a central role in the ongoing integration of household surveys in DHHS. The designs of two major DHHS national household surveys have been or are linked to the NHIS. The National Survey of Family Growth used the NHIS sampling frame in its first five cycles and the Medical Expenditure Panel Survey currently uses half of the NHIS sampling frame. Other linkage includes linking NHIS data to death certificates in the National Death Index (NDI). While the NHIS has been conducted continuously since 1957, the content of the survey has been updated about every 10-15 years. In 1996, a substantially revised NHIS questionnaire began field testing. This revised questionnaire, described in detail below, was implemented in 1997 and has improved the ability of the NHIS to provide important health information.

This study, commonly known as the Longitudinal Study of Aging (LSOA), was conducted by the National Center for Health Statistics (NCHS) in collaboration with the National Institute on Aging (NIA) and designed to (1) provide mortality rates by demographic, social, economic, and health characteristics that are not available from the vital statistics system, (2) measure change in the functional status and living arrangements of older people, and (3) provide measures of health care use. It was also designed to describe the continuum from functionally independent living in the community through dependence, possible institutionalization, and finally death. The LSOA is an extension of the National Health Interview Survey (NHIS) of 1984, following its sample of 16,148 noninstitutionalized elderly people (55 years and over) living in the United States, with a special focus on those who were 70 years and over in 1984. This release of the LSOA contains data on those respondents who had been 70 years and older at the time of their 1984 interviews. The data include 1986, 1988, and 1990 reinterviews, National Death Index matches from 1984-1989, and 1987 interviews with contact persons named by decedents, as well as selected variables from the 1984 NHIS core questionnaire and its two supplements, Health Insurance and the Supplement on Aging (SOA). Two Medicare files are also included: Part 2, Medicare Hospital Records, and Part 3, Other Medicare Use Records (which covers home health care, hospice, and outpatient use). Links also are provided to allow merging of additional variables from the NATIONAL HEALTH INTERVIEW SURVEY, 1984 (ICPSR 8659). (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08719.v7. We highly recommend using the ICPSR version as they have this dataset available in multiple data formats.

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

Abstract (en): This collection provides information on live births in the United States during calendar year 2005. The natality data in these files are a component of the vital statistics collection effort maintained by the federal government. Birth data is limited to births occurring in the United States to United States residents and nonresidents. Births occurring to United States citizens outside of the United States are not included in this data collection. Part 1 contains data on births occurring within the United States, while Part 2 contains data on births occurring in the United States territories of Puerto Rico, the Virgin Islands, Guam, American Samoa, and the Commonwealth of the Northern Mariana Islands. Beginning in 2005, the United States file no longer includes geographic detail (e.g., mother's state of residence). Geographic variables for the United States Territories file include the territory and county in which the birth occurred and in which the mother resided. Other variables describe the place of delivery, who was in attendance, and medical and health data such as the method of delivery, prenatal care, tobacco and alcohol use during pregnancy, pregnancy history, medical risk factors, and infant health characteristics. Birth and fertility rates and other statistics related to this study can be found in the National Vital Statistics Report in the codebook documentation. Demographic variables include the child's sex and month and year of birth, the parent's age, race, and ethnicity, as well as the mother's marital status, education level, and residency status. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created online analysis version with question text.. Live births in the United States during calendar year 2005. One-hundred percent of birth certificates in calendar year 2005. (1) The full product suite was not released for this study due to the large size of system and transport files. (2) Data for Vermont are based on an incomplete file of records. The total number of Vermont resident births is under-reported by about 3 percent. Information based on the complete file of Vermont resident births is available via the National Center for Health Statistics Web Site. (3) To protect respondent confidentiality, codes in variables MRACE1E through MRACE8E and FRACE1E through FRACE8E were blanked. (4) Beginning in 2005, the United States file no longer includes geographic detail. Tabulations of birth data by residence of mother for states and for counties with populations of 100,000 or more are available using the VitalStats online data access tool available via the National Center for Health Statistics Vital Stats Web site. Certain geographic-level data may also be available upon request from the National Center for Health Statistics. More information can be found via the Release and Access Policy for Microdata and Compressed Vital Statistics Files, 2007. (5) According to documentation from the principal investigator, United States residents who gave birth in Guam were considered residents of Guam and were assigned a code 1 in the RESTATUS variable. (6) The CASEID variable was created for use with online analysis. (7) Variables SEX, OTERR, and RCNTY_POP were converted from character to numeric. (8) System-missing values in character variables were recoded to 9. (9) Value labels for unknown codes were added in variables APNCU, U_APNCU, MBRACE, FBRACE, and OCNTY. (10) More information on natality studies may be found via the National Center for Health Statistics Web site.

BackgroundLarge socio-economic disparities exist in US dietary habits and cardiovascular disease (CVD) mortality. While economic incentives have demonstrated success in improving dietary choices, the quantitative impact of different dietary policies on CVD disparities is not well established. We aimed to quantify and compare the potential effects on total CVD mortality and disparities of specific dietary policies to increase fruit and vegetable (F&V) consumption and reduce sugar-sweetened beverage (SSB) consumption in the US.Methods and findingsUsing the US IMPACT Food Policy Model and probabilistic sensitivity analyses, we estimated and compared the reductions in CVD mortality and socio-economic disparities in the US population potentially achievable from 2015 to 2030 with specific dietary policy scenarios: (a) a national mass media campaign (MMC) aimed to increase consumption of F&Vs and reduce consumption of SSBs, (b) a national fiscal policy to tax SSBs to increase prices by 10%, (c) a national fiscal policy to subsidise F&Vs to reduce prices by 10%, and (d) a targeted policy to subsidise F&Vs to reduce prices by 30% among Supplemental Nutrition Assistance Program (SNAP) participants only. We also evaluated a combined policy approach, combining all of the above policies. Data sources included the Surveillance, Epidemiology, and End Results Program, National Vital Statistics System, National Health and Nutrition Examination Survey, and published meta-analyses.Among the individual policy scenarios, a national 10% F&V subsidy was projected to be most beneficial, potentially resulting in approximately 150,500 (95% uncertainty interval [UI] 141,400–158,500) CVD deaths prevented or postponed (DPPs) by 2030 in the US. This far exceeds the approximately 35,100 (95% UI 31,700–37,500) DPPs potentially attributable to a 30% F&V subsidy targeting SNAP participants, the approximately 25,800 (95% UI 24,300–28,500) DPPs for a 1-y MMC, or the approximately 31,000 (95% UI 26,800–35,300) DPPs for a 10% SSB tax.Neither the MMC nor the individual national economic policies would significantly reduce CVD socio-economic disparities. However, the SNAP-targeted intervention might potentially reduce CVD disparities between SNAP participants and SNAP-ineligible individuals, by approximately 8% (10 DPPs per 100,000 population). The combined policy approach might save more lives than any single policy studied (approximately 230,000 DPPs by 2030) while also significantly reducing disparities, by approximately 6% (7 DPPs per 100,000 population).Limitations include our effect estimates in the model; these estimates use interventional and prospective observational studies (not exclusively randomised controlled trials). They are thus imperfect and should be interpreted as the best available evidence. Another key limitation is that we considered only CVD outcomes; the policies we explored would undoubtedly have additional beneficial effects upon other diseases. Further, we did not model or compare the cost-effectiveness of each proposed policy.ConclusionsFiscal strategies targeting diet might substantially reduce CVD burdens. A national 10% F&V subsidy would save by far the most lives, while a 30% F&V subsidy targeting SNAP participants would most reduce socio-economic disparities. A combined policy would have the greatest overall impact on both mortality and socio-economic disparities.

This reports was prepared by the Government of the Republic of Palau.From the years 2002 to 2014 it has the following chapters and they are as follows:

Chapter 1-climate statics, Chapter 2 - population statistics, Chapter 3 - Migration statistics, Chapter 4- Housing statistics, Chapter 5- Labor force statistics, Chapter 6 - crime & offense statistics, Chapter 7- Government Finance & Banking Statistics, Chapter 8- National accounts balance of payments and customer price index statistics, Chapter 9- foreign trade, Chapter 10- Agriculture, Chapter 11 - Fisheries Statistics, Chapter 12- Tourism Statistics, Chapter 13- Education statistics, Chapter 14- Health and vital statistics, Chapter 15- Transportation and communication statistics, Chapter 16- Credit Union Statistics, Chapter 17- Construction statistics, Chapter 18- Foreign Investment Statistics, Chapter 19- Electric Power statistic, Chapter 20-Environmental statistics, Chapter 21- 2003 Palau community survey.

Abstract

The Kenya Integrated Household Budget Survey (KIHBS 2005/06) Project was to collect a wide spectrum of socio-economic indicators required to measure, monitor and analyse the progress made in improving living standards. Specifically, the KIHBS was designed to update and strengthen three vital aspects of the national statistical database, notably: the Consumer Price Index (CPI), poverty and inequality; and the System of National Accounts (SNA). The data collection phase of this survey took 12 months and data on demographics, housing, education, health, agriculture and livestock, enterprises, expenditure and consumption, among others, was collected.

Geographic coverage

The survey covered all the districts in Kenya. The data representativeness are at the following levels

-National -Urban/Rural -Provincial -District

Analysis unit

Households Indviduals within Households Community

Universe

The survey covered all household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

KNBS has established the NASSEP IV sampling frame based on the 1999 Population and Housing Census. The sample design of the KIHBS 2005/2006 is based on this frame. The survey drew a sample of clusters from the set of 540 urban clusters and the 1,260 rural clusters under NASSEP IV Sampling frame.

The KIHBS 2005/2006 covered a total of 1,343 clusters with a total sample of 13,430 households, stratified by district and by Urban/Rural.

In the first stage, using the KNBS Master Sample (NASSEP IV), 1,343 clusters were selected with equal probability within a district. In the second stage, 10 households were selected with equal probability in each cluster.

A total sample of 13,430 households (10 households in each of 1,430 Primary Sampling Units - called clusters,) was allocated into 136 explicit strata (the urban and rural sections of each of Kenya's 69 districts, except in Nairobi and Mombassa, which are wholly urban). The 1,343 clusters required by the KIHBS were selected from the CBS 1,800-cluster master sample. This selection was done with equal probability within each stratum, except for the six districts that contain urban areas qualified as municipalities. In these districts, the urban part of the sample was further stratified into six groups (five socio-economic classes in the municipality itself and other urban areas in the district,).

Sampling deviation

From the design sample of 1,343 clusters, only 1,339 clusters were covered the remianing 4 clusters were not covered due to Inaccessibilty and security issues.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were administered namely: -Socio-economic questionnaire and -Community questionnaire -Diary qustionares

The socio-economic module included the following sections

-Household member Information -Education -Health, Fertility and Household Deaths -Labour -Child Health and Anthropometry -Housing -Water, Sanitation and Energy use -Consumption of Food Items over the past week -Expenditre on regular Non-food items over the past month -Expenditure on Durables over the past 12 months -Agricultural holdings -Agricltral Outputs -Livestock -Hosehold Enterprises -Transfers -Other Income -Recent Shocks to Household -Credit to Household members

The Community Questionnaire was administered to capture community level information. The information collected related to; - Community facilities including access to schools, health facilities, roads, extension services and markets, - Community major events, - Land tenure,

The were two types of diaries one used to record goods and services purchased, and the other to record goods and services consumed by the household.

Cleaning operations

Data editing took place at the data collection in the field inluding; a) During data entry in the field, b) Structure checking and completeness and c) Structural checking of SPSS data files

Sampling error estimates

Estimates of sampling error was calculated for the poverty level estimates only using .............