Explore Public Health AmeriCorps FY22 and FY23 grantee application information using this dashboard. The user guide linked provides background on the data involved and how to navigate the dashboard.

The Performance Dashboard (formerly Performance Outcomes System) datasets are developed to improve outcomes and inform beneficiaries who receive Medi-Cal Specialty Mental Health Services (SMHS). The intent of the dashboard is to gather information relevant to particular mental health outcomes, which will provide useful summary reports to help ensure ongoing quality improvement and to support decision making. Please note: the Excel file Performance Dashboard has been discontinued and replaced with the SMHS Performance Dashboards found on Behavioral Health Reporting (ca.gov).

The Live Healthy Loudoun Community Health Dashboard is a web-based data resource for the Loudoun County community. This site enables anybody to explore existing population data. The Community Health Dashboard provides online access to data on a broad range of topics, links to existing local data resources, and highlights promising practices to promote community health and well-being. The Community Health Dashboard is a component of the Live Healthy Loudoun initiative, which aims to transform our communities together. To learn more about this initiative, please visit us on the web at: https://www.loudoun.gov/livehealthy

Philadelphia Council District Health Dashboard - Dataset and Codebook

Description

This dataset supports the Philadelphia Council District Health Dashboard, an interactive web application that visualizes health disparities and social determinants of health across Philadelphia's 10 City Council Districts. The dashboard provides district-level insights to guide equitable policy and investment decisions by City Council members and the public.

Background

Philadelphia residents experience drastically different health outcomes across the city – differences shaped by federal, state, and local policies rather than individual choices alone. This project maps key health indicators across all 10 Philadelphia City Council Districts to show how politics and geography intersect to shape Philadelphian health.

Data Sources

• US Census Bureau American Community Survey (ACS) 5-year estimates (2018-2022)
• Open Data Philly (2015-2024)

Data aggregated from original geographic units to City Council District boundaries using population-weighted methods.

Dataset Contents

Files:

• data_v1.csv - Main dataset containing health indicators by Philadelphia City Council District
• codebook_v1.csv - Complete metadata and variable documentation

Methodology

• Population-weighted aggregation for demographic/socioeconomic variables
• Area-weighted aggregation for environmental variables
• Count aggregation for incident data
• City averages calculated as population-weighted across districts

Geographic Coverage

• Unit: Philadelphia City Council Districts (n=10)
• Period: 2018-2022 (ACS), 2015-2024 (Open Data Philly)

Applications

Supports policy analysis, community advocacy, academic research, and public health planning at the district level.

Contact

Authors

Amber Bolli, Tamara Rushovich, Ran Li, Stephanie Hernandez, Alina Schnake-Mahl

Funding

Transform Academia for Equity grant from Robert Wood Johnson Foundation

Keywords

Philadelphia, City Council, Health Disparities, Social Determinants, Urban Health, Public Policy, Geospatial Analysis

EMSIndicators:The number of individual patients administered naloxone by EMSThe number of naloxone administrations by EMSThe rate of EMS calls involving naloxone administrations per 10,000 residentsData Source:The Vermont Statewide Incident Reporting Network (SIREN) is a comprehensive electronic prehospital patient care data collection, analysis, and reporting system. EMS reporting serves several important functions, including legal documentation, quality improvement initiatives, billing, and evaluation of individual and agency performance measures.Law Enforcement Indicators:The Number of law enforcement responses to accidental opioid-related non-fatal overdosesData Source:The Drug Monitoring Initiative (DMI) was established by the Vermont Intelligence Center (VIC) in an effort to combat the opioid epidemic in Vermont. It serves as a repository of drug data for Vermont and manages overdose and seizure databases. Notes:Overdose data provided in this dashboard are derived from multiple sources and should be considered preliminary and therefore subject to change. Overdoses included are those that Vermont law enforcement responded to. Law enforcement personnel do not respond to every overdose, and therefore, the numbers in this report are not representative of all overdoses in the state. The overdoses included are limited to those that are suspected to have been caused, at least in part, by opioids. Inclusion is based on law enforcement's perception and representation in Records Management Systems (RMS). All Vermont law enforcement agencies are represented, with the exception of Norwich Police Department, Hartford Police Department, and Windsor Police Department, due to RMS access. Questions regarding this dataset can be directed to the Vermont Intelligence Center at dps.vicdrugs@vermont.gov.Overdoses Indicators:The number of accidental and undetermined opioid-related deathsThe number of accidental and undetermined opioid-related deaths with cocaine involvementThe percent of accidental and undetermined opioid-related deaths with cocaine involvementThe rate of accidental and undetermined opioid-related deathsThe rate of heroin nonfatal overdose per 10,000 ED visitsThe rate of opioid nonfatal overdose per 10,000 ED visitsThe rate of stimulant nonfatal overdose per 10,000 ED visitsData Source:Vermont requires towns to report all births, marriages, and deaths. These records, particularly birth and death records are used to study and monitor the health of a population. Deaths are reported via the Electronic Death Registration System. Vermont publishes annual Vital Statistics reports.The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) captures and analyzes recent Emergency Department visit data for trends and signals of abnormal activity that may indicate the occurrence of significant public health events.Population Health Indicators:The percent of adolescents in grades 6-8 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who drank any alcohol in the past 30 daysThe percent of adolescents in grades 9-12 who binge drank in the past 30 daysThe percent of adolescents in grades 9-12 who misused any prescription medications in the past 30 daysThe percent of adults who consumed alcohol in the past 30 daysThe percent of adults who binge drank in the past 30 daysThe percent of adults who used marijuana in the past 30 daysData Sources:The Vermont Youth Risk Behavior Survey (YRBS) is part of a national school-based surveillance system conducted by the Centers for Disease Control and Prevention (CDC). The YRBS monitors health risk behaviors that contribute to the leading causes of death and disability among youth and young adults.The Behavioral Risk Factor Surveillance System (BRFSS) is a telephone survey conducted annually among adults 18 and older. The Vermont BRFSS is completed by the Vermont Department of Health in collaboration with the Centers for Disease Control and Prevention (CDC).Notes:Prevalence estimates and trends for the 2021 Vermont YRBS were likely impacted by significant factors unique to 2021, including the COVID-19 pandemic and the delay of the survey administration period resulting in a younger population completing the survey. Students who participated in the 2021 YRBS may have had a different educational and social experience compared to previous participants. Disruptions, including remote learning, lack of social interactions, and extracurricular activities, are likely reflected in the survey results. As a result, no trend data is included in the 2021 report and caution should be used when interpreting and comparing the 2021 results to other years.The Vermont Department of Health (VDH) seeks to promote destigmatizing and equitable language. While the VDH uses the term "cannabis" to reflect updated terminology, the data sources referenced in this data brief use the term "marijuana" to refer to cannabis. Prescription Drugs Indicators:The average daily MMEThe average day's supplyThe average day's supply for opioid analgesic prescriptionsThe number of prescriptionsThe percent of the population receiving at least one prescriptionThe percent of prescriptionsThe proportion of opioid analgesic prescriptionsThe rate of prescriptions per 100 residentsData Source:The Vermont Prescription Monitoring System (VPMS) is an electronic data system that collects information on Schedule II-IV controlled substance prescriptions dispensed by pharmacies. VPMS proactively safeguards public health and safety while supporting the appropriate use of controlled substances. The program helps healthcare providers improve patient care. VPMS data is also a health statistics tool that is used to monitor statewide trends in the dispensing of prescriptions.Treatment Indicators:The number of times a new substance use disorder is diagnosed (Medicaid recipients index events)The number of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation events)The number of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement events)The percent of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation rate)The percent of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement rate)The MOUD treatment rate per 10,000 peopleThe number of people who received MOUD treatmentData Source:Vermont Medicaid ClaimsThe Vermont Prescription Monitoring System (VPMS)Substance Abuse Treatment Information System (SATIS)

This interactive healthcare dashboard leverages patient-level data from Johns Hopkins University to offer a comprehensive, data-driven analysis of both clinical and financial performance in a healthcare setting. Developed using Power BI, the dashboard integrates diverse data sources—such as diagnoses, procedures, lab results, and billing records—to provide stakeholders with a unified view of population health, operational efficiency, and revenue cycles.

At the core of the dashboard is a risk stratification model, categorizing patients into Low Risk, Medium Risk, and High Risk segments based on parameters such as chronic conditions, abnormal lab values, and age. This segmentation enables a focused evaluation of healthcare delivery patterns, clinical burdens, and cost implications across risk categories. For instance, High-Risk patients, while representing a small proportion of the population, account for a disproportionately high share of average billing and extended lengths of stay—underscoring the need for proactive care management strategies.

Key features of the dashboard include:

Patient Risk Scoring based on clinical and demographic factors

Revenue Attribution by Diagnosis, highlighting top cost drivers like Type 2 diabetes, heart failure, and hypertension

Monthly Revenue Trends to monitor changes in financial performance

Department-Level Length of Stay Metrics, helping optimize bed utilization and throughput

Correlation of Age and Chronic Conditions with Revenue, revealing the impact of aging and multimorbidity on healthcare expenditure

The dashboard also presents a granular view of patient encounters, lab abnormalities, and procedure volumes—supporting decision-making for clinical operations, resource allocation, and preventive care initiatives. From a financial perspective, it highlights average revenue per patient and per encounter across risk levels, guiding strategic planning and budgeting efforts.

By combining clinical, operational, and financial metrics into one interactive platform, this dashboard empowers healthcare administrators, analysts, and clinicians to identify trends, assess performance, and take timely action. It supports value-based care initiatives by focusing on outcomes, efficiency, and high-cost patient cohorts, helping institutions achieve better health outcomes while maintaining financial sustainability.

There is no description available for this dataset.

IntroductionPublic health is not only threatened by diseases, pandemics, or epidemics. It is also challenged by deficits in the communication of health information. The current COVID-19 pandemic demonstrates that impressively. One way to deliver scientific data such as epidemiological findings and forecasts on disease spread are dashboards. Considering the current relevance of dashboards for public risk and crisis communication, this systematic review examines the state of research on dashboards in the context of public health risks and diseases.MethodNine electronic databases where searched for peer-reviewed journal articles and conference proceedings. Included articles (n = 65) were screened and assessed by three independent reviewers. Through a methodological informed differentiation between descriptive studies and user studies, the review also assessed the quality of included user studies (n = 18) by use of the Mixed Methods Appraisal Tool (MMAT).Results65 articles were assessed in regards to the public health issues addressed by the respective dashboards, as well as the data sources, functions and information visualizations employed by the different dashboards. Furthermore, the literature review sheds light on public health challenges and objectives and analyzes the extent to which user needs play a role in the development and evaluation of a dashboard. Overall, the literature review shows that studies that do not only describe the construction of a specific dashboard, but also evaluate its content in terms of different risk communication models or constructs (e.g., risk perception or health literacy) are comparatively rare. Furthermore, while some of the studies evaluate usability and corresponding metrics from the perspective of potential users, many of the studies are limited to a purely functionalistic evaluation of the dashboard by the respective development teams.ConclusionThe results suggest that applied research on public health intervention tools like dashboards would gain in complexity through a theory-based integration of user-specific risk information needs.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=200178, identifier: CRD42020200178.

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables.

Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021.

This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data.

This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score.

This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4.

The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

This dashboard for local public health shares data on the 2022-2023 Capacity Assessment conducted among municipalities participating in the Public Health Excellence Shared Services Grant Program. These data represent health departments’ self-reported ability to meet the Performance Standards for local public health.

In March 2023, the Public Health Agency of Canada launched a new, interactive dashboard that reports on the health of people in Canada. This collection of public health indicators helps us understand areas where we are doing well and where we need to improve. Ultimately, this dashboard provides a snapshot of the health of our population, while illustrating the wide range of health, socio-economic, and environmental factors that interact to keep us healthy and well. People in Canada generally live long and healthy lives, but not everyone has the same opportunity for good health. This dashboard explores the question: how healthy are people in Canada?

The Health Inequalities Dashboard presents data on health inequalities for England, English regions, clinical commissioning groups and local authorities. It presents measures of inequality for 19 indicators, mostly drawn from the Public Health Outcomes Framework (PHOF).

The dashboard measures trends in each indicator since a baseline period, with longer term data provided where these are available. Inequalities are considered across a range of dimensions, including:

• deprivation
• ethnic group
• sexual orientation
• employment status

The following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

Health dashboards can be used to highlight key metrics, including changes in a population’s health over time, how people choose to receive healthcare, or urgent public health information, such as vaccination rates during a global pandemic.

The dashboard is updated each Friday.

Laboratory surveillance data: California laboratories report SARS-CoV-2 test results to CDPH through electronic laboratory reporting. Los Angeles County SARS-CoV-2 lab data has a 7-day reporting lag. Test positivity is calculated using SARS-CoV-2 lab tests that has a specimen collection date reported during a given week. Specimens for testing are collected from patients in healthcare settings and do not reflect all testing for COVID-19 in California. Test positivity for a given week is calculated by dividing the number of positive COVID-19 results by the total number of specimens tested for that virus. Weekly laboratory surveillance data are defined as Sunday through Saturday.

Hospitalization data: Data on COVID-19 and influenza hospital admissions are from Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network (NHSN) Hospitalization dataset. The requirement to report COVID-19-associated hospitalizations was effective November 1, 2024. CDPH pulls NHSN data from the CDC on the Wednesday prior to the publication of the report. Results may differ depending on which day data are pulled. Admission rates are calculated using population estimates from the P-3: Complete State and County Projections Dataset (https://dof.ca.gov/forecasting/demographics/projections/) provided by the State of California Department of Finance. Reported weekly admission rates for the entire season use the population estimates for the year the season started. For more information on NHSN data including the protocol and data collection information, see the CDC NHSN webpage (https://www.cdc.gov/nhsn/index.html). Weekly hospitalization data are defined as Sunday through Saturday.

Death certificate data: CDPH receives weekly year-to-date dynamic data on deaths occurring in California from the CDPH Center for Health Statistics and Informatics. These data are limited to deaths occurring among California residents and are analyzed to identify COVID-19-coded deaths. These deaths are not necessarily laboratory-confirmed and are an underestimate of all COVID-19-associated deaths in California. Weekly death data are defined as Sunday through Saturday.

Note: On April 30, 2024, the Federal mandate for COVID-19 and influenza associated hospitalization data to be reported to CDC’s National Healthcare Safety Network (NHSN) expired. Hospitalization data beyond April 30, 2024, will not be updated on the Open Data Portal. Hospitalization and ICU admission data collected from summer 2020 to May 10, 2023, are sourced from the California Hospital Association (CHA) Survey. Data collected on or after May 11, 2023, are sourced from CDC's National Healthcare Safety Network (NHSN).

Data is from the California Department of Public Health (CDPH) Respiratory Virus State Dashboard at https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx.

Data are updated each Friday around 2 pm.

For COVID-19 death data: As of January 1, 2023, data was sourced from the California Department of Public Health, California Comprehensive Death File (Dynamic), 2023–Present. Prior to January 1, 2023, death data was sourced from the COVID-19 case registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023. Influenza death data was sourced from the California Department of Public Health, California Comprehensive Death File (Dynamic), 2020–Present.

COVID-19 testing data represent data received by CDPH through electronic laboratory reporting of test results for COVID-19 among residents of California. Testing date is the date the test was administered, and tests have a 1-day lag (except for the Los Angeles County, which has an additional 7-day lag). Influenza testing data represent data received by CDPH from clinical sentinel laboratories in California. These laboratories report the aggregate number of laboratory-confirmed influenza virus detections and total tests performed on a weekly basis. These data do not represent all influenza testing occurring in California and are available only at the state level.

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

On 6/16/2023 CDPH replaced the booster measures with a new “Up to Date” measure based on CDC’s new recommendations, replacing the primary series, boosted, and bivalent booster metrics The definition of “primary series complete” has not changed and is based on previous recommendations that CDC has since simplified. A person cannot complete their primary series with a single dose of an updated vaccine. Whereas the booster measures were calculated using the eligible population as the denominator, the new up to date measure uses the total estimated population. Please note that the rates for some groups may change since the up to date measure is calculated differently than the previous booster and bivalent measures.

This data is from the same source as the Vaccine Progress Dashboard at https://covid19.ca.gov/vaccination-progress-data/ which summarizes vaccination data at the county level by county of residence. Where county of residence was not reported in a vaccination record, the county of provider that vaccinated the resident is included. This applies to less than 1% of vaccination records. The sum of county-level vaccinations does not equal statewide total vaccinations due to out-of-state residents vaccinated in California.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

Totals for the Vaccine Progress Dashboard and this dataset may not match, as the Dashboard totals doses by Report Date and this dataset totals doses by Administration Date. Dose numbers may also change for a particular Administration Date as data is updated.

Previous updates:

• On March 3, 2023, with the release of HPI 3.0 in 2022, the previous equity scores have been updated to reflect more recent community survey information. This change represents an improvement to the way CDPH monitors health equity by using the latest and most accurate community data available. The HPI uses a collection of data sources and indicators to calculate a measure of community conditions ranging from the most to the least healthy based on economic, housing, and environmental measures.

• Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 16+ and age 5+ denominators have been uploaded as archived tables.

• Starting on May 29, 2021 the methodology for calculating on-hand inventory in the shipped/delivered/on-hand dataset has changed. Please see the accompanying data dictionary for details. In addition, this dataset is now down to the ZIP code level.

Data by medical encounter for the following conditions by age, race/ethnicity, and sex (gender):

Influenza (Flu) Flu/Pneumonia Pneumonia Urinary Tract Infections

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population. Blank Cells: Events less than 11 are suppressed. Starting with data year 2022, geographies with less than 20,000 population contain no age-adjusted rates and all rates based on events <20 are suppressed due to statistical instability. Rates not calculated in cases where zip code is unknown. SES: Is the median household income by Subregional Area (SRA) community. Data for SRA only.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS), 2022. California Department of Health Care Access and Information (HCAI), Emergency Department Discharge Database and Patient Discharge Database, 2022. SANDAG Population Estimates, 2022 (v11/23). 2022 population estimates were derived from the 2020 decennial census. Comparison of rates to prior years may not be appropriate. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, May 2024.

2022 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2022COREDataGuideandDataDictionary/Home

This Power BI dashboard shows the COVID-19 vaccination rate by key demographics including age groups, race and ethnicity, and sex for Tempe zip codes. Data Source: Maricopa County GIS Open Data weekly count of COVID-19 vaccinations. The data were reformatted from the source data to accommodate dashboard configuration. The Maricopa County Department of Public Health (MCDPH) releases the COVID-19 vaccination data for each zip code and city in Maricopa County at ~12:00 PM weekly on Wednesdays via the Maricopa County GIS Open Data website (https://data-maricopa.opendata.arcgis.com/). More information about the data is available on the Maricopa County COVID-19 Vaccine Data page (https://www.maricopa.gov/5671/Public-Vaccine-Data#dashboard). The dashboard’s values are refreshed at 3:00 PM weekly on Wednesdays. The most recent date included on the dashboard is available by hovering over the last point on the right-hand side of each chart. Please note that the times when the Maricopa County Department of Public Health (MCDPH) releases weekly data for COVID-19 vaccines may vary. If data are not released by the time of the scheduled dashboard refresh, the values may appear on the dashboard with the next data release, which may be one or more days after the last scheduled release. Dates: Updated data shows publishing dates which represents values from the previous calendar week (Sunday through Saturday). For more details on data reporting, please see the Maricopa County COVID-19 data reporting notes at https://www.maricopa.gov/5460/Coronavirus-Disease-2019.

Context

WHO is collecting data from all countries to monitor COVID 19 metrics at a global level

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?