As of April 15, 2023, there had been over 4.65 million confirmed cases of COVID-19 in Canada. As of this date, the coronavirus had been confirmed in every province and territory, with the province of Ontario having the highest number of confirmed cases.

COVID-19 vaccinations in Canada There have now been seven COVID-19 vaccines approved for use in Canada, the most widely distributed of which is manufactured by Pfizer and BioNTech. Around 63 million doses of the Pfizer/BioNTech vaccine have been distributed across Canada. As of January 1, 2023, around 83 percent of the population in Canada had received at least one COVID-19 vaccination dose.

As of April 15, 2023, there had been a total of around 51,921 deaths attributed to COVID-19 in Canada. As of this time, every province and territory has reported deaths, with Quebec and Ontario reporting the highest numbers.

COVID-19 in Canada Canada has recorded almost 4.65 million coronavirus cases since the first infection in the country was confirmed on January 25, 2020. The number of cases by province shows that Ontario and Quebec have been the most severely affected. The number of daily new cases reached record highs at the end of 2021 and began to decrease as spring arrived in 2022.

COVID-19 vaccinations in Canada Seven COVID-19 vaccines have now been approved for use in Canada and vaccines are widely available. As of January 1, 2023 around 83 percent of the Canadian population had received at least one dose of a COVID-19 vaccine. The provinces with the highest share of people fully vaccinated against COVID-19 are Newfoundland and Labrador and Nova Scotia. However, Ontario and Quebec are the provinces with the highest total number of people vaccinated.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Revisions added on 4/23/2020 are highlighted.Revisions added on 4/30/2020 are highlighted.Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Correction on 6/1/2020Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. Back-casting revisions: In the Johns Hopkins’ data, the structure is to provide the cumulative number of cases per day, which presumes an ever-increasing sequence of numbers, e.g., 0,0,1,1,2,5,7,7,7, etc. However, revisions do occur and would look like, 0,0,1,1,2,5,7,7,6. To accommodate this, we revised the lists to eliminate decreases, which make this list look like, 0,0,1,1,2,5,6,6,6.Reporting Interval: In the early weeks, Johns Hopkins' data provided reporting every day regardless of change. In late April, this changed allowing for days to be skipped if no new data was available. The day was still included, but the value of total cases was set to Null. The processing therefore was updated to include tracking of the spacing between intervals with valid values.100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent fourteen days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 42 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 14 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 14 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 14 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 14 days and less than past 2 days indicates slight positive trend, but likely still within peak trend time frame.Past five days is less than the past 14 days. This means a downward trend. This would be an

Number of Registered Deaths: Rural: CF: Smolensk Region data was reported at 4,519.000 Person in 2022. This records a decrease from the previous number of 5,721.000 Person for 2021. Number of Registered Deaths: Rural: CF: Smolensk Region data is updated yearly, averaging 7,362.000 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 9,117.000 Person in 2003 and a record low of 4,519.000 Person in 2022. Number of Registered Deaths: Rural: CF: Smolensk Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GD008: Number of Registered Deaths: Rural: by Region.

Number of Registered Deaths: Male: CF: Tambov Region data was reported at 7,565.000 Person in 2023. This records a decrease from the previous number of 7,978.000 Person for 2022. Number of Registered Deaths: Male: CF: Tambov Region data is updated yearly, averaging 9,731.000 Person from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 12,134.000 Person in 2002 and a record low of 7,498.000 Person in 2019. Number of Registered Deaths: Male: CF: Tambov Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GD006: Number of Registered Deaths: by Region.

In 2023, it was estimated that the mortality rate for cancer in Manitoba would be 192.4 deaths due to per 100,000 population. This statistic displays the estimated mortality rate of cancer in Canada by province in 2023.

This study contains selected demographic, social, economic, public policy, and political comparative data for Switzerland, Canada, France, and Mexico for the decades of 1900-1960. Each dataset presents comparable data at the province or district level for each decade in the period. Various derived measures, such as percentages, ratios, and indices, constitute the bulk of these datasets. Data for Switzerland contain information for all cantons for each decennial year from 1900 to 1960. Variables describe population characteristics, such as the age of men and women, county and commune of origin, ratio of foreigners to Swiss, percentage of the population from other countries such as Germany, Austria and Lichtenstein, Italy, and France, the percentage of the population that were Protestants, Catholics, and Jews, births, deaths, infant mortality rates, persons per household, population density, the percentage of urban and agricultural population, marital status, marriages, divorces, professions, factory workers, and primary, secondary, and university students. Economic variables provide information on the number of corporations, factory workers, economic status, cultivated land, taxation and tax revenues, canton revenues and expenditures, federal subsidies, bankruptcies, bank account deposits, and taxable assets. Additional variables provide political information, such as national referenda returns, party votes cast in National Council elections, and seats in the cantonal legislature held by political groups such as the Peasants, Socialists, Democrats, Catholics, Radicals, and others. Data for Canada provide information for all provinces for the decades 1900-1960 on population characteristics, such as national origin, the net internal migration per 1,000 of native population, population density per square mile, the percentage of owner-occupied dwellings, the percentage of urban population, the percentage of change in population from preceding censuses, the percentage of illiterate population aged 5 years and older, and the median years of schooling. Economic variables provide information on per capita personal income, total provincial revenue and expenditure per capita, the percentage of the labor force employed in manufacturing and in agriculture, the average number of employees per manufacturing establishment, assessed value of real property per capita, the average number of acres per farm, highway and rural road mileage, transportation and communication, the number of telephones per 100 population, and the number of motor vehicles registered per 1,000 population. Additional variables on elections and votes are supplied as well. Data for France provide information for all departements for all legislative elections since 1936, the two presidential elections of 1965 and 1969, and several referenda held in the period since 1958. Social and economic data are provided for the years 1946, 1954, and 1962, while various policy data are presented for the period 1959-1962. Variables provide information on population characteristics, such as the percentages of population by age group, foreign-born, bachelors aged 20 to 59, divorced men aged 25 and older, elementary school students in private schools, elementary school students per million population from 1966 to 1967, the number of persons in household in 1962, infant mortality rates per million births, and the number of priests per 10,000 population in 1946. Economic variables focus on the Gross National Product (GNP), the revenue per capita per household, personal income per capita, income tax, the percentage of active population in industry, construction and public works, transportation, hotels, public administration, and other jobs, the percentage of skilled and unskilled industrial workers, the number of doctors per 10,000 population, the number of agricultural cooperatives in 1946, the average hectares per farm, the percentage of farms cultivated by the owner, tenants, and sharecroppers, the number of workhorses, cows, and oxen per 100 hectares of farmland in 1946, and the percentages of automobiles per 1,000 population, radios per 100 homes, and cinema seats per 1,000 population. Data are also provided on the percentage of Communists (PCF), Socialists, Radical Socialists, Conservatives, Gaullists, Moderates, Poujadists, Independents, Turnouts, and other political groups and p

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

The number of new cases, age-standardized rates and average age at diagnosis of cancers diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Cancer incidence rates are age-standardized using the direct method and the final 2011 Canadian postcensal population structure. Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

Number of Registered Deaths: CF: Smolensk Region data was reported at 13,167.000 Person in 2023. This records a decrease from the previous number of 15,916.000 Person for 2022. Number of Registered Deaths: CF: Smolensk Region data is updated yearly, averaging 18,742.000 Person from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 22,824.000 Person in 2003 and a record low of 13,167.000 Person in 2023. Number of Registered Deaths: CF: Smolensk Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GD006: Number of Registered Deaths: by Region.

Data and Stata code for recreation of "Impact of Adjustment for Differential Testing by Age and Sex on Apparent Epidemiology of SARS-CoV-2 Infection in Ontario, Canada". For questions about analysis please contact me directly at david.fisman@gmail.com or david.fisman@utoronto.ca.Paper abstract: Surveillance of communicable diseases typically relies on case counts for estimates of risk, and counts can be strongly influenced by testing rates. In the Canadian province of Ontario, testing rates varied markedly by age, sex, geography and time over the course of the SARS-CoV-2 pandemic. We applied a standardization-based approach to test-adjustment to better understand pandemic dynamics from 2020 to 2022, and to better understand when test-adjustment is necessary for accurate estimation of risk. SARS-CoV-2 case counts by age, sex, public health unit and week were obtained from Ontario’s Case and Contact Management system (CCM), which includes all SARS-CoV-2 cases from March 2020 to August 2022. Complete data on testing volumes was obtained from the Ontario Laboratory Information System (OLIS). Case counts were adjusted for under-testing using a previously published standardization-based approach that estimates case numbers that would have been expected if the entire population was tested at the same rate as most-tested age and sex groups. Logistic regression was used to identify threshold testing rates beyond which test-adjustment was unnecessary. Testing rates varied markedly by age, sex, public health unit and pandemic wave. After adjustment for under-testing, overall case counts increased threefold. Adjusted epidemic curves suggested, in contrast to reported case counts, that the first two pandemic waves were equivalent in size, and that there were three distinct pandemic waves in 2022, due to the emergence of Omicron variants. Under-reporting was greatest in children and young males, and varied significantly across public health units, with variation explained partly by testing rates and prevalence of multigenerational households. Test adjustment resulted in little change in the epidemic curve during pandemic waves when testing rates were highest; we found that test-adjustment did not increase case counts once weekly per capita testing rates exceeded 6.3%. We conclude that standardization-based adjustment for differential testing by age and sex, and for dynamic changes in testing over time, results in a different picture of infection risk during the SARS-CoV-2 pandemic in Ontario; test-adjusted epidemic curves are concordant with observed patterns of mortality during the pandemic and have face validity. This methodology offers an alternative to sero-epidemiology for identification of true burden of infection when reinfection, sero-reversion, and non-specificity of serological assays make sero-epidemiology challenging.Update, December 15, 2023This data source is being updated in relation to work in progress showing the importance of test-adjusting for accurate estimation of the impacts of community masking mandates, as were introduced in Ontario in summer 2020 (see https://www.medrxiv.org/content/10.1101/2023.07.26.23293155v1). New files include a dataset that can be used to run updated analyses, Stata macros that create test-adjusted case counts by public health unit, age group, gender and week, and a spreadsheet that shows the estimated impact of mask mandates as compared to a counterfactual where they were not introduced.

We publish Canada’s greenhouse gas (GHG) and air pollutant emissions projections annually. These projections help measure progress in reducing emissions and combating climate change. GHG projections are presented for various scenarios. Air pollutant emissions projections reflect our efforts to reduce air pollution.

Site Contents:

• current_projections_actuelles: Contains the latest projections from Canada's First Biennial Transparency Report (2024).

• previous_projections_precedentes: Includes projections reported since 2017.

From 2021, a file named “combined-table-tableau-combiné.xlsx” is also included in the top folder. This file contains a summary of all data tables included in the “GHG – GES” and "Energy - Énergie" sub-folders

Key Folders:

• GHG – GES (introduced in 2018): Contains GHG and air pollutant emissions data. From 2021, includes LULUCF net GHG fluxes and accounting contributions. From 2024, includes net GHG flux historical estimates and projections from provinces and territories by land category.

• Energy – Énergie (introduced in 2018): Includes energy and macroeconomic data. From 2021, includes emission factors for flaring, venting, and fugitive emissions for the Oil and Gas sector. From 2022, includes sub-folders with results for the reference case and additional measures scenarios. From 2023, includes emissions per capita by province/territory and for Canada. From 2024, includes a document outlining the calculation of electricity grid intensities with and without biogenic carbon dioxide emissions.

Additional Sub-Folders (introduced in 2022):

• Reference Scenario de reference: Reflects the current Reference Case scenario.

• AM Scenario AMS: Reflects the current Additional Measures scenario. From 2023, includes macroeconomic assumptions.

Current and previous projections report can be accessed through our webpage: https://www.canada.ca/en/environment-climate-change/services/climate-change/greenhouse-gas-emissions/projections.html.

Between 2023 and 2024, the Canadian province with the most deaths was Ontario, with a total of 130,556 deaths.

This dataset includes information on the number of tests of individuals for COVID-19 infection performed in New York State beginning March 1, 2020, when the first case of COVID-19 was identified in the state. The primary goal of publishing this dataset is to provide users timely information about local disease spread and reporting of positive cases. The data will be updated daily, reflecting tests completed by 12:00 am (midnight) the day of the update (i.e., all tests reported by the end of the day on the day before the update).

Note: On November 14, 2020, only 14 hours of laboratory data was collected and shared. A 2:00 pm cutoff time was implemented, allowing the NYSDOH to enhance data quality reviews. All other published laboratory data represented 24 hours of data collection.

Reporting of SARS-CoV2 laboratory testing results is mandated under Part 2 of the New York State Sanitary Code. Clinical laboratories, as defined in Public Health Law (PHL) § 571 electronically report test results to the New York State Department of Health (DOH) via the Electronic Clinical Laboratory Reporting System (ECLRS). The DOH Division of Epidemiology’s Bureau of Surveillance and Data System (BSDS) monitors ECLRS reporting and ensures that all positives and negatives are accurate. Starting September 30, 2020, this data also includes pooled/batch tests reported by institutions of higher education. This is also known as surveillance testing and not performed by a clinical laboratory.

Test counts reflect those reported to DOH each day. A person may have multiple specimens tested on one day, these would be counted one time, i.e., if two specimens are collected from an individual at the same time and then evaluated, the outcome of the evaluation of those two samples to diagnose the individual is counted as a single test of one person, even though the specimens may be tested separately. Conversely, if an individual is tested on more than one day, the data will show two tests of an individual, one for each date the person was tested. An individual will only be counted positive one time.

Test counts are assigned to a county based on this order of preference: 1) the patient’s address, 2) the ordering healthcare provider/campus address, or 3) the ordering facility/campus address.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Attribute DALYs of HFMD due to temperature in Guangdong, 2010–2012.

Number of Registered Deaths: Urban: Male: CF: Lipetsk Region data was reported at 5,211.000 Person in 2022. This records a decrease from the previous number of 6,784.000 Person for 2021. Number of Registered Deaths: Urban: Male: CF: Lipetsk Region data is updated yearly, averaging 5,429.000 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 6,784.000 Person in 2021 and a record low of 3,759.000 Person in 1990. Number of Registered Deaths: Urban: Male: CF: Lipetsk Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GD007: Number of Registered Deaths: Urban: by Region.

Number of Registered Deaths: Rural: Female: UF: Tumen Region data was reported at 3,764.000 Person in 2022. This records a decrease from the previous number of 4,864.000 Person for 2021. Number of Registered Deaths: Rural: Female: UF: Tumen Region data is updated yearly, averaging 4,180.000 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 4,864.000 Person in 2021 and a record low of 3,253.000 Person in 1990. Number of Registered Deaths: Rural: Female: UF: Tumen Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GD008: Number of Registered Deaths: Rural: by Region.

Nova Scotia has the highest cancer incidence rate of any province in Canada, followed by Newfoundland and Labrador, and Ontario. However, Nunavut has the highest cancer mortality rate of the provinces. In Nunavut there are around 310 deaths from cancer per 100,000 population, compared to a rate of 218 deaths per 100,000 in Newfoundland and Labrador.

New cancer cases

As of 2023, there were around 513 new cancer cases in Canada per 100,000 population. The most common types of cancer in Canada include lung and bronchus cancer, breast cancer, and prostate cancer. Breast cancer is the most common type of cancer among women, while prostate cancer is the second most common type among men. Men have slightly higher rates of lung and bronchus cancer and colorectal cancer.

Cancer mortality

Lung and bronchus cancers have the highest mortality rate of any cancer in Canada, followed by colorectal and pancreas cancer. Men in Canada have around a five percent chance of dying as a result of lung and bronchus cancer. The lifetime probability of dying from any cancer type for males in Canada is around 24 percent.