The North Carolina State Center for Health Services (SCHS) collects yearly vital statistics. The Odum Institute holds vital statistics beginning in 1968 for deaths, marriages and divorce. Public marriage and divorce data are available through 1999 only. Vital statistics for births, fetal deaths, and birth/infant deaths may be obtained directly from SCHS by submitting a request to SCHS.Info@dhhs.nc.gov This study focuses on deaths in North Carolina in 2020. Death is defined as the permanent disappearance of any evidence of life at any time after live birth. This definition excludes fetal deaths. The data kept for deaths includes the age, race, marital status, and sex of the individual; date, time, cause and location of death; and mode of burial. Minor changes to the files beginning in 2014 reflect the release of an updated NC Death Certificate form in that year. The data are strictly numerical; there is no identifying information given about the individuals.

Input datasets on Ohio Birth and Autism will not be made accessible to the public due to the fact that they include individual-level data with PII. Output data are all available in tabulated form within the published manuscript. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Input data can be obtained from Applications from owners of the data (Children's Hospital and Ohio Department of Health). The tabulated output data is found in the manuscript. Format: Input datasets on Ohio Birth and Autism will not be made accessible to the public due to the fact that they include individual-level data with PII. Output data are all available in tabulated form within the published manuscript (e.g., results of regression models, measures of central tendency, population characteristics, etc.). This dataset is associated with the following publication: Kaufman, J., M. Wright, G. Rice, N. Connolly, K. Bowers, and J. Anixt. AMBIENT OZONE AND FINE PARTICULATE MATTER EXPOSURES AND AUTISM SPECTRUM DISORDER IN METROPOLITAN CINCINNATI, OHIO. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 171: 218-227, (2019).

The AIDS Public Information Data Set (APIDS) for years 1981-2002 on CDC WONDER online database contains counts of AIDS (Acquired Immune Deficiency Syndrome) cases reported by state and local health departments, by demographics; location (region and selected metropolitan areas); case-definition; month/year and quarter-year of diagnosis, report, and death (if applicable); and HIV exposure group (risk factors for AIDS). Data are produced by the US Department of Health and Human Services (US DHHS), Public Health Service (PHS), Centers for Disease Control and Prevention (CDC), National Center for HIV, STD and TB Prevention (NCHSTP), Division of HIV/AIDS Prevention (DHP).

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Data for deaths by leading cause of death categories are now available in the death profiles dataset for each geographic granularity.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Cause of death categories for years 1999 and later are based on tenth revision of International Classification of Diseases (ICD-10) codes. Comparable categories are provided for years 1979 through 1998 based on ninth revision (ICD-9) codes. For more information on the comparability of cause of death classification between ICD revisions see Comparability of Cause-of-death Between ICD Revisions.

This data visualization presents county-level provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. County-level provisional counts include deaths occurring within the 50 states and the District of Columbia, as of the date specified and may not include all deaths that occurred during a given time period. Provisional counts are often incomplete and causes of death may be pending investigation resulting in an underestimate relative to final counts (see Technical Notes). The provisional data presented on the dashboard below include reported 12 month-ending provisional counts of death due to drug overdose by the decedent’s county of residence and the month in which death occurred. Percentages of deaths with a cause of death pending further investigation and a note on historical completeness (e.g. if the percent completeness was under 90% after 6 months) are included to aid in interpretation of provisional data as these measures are related to the accuracy of provisional counts (see Technical Notes). Counts between 1-9 are suppressed in accordance with NCHS confidentiality standards. Provisional data presented on this page will be updated on a quarterly basis as additional records are received. Technical Notes Nature and Sources of Data Provisional drug overdose death counts are based on death records received and processed by the National Center for Health Statistics (NCHS) as of a specified cutoff date. The cutoff date is generally the first Sunday of each month. National provisional estimates include deaths occurring within the 50 states and the District of Columbia. NCHS receives the death records from the state vital registration offices through the Vital Statistics Cooperative Program (VSCP). The timeliness of provisional mortality surveillance data in the National Vital Statistics System (NVSS) database varies by cause of death and jurisdiction in which the death occurred. The lag time (i.e., the time between when the death occurred and when the data are available for analysis) is longer for drug overdose deaths compared with other causes of death due to the time often needed to investigate these deaths (1). Thus, provisional estimates of drug overdose deaths are reported 6 months after the date of death. Provisional death counts presented in this data visualization are for “12 month-ending periods,” defined as the number of deaths occurring in the 12 month period ending in the month indicated. For example, the 12 month-ending period in June 2020 would include deaths occurring from July 1, 2019 through June 30, 2020. The 12 month-ending period counts include all seasons of the year and are insensitive to reporting variations by seasonality. These provisional counts of drug overdose deaths and related data quality metrics are provided for public health surveillance and monitoring of emerging trends. Provisional drug overdose death data are often incomplete, and the degree of completeness varies by jurisdiction and 12 month-ending period. Consequently, the numbers of drug overdose deaths are underestimated based on provisional data relative to final data and are subject to random variation. Cause of Death Classification and Definition of Drug Deaths Mortality statistics are compiled in accordance with the World Health Organizations (WHO) regulations specifying that WHO member nations classify and code causes of death with the current revision of the International Statistical Classification of Diseases and Related Health Problems (ICD). ICD provides the basic guidance used in virtually all countries to code and classify causes of death. It provides not only disease, injury, and poisoning categories but also the rules used to select the single underlying cause of death for tabulation from the several diagnoses that may be reported on a single death certificate, as well as definitions, tabulation lists, the format of the death certificate, and regul

Deaths were categorized into non-AIDS-related, AIDS-related, and of unknown cause. Columns provide number (percentage) unless otherwise specified.IQR: interquartile range, MSM: men who have sex with men, IDU: injection drug use, HAART: highly active antiretroviral treatment.*Defined as use of HAART for at least 12 weeks.

Abstract copyright UK Data Service and data collection copyright owner.The purpose of the Vital Statistics for England and Wales data is to record the numbers of conceptions, live births, stillbirths, deaths and causes of death for persons in England and Wales, by gender and age. Data are available at local authority, health authority and ward level. Individual studies in the series record various parts of these data. Changes have been made over time to the way in which the Office for National Statistics (ONS) collects vital statistics data, resulting in some variation in the content of later studies in the series. Further information may be found in the Key Population and Vital Statistics reports available from the ONS web site. During 2006, Sam Smith and colleagues at ESDS Government carried out work on various studies in the series prior to 2002, to improve the data format. The resulting files have been redeposited at the UKDA. More information is available in the documentation for the studies concerned. SN:4817 includes annual summary figures for 2002 for each local authority in England and Wales on populations, births and deaths; fertility and mortality rates; comparative numbers and rates for the region and England and Wales. Main Topics: The data in the tables include all deaths registered in 2002. The 2001 mid-year population estimates are used in the 2002 VS1 table. Births data are defined by mother's area of usual residence. Deaths data are defined by usual residence of the deceased as stated at death registration. Births and deaths where the address of usual residence was outside England and Wales have been included in the 'England, Wales and Elsewhere' aggregate, but excluded from the figures given for individual health and local authorities.

Russia Rosstat Forecast: Mean: Births data was reported at 1,387,829.000 NA in 2035. This records an increase from the previous number of 1,364,608.000 NA for 2034. Russia Rosstat Forecast: Mean: Births data is updated yearly, averaging 1,387,829.000 NA from Dec 2017 (Median) to 2035, with 19 observations. The data reached an all-time high of 1,683,526.000 NA in 2017 and a record low of 1,318,351.000 NA in 2030. Russia Rosstat Forecast: Mean: Births data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GD012: Vital Statistics: Forecast: Rosstat: Annual.

Russia Rosstat Forecast: Mean: Natural Increase data was reported at -541,194.000 NA in 2035. This records a decrease from the previous number of -540,267.000 NA for 2034. Russia Rosstat Forecast: Mean: Natural Increase data is updated yearly, averaging -409,061.000 NA from Dec 2017 (Median) to 2035, with 19 observations. The data reached an all-time high of -155,731.000 NA in 2017 and a record low of -541,194.000 NA in 2035. Russia Rosstat Forecast: Mean: Natural Increase data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GD012: Vital Statistics: Forecast: Rosstat: Annual.

Please note: This is a Synthetic data file, also known as a Dummy file - it is not real data. This synthetic file should not be used for purposes other than to develop an test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical package 'code' (e.g. SPSS syntax, SAS programs, etc.) in preperation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Resource Data Centres. In the fall of 1991, the National Health Information Council recommended that an ongoing national survey of population health be conducted. This recommendation was based on consideration of the economic and fiscal pressures on the health care systems and the requirement for information with which to improve the health status of the population in Canada. Commencing in April 1992, Statistics Canada received funding for development of a National Population Health Survey (NPHS). The NPHS collects information related to the health of the Canadian population and related socio-demographic information to: aid in the development of public policy by providing measures of the level, trend and distribution of the health status of the population, provide data for analytic studies that will assist in understanding the determinants of health, and collect data on the economic, social, demographic, occupational and environmental correlates of health. In addition the NPHS seeks to increase the understanding of the relationship between health status and health care utilization, including alternative as well as traditional services, and also to allow the possibility of linking survey data to routinely collected administrative data such as vital statistics, environmental measures, community variables, and health services utilization. The NPHS collects information related to the health of the Canadian population and related socio-demographic information. It is composed of three components: the Households, the Health Institutions, and the North components. The Household component started in 1994/1995 and is conducted every two years. The first three cycles (1994/1995, 1996/1997, 1997/1998) were both cross-sectional and longitudinal. The NPHS longitudinal sample includes 17,276 persons from all ages in 1994/1995 and these same persons are to be interviewed every two years. Beginning in Cycle 4 (2000/2001) the survey became strictly longitudinal (collecting health information from the same individuals each cycle). The cross-sectional and longitudinal documentation of the Household component is presented separately as well as the documentation for the Health Institutions and North components. The cross-sectional component of the Population Health Survey Program has been taken over by the Canadian Community Health Survey (CCHS). With the introduction of the Canadian Community Health Survey (CCHS), there were many changes to the 2000-2001 National Population Health Survey - Household questionnaire. Since NPHS is strictly a longitudinal survey, some content was migrated to the CCHS (such as the two-week disability section and certain questions on place where health care was provided) or was dropped (e.g. certain chronic conditions), while the order of the questionnaire changed. As only the longitudinal respondent is now surveyed, it was no longer necessary to distinguish between the General questionnaire and the Health component. Health Canada, Public Health Agency of Canada and provincial ministries of health use NPHS longitudinal data to plan, implement and evaluate programs and health policies to improve health and the efficiency of health services. Non-profit health organizations and researchers in the academic fields use the information to move research ahead and to improve health.

[Metadata] Department of Health Primary Care Service Areas: Received from State Dept. of Health, December, 2019. Boundaries are based on census tracts. Primary care service areas were selected to describe the delivery of primary health services in the State of Hawai‘i through participation of multiple stakeholders in the early 1990’s. The rural health associations of the Hawai‘i, Maui and Kaua‘i Counties delineated rational service areas under their respective jurisdictions. The Needs Assessment Committee of the Primary Care Roundtable participated in the delineation of rational service areas... referred to as primary care service areas. Clustering of neighborhoods into these primary care service areas was intended to provide information below the county or island level with demarcation between adjacent neighborhoods. Census tracts were used in defining these areas due to their availability in census and vital statistic data. The size of the population in these areas, based on the 2010 u.S. Census data, vary from 170 individuals in Ni‘ihau and 2,291 in Hāna to 115,164 in Ko‘olaupoko. Thus direct comparisons in estimates between primary care service areas are limited. Clusters of census tracts were defined using both 2010 and 2000 census tracts depending on the data source. For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/Primary_Care_Service_Areas.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

Analysis of ‘In Hospital Mortality Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/saurabhshahane/in-hospital-mortality-prediction on 28 January 2022.

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

Context

The predictors of in-hospital mortality for intensive care units (ICU)-admitted HF patients remain poorly characterized. We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.

Content

Using Structured Query Language queries (PostgreSQL, version 9.6), demographic characteristics, vital signs, and laboratory values data were extracted from the following tables in the MIMIC III dataset: ADMISSIONS, PATIENTS, ICUSTAYS, D_ICD DIAGNOSIS, DIAGNOSIS_ICD, LABEVENTS, D_LABIEVENTS, CHARTEVENTS, D_ITEMS, NOTEEVENTS, and OUTPUTEVENTS. Based on previous studies 7-9 13-15, clinical relevance, and general availability at the time of presentation, we extracted the following data: demographic characteristics (age at the time of hospital admission, sex, ethnicity, weight, and height); vital signs (heart rate, (HR), systolic blood pressure [SBP], diastolic blood pressure [DBP], mean blood pressure, respiratory rate, body temperature, saturation pulse oxygen [SPO2], urine output [first 24 h]); comorbidities (hypertension, atrial fibrillation, ischemic heart disease, diabetes mellitus, depression, hypoferric anemia, hyperlipidemia, chronic kidney disease (CKD), and chronic obstructive pulmonary disease [COPD]); and laboratory variables (hematocrit, red blood cells, mean corpuscular hemoglobin [MCH], mean corpuscular hemoglobin concentration [MCHC], mean corpuscular volume [MCV], red blood cell distribution width [RDW], platelet count, white blood cells, neutrophils, basophils, lymphocytes, prothrombin time [PT], international normalized ratio [INR], NT-proBNP, creatine kinase, creatinine, blood urea nitrogen [BUN] glucose, potassium, sodium, calcium, chloride, magnesium, the anion gap, bicarbonate, lactate, hydrogen ion concentration [pH], partial pressure of CO2 in arterial blood, and LVEF), using Structured Query Language (SQL) with PostgreSQL (version 9.6). Demographic characteristics and vital signs extracted were recorded during the first 24 hours of each admission and laboratory variables were measured during the entire ICU stay. Comorbidities were identified using ICD-9 codes. For variable data with multiple measurements, the calculated mean value was included for analysis. The primary outcome of the study was in-hospital mortality, defined as the vital status at the time of hospital discharge in survivors and non-survivors.

Acknowledgements

Zhou, Jingmin et al. (2021), Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database, Dryad, Dataset, https://doi.org/10.5061/dryad.0p2ngf1zd

LICENSE - CC0 1.0 Universal (CC0 1.0) Public Domain Dedication

Target Variable - Outcome 0 - Alive 1 - Death

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

Russia Rosstat Forecast: Mean: per 1000 Population: Births data was reported at 9.600 NA in 2035. This records an increase from the previous number of 9.500 NA for 2034. Russia Rosstat Forecast: Mean: per 1000 Population: Births data is updated yearly, averaging 9.600 NA from Dec 2017 (Median) to 2035, with 19 observations. The data reached an all-time high of 11.500 NA in 2017 and a record low of 9.100 NA in 2031. Russia Rosstat Forecast: Mean: per 1000 Population: Births data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GD012: Vital Statistics: Forecast: Rosstat: Annual.

The limitation of studies on the coverage and completeness of death records is one of the main problems regarding vital statistics in Brazil. In 2020, the number of information systems on death records in Brazil increased due to the COVID-19 pandemic, generating more uncertainties about the quality of death records. This study proposed an evaluation of the quality of death records due to COVID-19. Three methodological stages were considered: estimation of deaths under-registration; redistribution of deaths from nonspecific causes (Garbage Codes), and redistribution of deaths from ill-defined causes to COVID-19 data. The proposal was applied in the State of Paraíba, Brazil, and its municipalities in 2020, by using the official records of the Brazilian Mortality Information System of the Brazilian Ministry of Health. In total, 1,281 deaths were retrieved, besides the 3,426 deaths officially recorded for Paraíba State, an increase of 37.4% in deaths from COVID-19. The proposal was effective, easy to apply, and can be used by managers of governmental spheres and people interested in it as a tool to assess the quality of death records for any geographic space, thus, contributing to a better understanding of the real effect of the pandemic.

[Metadata] Dental Health Professional Shortage Areas as of April 2024. Source - Hawaii State Department of Health. Description: Designation of Health Professional Shortage Areas for Dental Health. See also Mental Health and Primary Care Health Professional Shortage Areas. A Health Professional Shortage Area (HPSA) means any of the following which has a shortage of health professionals: (a) an urban or rural area which is a rational service area for the delivery of health services, (b) a population group, or (c) a public or nonprofit private medical facility. HPSAs are divided into three major categories according to the type of health professional shortage: primary care, dental or mental health HPSAs. For more information about HPSA’s, visit the Hawaii State Department of Health HPSA website at https://health.hawaii.gov/opcrh/home/health-professional-shortage-area-hpsa/. Hawaii Statewide GIS Program staff downloaded data from https://data.hrsa.gov/data/download?hmpgtitle=hmpg-hrsa-data April 2024. Projected to UTM Zone 4 NAD 83 HARN, and clipped to coastline. For additional information, please refer to summary metadata at https://files.hawaii.gov/dbedt/op/gis/data/hpsa.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

Abstract: The study aimed to compare two concepts of rurality, one proposed by the Brazilian Institute of Geography and Statistics (IBGE) and the other by the World Bank, to determine which is better fitted to the territory’s reality, besides analyzing the infant mortality profile of rural municipalities (counties) in the state of Paraíba, Brazil, according to the best criterion for rurality. This was an observational epidemiological study conducted in the state of Paraíba. The method for analyzing rural/urban typologies was based on data mining, using the Apriori algorithm of association. Infant mortality was analyzed with descriptive statistics. The data were obtained from the Mortality Information System of the Brazilian Ministry of Health, from 2007 to 2016, and municipal indicators were from IBGE. The World Bank definition of rurality showed kappa = 0.337, compared to the IBGE definition, with kappa = 0.616. Among the 223 municipalities that were analyzed, the World Bank classified 130 (65.66%) correctly, and the IBGE 183 (82.06%). The predominant epidemiological profile of infant mortality in rural municipalities in Paraiba state was male gender (57.4%), brown skin color (61.1%), age from 0 to 7 days (52.4%), low birthweight (44%), and gestational age less than 37 weeks (43.2%). Underlying cause of death was classified as avoidable death via interventions by the Brazilian Unified National Health System (65.2%). The urban/rural typology adopted by the IBGE was better than the World Bank at classifying the municipalities in Paraiba state. This classification allowed studying the infant mortality profile in rural municipalities, which was similar to the overall profile, except for maternal schooling.

[Metadata] Mental Health Professional Shortage Areas as of April 2024. Source - Hawaii State Department of Health. Description: Designation of Health Professional Shortage Areas for Mental Health. See also Health Professional Shortage Areas for Dental Health and Primary Care. A Health Professional Shortage Area (HPSA) means any of the following which has a shortage of health professionals: (a) an urban or rural area which is a rational service area for the delivery of health services, (b) a population group, or (c) a public or nonprofit private medical facility. HPSAs are divided into three major categories according to the type of health professional shortage: primary care, dental or mental health HPSAs. For more information about HPSA’s, visit the Hawaii State Department of Health HPSA website at https://health.hawaii.gov/opcrh/home/health-professional-shortage-area-hpsa/. Hawaii Statewide GIS Program staff downloaded data from https://data.hrsa.gov/data/download?hmpgtitle=hmpg-hrsa-data April 2024. Projected to UTM Zone 4 NAD 83 HARN, and clipped to coastline. For additional information, please refer to summary metadata at https://files.hawaii.gov/dbedt/op/gis/data/hpsa.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

This dataset contains counts of live births to California residents by ZIP Code based on information entered on birth certificates. Final counts are derived from static data and include out-of-state births to California residents. The data tables include births to residents of California by ZIP Code of residence (by residence).

Note that ZIP Codes are intended for mail delivery routing and do not represent geographic regions. ZIP Codes are subject to change over time and may not represent the same locations between different time periods. All ZIP Codes in the list of California ZIP Codes used for validation are included for all years, but this does not mean that the ZIP Code was in use at that time.