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NCHS - Drug Poisoning Mortality by County: United States
healthdata.gov | Last Updated 2023-07-25T17:57:16.000ZThis dataset contains model-based county estimates for drug-poisoning mortality. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8). REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. 2. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html. 3. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013. 4. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014. 5. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf. 6. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e. 7. National Center for Health Statistics. County geog
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Animal Contact Exhibits_Legal Epidemiology Research Procedure and Code Book_2016
healthdata.gov | Last Updated 2023-07-26T01:28:18.000ZAnimals at petting zoos and agricultural fairs can be carriers of pathogens, such as Escherichia coli. Disease outbreaks at animal contact exhibits can be prevented by handwashing after contact with animals and keeping food and beverage away from exhibits. This research procedure and code book accompanies the data set, Animal Contact Exhibits_Legal Epidemiology Dataset_2016, which catalogs and analyzes a collection of state hand sanitation laws for the following categories of animal contact exhibits: a. Petting zoos b. Agricultural fairs c. County or state fairs d. Exotic animal exhibits e. Circuses f. Zoos
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Department of Human Services(DHS): Child Support Enforcement Administration Performance Measures
healthdata.gov | Last Updated 2024-06-21T04:01:20.000ZCHILD SUPPORT ENFORCEMENT ADMINISTRATION The performance measures are used to evaluate each State's performance and measure results in the Child Support Enforcement program. These measures emphasize paternity establishment, support order establishment, collection of current support, collection of arrearages, and cost effectiveness. The performance measures, except cost effectiveness which can only be measured annually, are calculated from data which is reported on federal form OCSE 157. CASES WITH SUPPORT ORDERS This metric measures the proportion of IV-D cases with support orders established. Equation: Number of IV-D Cases with Support Orders divided by Total Number of IV-D Cases IV-D Paternity Establishment Percentage: This metric measures the proportion of children in the IV-D caseload as of the end of the preceding FFY who were born out of wedlock is the total number of children in the IV-D caseload in the federal fiscal year born out of wedlock with paternity established or acknowledged divided by the total number of children in the IV-D caseload as of the end of the preceding FFY who were born out of wedlock. . Equation: Total # of Children in IV-D Caseload in the Federal Fiscal Year or, as of the end of the Fiscal Year who were born out of wedlock with Paternity Established or Acknowledged divided by Total # of Children in IV-D Caseload as of the end of the preceding Federal Fiscal Year who were Born Out of Wedlock COLLECTIONS ON CURRENT SUPPORT This measure focuses on the proportion of current support due that is collected on IV-D cases. Equation: Total Dollars Collected for Current Support in IV-D Cases during the Federal Fiscal Year divided by Total Dollars Owed for Current Support in IV-D Cases during the Federal Fiscal Year COLLECTIONS ON ARREARS The measure assesses efforts to collect money from those cases with an arrearage due. The measure specifically counts paying cases, and not total arrears dollars collected. Equation: Total number of IV-D cases paying toward arrears during the Federal Fiscal Year divided by Total number of IV-D cases with arrears due
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Children by Disposition
healthdata.gov | Last Updated 2023-07-25T18:18:16.000ZThe numbers of children (duplicate count) are counted once for each investigation response or alternative response that reached a disposition (finding) for the most recent federal fiscal year for which data are available. *11/29/2021: Added column including year in which data was collected. To view more National Child Abuse and Neglect Data System (NCANDS) findings, click link to summary page below: https://healthdata.gov/stories/s/kaeg-w7jc
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Approved Animal Drug Products (Green Book)
healthdata.gov | Last Updated 2023-07-25T18:40:13.000ZOn November 16, 1988, the President of the United States signed into law the Generic Animal Drug and Patent Restoration Act (GADPTRA). Among its major provisions, the Act extends eligibility for submission of Abbreviated New Animal Drug Applications (ANADAs) to all animal drug products approved for safety and effectiveness under the Federal Food, Drug, and Cosmetic Act. The Act also requires that each sponsor of an approved animal drug product submit to the FDA certain information regarding patents held for the animal drug or its method of use. The Act requires that this information, as well as a list of all animal drug products approved for safety and effectiveness, be made available to the public. This list must be updated monthly under the provisions of the Act. This publication, which is known as the �Green Book�, was first published in January of 1989. Updates have been added monthly since then. The list is published in its entirety each January.
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Forensic vs. Civil Commitment Population
healthdata.gov | Last Updated 2024-02-02T04:00:56.000ZThis data set shows the count of patients committed to the California State Hospitals during fiscal years 2006-2022. The Department of State Hospitals (DSH) population consists of patients that are mandated for treatment by a criminal or civil court. Patients in this data set that are sent to DSH through the criminal court system and have committed or have been accused of committing a crime linked to their mental illness are referred to as "forensic" commitments. Patients in this data set committed to DSH from civil courts because they are a danger to themselves or others are referred to as "civil" commitments.
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Low Income Home Energy Assistance Program FY 2008 Household Data
healthdata.gov | Last Updated 2023-07-25T18:42:49.000Z<p>State-reported annual data collected on the presence of elderly, disabled, and young children in eligible households receiving Low Income Home Energy Assistance Program (LIHEAP) heating assistance, cooling assistance, crisis assistance or weatherization assistance.</p>
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COVID-19 Blueprint for a Safer Economy Data Chart (ARCHIVED)
healthdata.gov | Last Updated 2023-07-26T01:31:58.000Z__Note__: Blueprint has been retired as of June 15, 2021. This dataset will be kept up for historical purposes, but will no longer be updated. California has a new blueprint for reducing COVID-19 in the state with revised criteria for loosening and tightening restrictions on activities. Every county in California is assigned to a tier based on its test positivity and adjusted case rate for tier assignment. Additionally, a new health equity metric took effect on October 6, 2020. In order to advance to the next less restrictive tier, each county will need to meet an equity metric or demonstrate targeted investments to eliminate disparities in levels of COVID-19 transmission, depending on its size. The California Health Equity Metric is designed to help guide counties in their continuing efforts to reduce COVID-19 cases in all communities and requires more intensive efforts to prevent and mitigate the spread of COVID-19 among Californians who have been disproportionately impacted by this pandemic. Please see https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/COVID19CountyMonitoringOverview.aspx for more information. Also, in lieu of a Data Dictionary, please refer to the detailed explanation of the data columns in Appendix 1 of the above webpage. Because this data is in machine-readable format, the merged headers at the top of the source spreadsheet have not been included: - The first 8 columns are under the header "County Status as of Tier Assignment" - The next 3 columns are under the header "Current Data Week Tier and Metric Tiers for Data Week" - The next 4 columns are under the header "Case Rate Adjustment Factors" - The next column is under the header "Small County Considerations" - The last 5 columns are under the header "Health Equity Framework Parameters"
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Population Distribution for Medi-Cal Enrollees by Met and Unmet Share of Cost (SOC)
healthdata.gov | Last Updated 2023-07-25T18:42:58.000ZThis dataset represents the counts of those individuals who have been determined to have a share of cost (SOC) obligation, which is the monthly amount of medical expenses they must incur before they are eligible to receive Medi-Cal benefits. The dataset includes individuals who have a met or unmet monthly SOC obligation. Individuals who have not met their monthly SOC obligation are not eligible for Medi-Cal. SOC obligations are calculated during the eligibility determination process based on household income.
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Living Wage
healthdata.gov | Last Updated 2023-07-25T20:47:48.000ZThis table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the [Living Wage Calculator](http://livingwage.mit.edu/) and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.