Population Health (BRFSS: HRQOL)

Examining Trends, Disparities and Determinants of Health in the US Population

By Health [source]

About this dataset

The Behavioral Risk Factor Surveillance System (BRFSS) offers an expansive collection of data on the health-related quality of life (HRQOL) from 1993 to 2010. Over this time period, the Health-Related Quality of Life dataset consists of a comprehensive survey reflecting the health and well-being of non-institutionalized US adults aged 18 years or older. The data collected can help track and identify unmet population health needs, recognize trends, identify disparities in healthcare, determine determinants of public health, inform decision making and policy development, as well as evaluate programs within public healthcare services.

The HRQOL surveillance system has developed a compact set of HRQOL measures such as a summary measure indicating unhealthy days which have been validated for population health surveillance purposes and have been widely implemented in practice since 1993. Within this study's dataset you will be able to access information such as year recorded, location abbreviations & descriptions, category & topic overviews, questions asked in surveys and much more detailed information including types & units regarding data values retrieved from respondents along with their sample sizes & geographical locations involved!

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How to use the dataset

This dataset tracks the Health-Related Quality of Life (HRQOL) from 1993 to 2010 using data from the Behavioral Risk Factor Surveillance System (BRFSS). This dataset includes information on the year, location abbreviation, location description, type and unit of data value, sample size, category and topic of survey questions.

Using this dataset on BRFSS: HRQOL data between 1993-2010 will allow for a variety of analyses related to population health needs. The compact set of HRQOL measures can be used to identify trends in population health needs as well as determine disparities among various locations. Additionally, responses to survey questions can be used to inform decision making and program and policy development in public health initiatives.

Research Ideas

• Analyzing trends in HRQOL over the years by location to identify disparities in health outcomes between different populations and develop targeted policy interventions.
• Developing new models for predicting HRQOL indicators at a regional level, and using this information to inform medical practice and public health implementation efforts.
• Using the data to understand differences between states in terms of their HRQOL scores and establish best practices for healthcare provision based on that understanding, including areas such as access to care, preventative care services availability, etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:----------------------------------------------------------| | Year | Year of survey. (Integer) | | LocationAbbr | Abbreviation of location. (String) | | LocationDesc | Description of location. (String) | | Category | Category of survey. (String) | | Topic | Topic of survey. (String) | | Question | Question asked in survey. (String) | | DataSource | Source of data. (String) | | Data_Value_Unit | Unit of data value. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Footnote_Symbol | Footnote symbol for data value. (String) | | Data_Value_Std_Err | Standard error of the data value. (Float) | | Sample_Size | Sample size used in sample. (Integer) | | Break_Out | Break out categories used. (String) | | Break_Out_Category | Type break out assessed. (String) | | **GeoLocation*...

This United States Environmental Protection Agency (US EPA) feature layer represents monitoring site data, updated hourly concentrations and Air Quality Index (AQI) values for the latest hour received from monitoring sites that report to AirNow.Map and forecast data are collected using federal reference or equivalent monitoring techniques or techniques approved by the state, local or tribal monitoring agencies. To maintain "real-time" maps, the data are displayed after the end of each hour. Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here.

This is the complete dataset for the 500 Cities project 2017 release. This dataset includes 2015, 2014 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2015, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2011-2015, 2010-2014 estimates. Because some questions are only asked every other year in the BRFSS, there are 7 measures from the 2014 BRFSS that are the same in the 2017 release as the previous 2016 release. More information about the methodology can be found at www.cdc.gov/500cities.

This is the complete dataset for the 500 Cities project 2018 release. This dataset includes 2016, 2015 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2016, 2015), Census Bureau 2010 census population data, and American Community Survey (ACS) 2012-2016, 2011-2015 estimates. Because some questions are only asked every other year in the BRFSS, there are 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, cholesterol screening) from the 2015 BRFSS that are the same in the 2018 release as the previous 2017 release. More information about the methodology can be found at www.cdc.gov/500cities.

Abstract Background: Smoking and an inadequate diet are behavioral risk factors that contribute to the majority of deaths and disabilities caused by noncommunicable diseases. Objectives: To estimate the prevalence of the co-occurrence of smoking and inadequate diet and identify associated factors in adults. Methods: A cross-sectional population-based study was conducted with a sample of 28,950 Brazilian adults (18 to 59 years old). Data were obtained from Sistema de Vigilância por Inquérito Telefônico (Vigitel [Brazilian Health Surveillance Telephone Survey]) in 2014. Independent associations were investigated using Poisson hierarchical regression analysis with 5% significance level. Results: The prevalence of the co-occurrence of smoking and unhealthy eating was 8.6% (95% CI: 7.9-9.3) and was higher among individuals residing in the southern region of the country than in those living in the central western region (PR = 1.50; 95% CI: 1.18-1.89), those with no private health insurance (PR = 1.14; 95% CI: 1.03-1.25), those who drank alcohol abusively (binge drinkers) (PR = 3.22; 95% CI: 2.70-3.85) and those who self-rated their health as fair (PR = 1.65; 95% CI: 1.36-1.99) or poor/very poor (PR = 1.70; 95% CI: 1.18-2.44). The prevalence of both factors was lower among individuals residing in the northeastern region of the country, women, individuals with brown skin color, those with a spouse, the more educated ones and those with overweight or obesity. Conclusion: The more vulnerable segments to the co-occurrence of the risk factors studied were men residing in the southern region of the country, individuals with a lower socioeconomic status and those who reported binge drinking. Interventions addressing multiple behavioral risk factors adapted to specific contexts could have a greater impact on the Brazilian population.

This is the complete dataset for the 500 Cities project 2019 release. This dataset includes 2017, 2016 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2017, 2016), Census Bureau 2010 census population data, and American Community Survey (ACS) 2013-2017, 2012-2016 estimates. Because some questions are only asked every other year in the BRFSS, there are 7 measures (all teeth lost, dental visits, mammograms, pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) from the 2016 BRFSS that are the same in the 2019 release as the previous 2018 release. More information about the methodology can be found at www.cdc.gov/500cities.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset contains model-based place (incorporated and census designated places) level estimates for the PLACES project 2020 release. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 27 measures: 5 chronic disease-related unhealthy behaviors, 13 health outcomes, and 9 on use of preventive services. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This is a rice plant dataset that contains both healthy and unhealthy images. I collected this dataset for my research work on the diseases in plants and mainly focused on rice plants because rice is one of the economic crops of Pakistan. This dataset was collected from different cities in Pakistan such as Kandhkot, Shikarpur, Sukkur, Moro, and Kashmore.

I used the DSLR (a megapixel camera) to capture the images and tried my best to collect the most helpful dataset. I used this dataset for my research on detecting diseases in plants such as fungal blast disease. I successfully published a paper using this dataset entitled "Fungal Blast Disease Detection in Rice Seed Using Machine Learning", published in IJACSA (International Journal of Advanced Computer Science and Applications).

This dataset is already tuned and fined with image processing steps. I performed all the necessary tasks of data augmentation to make this dataset usable. Such as rescaling, cropping, enhancement, contrast, flipping, and saturation that make the dataset more visually.

In case of a query or question you can directly contact me regarding this dataset. I am available to help you.

NOTE: PLEASE DON'T FORGET TO CITE THIS DATASET WITH MY REFERENCES PAPER GIVEN BELOW.

Raj Kumar, Gulsher Baloch, Pankaj, Abdul Baseer Buriro and Junaid Bhatti, “Fungal Blast Disease Detection in Rice Seed using Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 12(2), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120232

DOI Link: https://dx.doi.org/10.14569/IJACSA.2021.0120232

Thanks and regards,

Engr. Raj Kumar | Research Scholar @ Jeju National University, South Korea

This dataset contains model-based county-level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2018 or 2017 county population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2015 county boundary file in a GIS system to produce maps for 27 measures at the county level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

ABSTRACT: Introduction: Inadequate dietary patterns in childhood and adolescence are a major risk factor for the early onset of obesity and other chronic diseases. Objectives: To identify and to analyze dietary patterns among Brazilian adolescents. Methods: Data from the Brazilian National School Health Survey (PeNSE) of 2015 were used (n = 10,926 adolescents). The identification and analyses of dietary patterns were calculated using principal component analysis and linear regression, respectively. Results: Two dietary patterns were identified. The first one was characterized by the presence of markers of unhealthy eating, and the second one by markers of healthy eating. The adherence to the unhealthy pattern was positively associated with female adolescents, which mothers had completed, at least, the elementary school, residents in developed regions and urban areas, as well as those students who usually chose to skip breakfast, to not have meals with their parents/guardians, and who usually had meals while watching TV/studying, and at fast food restaurants. Discussion: Analyses of the consumption of isolated food groups, which do not consider the diet in all its complexity, have been insufficient in explaining the main outcomes of the public health nutrition area. Conclusion: Our findings contributed to the identification of the most vulnerable population groups to unhealthy dietary patterns and to the understanding of the coexistence of different food habit determinants among adolescents.

This layer includes contains air quality and meteorologic measurements from air monitoring stations in Michigan that is sourced from AirNow. This dataset contains only the most recent recorded values. Note that this data is preliminary and is subject to validation and changes.

Field Name

Alias

Description

OBJECTID

N/A

N/A

StationID

Station ID

The station ID assigned by EGLE

StationName

Station Name

Station name of the air monitoring station. StationType

Station TypeThe type of air monitoring station. The value 'Permanent' indicates the station is a fixed, long-term installation.

StationStatus

Station Status

Activity status of the station.

LastObservation

Last Observation

Date and time of the most recent recorded observation.

shape

shape

ESRI geometry field.

WD_DEGREES

Wind Direction

Wind direction for current observation expressed in degrees.

WS_MS

Wind Speed

Wind speed measured in meters per second.

TEMP_CTemperatureTemperature measure in degrees Celsius.

PM25_UGM3

PM 2.5

Concentration of particulate matter ≤ 2.5 micrometers (PM2.5) measured in micrograms per cubic meter (µg/m³).

OZONE_PPBOzone

Concentration of ozone (O3) measured in parts per billion (ppb).

NO2_PPB

NO2

Concentration of nitrogen dioxide (NO₂) measured in parts per billion (ppb).

SO2_PPB

SO2Concentration of sulfur dioxide (SO₂) measured in parts per billion (ppb).

CO_PPM

CO

Concentration of carbon monoxide (CO) measured in parts per million (ppm).

NO_PPB

NOConcentration of nitrogen monoxide (NO) measured in parts per billion (ppb).

PM10_UGM3

PM 10

Concentration of particulate matter ≤ 10 micrometers (PM10) measured in micrograms per cubic meter (µg/m³). NOX_PPB

NOxConcentration of nitrogen oxides (NOx) measured in parts per billion (ppb).RWD_DEGREESResultant Wind Direction The average wind direction expressed in degrees. NOY_PPB

NOy

Concentration of total reactive nitrogen (NOy) measured in parts per billion (ppb). RWS_KNOTS

Resultant Wind Speed

The average wind speed measured in knots.

If you have questions related to air quality, please reach out to Susan Kilmer (KilmerS@Michigan.gov or 517-242-2655). If you have map suggestions or functionality issues, please reach out to EGLE-Maps@Michigan.gov.From EPA AirNow:Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here. Visit Michigan.gov/EGLE for more information about air monitoring in Michigan.

The aim of the present study was to evaluate the efficacy of a proactive electronic screening and brief intervention (internet-based brief intervention) providing personalized feedback and information on alcohol use and its consequences among young men in the general population. It consists of two studies: a secondary prevention study (for those with unhealthy alcohol use, defined as reporting >14 drinks per week OR at least one episode of binge drinking (6 or more drinks per occasion) per month OR an Alcohol Use Disorders Identification Test score >8) and a primary prevention study (for those without unhealthy alcohol use). It is hypothesized that the internet-based brief intervention will decrease later alcohol use and related consequences among individuals with unhealthy alcohol use (secondary prevention study) and will prevent the increase of alcohol use among individuals without unhealthy alcohol use (primary prevention study). The study is a parallel-group randomized controlled trial: a total of 1633 participants were included. 737 participated in the secondary prevention study and 896 in the primary prevention study. In both studies, participants were randomly assigned to receive electronic personalized feedback or not and followed at 1 month and at 6 months to evaluate their alcohol use. The primary outcomes were weekly alcohol consumption and prevalence of monthly risky single occasion drinking (or "binge"). Participants were Swiss young men from a general population sample.

IntroductionUrinary incontinence (UI) significantly impairs women’s quality of life. Identifying its risk factors is essential for developing effective interventions. Sarcopenia, characterized by the accelerated loss of muscle mass and function, is an emerging concern often linked to obesity and abnormal metabolic status, exacerbating various adverse health outcomes. This population-based study aimed to explore the independent and joint associations of sarcopenia, obesity, and metabolic health with UI risk, as well as to evaluate the mediating role of metabolic indicators in these associationsMethodsA total of 3,557 women aged ≥20 years from the National Health and Nutrition Examination Survey were included. Sarcopenia was assessed using the appendicular lean mass index (ALMI), and obesity was defined by body mass index and waist circumference. Metabolic health was evaluated using revised criteria from the National Cholesterol Education Program-Adult Treatment Panel III. UI was identified through responses to the “Kidney Conditions-Urology” questionnaire and classified into stress UI (SUI), urgency UI (UUI), and mixed UI (MUI). Multivariable logistic regression and restricted cubic spline models were used to evaluate the associations and visualize the relationship between ALMI and UI. Mediation models were constructed to assess the mediating role of metabolic indicators.ResultsWe found that sarcopenia was significantly associated with an increased risk of MUI in the general population. Age-specific analysis revealed that sarcopenia is an independent risk factor for SUI in women aged ≥60, and for MUI in women aged 40–59 years. Sarcopenic obesity, particularly under central obesity criteria, further elevated the risk of UI. Notably, women with the metabolically unhealthy obese phenotype with sarcopenia were at the highest risk for both SUI and MUI. Metabolically unhealthy status, glycohemoglobin, vitamin D, and serum albumin levels were partial mediators of these associations.ConclusionOur findings elucidated the complex interactions between sarcopenia, obesity, and metabolic health, underscoring the critical need for integrated therapeutic strategies that address both metabolic health and targeted nutritional interventions, aiming to enhance muscular health and effectively manage and prevent UI.

2017, 2016. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level. There are 7 measures (all teeth lost, dental visits, mammograms, Pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) in this 2019 release from the 2016 BRFSS that were the same as the 2018 release.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

API operated by Louisville Metro that returns AQI information from local sensors operated by APCD. Shows the latest hourly data in a JSON feed.The Air Quality Index (AQI) is an easy way to tell you about air quality without having to know a lot of technical details. The “Metropolitan Air Quality Index” shows the AQI from the monitor in Kentuckiana that is currently detecting the highest level of air pollution. See: https://louisvilleky.gov/government/air-pollution-control-district/servi...See the air quality map (Louisville Air Watch) for more details: airqualitymap.louisvilleky.gov/#Read the FAQ for more information about the AQI data: https://louisvilleky.gov/government/air-pollution-control-district/louis...If you'd prefer air quality forecast data (raw data, maps, API) instead, please see AIRNow: https://www.airnow.gov/index.cfm?action=airnow.local_city&zipcode=40204&...See the Data Dictionary section below for information about what the AQI numbers mean, their corresponding colors, recommendations, and more info and links.To download daily snapshots of AQI for the last 25 years, visit the EPA website, set your year range, and choose, Louisville KY. Then download with the CSV link at the bottom of the page.IFTTT integration trigger that fires and after retrieving air quality from Louisville Metro air sensors via the APIGives a forecast instead of the current conditions, so you can take action before the air quality gets bad.The U.S. EPA AirNow program (www.AirNow.gov) protects public health by providing forecast and real-time observed air quality information across the United States, Canada, and Mexico. AirNow receives real-time air quality observations from over 2,000 monitoring stations and collects forecasts for more than 300 cities.Sign up for a free account and get started using the RSS data feed for Louisville. https://docs.airnowapi.org/feedsAir Quality Forecast via AirNowAQI Level - Value and Related Health Concerns LegendGood 0-50 GreenAir quality is considered satisfactory, and air pollution poses little or no risk.Moderate 51-100 YellowAir quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups 101-150 OrangeMembers of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy 151-200 RedEveryone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy 201-300 PurpleHealth alert: everyone may experience more serious health effects.Hazardous > 300 Dark PurpleHealth warnings of emergency conditions. The entire population is more likely to be affected.Here are citizen actions APCD recommends on air quality alert days, that is, days when the forecast is for the air quality to reach or exceed the “unhealthy for sensitive groups” (orange) level:Don’t idle your car. (Recommended all the time; see the second link below.)Put off mowing grass with a gas mower until the alert ends.“Refuel when it’s cool” (pump gasoline only in the evening or night).Avoid driving if possible. Share rides or take TARC.Check on neighbors with breathing problems.Here are some links in relation to the recommendations:KAIRE, www.helptheair.org/Idle Free Louisville, www.helptheair.org/idle-freeTARCTicket to Ride, tickettoride.org/Lawn Care for Cleaner Air (rebates)Contact:Bryan FrazerBryan.Frazar@louisvilleky.gov

2015, 2014. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census tract-level data in GIS-friendly format can be joined with census tract spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-Census-Tract-Boundaries/x7zy-2xmx) in a geographic information system (GIS) to produce maps of 27 measures at the census tract level. Because some questions are only asked every other year in the BRFSS, there are 7 measures in this 2017 release from the 2014 BRFSS that were the same as the 2016 release.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

IntroductionPolicy makers increasingly need to prioritise between competing health technologies or patient populations. When aiming to align allocation decisions with societal preferences, knowledge and operationalisation of such preferences is indispensable. This study examines the distribution of three views on healthcare priority setting in the Netherlands, labelled “Equal right to healthcare”, “Limits to healthcare”, and “Effective and efficient healthcare”, and their relationship with preferences in willingness to trade-off (WTT) exercises.MethodsA survey including four reimbursement scenarios was conducted in a representative sample of the adult population in the Netherlands (n = 261). Respondents were matched to one of the three views based on their agreement with 14 statements on principles for resource allocation. We tested for WTT differences between respondents with different views and applied logit regression models for examining the relationship between preferences and background characteristics, including views.ResultsNearly 65% of respondents held the view “Equal right to healthcare”, followed by “Limits to healthcare” (22.5%), and “Effective and efficient healthcare” (7.1%). Most respondents (75.9%) expressed WTT in at least one scenario and preferred gains in quality of life over life expectancy, maximising gains over limiting inequality, treating children over elderly, and those with adversity over those with an unhealthy lifestyle. Various background characteristics, including the views, were associated with respondents’ preferences.ConclusionsMost respondents held an egalitarian view on priority setting, yet the majority was willing to prioritise regardless of their view. Societal views and preferences concerning healthcare priority setting are related. However, respondents’ views influence preferences differently in different reimbursement scenarios. As societal views and preferences are heterogeneous and may conflict, aligning allocation decisions with societal preferences remains challenging and any decision may be expected to receive opposition from some group in society.

BackgroundThis study investigates the joint effect of sleep patterns and oxidative balance score (OBS) on all-cause and CVD mortality in the general population.MethodsWe examined 21,427 individuals aged 18–85 from NHANES 2005–2014, connecting them to mortality data until December 31, 2019, using interview and physical examination dates. Surveys collected data on sleep duration, self-reported sleep disturbance, and doctor-told sleep disorders, classified into healthy, intermediate, and unhealthy sleep patterns. OBS was calculated based on twenty oxidative stress-related exposures to dietary and lifestyle factors. Cox proportional hazards model was conducted to evaluate the association between sleep patterns or OBS alone and combined with all-cause and CVD mortality.ResultsPoor sleep patterns and pro-oxidant OBS (Q1 & Q2) were identified as risk factors for mortality. Each point increase in OBS was associated with a 3% decrease in both all-cause mortality and CVD mortality. There was an interaction between sleep patterns and OBS (P for interaction = 0.013). Joint analyses revealed that participants with combined unhealthy (intermediate and poor) sleep pattern and pro-oxidant OBS were significantly associated with increased risk of all-cause (HR = 1.45 [1.21–1.74]) and CVD mortality (HR = 1.60 [1.12–2.28]). Furthermore, stratified analysis highlighted that this joint effect was more prominent among individuals without hypertension or diabetes; more notable for all-cause mortality in younger individuals and for CVD mortality in the elderly.ConclusionWe identified a significant interaction between sleep patterns and OBS affecting all-cause mortality. Unhealthy sleep patterns and pro-oxidant OBS were jointly and positively associated with an increased risk of all-cause and CVD mortality. Interventions targeting healthy sleep patterns and antioxidant lifestyles may promote health outcomes.

The growth in longevity in Brazil has drawn attention to more useful population health measures to complement mortality. In this paper, we investigate socio-spatial differences in life expectancy and healthy life expectancy based on information from the Brazilian National Health Survey (PNS), 2013 and 2019. A three-stage cluster sampling with stratification of the primary sampling units and random selection in all stages was used in both PNS editions. Healthy life expectancy was estimated by Sullivan’s method by sex, age, and Federated Units (UF). Severe limitations to at least one noncommunicable chronic disease (NCD) or poor self-rated health were used to define the unhealthy state. Inequality indicators and a Principal Component analysis were used to investigate socio-spatial inequalities. From 2013 to 2019, both life expectancy and healthy life expectancy increased. The analysis by UF show larger disparities in healthy life expectancy than in life expectancy, with healthy life expectancy at age 60 varying from 13.6 to 19.9 years, in 2013, and from 14.9 to 20.1, in 2019. Healthy life expectancy in the wealthiest quintile was 20% longer than for those living in the poorest quintile. Wide socio-spatial disparities were found with the worst indicators in the UF located in the North and Northeast regions, whether considering poverty concentration or health care utilization. The socio-spatial inequalities demonstrated the excess burden of poor health experienced by older adults living in the less developed UF. The development of strategies at subnational levels is essential not only to provide equal access to health care but also to reduce risk exposures and support prevention policies for adoption of health behaviors.

BackgroundWe comparatively assessed the performance of six simple obesity indices to identify adults with cardiovascular disease (CVD) risk factors in a diverse and contemporary South Asian population.Methods8,892 participants aged 20–60 years in 2010–2011 were analyzed. Six obesity indices were examined: body mass index (BMI), waist circumference (WC), waist-height ratio (WHtR), waist-hip ratio (WHR), log of the sum of triceps and subscapular skin fold thickness (LTS), and percent body fat derived from bioelectric impedance analysis (BIA). We estimated models with obesity indices specified as deciles and as continuous linear variables to predict prevalent hypertension, diabetes, and high cholesterol and report associations (prevalence ratios, PRs), discrimination (area-under-the-curve, AUCs), and calibration (index χ2). We also examined a composite unhealthy cardiovascular profile score summarizing glucose, lipids, and blood pressure.ResultsNo single obesity index consistently performed statistically significantly better than the others across the outcome models. Based on point estimates, WHtR trended towards best performance in classifying diabetes (PR = 1.58 [1.45–1.72], AUC = 0.77, men; PR = 1.59 [1.47–1.71], AUC = 0.80, women) and hypertension (PR = 1.34 [1.26,1.42], AUC = 0.70, men; PR = 1.41 [1.33,1.50], AUC = 0.78, women). WC (mean difference = 0.24 SD [0.21–0.27]) and WHtR (mean difference = 0.24 SD [0.21,0.28]) had the strongest associations with the composite unhealthy cardiovascular profile score in women but not in men.ConclusionsWC and WHtR were the most useful indices for identifying South Asian adults with prevalent diabetes and hypertension. Collection of waist circumference data in South Asian health surveys will be informative for population-based CVD surveillance efforts.