Financial overview and grant giving statistics of California Environmental Health Asso

Financial overview and grant giving statistics of California Conference of Directors of Environmental Health

This table contains data on the modified retail food environment index for California, its regions, counties, cities, towns, and census tracts. An adequate, nutritious diet is a necessity at all stages of life. Pregnant women and their developing babies, children, adolescents, adults, and older adults depend on adequate nutrition for optimum development and maintenance of health and functioning. Nutrition also plays a significant role in causing or preventing a number of illnesses, such as cardiovascular disease, some cancers, obesity, type-2 diabetes, and anemia. Peoples’ food choices and their likelihood of being overweight or obese are also influenced by their food environment: the foods available in their neighborhoods including stores, restaurants, schools, and worksites.

The modified retail food environment index table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf

The format of the modified retail food environment table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

This table contains data on access to parks measured as the percent of population within ½ a mile of a parks, beach, open space or coastline for California, its regions, counties, county subdivisions, cities, towns, and census tracts. More information on the data table and a data dictionary can be found in the Data and Resources section. As communities become increasingly more urban, parks and the protection of green and open spaces within cities increase in importance. Parks and natural areas buffer pollutants and contribute to the quality of life by providing communities with social and psychological benefits such as leisure, play, sports, and contact with nature. Parks are critical to human health by providing spaces for health and wellness activities. The access to parks table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. The format of the access to parks table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

aIndicated the Cd exposure level of subjects enrolled in the selected village about 30 km far from the smelter opposite the leeward in CA.bIndicated the Cd exposure level of subjects enrolled in the two selected villages about 2 km to 4 km far from the smelter on the leeward in CA.cIndicated the Cd exposure level of subjects enrolled in the two selected villages about 10 km far from the copper smelter opposite the leeward in CB.dIndicated the Cd exposure level of subjects enrolled in the three selected villages about 2 km to 3 km far from the copper smelter on the leeward in CB.

aIndicated the Cd exposure level of subjects enrolled in the selected village about 30 km far from the smelter opposite the leeward in CA.bIndicated the Cd exposure level of subjects enrolled in the two selected villages about 2 km to 4 km far from the smelter on the leeward in CA.cIndicated the Cd exposure level of subjects enrolled in the two selected villages about 10 km far from the copper smelter opposite the leeward in CB.dIndicated the Cd exposure level of subjects enrolled in the three selected villages about 2 km to 3 km far from the copper smelter on the leeward in CB.eCompared with the counterpart value of subjects with high-level Cd exposures, P

aCorresponding 90th percentiles of UB2M (i.e. 1948.8 µg/g cr and 1608.7 µg/g cr) and UNAG (i.e. 90.2 U/g cr and 6.8 U/g cr) among the low-Cd exposed subjects were adopted as the thresholds for hyperB2Muria and hyperNAGuria in CA and CB, respectively. And BMD approach was used for the county-by-county data to generate BMD/BMDL values.bIn CA.cIn CB.dP values were obtained from the chi-square test with the Pearson goodness of fit test; if P>0.1 then the model is a good fit.eLogProbit model: P[response = background+(1-background) ×CumNorm×[intercept +slope×Log(dose)].fLoglogistic model: P[response] = background+(1-background)/[1+EXP(-intercept–slope×Log (dose)) ].gQuantal-linear model: P[response] = background+(1-background)×[1-EXP(-slope×dose)].

Metadata and Conversion Code for the datasets are available for download. This dataset is comprised of policy data, performance data, accompanying URL links on each data entry if available, and indicator category average data. The table of attributes contains data across 29 sustainability indicators, with upwards to 28,000 data entries. Several empty columns exist within the sheet as placeholders for future data entries, with a majority of columns to be used for future performance data and a few columns to be used for category averages once additional performance data has been collected.The Datasheet builds upon the previous iteration of the GRI, version 3.6, expanding the GRI from 28 sustainability indicators and 11 categories to 29 indicators and 12 categories. The dataset is used to develop the Green Region Initiative Sustainability Indicators Map which will provide a visual representation of both policy and performance progress being made in cities and counties throughout the SCAG region. It provides a resource for local governments to visualize sustainability progress, explore best practices, collaborate on programs, identify areas of opportunity, and assess and target the needs of communities. Data sourcePolicy Data: Developed by 2014-2015, 2015-2016, 2017-2018, 2018-2019, and 2019-2020 CivicSpark Fellows Primary data-collection sourced from policies or plans unique to each city or county for each indicator. Using the primary data-collection, the CivicSpark Fellows developed unique metrics for each indicator. A metric system developed by Environmental Science Associates for the Regional Climate Adaptation Framework in January 2020 was incorporated into the Adaptation Planning Indicator. The Governor’s Office of Business and Development shared their data from 2019-2020 on AB 1236 compliance throughout the state with SCAG to inform the Electric Vehicle Permitting Indicator Policy map.Performance Data: Developed by 2016-2017 CivicSpark FellowsPrimary data-collection sourced from various providers (i.e. California, Solar Statistics, Cool California, Energy Star, LEED, etc.). Using the primary data-collection, the CivicSpark Fellows developed unique metrics for each indicator. Category Average: Developed by 2016-2017 and 2017-2018 CivicSpark FellowsPrimary data-collection sourced from existing Policy and Performance Data developed by CivicSpark Fellows. Metrics based on a 4-tiered system. Category Averages were recalculated in 2019-20 for Built Environment, Climate Action, Health, Motorized Transportation, Open Space, and Urban Greening. Category Average was removed from Water in 2019-20 due to the reassignment of the Stormwater Management Indicator to Urban Greening and no longer having enough data to calculate Category Average. Category Average created for the new Urban Greening Topic, which contains the Parks, Stormwater Management, and Urban Forestry Indicators. Open Space no longer has a Category Average for Performance because the Parks Indicator was moved into Urban Greening and the Farmland Indicator does not have Performance metrics.Data yearDue to the Datasheet building upon previous iterations of the GRI, there are multiple data years for which the data was collected, thus limiting the validity of the current data. The following lists categorize the Policy, Performance, and Category Average data by Indicator from date of validity, beginning with the most recent. The date of validity represents the year the data was researched and updated in. A list of incomplete data is included.Policy Data:Valid as of March 2020Adaptation & Resilience PlanningClimate Action Planning Electric Vehicle PermittingValid as of May 2018Renewable EnergyCommunity Energy EfficiencyMunicipal Energy EfficiencyCommunity GHG Emissions InventoryMunicipal GHG Emissions InventoryAffordable HousingBikesPedestriansComplete StreetsElectric Vehicles Municipal Alternative Fuel FleetWater ConservationStormwater ManagementHealthy Food AccessValid as of September 2016 Municipal Green BuildingCommunity Green BuildingParking ManagementUrban ForestryParksNatural LandsFarmlandWaste MinimizationPublic HealthSustainability GrantsGreen Business Program/EPPParticipation/Collaboration PolicyIncomplete DataN/APerformance Data:Valid as of September 2017Renewable EnergyCommunity Energy EfficiencyClimate Action PlanningCommunity GHG Emissions InventoryMunicipal GHG Emissions InventoryMunicipal Green BuildingCommunity Green BuildingUrban ForestryBikesPedestriansComplete StreetsParksNatural LandsWaste MinimizationSustainability GrantsIncomplete DataCommunity Energy EfficiencyMunicipal Energy EfficiencyAffordable HousingParking ManagementElectric VehiclesMunicipal Alternative Fuel FleetFarmlandWater ConservationStormwater ManagementHealthy Food AccessGreen Business Program/EPPParticipation/Collaboration PolicyAdaptation

Descriptive statistics of study population and hospital admission characteristics for the Municipality of Tijuana and County of San Diego before and after the wildfire smoke event.

These data tables present national data on concentrations of environmental chemicals in Canadians. These data were collected as part of the Canadian Health Measures Survey (CHMS), an ongoing national direct health measures survey. Statistics Canada, in partnership with Health Canada and the Public Health Agency of Canada, launched the CHMS in 2007 to collect health and wellness data and biological specimens on a nationally representative sample of Canadians. Biological specimens were analyzed for indicators of health status, chronic and infectious diseases, nutritional status, and environmental chemicals.

These data tables present national data on concentrations of environmental chemicals in Canadians. These data were collected as part of an ongoing national direct health measures survey called the Canadian Health Measures Survey (CHMS). Statistics Canada, in partnership with Health Canada and the Public Health Agency of Canada, launched the CHMS in 2007 to collect health and wellness data and biological specimens on a nationally representative sample of Canadians. Biological specimens were analyzed for indicators of health status, chronic and infectious diseases, nutritional status, and environmental chemicals.

aCorresponding 90th percentiles of UB2M (i.e. 1948.8 µg/g cr and 1608.7 µg/g cr) and UNAG (i.e. 90.2 U/g cr and 6.8 U/g cr) among the low-Cd exposed subjects were adopted as the thresholds for hyperB2Muria and hyperNAGuria in CA and CB, respectively. And BMD approach was used for the county-by-county data to generate BMD/BMDL values.

The Canadian Optimized Statistical Smoke Model (CanOSSEM) was developed by the Environmental Health Services of the BC Centre for Disease Control and used to produce estimated concentrations of fine particulate across all populated regions of Canada. The estimates are optimized for wildfire smoke through use of multiple variables that are specific to this source. NOTE: Daily data indexed to postal codes will be available shortly. Un-indexed grid files are available on request to naman.paul@bccdc.ca.CanOSSEM is a random forest machine learning model that uses potential predictor variables integrated from multiple data sources and estimates daily mean (24-hour) PM2.5 concentrations at a 5 km × 5 km spatial resolution. The training and prediction datasets were generated using observations from National Air Pollution Surveillance (NAPS) network. The Root Mean Squared Error (RMSE) between predicted and observed PM2.5 concentration was 2.85 µg/m3 for the entire prediction set, with over 95% of the predictions lying within an absolute difference of 5 µg/m3 from the NAPS PM2.5 measurements. The model was evaluated using 10-fold cross-validation, leave-one-region-out and leave-one-year-out cross-validation.

Human biomonitoring is used to estimate exposure to environmental chemicals by measuring the chemical, its metabolites, or reaction products in biological specimens. Since 2007, the biomonitoring component of the Canadian Health Measures Survey (CHMS) has measured hundreds of chemicals in blood, urine, hair, or pooled serum. The CHMS is an ongoing national survey with data collected in two-year cycles. Biomonitoring data are available through an interactive online tool called the Canadian Biomonitoring Dashboard (https://health-infobase.canada.ca/biomonitoring/). New data will be added to the dashboard as they become available. Information specific to the biomonitoring component of the CHMS, including general information on the survey design, fieldwork, laboratory and statistical analyses, and considerations for data interpretation can be found in Health Canada’s biomonitoring reports. These archived reports as well as biomonitoring resources such as a biomonitoring content summary and fact sheets are available on the Resources (https://health-infobase.canada.ca/biomonitoring/resources.html) tab of the Canadian Biomonitoring Dashboard. More information on the full survey can be found on the Statistics Canada website (https://www.statcan.gc.ca/en/survey/household/5071).

Canada CA: Mortality Rate Attributed to Unintentional Poisoning: Female: per 100,000 Female Population data was reported at 0.300 Ratio in 2016. This stayed constant from the previous number of 0.300 Ratio for 2015. Canada CA: Mortality Rate Attributed to Unintentional Poisoning: Female: per 100,000 Female Population data is updated yearly, averaging 0.300 Ratio from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 0.300 Ratio in 2016 and a record low of 0.300 Ratio in 2016. Canada CA: Mortality Rate Attributed to Unintentional Poisoning: Female: per 100,000 Female Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank: Health Statistics. Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

The file comprises COVID-19 case counts, population, demographic and air pollution data by Toronto neighbourhood. The data were employed in an ecological study of the association between air pollution and incidence of COVID-19. Data were obtained from the Toronto Open Data portal, McGill University, the University of Toronto, the Canadian Urban Environmental Health Research Consortium (CANUE) and Statistics Canada. The study found that there was a positive association between COVID-19 incidence and long-term exposure to reactive oxygen species in fine particulate matter (PM2.5). The association was larger in magnitude in neighbourhoods with a higher proportion of Black residents. The results require further examination using studies based on individual-level rather than area-level data. Supporting documentation: https://doi.org/10.1164/rccm.202011-4142OC

Characteristics include: well-being, health conditions, health behaviours, health system, accessibility, environmental factors, deaths by cause, life expectancy, personal resources, living and working conditions, community characteristics. Includes counts and rates, high and low 95% confidence intervals, coefficient of variation, significance vis-a-vis Canada, province, peer group rate, and previous reference period.

This web experience contains a dashboard that displays spatial data and summary statistics for the 2025 Risk Assessment for domestic wells and state small water systems. Users can navigate between four tabs that display the combined risk as well as specific risk indicators for water quality, water shortage, and socioeconomic risk. Users can filter the map display and summary statistics to specific geographic areas, distance to a community water system, or risk bin.Water quality data is from the State Water Resources Control Board Aquifer Risk Map.Water shortage data is from the Department of Water Resources Water Shortage Vulnerability Assessment Scoring.Socioeconomic data is from the Office of Environmental Health Hazard Assessment.Domestic well counts are from the Department of Water Resources OSWCR database.State small water system counts are from the Division of Drinking Water.For more information, including the methodology write-up, please refer to the SWRCB Needs Assessment homepage.To connect to the GIS data in this dashboard and the GIS data for previous years risk assessments, please use the links below:2025 Risk Assessment REST Endpoint URL/SWRCB Portal page2024 Risk Assessment REST Endpoint URL/SWRCB Portal page2023 Risk Assessment REST Endpoint URL/SWRCB Portal page2022 Risk Assessment REST Endpoint URL/SWRCB Portal page

The Canadian Optimized Statistical Smoke Model (CanOSSEM) was developed by the Environmental Health Services of the BC Centre for Disease Control and used to produce estimated concentrations of fine particulate across all populated regions of Canada. The estimates are optimized for wildfire smoke through use of multiple variables that are specific to this source. NOTE: Daily data indexed to postal codes will be available shortly. Un-indexed grid files are available on request to naman.paul@bccdc.ca.CanOSSEM is a random forest machine learning model that uses potential predictor variables integrated from multiple data sources and estimates daily mean (24-hour) PM2.5 concentrations at a 5 km × 5 km spatial resolution. The training and prediction datasets were generated using observations from National Air Pollution Surveillance (NAPS) network. The Root Mean Squared Error (RMSE) between predicted and observed PM2.5 concentration was 2.85 µg/m3 for the entire prediction set, with over 95% of the predictions lying within an absolute difference of 5 µg/m3 from the NAPS PM2.5 measurements. The model was evaluated using 10-fold cross-validation, leave-one-region-out and leave-one-year-out cross-validation.

This dataset was produced through the joint collection of Statistics Canada's Canadian Wastewater Survey (CWS) with the Public Health Agency of Canada. The CWS measures levels of SARS-CoV-2 in the wastewater of five Canadian municipalities: Vancouver, Edmonton, Toronto, Montreal, and Halifax. The dataset includes measurements by RT-qPCR of the concentration of SARS-CoV-2 and Pepper Mild Mottle Virus (PMMV) in wastewater from 2021/04/01 to 2021/12/15 reported in the Public Health Environmental Surveillance Open Data Model v1.1.