Context

Data Review: How many people die from air pollution?

Content

Datas come from https://ourworldindata.org/data-review-air-pollution-deaths

Acknowledgements

https://www.site-shot.com/cached_image/s4WAhFOPEey54gJCrBEAAg" alt="Pollution Deaths">

Inspiration

Compare pollution deaths by country

Dataset contains information on New York City air quality surveillance data.

Air pollution is one of the most important environmental threats to urban populations and while all people are exposed, pollutant emissions, levels of exposure, and population vulnerability vary across neighborhoods. Exposures to common air pollutants have been linked to respiratory and cardiovascular diseases, cancers, and premature deaths. These indicators provide a perspective across time and NYC geographies to better characterize air quality and health in NYC. Data can also be explored online at the Environment and Health Data Portal: http://nyc.gov/health/environmentdata.

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.

Dataset

This dataset was created by valcho valev

Contents

United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data was reported at 17.000 NA in 2016. United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data is updated yearly, averaging 17.000 NA from Dec 2016 (Median) to 2016, with 1 observations. United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Lung Cancer Prediction

Air Pollution, Alcohol, Smoking & Risk of Lung Cancer

About this dataset

This dataset contains information on patients with lung cancer, including their age, gender, air pollution exposure, alcohol use, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, smoking, passive smoker, chest pain, coughing of blood, fatigue, weight loss ,shortness of breath ,wheezing ,swallowing difficulty ,clubbing of finger nails and snoring

How to use the dataset

Lung cancer is the leading cause of cancer death worldwide, accounting for 1.59 million deaths in 2018. The majority of lung cancer cases are attributed to smoking, but exposure to air pollution is also a risk factor. A new study has found that air pollution may be linked to an increased risk of lung cancer, even in nonsmokers.

The study, which was published in the journal Nature Medicine, looked at data from over 462,000 people in China who were followed for an average of six years. The participants were divided into two groups: those who lived in areas with high levels of air pollution and those who lived in areas with low levels of air pollution.

The researchers found that the people in the high-pollution group were more likely to develop lung cancer than those in the low-pollution group. They also found that the risk was higher in nonsmokers than smokers, and that the risk increased with age.

While this study does not prove that air pollution causes lung cancer, it does suggest that there may be a link between the two. More research is needed to confirm these findings and to determine what effect different types and levels of air pollution may have on lung cancer risk

Research Ideas

• predicting the likelihood of a patient developing lung cancer
• identifying risk factors for lung cancer
• determining the most effective treatment for a patient with lung cancer

Acknowledgements

License

See the dataset description for more information.

Columns

File: cancer patient data sets.csv | Column name | Description | |:-----------------------------|:--------------------------------------------------------------------| | Age | The age of the patient. (Numeric) | | Gender | The gender of the patient. (Categorical) | | Air Pollution | The level of air pollution exposure of the patient. (Categorical) | | Alcohol use | The level of alcohol use of the patient. (Categorical) | | Dust Allergy | The level of dust allergy of the patient. (Categorical) | | OccuPational Hazards | The level of occupational hazards of the patient. (Categorical) | | Genetic Risk | The level of genetic risk of the patient. (Categorical) | | chronic Lung Disease | The level of chronic lung disease of the patient. (Categorical) | | Balanced Diet | The level of balanced diet of the patient. (Categorical) | | Obesity | The level of obesity of the patient. (Categorical) | | Smoking | The level of smoking of the patient. (Categorical) | | Passive Smoker | The level of passive smoker of the patient. (Categorical) | | Chest Pain | The level of chest pain of the patient. (Categorical) | | Coughing of Blood | The level of coughing of blood of the patient. (Categorical) | | Fatigue | The level of fatigue of the patient. (Categorical) | | Weight Loss | The level of weight loss of the patient. (Categorical) | | Shortness of Breath | The level of shortness of breath of the patient. (Categorical) | | Wheezing | The level of wheezing of the patient. (Categorical) | | Swallowing Difficulty | The level of swallowing difficulty of the patient. (Categorical) | | Clubbing of Finger Nails | The level of clubbing of finger nails of the patient. (Categorical) |

Air pollution results from the introduction of a range of substances into the atmosphere from a wide variety of sources. It can cause both short term and long term effects on health, but also on the wider environment. The air quality in Northern Ireland is generally better now than it has been at any time since before the Industrial Revolution.

These improvements have been achieved through the introduction of legislation enforcing tighter controls on emissions of pollutants from key sources, notably industry, domestic combustion and transport. However, despite the improvements made, air pollution is still recognised as a risk to health, and many people are concerned about pollution in the air that they breathe.

Government statistics estimate that air pollution in the UK reduces the life expectancy of every person by an average of 7-8 months, with an associated cost of up to £20 billion each year. Legislation and Policies aiming to further minimise and track the impact of air pollution on health and the environment have been introduced in Europe, the UK and Northern Ireland.

U.S. Daily PM2.5

2003-2008 Annual Average Rates by State

By Health [source]

About this dataset

This dataset presents the annual average of daily fine particulate matter (PM2.5) concentrations for locations across the United States from 2003-2008. PM2.5 particles are air pollutants with an aerodynamic diameter of less than 2.5 micrometers and can have a range of adverse health effects, including but not limited to decreased lung function, irregular heartbeats, asthma attacks, and even premature death in extreme cases. This dataset provides a snapshot of the status of air quality across the US during this time period and highlights areas where pollution levels were above safe limits established by regulatory standards set forth by the EPA. The data are geographically aggregated according to county-level as well as region, division, state selectivity collected with 10km square spatial resolution grids identified by their geographic location such as counties or states and provided in micrograms per cubic meter (PM2.5) ''µg/m''³). Included here are only those measurements which provide an annual average divided into five columns: PM2.5 concentration rate; number of observations; minimum range value; maximum range value;and standard deviation. Understanding these patterns is essential for developing strategies that protect public health from hazardous air pollutant exposure to work towards attaining improved air quality standards throughout our communities nationally ad internationally

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How to use the dataset

This dataset is great for anyone who is interested in understanding the daily concentrations of fine particulate matter across the United States from 2003 to 2008. PM2.5 particles are air pollutants with an aerodynamic diameter less than 2.5 micrometers, and can be harmful to our health if levels get too high, so understanding areas and times where this pollutant is present helps us better understand how to tackle issues of air pollution around the country.

The columns present in this dataset give users invaluable insights into different states, regions, divisions, counties, and ranges of PM2.5 values that were observed over a given period of time. To begin exploring this data set it’s helpful to understand some basic concepts: - The ‘place’ column indicates which area we are looking at on a geographical level; this could be a state (e.g., 'Alabama'), region (e.g., 'West South Central'), division (Midwest) or county (Coos). - The ‘Value’ contains information around what was measured—for any given location there will be one value attached which outlines the average concentration of PM2.5 over that year based on observations taken from NASA satellites and EPA ground stations during different times of day or nights over about two weeks per month - The ‘Numberofobservation’ column reports how many observations were taken for any given place; areas with more populated cities like New York or California may have thousands more observations compared to rural locations like Alaska where numbers may be lower since there are not as many people—this subset gives us information about exactly how much data was collected
- Finally we also have information around minimum/maximum/standard deviation corresponding with each observation; this provides valuable statistical insight into any given area

Research Ideas

• Analyzing the relationship between fine particulate matter levels and health outcomes, such as the prevalence of asthma or other respiratory diseases.
• Comparing average PM2.5 values between different states to pinpoint areas where air pollution is higher than others and suggest further efforts at tackling air pollution in those areas.
• Using county-level PM2.5 data to determine potential correlations between populations, industrial output, and also geographic features such as proximity to bodies of water with respect to air pollution trends over time

Acknowledgements

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

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute you...

Analysis of ‘Daily Air Pollution Data - India & USA’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sumandey/daily-air-quality-dataset-india on 30 September 2021.

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

Context

Air Pollution is a major health concern of many. However, the COVID-19 pandemic might have some role to play in bringing some changes to the overall quality of air.

Content

The dataset consists of pm2.5 measurements from Jan 2019 to May 2021 of the Major Cities of India & the United States. You also need to understand how pm2.5 classifies Air Quality.

Acknowledgements

Special thanks go to https://aqicn.org for making the data open-source and use it for research purposes.

Inspiration

This data could be used to answer several questions -

• How the air quality been pre and post-Covid.
• How Air Quality varies across different cities.
• Can you forecast the values for the next 1 month.

You are open to coming up with your own analysis as well.

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

Analysis of ‘COVID-19 State Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nightranger77/covid19-state-data on 30 September 2021.

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

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

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

This data set illustrates three issues relating to water pollution across the globe. Suspended solids, freshwater pollution, and dissolved oxygen concentration are these issues. A value of -1 means that no data was available. http://www.nationmaster.com/graph/env_wat_sus_sol-environment-water-suspended-solids http://www.nationmaster.com/graph/env_wat_fre_pol-environment-water-freshwater-pollution http://www.nationmaster.com/graph/env_wat_dis_oxy_con-environment-water-dissolved-oxygen-concentration September 17, 2007

OpenAQ is an open-source project to surface live, real-time air quality data from around the world. Their “mission is to enable previously impossible science, impact policy and empower the public to fight air pollution.” The data includes air quality measurements from 5490 locations in 47 countries.

Scientists, researchers, developers, and citizens can use this data to understand the quality of air near them currently. The dataset only includes the most current measurement available for the location (no historical data).

Update Frequency: Weekly

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.openaq.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Dataset Source: openaq.org

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source and is provided "AS IS" without any warranty, express or implied.

INTRO

This dataset covers the most recent and updated health statistics of the world (countries recognized by WHO- all), BUT the data could not be directly used as the major indicator of various subtopics in the dataset was mixed so I have filtered based on various indicators and hence, divided into subcategories. I know so many datasets seem overwhelming, but I will be giving the various categories they belong to and what they represent so do not worry!)

The dataset was filtered to increase user readability and create amazing and beautiful visualizations and EDA’s. Listed below will be the various datasets (named csv’s) and what they represent under their categories.

Also, before starting I will soon be uploading a viz for the same and this data cleaning and filtering has along with compiling has been a great task so...

Let us get started.

DATASET (CONTENTS)

Life expectancy and Healthy life expectancy

lifeExpectancyAtBirth.csv -> Life expectancy at birth, country wise mentioned in age (years). HALElifeExpectancyAtBirth.csv -> Healthy life expectancy (HALE) at birth, country wise mentioned in age(years).csv WHOregionLifeExpectancAtBirth.csv -> Life expectancy at birth, Region wise mentioned in age (years). HAleWHOregionLifeExpectancy.csv -> Healthy life expectancy at birth, region wise mentioned in age(years). %HaleInLifeExpectancy.csv -> Healthy life and life expectancy at birth with the % of HALE in life expectancy.

Maternal mortality

Data from 2014 to 2019 indicate that approximately 81% of all births globally took place in the presence of skilled health personnel, an increase from 64% in the 2000–2006 period

maternalMortalityRatio.csv-> Maternal mortality ratio per 100,000 births birthAttendedBySkilledPersonal.csv-> Births attended by skilled personals (percentile)

Newborn and child mortality

infantMortalityRate.csv-> Probability of dying between birth and age 1 per 1000 live births. neonatalMortalityRate.csv -> Probability of children dying in the first 28 days of life. under5MortalityRate.csv- > Probability of children dying below the age of 5 per 1000 live births.

Communicable Diseases

incedenceOfMalaria.csv-> Malaria incidence per 1000 population at risk incedenceOfTuberculosis.csv-> Incidence of TB per 100,000 population per year. hepatitusBsurfaceAntigen.csv -> Hepatitis B surface antigen (HBsAg) prevalence among children under 5 years) interventionAgianstNTD's.csv -> Reported number of people requiring interventions against NTDs. newHivInfections.csv ->New HIV infections per 1000 uninfected population

Noncommunicable diseases and mental health

30-70cancerChdEtc.csv -> Probability of dying between the age of 30 and exact age of 70 from any of the cardiovascular disease, cancer, diabetes, or chronic respiratory disease. crudeSuicideRates.csv -> Crude suicide rates per 100,000 population

Substance abuse

AlcoholSubstanceAbuse.csv -> Total (recorded + unrecorded) alcohol per capita (15 +) consumption’s

Road Traffic Injuries

roadTrafficDeaths.csv -> Estimated road traffic death rate per 100,000 population

Sexual and reproductive health

reproductiveAgeWomen.csv -> Married or in-union women of reproductive age who have their need for family planning satisfied with modern methods (%) adolescentBirthRate.csv -> Adolescent birth rate per 1000 women aged 15-19 years

Achieve universal health coverage (UHC) including financial risk protection

uhcCoverage.csv ->UHC index of service coverage (SCI) dataAvailibilityForUhc.csv ->Data availability of UHC index of essential service coverage (%) population10%SDG3.8.2.csv ->Population with household expenditures on health greater than 10% of total household expenditure or income (SDG indicator 3.8.2) (%) population25%SDG3.8.2.csv -> Population with household expenditures on health greater than 25% of total household expenditure or income (SDG indicator 3.8.2) (%)

Mortality from environment pollution

airPollutionDeathRate.csv -> Ambient and household air pollution attributable death rate per 100,00 population and the same data with age-standardized. mortalityRateUnsafeWash.csv -> Mortality rate attributed to exposure to unsafe WASH services per 100,000 population SDG3.9.2 mortalityRatePoisoning.csv -> Mortality rate attributed to unintentional poisoning per 100,000 population

Tobacco control

tobaccoAge15.csv ->Prevalence of current tobacco use among persons aged 15 years and older (age- standardized rate)

Health Workforce

medicalDoctors.csv -> Medical doctors per 10,000 population. nursingAndMidwife.csv -> Nursing and midwifery personnel per 10,000 ...

The HES-ONS linked mortality dataset combines Hospital Episode Statistics (HES), which contains clinical information on patients' hospital activity and treatment such as diagnoses and procedures, with mortality records from the Office for National Statistics (ONS). The linkage captures deaths of people who have been treated in English hospitals, irrespective of whether they died in hospital or not.

While HES data alone can be used to identify if a patient died in hospital and analysed on the basis of their primary diagnosis, it cannot determine the cause of death or capture deaths after discharge. The ONS mortality data is a richer source of information on deaths, containing cause of death, date and place of death for all deaths registered in England and Wales, based on information from death certificates.

By linking the two datasets, it is possible to analyse deaths both in and outside hospital, assess outcomes such as postoperative mortality (e.g. deaths within 30, 60 or 90 days), track provider performance, and conduct long-term follow-up on survival rates. The dataset only includes mortality information for individuals who have had a record in HES.

SAHSU holds a data sharing agreement with NHS England and has access to a restricted subset of variables from the full dataset.

The dataset may be internally linkable with SAHSU's environmental datasets, including air pollution and green space.

BackgroundWhile a great number of papers have been published on the short-term effects of air pollution on mortality, few have tried to assess whether this association varies according to the neighbourhood socioeconomic level and long-term ambient air concentrations measured at the place of residence. We explored the effect modification of 1) socioeconomic status, 2) long-term NO2 ambient air concentrations, and 3) both combined, on the association between short-term exposure to NO2 and all-cause mortality in Paris (France).MethodsA time-stratified case-crossover analysis was performed to evaluate the effect of short-term NO2 variations on mortality, based on 79,107 deaths having occurred among subjects aged over 35 years, from 2004 to 2009, in the city of Paris. Simple and double interactions were statistically tested in order to analyse effect modification by neighbourhood characteristics on the association between mortality and short-term NO2 exposure. The data was estimated at the census block scale (n=866).ResultsThe mean of the NO2 concentrations during the five days prior to deaths were associated with an increased risk of all-cause mortality: overall Excess Risk (ER) was 0.94% (95%CI=[0.08;1.80]. A higher risk was revealed for subjects living in the most deprived census blocks in comparison with higher socioeconomic level areas (ER=3.14% (95%CI=[1.41-4.90], p

Percentage of deaths attributable to PM2.5 among adults in Arak during the study period.

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

The AQI describes the five main types of air pollution regulated by the Clean Air Act: sulfur dioxide, nitrogen dioxide, carbon monoxide, ground-level ozone, and particle pollution. The EPA and its partners take regular readings of these pollutants and converts the results into a number ranging from 0 to 500, along with a specific color corresponding to a level of health concern. Generally, if the air quality is good, the air quality index is low (0 to 50) or moderate (51-100), and the color associated with it is green or yellow. As the air quality gets worse, the numbers go up, and the color linked with it goes from orange, to red, to purple, all the way to a dark shade of maroon for hazardous (300+).

This dataset contains the AQI scores by metropolitant area (CBSA) during 2017. I've enhanced some publically available data from the EPA's airnow website with census data, to be able to provide context about the number of people who are actually impacted when an AQI score is high or low in a given area.

Related datasets on relative composition of air pollution by source: typical distribution and during wildfire season are available here.

CalEnviroScreen is a mapping tool that helps identify California communities that are most affected by many sources of pollution, and where people are often especially vulnerable to pollution’s effects.
CalEnviroScreen uses environmental, health, and socioeconomic information to produce scores for every census tract in the state.
The scores are mapped so that different communities can be compared. An area with a high score is one that experiences a much higher pollution burden than areas with low scores.
CalEnviroScreen ranks communities based on data that are available from state and federal government sources.

Information on hospitalizations of COPD patients from electronic health records linked to air pollution concentrations for the study period. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Can be requested through NCTracts https://tracs.unc.edu/index.php/services/comparative-effectiveness-research/data-linkage. Format: Data used in this analysis include electronic health records from the UNC healthcare system. This dataset is associated with the following publication: Cowan, K., L. Wyatt, T. Luben, J. Sacks, C. Ward-Caviness, and K. Rappazzo. Effect measure modification of the association between short-term exposures to PM2.5 and hospitalizations by longs-term PM2.5 exposure among a cohort of people with Chronic Obstructive Pulmonary Disease (COPD) in North Carolina, 2002–2015. ENVIRONMENTAL HEALTH. Academic Press Incorporated, Orlando, FL, USA, 22: 49, (2023).