2011–2023. The tobacco disparities dashboard data utilized the Behavioral Risk Factor Surveillance System (BRFSS) data to measure cigarette smoking disparities by age, disability, education, employment, income, mental health status, race and ethnicity, sex, and urban-rural status. The disparity value is the relative difference in the cigarette smoking prevalence among adults 18 and older in a focus group divided by the cigarette smoking prevalence among adults 18 and older in a reference group. A disparity value above 1 indicates that adults in the focus group smoke cigarettes at a higher rate, as reflected by the disparity value, compared with the rate among adults in the reference group who smoke cigarettes. A disparity value below 1 indicates that adults in the focus group smoke cigarettes at a lower rate, as reflected by the disparity value, compared with the rate among adults in the reference group who smoke cigarettes. A disparity value of 1 means there is no relative difference in the rate of adults who smoke cigarettes for the two groups compared.

The smoking prevalence in the United States was forecast to continuously decrease between 2024 and 2029 by in total two percentage points. After the eighth consecutive decreasing year, the smoking prevalence is estimated to reach 19.93 percent and therefore a new minimum in 2029. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke on a daily basis. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smoking prevalence in countries like Canada and Mexico.

United States US: Smoking Prevalence: Total: % of Adults: Aged 15+ data was reported at 21.800 % in 2016. This records a decrease from the previous number of 22.300 % for 2015. United States US: Smoking Prevalence: Total: % of Adults: Aged 15+ data is updated yearly, averaging 23.900 % from Dec 2000 (Median) to 2016, with 9 observations. The data reached an all-time high of 31.400 % in 2000 and a record low of 21.800 % in 2016. United States US: Smoking Prevalence: Total: % of Adults: Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of smoking is the percentage of men and women ages 15 and over who currently smoke any tobacco product on a daily or non-daily basis. It excludes smokeless tobacco use. The rates are age-standardized.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Smoking rates for each Census Tract in Allegheny County were produced for the study “Developing small-area predictions for smoking and obesity prevalence in the United States.” The data is not explicitly based on population surveys or data collection conducted in Allegheny County, but rather estimated using statistical modeling techniques. In this technique, researchers applied the smoking rate of a demographically similar Census Tract to one in Allegheny County to compute a smoking rate.

US: Smoking Prevalence: Females: % of Adults data was reported at 19.100 % in 2016. This records a decrease from the previous number of 19.600 % for 2015. US: Smoking Prevalence: Females: % of Adults data is updated yearly, averaging 21.100 % from Dec 2000 (Median) to 2016, with 9 observations. The data reached an all-time high of 28.400 % in 2000 and a record low of 19.100 % in 2016. US: Smoking Prevalence: Females: % of Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of smoking, female is the percentage of women ages 15 and over who currently smoke any tobacco product on a daily or non-daily basis. It excludes smokeless tobacco use. The rates are age-standardized.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Historical chart and dataset showing U.S. smoking rate by year from 2000 to 2022.

This dataset include the Tobacco/Nicotine Use Rates and Counts Table which provides rates and counts of tobacco and nicotine use for adults in Virginia, stratified by demographics, utilizing data sourced from the Behavioral Risk Factor Surveillance System (BRFSS), Virginia Adult Health Survey (VAHS), and Adult Tobacco Survey (ATS).

United States US: Smoking Prevalence: Males: % of Adults data was reported at 24.600 % in 2016. This records a decrease from the previous number of 25.100 % for 2015. United States US: Smoking Prevalence: Males: % of Adults data is updated yearly, averaging 26.800 % from Dec 2000 (Median) to 2016, with 9 observations. The data reached an all-time high of 34.500 % in 2000 and a record low of 24.600 % in 2016. United States US: Smoking Prevalence: Males: % of Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of smoking, male is the percentage of men ages 15 and over who currently smoke any tobacco product on a daily or non-daily basis. It excludes smokeless tobacco use. The rates are age-standardized.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Iranian Tobacco Co. Institut National de la Statistique (Tunisia) HM Revenue & Customs (UK) Eidgenössisches Finanzdepartement EFD/Département...

This Dataset consists of Year, State, District and Season (Kharif and Rabi)-wise Area, Production and Yield statistics for Tobacco

This dataset includes Centers for Disease Control and Prevention (CDC) information related to state legislation on smokefree indoor air in various areas per state. The State System houses current and historical state-level legislative dataset on tobacco use prevention and control policies data are reported on a quarterly basis.

US E-Cig Tax Regulations

1995-2016 State Taxation Policies

By Health [source]

About this dataset

This dataset, provided by the Centers for Disease Control and Prevention (CDC) through the State Tobacco Activities Tracking and Evaluation (STATE) System, contains information on state-level legislative data on tobacco use prevention and control policies related to e-cigarette taxes. It captures various measures of state excise taxes for e-cigarettes implemented over a span of almost two decades. The STATE System stores comprehensive historical data which can be used to track changes in these policies at the state level over time. This dataset includes fields such as location abbreviations, topic descriptions, measure descriptions, provision value, provision description, citations and more that provide valuable insight into understanding how these measures have evolved overtime across states in the US

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How to use the dataset

This dataset contains information on state-level legislative data on tobacco use prevention and control policies related to e-cigarette taxes from 1995-2016. It includes the following columns: location abbreviations, location descriptions, topic descriptions, measure descriptions, data sources, provision group descriptions, provision descriptions, provision values, citations for the provisions cited in the dataset as well as alternative values for those provisions if they are used. Additionally it contains dates when certain provisions become effective or enacted and also geographic locations of the data which can be used as a helpful reference point.

In order to best use this dataset you should familiarize yourself with its columns and their definitions. This will help you better understand how each element relates to others within the set and give you an idea of what type of analyses can be conducted using it. You should also take note of any relevant comments that may shed light on specific elements or provide additional information not captured in other columns. After understanding the contents of this dataset it is suggested that individuals analyze it according to their individual needs and interests but some general uses may include exploring trends in e-cigarette taxation over time by examining yearly changes in tax rates or seeing how tax regulation varies among states depending on location abbreviations provided in each row entry etc.. With these tools one could potentially make meaningful connections between different variables within this set and gain valuable insights into how US states legislate taxes related to tobacco use prevention methods

Research Ideas

• Analyzing the impact of e-cigarette taxes on usage rates in different states, in order to inform tax policy decisions.
• Examining the differences between enacted and effective dates for legislations by state and across the country, in order to gain a better understanding of how long it takes for new laws to become implemented.
• Tracking changes of e-cigarette regulation over time and studying how they correlate with measures such as number of youth users or youth perception on risk associated with e-cigarettes by state

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 your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: CDC_STATE_System_E-Cigarette_Legislation_-_Tax.csv | Column name | Description | |:-----------------------|:------------------------------------------------------------------| | YEAR | Year of the policy (Integer) ...

This dataset includes two data tables that contain the percentage of licensed tobacco retailers that sold tobacco to underage youth and young adults: (1) Tobacco Sold to Youth Under 18, and (2) Tobacco Sold to Young Adults Under 21. The California Youth Tobacco Survey (YTPS), conducted between 1997 and 2018, assessed the rate of illegal tobacco sales to youth under the age of 18. Due to California raising the minimum legal sales age for tobacco products from 18 to 21 years old, YTPS was replaced with the Synar Tobacco Purchase Survey (STPS) in 2019. STPS is an annual survey that assesses the rate of illegal tobacco sales to young adults under the age of 21 years of age. Both surveys were conducted in accordance with the California’s Stop Tobacco Access to Kids Enforcement Act (STAKE Act) and the federal Synar Amendment.

This data package contains dataset on prevalence rates of health conditions and diseases like obesity, diabetes and hearing loss and health risk factors for diseases like tobacco, alcohol and drug use.

This dataset include the Tobacco/Nicotine Use Rates and Counts Table which provides rates and counts of tobacco and nicotine use for adults in Virginia, its health regions, and health districts utilizing data sourced from the Behavioral Risk Factor Surveillance System (BRFSS), Virginia Adult Health Survey (VAHS), and Adult Tobacco Survey (ATS).

This dataset provides tax rates (Value Added Tax, Entry Tax, Luxury Tax) on tobacco products in 10 Indian States (Karnataka, Kerala, Goa, Madhya Pradesh, Gujarat, Haryana, Bihar, West Bengal, Meghalaya, and Nagaland) for the period of 1990-2017. The dataset provides tax rates for three major categories of tobacco products (cigarettes, bidis, and smokeless products) month-wise starting from the financial year April 1990 - March 1991 till the financial year April 2016 - March 2017. These data were collected from relevant statutes, notifications, public notices by concerned state governments typically available through state government commercial tax department websites occasionally supplemented by free internet searches for specific documents not available or accessible on state government websites.The following points will help better understand the dataset and its strengths and limitations:The numerical data in each cell refers to the rate of the tax on given tobacco product that prevailed at a given time (month/year). The data provided is a decimal fraction and is to be multiplied by 100 to derive the percentage e.g. 0.01 in the dataset imply 1% of tax rate.The Value Added Tax (VAT) Acts were enacted in Indian states in early 2000s and generally came to be implemented around the year 2005. In our dataset, we capture the VAT rates on tobacco from March 2005 onward. However, the actual implementation could have been a little earlier in some states. VAT rates are generally provided till March 2017 after which, VAT was subsumed in the Goods and Services Tax.In case of the Entry Tax and the Luxury Tax, only some of the states levied such taxes on tobacco products. In case of the states that levied these taxes on tobacco, we have captured data from March 1990 onward as our study period was 1990-2017. This does not necessarily imply that such taxes were not levied on tobacco before March 1990.Blank cells or cells with missing values denote that the given tax type was not levied on the given tobacco products for that time point.At times, additional tax or surcharge was levied under the VAT Act in addition to the VAT rate for tobacco. The dataset provides the VAT rates that are inclusive of such additional tax or surcharge and in such cases, a comment clarifying this has been inserted in the dataset.At times, different smokeless tobacco products had different tax rates levied on them. In such cases, we have generally indicated the highest tax rate in the dataset while including a comment clarifying the different rates for different smokeless tobacco items.Rarely, the VAT rate was levied in form of a fixed amount per certain number of products (cigarette sticks) instead of a fixed percentage of the product value. In such instance, we have inserted a comment in the dataset clarifying this.We found it complex to track all the changes done in tax rates on tobacco over time under these three tax categories. There were several amendments to the tax legislations and several notifications issued under these tax legislations regarding changes in tax rates on tobacco. It is likely that we missed out capturing all these changes, especially as some of the notifications were missing from the government websites. So, there are likely to be errors in terms of the tax rates and the exact period for which specific rates prevailed. We tried our best to capture data from authoritative sources as much as possible given the limited time and resources we had.This dataset was produced as part of the broader research project that explored the political economy of tobacco, titled “Deciphering an epidemic of epic proportion: the role of state and tobacco industry in tobacco control in post-liberalised India (1990-2017)”. We thank the DBT/Wellcome Trust India Alliance for funding this project through the Intermediate (Clinical and Public Health) Fellowship awarded to Upendra Bhojani (IA/CPHI/17/1/503346). While collecting these data, an earlier document compiling tax rates on tobacco at state level by Mr. Gaurav Gupta of the Campaign for Tobacco-Free Kids for the period 2010-2011 to 2016-2017 served as a useful reference. We thank him for sharing such resource with us.

Tobacco tax rates were last changed on March 29, 2018. The current rates are:

• 18.475¢ per cigarette
• 18.475¢ per gram or part gram of cut tobacco
• Tax on cigars is 56.6% of the taxable price

Tobacco tax is:

• $3.70 on a pack of 20 cigarettes
• $4.62 on a pack of 25 cigarettes
• $36.95 on a carton of 200 cigarettes

You can download the dataset to view the historical price points for this tax.

About tobacco tax

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 28 January 2022.

--- 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 ---

1970-2019. Orzechowski and Walker. Tax Burden on Tobacco. Tax burden data was obtained from the annual compendium on tobacco revenue and industry statistics, The Tax Burden on Tobacco. Data are reported on an annual basis; Data include federal and state-level information regarding taxes applied to the price of a pack of cigarettes.

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.