This is a point coverage of the 1990 Census of Population and Housing for the conterminous United States. (Alaska and Hawaii are available separately). The coverage contains the location of population points retrieved at the block group summary level and shows the total number of persons and housing units enumerated in the "100 percent processing" component of the decennial census. The data was extracted from CD-ROMs containing Public Law 94-171 counts. These are counts that States use in redistricting.

More than ** percent of the U.S. population were covered by at least one ** mobile network as of late 2023, while ** percent were covered by two or more networks. Mobile network operators T-Mobile U.S., AT&T, and Verizon dominate the U.S. wireless market, and seek to compete on the quality and availability of their ** services.

Japan 4G Population Coverage grew 20.7points in 2014, compared to a year earlier.

In 2023, **** percent of the North American population was covered by a 5G network, the highest share among global regions. The East Asia and Pacific region had the second-highest coverage at **** percent.

A point coverage was created from the 1980 Master Area Reference File (MARF) of the U.S. Census Bureay. Each point represents the center of a census tract, though some tracts were split. A 1980 population is associated with each point. Populations for 1970, 1982, 1984, and 1985 were inferred from county population data.

5G technology is forecast to cover about ** percent of the global population by 2031. While this coverage remains lower than LTE and 3GPP networks, the latter which remains stable at ** percent coverage of the world population, 5G networks have seen an exponential increase - from *** percent in 2019 to ***percent in 2025 - and are expected to continue accelerating their coverage from 2025 onwards even though short term factors point to a slower pace in certain countries due to potential delays in the licensing of 5G spectrum due to COVID-19.

The resource allows you to view 5-year period population estimates by health insurance coverage status and age for the civilian non-institutionalized population. Information is provided for Iowa and other states, the nation, and counties, cities, metropolitan and micropolitan areas, and school districts in Iowa.

Population coverage rates and uninsured population groups under the HF schemes.

This data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014. Product: SAHIE File Layout Overview Small Area Health Insurance Estimates Program - SAHIE Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014 Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau. Internet Release Date: May 2016 Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions

The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties.

For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of: •5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64

•3 sex categories: both sexes, male, and female

•6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold

•4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race).

In addition, estimates for age category 0-18 by the income categories listed above are published.

Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured.

This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges.

We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response.

The SAHIE program models health insurance coverage by combining survey data from several sources, including: •The American Community Survey (ACS) •Demographic population estimates •Aggregated federal tax returns •Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program •County Business Patterns •Medicaid •Children's Health Insurance Program (CHIP) participation records •Census 2010

Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level.

Abstract The objective of this study is to describe the profile of use of primary health care services, estimated by the PNS, of the population living in households registered and not registered with the Famly Health Strategy - FHS, in the years 2013 and 2019. Cross-sectional study carried out using microdata from national health surveys 2013 and 2019. The sample originated from a master sample, consisting of a set of units from selected areas in a register..The variables sex, age, skin color, income, education, self-perceived health, home registered with the FHS, medical care in the last year, type of service you seek when you are ill were selected. The dependent variables were use of health services and use of public health services. The dependent and independent variables were described with the respective confidence interval and adjusted logistic regression was performed for each outcome analyzed. In public health services, lower income, have chronic diseases (arterial hypertension or high cholesterol), be pregnant, and having a bad self-perception of health were associated with used more health services in both periods. Living in registered households was associated with more used health services (public or private). The family health strategy is an important strategy for expanding access equally.

The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan.

Abstract There is an estimated deficit of six million nurses worldwide. Despite its importance for health systems, sociodemographic studies are scarce due to the absence of systematized data specific to nurses. The objective of this study was to compare the population coverage of nurses in Brazil based on sources from the Brazilian Institute of Geography and Statistics (IBGE), in the years 2010 and 2015, and the Federal Nursing Council (Cofen), in the years 2013 and 2019. In both sources, there was an average increase of 164 thousand nurses throughout Brazil. The growth rate for the period of the IBGE surveys (15.7% per year) was triple that recorded in the Cofen data (5.3% per year). Coverage in the states of Brazil remains below the international recommendation (40 nurses per 10 thousand inhabitants), with greater deficits in the states of the North and Northeast regions. The comparisons in this study reiterate the importance of the availability of standardized and systematized data for Nursing in Brazil. Accurate health indicators subsidize public policies to reduce health inequities, with emphasis on the coverage of nurses, especially in regions with high socioeconomic vulnerabilities.

Present scenario and projections for reduced number of delivery sites. Network analysis.

This dataset includes information regarding civilian noninstitutionalized population without health Insurance coverage for persons under the age of 65 years in the United States and Puerto Rico by territory, state and age from year 2009 through 2016.

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).

Egypt EG: Coverage: Social Insurance Programs: % of Population data was reported at 21.304 % in 2008. Egypt EG: Coverage: Social Insurance Programs: % of Population data is updated yearly, averaging 21.304 % from Dec 2008 (Median) to 2008, with 1 observations. Egypt EG: Coverage: Social Insurance Programs: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank.WDI: Social Protection. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

More than ** percent of the rural United States population were covered by at least one ** network as of late 2023, while around ** percent were covered by two or more. Expanding rural ** coverage presents a challenge for U.S. mobile network operators, with low density and difficult terrain driving up the cost per potential customer.

This layer shows health insurance coverage sex and race by age group. This is shown by county boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black)This layer is symbolized to show the percent of population with no health insurance coverage.

Population coverage using IEDB population coverage tool.

BackgroundPre-clinical development and in-human trials of ‘off-the-shelf’ immune effector cell therapy (IECT) are burgeoning. IECT offers many potential advantages over autologous products. The relevant HLA matching criteria vary from product to product and depend on the strategies employed to reduce the risk of GvHD or to improve allo-IEC persistence, as warranted by different clinical indications, disease kinetics, on-target/off-tumor effects, and therapeutic cell type (T cell subtype, NK, etc.).ObjectiveThe optimal choice of candidate donors to maximize target patient population coverage and minimize cost and redundant effort in creating off-the-shelf IECT product banks is still an open problem. We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing.Study designWe developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). We then compared the efficiency of different optimization algorithms – a greedy solution, a linear programming (LP) solution, and integer linear programming (ILP) -- as well as random donor selection (average of 5 random trials) to show that an optimization can be performed at the entire population level.ResultsThe average additional population coverage per donor decrease with the number of donors, and varies with the scenario. The Greedy, LP and ILP algorithms consistently achieve the optimal coverage with far fewer donors than the random choice. In all cases, the number of randomly-selected donors required to achieve a desired coverage increases with increasing population. However, when optimal donors are selected, the number of donors required may counter-intuitively decrease with increasing population size. When comparing recruiting more donors vs gene editing, the latter was generally more expensive. When choosing donors and patients from different populations, the number of random donors required drastically increases, while the number of optimal donors does not change. Random donors fail to cover populations different from their original populations, while a small number of optimal donors from one population can cover a different population.DiscussionGraph-based coverage optimization algorithms can flexibly handle various HLA matching criteria and accommodate additional information such as KIR genotype, when such information becomes routinely available. These algorithms offer a more efficient way to develop off-the-shelf IECT product banks compared to random donor selection and offer some possibility of improved transparency and standardization in product design.