The estimated median age gives an idea of the age distribution of the population in a given area. A greater median age would suggest that the area of interest has a relatively large number of older residents, while a lower median age suggests that the area has a relatively large number of younger residents.

Champaign County’s estimated median age has risen for over a decade, but has always stayed between 28 and 31. Year-to-year changes from 2017 to 2019 were statistically significant, but not from 2019 to 2023. The Champaign County estimated median age has been consistently younger than the estimated median ages of the United States and State of Illinois. Champaign County’s figure is likely impacted to some degree by the large student population associated with the University of Illinois.

The estimated median age does not provide a significant amount of detail, and it does not provide any information on why the estimated median age is what it is. However, when placed in the context of other pieces of data and other indicators, it is a valuable starting point in understanding county demographics.

Estimated median age data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Median Age by Sex.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using data.census.gov; (8 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using data.census.gov; (6 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using data.census.gov; (13 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using data.census.gov; (7 April 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using data.census.gov; (7 April 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0101; generated by CCRPC staff; using American FactFinder; (16 March 2016).

BackgroundPrevious studies have shown that national cultural traits, such as collectivism–individualism and tightness–looseness, are associated with COVID-19 infection and mortality rates. However, although East Asian countries have outperformed other countries in containing COVID-19 infections and lowering mortality in the first pandemic waves, no studies to date have examined flexibility-monumentalism, a cultural trait that uniquely distinguishes East Asia from the rest of the world. Moreover, none of the previous studies have explored mechanisms underpinning the association between national culture and COVID-19 mortality.AimsOur study fills in these gaps by examining the association between flexibility-monumentalism and COVID-19 mortality, adjusting for important covariates and by analyzing mask wearing and fear of COVID-19 during the first weeks of the pandemic as plausible mechanisms underpinning this association.MethodsWe constructed and analyzed a dataset including 37 countries that have valid information on flexibility-monumentalism, COVID-19 deaths as of 31 October 2020 (before the start of vaccination campaigns), and relevant covariates including two other national cultural traits (individualism–collectivism and tightness–looseness) and other national characteristics (economic, political, demographic and health). Multiple linear regression with heteroscedasticity-consistent standard errors was used to assess the independent effect of flexibility-monumentalism on COVID-19 mortality. Mediation was assessed by examining the indirect effects of flexibility through mask wearing and fear of COVID-19 and determining the statistical significance through bootstrapping. Graphical and delete-one analysis was used to assess the robustness of the results.ResultsWe found that flexibility was associated with a significant reduction in COVID-19 mortality as of 31 October 2020, independent of level of democracy, per capita GDP, urbanization, population density, supply of hospital beds, and median age of the population. This association with mortality is stronger and more robust than for two other prominent national cultural traits (individualism–collectivism and tightness–looseness). We also found tentative evidence that the effect of flexibility on COVID-19 mortality may be partially mediated through mask wearing in the first weeks of the pandemic.

Context

Coronavirus infection is currently the most important health topic. It surely tested and continues to test to the fullest extent the healthcare systems around the world. Although big progress is made in handling this pandemic, a tremendous number of questions are needed to be answered. I hereby present to you the local Bulgarian COVID-19 dataset with some context. It could be used as a comparator because it stands out compared to other countries and deserves analysis.

Context for Bulgarian population: Population - 6 948 445 Median age - 44.7 years Aged >65 - 20.801 % Aged >70 - 13.272%

Summary of the results: - first pandemic wave was weak, probably because of the early state of emergency (5 days after the first confirmed case). Whether this was a good decision or it was too early and just postpone the inevitable is debatable. -healthcare system collapses (probably due to delayed measures) in the second and third waves which resulted in Bulgaria gaining the top ranks for mortality and morbidity tables worldwide and in the EU. - low percentage of vaccinated people results in a prolonged epidemic and delaying the lifting of the preventive measures.

Some of the important moments that should be considered when interpreting the data: 08.03.2020 - Bulgaria confirmed its first two cases. The government issued a nationwide ban on closed-door public events (first lockdown); 13.03.2020- after 16 reported cases in one day, Bulgaria declared a state of emergency for one month until 13.04.2020. Schools, shopping centres, cinemas, restaurants, and other places of business were closed. All sports events were suspended. Only supermarkets, food markets, pharmacies, banks, and gas stations remain open. 03.04.2020 - The National Assembly approved the government's proposal to extend the state of emergency by one month until 13.05.2020; 14.05.2020 - the national emergency was lifted, and in its place was declared a state of an emergency epidemic situation. Schools and daycares remain closed, as well as shopping centers and indoor restaurants; 18.05.2020 - Shopping malls and fitness centers opened; 01.06.2020 - Restaurants and gaming halls opened; 10.07.2020 - discos and bars are closed, the sports events are without an audience; 29.10.2020 - High school and college students are transitioning to online learning; 27.11.2020 - the whole education is online, restaurants, nightclubs, bars, and discos are closed (second lockdown 27.11 - 21.12); 05.12.2020 - the 14-day mortality rate is the highest in the world; 16.01.2021 - some of the students went back to school; 01.03.2021 - restaurants and casinos opened; 22.03.2021 - restaurants, shopping malls, fitness centers, and schools are closed (third lockdown for 10 days - 22.03 - 31.03); 19.04.2021 - children daycare facilities, fitness centers, and nightclubs are opened;

Content

This dataset consists of 447 rows with 29 columns and covers the period 08.03.2020 - 28.05.2021. In the beginning, there are some missing values until the proper statistical report was established.

Inspiration

A publication proposal is sent to anyone who wishes to collaborate. Based on the results and the value of the findings and the relevance of the topic it is expected to publish: - in a local journal (guaranteed); - in a SCOPUS journal (highly probable); - in an IF journal (if the results are really insightful).

The topics could be, but not limited to: - descriptive analysis of the pandemic outbreak in the country; - prediction of the pandemic or the vaccination rate; - discussion about the numbers compared to other countries/world; - discussion about the government decisions; - estimating cut-off values for step-down or step-up of the restrictions.

Error or query reporting

If you find an error, have a question, or wish to make a suggestion, I encourage you to reach me.

Context

Late in December 2019, the World Health Organisation (WHO) China Country Office obtained information about severe pneumonia of an unknown cause, detected in the city of Wuhan in Hubei province, China. This later turned out to be the novel coronavirus disease (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) of the coronavirus family. The disease causes respiratory illness characterized by primary symptoms like cough, fever, and in more acute cases, difficulty in breathing. WHO later declared COVID-19 as a Pandemic because of its fast rate of spread across the Globe.

Content

The COVID-19 datasets organized by continent contain daily level information about the COVID-19 cases in the different continents of the world. It is a time-series data and the number of cases on any given day is cumulative. The original datasets can be found on this John Hopkins University Github repository. I will be updating the COVID-19 datasets on a regular basis with every update from John Hopkins University. I have also included the World COVID-19 tests data scraped from Worldometer and 2020 world population also scraped from worldometer.

The datasets

COVID-19 cases covid19_world.csv. It contains the cumulative number of COVID-19 cases from around the world since January 22, 2020, as compiled by John Hopkins University. covid19_asia.csv, covid19_africa.csv, covid19_europe.csv, covid19_northamerica.csv, covid19.southamerica.csv, covid19_oceania.csv, and covid19_others.csv. These contain the cumulative number of COVID-19 cases organized by the continent.

Field description - ObservationDate: Date of observation in YY/MM/DD - Country_Region: name of Country or Region - Province_State: name of Province or State - Confirmed: the number of COVID-19 confirmed cases - Deaths: the number of deaths from COVID-19 - Recovered: the number of recovered cases - Active: the number of people still infected with COVID-19 Note: Active = Confirmed - (Deaths + Recovered)

COVID-19 tests covid19_tests.csv. It contains the cumulative number of COVID tests data from worldometer conducted since the onset of the pandemic. Data available from June 01, 2020.

Field description Date: date in YY/MM/DD Country, Other: Country, Region, or dependency TotalTests: cumulative number of tests up till that date Population: population of Country, Region, or dependency Tests/1M pop: tests per 1 million of the population 1 Testevery X ppl: 1 test for every X number of people

2020 world population world_population(2020).csv. It contains the 2020 world population as reported by woldometer.

Field description Country (or dependency): Country or dependency Population (2020): population in 2020 Yearly Change: yearly change in population as a percentage Net Change: the net change in population Density(P/km2): population density Land Area(km2): land area Migrants(net): net number of migrants Fert. Rate: Fertility Rate Med. Age: median age Urban pop: urban population World Share: share of the world population as a percentage

Acknowledgements

1. John Hopkins University for making COVID-19 datasets available to the public: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports
2. John Hopkins University COVID-19 Dashboard: https://coronavirus.jhu.edu/map.html
3. COVID-19 Africa dashboard: http://covid-19-africa.sen.ovh/
4. Worldometer: https://www.worldometers.info/
5. United Nations Department of General Assembly and Conference Management: https://www.un.org/depts/DGACM/RegionalGroups.shtml
6. wallpapercave.com: https://wallpapercave.com/covid-19-wallpapers

AbstractBackground Moral injury is an emerging explanation of burnout and suicidality, but remains poorly quantified in at-risk practitioners. We hypothesized that COVID-19 pandemic-related moral injury differs between frontline clinicians, genders, age, and country of practice. Methods We conducted an online cross-sectional survey of international physicians, nurses, nurse practitioners, paramedics and respiratory therapists between April and June 2020. We included the adapted version of the Expressions of Moral Injury Scale (EMIS). The primary outcome was differences in moral injury scores between clinician roles. Results Three hundred and two clinicians participated, including physicians (61% [n=184]), nurses (28% [n=85]), and nurse practitioners (5% [n=14]). The median age was 39 (IQR 32-76), females comprised 54% of the respondents, and the majority resided in Canada (n =183 [61%]) or the United States (US; n = 106 [35%]). Emergency medicine (88% [n=265]), and intensive care (6% [n=17]) were the main specialties responding. Median moral injury scores across multiple domains were higher for nurses compared to physicians, as well as for younger, and female respondents. Moral injury scores were also significantly higher for respondents from the United States, the United Kingdom and Australia, compared to Canada. Conclusions Our research suggests that during COVID-19, measures of moral injury differ across roles, gender and place of work. Future research is warranted to better understand the impact of moral injury on clinicians’ psychological well-being during the COVID-19 pandemic. MethodsThis dataset was collected through the Qualtrics online survey application.

The purpose of the study: to explore the views of the Lithuanian employed population on the social consequences of the COVID-19 pandemic and quarantine with a particular focus on changes in employment and working practices. Major investigated questions: respondents who are working were asked how safe they currently feel in general. Given the block of questions, they had to assess how the quarantine has affected their daily routine in general in various areas (financial situation, work - 7 choices in total). They were asked how they assess the risk of contracting COVID-19 personally. They were asked to assess the financial situation of the household over the upcoming 12 months and to provide their work status. Opinions on how life in Lithuania and their personal lives have changed over the last 12 months and when the coronavirus pandemic will end were analysed. Later, respondents were asked whether there was a change in the average number of working hours, earnings, workload and stress at work since the start of the coronavirus pandemic. There was a need to know how work relationships with colleagues, supervisors and clients have changed as a result of the pandemic and whether respondents had received help at work from these individuals since the pandemic. They were asked if they had experienced any inappropriate behaviour at work from supervisors, colleagues or clients (e.g., harassment, intimidation, terror, psychological abuse, insults, threats, physical aggression) and where or to whom they would first turn if they encountered such inappropriate behaviour in a workplace. The survey assessed the views on whether the coronavirus pandemic has increased the personal risk of losing a job. The aim was to find out whether there was a period of self-isolation due to the pandemic and its impact on respondents' incomes. Lithuanian workers who had lost their jobs in the wake of the coronavirus pandemic were asked what economic activity they were engaged in. They were asked whether it be difficult or easy to find a new job that suited them if they were to lose their job now. Later on, the survey went on to find out how the respondents' current employer takes care of the safety of its employees and whether there is an existing trade union or a works council in a workplace, and how the activities of these institutions have changed since the pandemic. The question about an average monthly net income was asked. Those Lithuanian workers who had to work remotely in the wake of the coronavirus pandemic were asked to rate a number of statements related to remote work (I have the right conditions at home for remote work, I have the necessary technical tools for remote working - 8 choices in total). The aim was to find out whether the employer provided the necessary tools for remote work (computer, telephone, printer, etc.) and what impact remote work has on work performance. The statement that "once the pandemic is over, I will no longer have my own workspace at my workplace or I will have to share it with another worker" was assessed. All respondents were then asked whether they would like to work remotely in the future and whether the coronavirus pandemic might require them to change their current qualifications and/or elevate their existing skills. They were asked whether they intend to get vaccinated once the coronavirus vaccine becomes available and whether they have any loans. At the end of the survey, there was a block of statements provided about different experiences and respondents had to answer whether they were suitable to describe their current experiences (there are enough people I can turn to in times of trouble - 6 choices in total). Socio-demographic characteristics: gender, age, place of residence, size of settlement, marital status, education, household size, age of children, nationality, economic activity, category of workers, square metres of living space, number of rooms in the apartment.

Importance: COVID-19 vaccine development has progressed at unprecedented speed. Widespread public uptake of the vaccine is crucial to stem the pandemic. Objective: To examine the factors associated with survey participants’ self-reported likelihood of selecting and receiving a hypothetical COVID-19 vaccine. Design, Setting and Participants: A survey of a nonprobability convenience sample of 2000 recruited participants including a choice-based conjoint analysis was conducted to estimate respondents’ probability of choosing a vaccine and willingness to receive vaccination . Participants were then asked to evaluate their willingness to receive each vaccine individually. The survey presented respondents with 5 choice tasks. In each, participants evaluated 2 hypothetical COVID-19 vaccines and were asked whether they would choose vaccine A, vaccine B, or neither vaccine . Vaccine attributes included efficacy, protection duration, major side effects, minor side effects, US Food and Drug Administration (FDA) approval process, national origin of vaccine, and endorsement. Levels of each attribute for each vaccine were randomly assigned and attribute order was randomized across participants. Survey data wereas collected on July 9, 2020. Main Outcomes and Measures: Average marginal component effect sizes and marginal means were calculated to estimate the relationship between each vaccine attribute-level and the probability of the respondent choosing a vaccine and self-reported willingness to receive vaccination . Results: A total of 1,971 US adults responded to the survey (median age 43; IQR: 30 to 58); 999 (51%) were women, 1,432 (73%) White, 277 (14%) Black, and 190 (10%) Latinx. An increase in efficacy from 50% to 70% was associated with a higher n increased the estimated probability of choosing a vaccine ofby .07 [95% CI: .06 to .09]; and an increase from 50% to 90% was associated with a higher probability of choosing a vaccine of .16 [95% CI: .15 to .18]. An increase in protection duration from 1 to 5 years was associated with a higher probability of choosing a vaccine of .05 [95% CI: .04 to .07]. A decrease in the incidence of major side effects from 1 in 10,000 to 1 in 1,000,000 was associated with a higher probability of choosing a vaccine of .07 [95% CI: .05 to .08]. An FDA emergency use authorization was associated with a lower probability of choosing a vaccine of -.03 [95% CI: -.01 to -.04] compared with full FDA approval. A vaccine that originated from a non-US country was associated with a lower probability of choosing a vaccine [China: -.13 (95% CI: -.11 to -.15 UK: -.04 (95% CI: -.02 to -.06)]. Endorsements from the US Centers for Disease Control and Prevention [.09 (95% CI: .07 to .11)] and World Health Organization [.06 (95% CI: .04 to .08)], compared with an endorsement from President Trump, were associated with higher probabilities of choosing a vaccine. Analyses of participants’ willingness to receive each vaccine when assessed individually yield similar results. Efficacy was the most important factor. An increase in efficacy from 50% to 90% was associated with a 10% higher marginal mean willingness to receive a vaccine [.51 to .61]. A reduction in the incidence of major side effects was associated with a 4% higher marginal mean willingness to receive a vaccine [.54 to .58]. A vaccine originating in China was associated with a 10% lower willingness to receive a vaccine versus one developed in the US [.60 to .50] Endorsements from the CDC and WHO were associated with substantial increases in willingness to receive a vaccine, 7% and 6%, respectively , from a baseline endorsement by President Trump [.52 to .59; .52 to .58]. Conclusions and Relevance: In this survey study of US adults, vaccine-related attributes and political characteristics were associated with self-reported preferences for choosing a hypothetical COVID-19 vaccine and self-reported willingness to receive vaccination. These results may help inform public health campaigns to address vaccine hesitancy when a COVID-19 vaccine becomes available.

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

This dataset presents data on the population of a region by age group for the Statistical Area Level 4 (SA4) regions as of December 2021. The boundaries for this dataset follow the 2016 edition of the Australian Statistical Geography Standard (ASGS).

The Australian Department of Education, Skills and Employment publishes a range of labour market data on its Labour Market Information Portal. The data provided includes unemployment rate, employment rate, participation rate, youth unemployment rate, unemployment duration, population by age group and employment by industry and occupation.

AURIN has spatially enabled the original data. Data Source: ABS Labour Force Survey, 12 month average, December 2021. The ABS advises that analysis of regional labour force estimates should typically be based on annual averages, which are important for understanding the state of the labour market and providing medium and long-term signals. The application of annual averages, however, is unlikely to accurately or quickly detect turning points in the regional data during periods of significant change (such as during the onset of the COVID-19 pandemic). Original data at the ABS Statistical Area 4 (SA4) level can be found in Table 16. The region named "Western Australia - Outback (North and South)" in the original data has been omitted as it did not match a region within the SA4 2016 ASGS.

Background: Due to the nature of their job, paramedics are at high risk for COVID-19 infection and transmission as they perform various procedures and treatments that may increase their exposure to the virus. Aims of the CITF funded study: CORSIP-Canada aimed to determine past infection prevalence and risk of infection by SARS-CoV-2 among paramedics by measuring infection-induced and vaccine-acquired antibodies, and vaccination. It also aimed to investigate their attitudes towards vaccines and risk factors in the workplace to establish optimal strategies and safety guidelines for protection. Methods: Paramedics above the age of 19 in British Columbia, Ontario, Saskatchewan, Alberta, and Manitoba were individually recruited into a cohort study via institutional promotion and external advertisements. Participants submitted a baseline questionnaire and blood sample and completed follow-up questionnaires and blood samples every 6 months for one year. Contributed dataset contents: The datasets include 3709 participants who completed baseline questionnaires between January 2021 and March 2023. 76% of participants gave one or more dried blood spots or blood samples for SARS-CoV-2 serology between Jan 2021 and Feb 2023. About 1600 participants provided longitudinal data with serology results for up to 2 years with a median follow-up time of 11 months (median 3 samples). Approximately 380 additional participants gave dried blood spots or blood samples for SARS-CoV-2 serology without completing a questionnaire. Questionnaire variables include data in the following areas of information: demographics (age, gender, race-ethnicity and indigeneity, province, education, household composition, occupations), general health (tobacco use; chronic conditions; height and weight; flu vaccine), COVID infection test results, symptoms and exposure risks, SARS-CoV-2 vaccination.

Footnotes:1Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender.2Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol.3Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date).4The median income of a specified group is the amount that divides the income distribution of that group into two halves, i.e., the incomes of half of the units in that group are below the median, while those of the other half are above the median. Median incomes of individuals are calculated for those with income (positive or negative).5Average income of a specified group is calculated by dividing the aggregate income of that group by the number of units in that group. Average incomes are calculated for those with income (positive or negative).6Total income refers to the sum of certain incomes (in cash and, in some circumstances, in kind) of the statistical unit during a specified reference period. The components used to calculate total income vary between: – Statistical units of social statistical programs such as persons, private households, census families and economic families; – Statistical units of business statistical programs such as enterprises, companies, establishments and locations; and – Statistical units of farm statistical programs such as farm operator and farm family. In the context of persons, total income refers to receipts from certain sources, before income taxes and deductions, during a specified reference period. In the context of census families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of economic families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of households, total income refers to receipts from certain sources of all household members, before income taxes and deductions, during a specified reference period. The monetary receipts included are those that tend to be of a regular and recurring nature. Receipts that are included as income are: * employment income from wages, salaries, tips, commissions and net income from self-employment (for both unincorporated farm and non-farm activities); * income from investment sources, such as dividends and interest on bonds, accounts, guaranteed investment certificates (GICs) and mutual funds; * income from employer and personal pension sources, such as private pensions and payments from annuities and registered retirement income funds (RRIFs); * other regular cash income, such as child support payments received, spousal support payments (alimony) received and scholarships; * income from government sources, such as social assistance, child benefits, Employment Insurance benefits, Old Age Security benefits, COVID-19 benefits and Canada Pension Plan and Québec Pension Plan benefits and disability income. Receipts excluded from this income definition are: * one-time receipts, such as lottery winnings, gambling winnings, cash inheritances, lump-sum insurance settlements and tax-free savings account (TFSA) or registered retirement savings plan (RRSP) withdrawals; * capital gains because they are not by their nature regular and recurring. It is further assumed that they are more relevant to the concept of wealth than the concept of income; * employers' contributions to registered pension plans, Canada Pension Plan, Québec Pension Plan and Employment Insurance; * voluntary inter-household transfers, imputed rent, goods and services produced for barter and goods produced for own consumption.7The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.8The sum of employment income (wages, salaries and commissions, net self-employment income from farm or non-farm unincorporated business and/or professional practice), investment income, private retirement income (retirement pensions, superannuation and annuities, including those from registered retirement savings plans [RRSPs] and registered retirement income funds [RRIFs]) and other money income from market sources during the reference period. It is equivalent to total income minus government transfers. It is also referred to as income before transfers and taxes.9The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.10All income received as wages, salaries and commissions from paid employment and net self-employment income from farm or non-farm unincorporated business and/or professional practice during the reference period.11The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.12Gross wages and salaries before deductions for such items as income taxes, pension plan contributions and employment insurance premiums during the reference period. While other employee remuneration such as security options benefits, board and lodging and other taxable allowances and benefits are included in this source, employer's contributions to pension plans and employment insurance plans are excluded. Other receipts included in this source are military pay and allowances, tips, commissions and cash bonuses associated with paid employment, benefits from wage-loss replacement plans or income-maintenance insurance plans, supplementary unemployment benefits from an employer or union, research grants, royalties from a work or invention with no associated expenses and all types of casual earnings during the reference period.13The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.14Net income (gross receipts minus cost of operation and capital cost allowance) received during the reference period from self-employment activities, either on own account or in partnership. In the case of partnerships, only the person's share of income is included. Net partnership income of a limited or non-active partner is excluded. It includes farming income, fishing income and income from unincorporated business or professional practice. Commission income for a self-employed commission salesperson and royalties from a work or invention with expenses associated are also included in this source.15The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.16All cash benefits received from federal, provincial, territorial or municipal governments during the reference period. It includes: * Old Age Security pension, Guaranteed Income Supplement, Allowance or Allowance for the Survivor; * retirement, disability and survivor benefits from Canada Pension Plan and Québec Pension Plan; * benefits from Employment Insurance and Québec parental insurance plan; * child benefits from federal and provincial programs; * social assistance benefits; * workers' compensation benefits; * Canada workers benefit (CWB); * Goods and services tax credit and harmonized sales tax credit; * other income from government sources. For the 2021 Census, this includes various benefits from new and existing federal, provincial and territorial government income programs intended to provide financial support to individuals affected by the COVID-19 pandemic and the public health measures implemented to minimize the spread of the virus.17The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.18Refers to the sum of payments received from COVID-19 - Emergency and recovery benefits and Employment Insurance (EI) benefits.19The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars. In 2019, earning replacement benefits is equal to Employment Insurance (EI) benefits.20All Employment Insurance (EI) benefits received during the reference period, before income tax deductions. It includes benefits for unemployment, sickness, maternity, paternity, adoption, compassionate care, work sharing, retraining, and benefits to self-employed fishers

Footnotes:1Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender.2Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol.3Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date).4This category includes women and girls, as well as some non-binary persons.5The median income of a specified group is the amount that divides the income distribution of that group into two halves, i.e., the incomes of half of the units in that group are below the median, while those of the other half are above the median. Median incomes of individuals are calculated for those with income (positive or negative).6Average income of a specified group is calculated by dividing the aggregate income of that group by the number of units in that group. Average incomes are calculated for those with income (positive or negative).7Total income refers to the sum of certain incomes (in cash and, in some circumstances, in kind) of the statistical unit during a specified reference period. The components used to calculate total income vary between: – Statistical units of social statistical programs such as persons, private households, census families and economic families; – Statistical units of business statistical programs such as enterprises, companies, establishments and locations; and – Statistical units of farm statistical programs such as farm operator and farm family. In the context of persons, total income refers to receipts from certain sources, before income taxes and deductions, during a specified reference period. In the context of census families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of economic families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of households, total income refers to receipts from certain sources of all household members, before income taxes and deductions, during a specified reference period. The monetary receipts included are those that tend to be of a regular and recurring nature. Receipts that are included as income are: * employment income from wages, salaries, tips, commissions and net income from self-employment (for both unincorporated farm and non-farm activities); * income from investment sources, such as dividends and interest on bonds, accounts, guaranteed investment certificates (GICs) and mutual funds; * income from employer and personal pension sources, such as private pensions and payments from annuities and registered retirement income funds (RRIFs); * other regular cash income, such as child support payments received, spousal support payments (alimony) received and scholarships; * income from government sources, such as social assistance, child benefits, Employment Insurance benefits, Old Age Security benefits, COVID-19 benefits and Canada Pension Plan and Québec Pension Plan benefits and disability income. Receipts excluded from this income definition are: * one-time receipts, such as lottery winnings, gambling winnings, cash inheritances, lump-sum insurance settlements and tax-free savings account (TFSA) or registered retirement savings plan (RRSP) withdrawals; * capital gains because they are not by their nature regular and recurring. It is further assumed that they are more relevant to the concept of wealth than the concept of income; * employers' contributions to registered pension plans, Canada Pension Plan, Québec Pension Plan and Employment Insurance; * voluntary inter-household transfers, imputed rent, goods and services produced for barter and goods produced for own consumption.8The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.9The sum of employment income (wages, salaries and commissions, net self-employment income from farm or non-farm unincorporated business and/or professional practice), investment income, private retirement income (retirement pensions, superannuation and annuities, including those from registered retirement savings plans [RRSPs] and registered retirement income funds [RRIFs]) and other money income from market sources during the reference period. It is equivalent to total income minus government transfers. It is also referred to as income before transfers and taxes.10The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.11All income received as wages, salaries and commissions from paid employment and net self-employment income from farm or non-farm unincorporated business and/or professional practice during the reference period.12The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.13Gross wages and salaries before deductions for such items as income taxes, pension plan contributions and employment insurance premiums during the reference period. While other employee remuneration such as security options benefits, board and lodging and other taxable allowances and benefits are included in this source, employer's contributions to pension plans and employment insurance plans are excluded. Other receipts included in this source are military pay and allowances, tips, commissions and cash bonuses associated with paid employment, benefits from wage-loss replacement plans or income-maintenance insurance plans, supplementary unemployment benefits from an employer or union, research grants, royalties from a work or invention with no associated expenses and all types of casual earnings during the reference period.14The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.15Net income (gross receipts minus cost of operation and capital cost allowance) received during the reference period from self-employment activities, either on own account or in partnership. In the case of partnerships, only the person's share of income is included. Net partnership income of a limited or non-active partner is excluded. It includes farming income, fishing income and income from unincorporated business or professional practice. Commission income for a self-employed commission salesperson and royalties from a work or invention with expenses associated are also included in this source.16The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.17All cash benefits received from federal, provincial, territorial or municipal governments during the reference period. It includes: * Old Age Security pension, Guaranteed Income Supplement, Allowance or Allowance for the Survivor; * retirement, disability and survivor benefits from Canada Pension Plan and Québec Pension Plan; * benefits from Employment Insurance and Québec parental insurance plan; * child benefits from federal and provincial programs; * social assistance benefits; * workers' compensation benefits; * Canada workers benefit (CWB); * Goods and services tax credit and harmonized sales tax credit; * other income from government sources. For the 2021 Census, this includes various benefits from new and existing federal, provincial and territorial government income programs intended to provide financial support to individuals affected by the COVID-19 pandemic and the public health measures implemented to minimize the spread of the virus.18The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.19Refers to the sum of payments received from COVID-19 - Emergency and recovery benefits and Employment Insurance (EI) benefits.20The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars. In 2019, earning replacement benefits is equal to Employment Insurance (EI) benefits.21All Employment Insurance (EI) benefits received during the reference period, before income tax deductions. It includes benefits for unemployment, sickness, maternity, paternity, adoption, compassionate

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

Assaults Disorders of the Teeth and Jaw Drowning Falls Firearm-Related Injuries
Heat-Related Illnesses and Injuries
Hip Fractures
Homicide (See Assault Death)
Injuries
Motor Vehicle Injuries
Motor Vehicle Injuries to Pedalcyclist
Motor Vehicle Injuries to Pedestrian
Poisoning
Unintentional Injuries

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.
Blank Cells: Rates not calculated for fewer than 11 events. Rates not calculated in cases where zip code is unknown. Geography not reported where there are no cases reported in a given year. SES: Is the median household income by SRA community. Data for SRAs only.
*The COVID-19 pandemic was associated with increases in all-cause mortality. COVID-19 deaths have affected the patterns of mortality including those of Injury conditions.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS). California Department of Health Care Access and Information (HCAI), Emergency Department Database and Patient Discharge Database, 2020. SANDAG Population Estimates, 2020 (vintage: 09/2022). Population estimates were derived using the 2010 Census and data should be considered preliminary. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, February 2023.

2020 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2020CommunityProfilesDataGuideandDataDictionaryDashboard_16763944288860/HomePage