Totals may be slightly different due to rounding. Region-specific estimates are given to 3 d.p. due to some small numbers of infected individuals, but this level of accuracy is unlikely to be supported by the model.Global and regional estimates of the number of existing (prevalent) cases of genital HSV-1 infection among 15–49 year olds in 2012 by age and sex, in millions (percentage of population with prevalent infection shown in parentheses), as a function of the assumed proportion of incident HSV-1 infections in this age group that are genital.

WHO: MERS-CoV: No of Cases: Qatar data was reported at 16.000 Person in 23 Mar 2016. This stayed constant from the previous number of 16.000 Person for 22 Mar 2016. WHO: MERS-CoV: No of Cases: Qatar data is updated daily, averaging 9.000 Person from Sep 2012 (Median) to 23 Mar 2016, with 1278 observations. The data reached an all-time high of 16.000 Person in 23 Mar 2016 and a record low of 1.000 Person in 22 Nov 2012. WHO: MERS-CoV: No of Cases: Qatar data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D001: World Health Organization: No. of Cases.

Totals may be slightly different due to rounding. Sex-specific estimates for Africa, Eastern Mediterranean, South-East Asia and Western Pacific were generated by applying modelled incidence, calibrated without stratification by sex, to sex-specific population sizes. Region-specific estimates are given to 3 d.p. due to some small numbers of infected individuals, but this level of accuracy is unlikely to be supported by the model.Global and regional estimates of the number of new (incident) cases of HSV-1 infection in 2012 by age and sex, in millions (percentage of population with incident infection shown in parentheses).

This dataset provides a curated and comprehensive overview of global health, demographic, economic, and environmental metrics for 188 recognized countries over a period of 10 years (2012-2021). It was created by combining reliable data from the World Bank and the World Health Organization (WHO). Due to the absence of a single source containing all necessary indicators, over 60 datasets were analyzed, cleaned, and merged, prioritizing completeness and significance.

The dataset includes 29 key indicators, ranging from life expectancy, population metrics, and economic factors to environmental conditions and health-related behaviors. Missing values were carefully handled, and only the most relevant data with substantial coverage were retained.

This dataset is ideal for researchers, analysts, and policymakers interested in exploring relationships between economic development, health outcomes, and environmental factors at a global scale.

WHO: MERS-CoV: No of Cases: Jordan data was reported at 24.000 Person in 23 Mar 2016. This stayed constant from the previous number of 24.000 Person for 22 Mar 2016. WHO: MERS-CoV: No of Cases: Jordan data is updated daily, averaging 7.000 Person from Nov 2012 (Median) to 23 Mar 2016, with 1212 observations. The data reached an all-time high of 24.000 Person in 23 Mar 2016 and a record low of 2.000 Person in 26 Jan 2014. WHO: MERS-CoV: No of Cases: Jordan data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D001: World Health Organization: No. of Cases.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format. Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc. Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Abstract

This dataset contains statistics on deaths in South Africa in 2012. The registration of deaths in South Africa is regulated by the Births and Deaths Registration Act, 51 of 1992. The South African Department of Home Affairs (DHA) is responsible for the registration of deaths in South Africa. The data is collected with two instruments: The death register and the medical certificate in respect of death. The staff of the DHA Registrar of Deaths section fills in the former while the medical practitioner attending to the death completes the latter. Causes of death are coded by the Department of Home Affairs according to the tenth revision of the International Classification of Diseases (ICD-10) ICD-10, as required by the World Health Organization for their member countries. The data is used by the Department of Home Affairs to update the Population Register. The forms are sent to Statistics South Africa (Stats SA) for their use for statistical purposes. From the two forms sent to Stats SA, the following data items of the deceased are extracted: place of residence, place of death, date of death, month and year of registration, sex, marital status, occupation, underlying cause of death, whether or not the death was certified by a medical practitioner, and whether or not the deceased died in a health institution or nursing home. From 1991 death notifications do not require data on population group, and therefore this dataset includes death data for all population groups. This dataset excludes 2012 deaths that were not registered, and late registrations which would not have been available to Stats SA in time for the production of the dataset.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The data covers all deaths that occurred in 2012 and registered at the Department of Home Affairs in South Africa.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

The data is collected with two instruments: the death register and the medical certificate in respect of death.

WHO: EVD: Total Deaths: Uganda data was reported at 4.000 Person in 28 Nov 2012. This records a decrease from the previous number of 5.000 Person for 23 Nov 2012. WHO: EVD: Total Deaths: Uganda data is updated daily, averaging 71.500 Person from Oct 2000 (Median) to 28 Nov 2012, with 46 observations. The data reached an all-time high of 173.000 Person in 09 Jan 2001 and a record low of 4.000 Person in 28 Nov 2012. WHO: EVD: Total Deaths: Uganda data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D001: World Health Organization: Ebola Virus Disease (EVD): By Countries.

WHO: MERS-CoV: No of Deaths: Saudi Arabia data was reported at 534.000 Person in 23 Mar 2016. This records an increase from the previous number of 530.000 Person for 22 Mar 2016. WHO: MERS-CoV: No of Deaths: Saudi Arabia data is updated daily, averaging 264.000 Person from Nov 2012 (Median) to 23 Mar 2016, with 1217 observations. The data reached an all-time high of 534.000 Person in 23 Mar 2016 and a record low of 2.000 Person in 27 Nov 2012. WHO: MERS-CoV: No of Deaths: Saudi Arabia data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D002: World Health Organization: No. of Deaths.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format. Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc. Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

This statistic shows estimated age-standardized suicide rates worldwide in 2012, sorted by income group as defined by the World Health Organization for its member states. For that year, the WHO estimated that there were around 11.4 suicides per every 100 thousand population worldwide. More than 80 percent of all suicides globally were conducted in poorer member states.

Abstract

The Uruguay GSHS was a school-based survey of students in grades 2 CB, 3 CB, and 1 BD.

The purpose of the GSHS is to provide data on health behaviors and protective factors among students to: - Help countries develop priorities, establish programs, and advocate for resources for school health and youth health programs and policies; - Allow international agencies, countries, and others to make comparisons across countries regarding the prevalence of health behaviors and protective factors; and - Establish trends in the prevalence of health behaviors and protective factors by country for use in evaluation of school health and youth health promotion.

Geographic coverage

National coverage

Analysis unit

Students aged 13-15 years

Kind of data

Sample survey data [ssd]

Sampling procedure

The Uruguay GSHS was a school-based survey of students in grades 2 CB, 3 CB, and 1 BD. A two-stage cluster sample design was used to produce data representative of all students in grades 2 CB, 3 CB, and 1 BD in Uruguay. At the first stage, schools were selected with probability proportional to enrollment size. At the second stage, classes were randomly selected and all students in selected classes were eligible to participate.

National: A total of 3524 students participated in the Uruguay GSHS. Montevideo: A total of 1721 students participated in the Uruguay (Montevideo) GSHS. Rest of Country: A total of 1803 students participated in the Uruguay (Rest of Country) GSHS.

Mode of data collection

Face-to-face [f2f]

Research instrument

The GSHS uses a standardized scientific sample selection process; common school-based methodology; and core questionnaire modules, core-expanded questions, and country-specific questions that are combined to form a self-administered questionnaire that can be administered during one regular class period.

The 10 core questionnaire modules address the leading causes of morbidity and mortality among children and adults worldwide. - Alcohol use - Dietary behaviors - Drug use - Hygiene - Mental health - Physical activity - Protective factors - Sexual behaviors that contribute to HIV infection, other sexually-transmitted infections, and unintended pregnancy - Tobacco use - Violence and unintentional injury

Cleaning operations

Students self-reported their responses to each question on a computer scannable answer sheet.

Response rate

National: The school response rate was 100%, the student response rate was 77%, and the overall response rate was 77%. Montevideo: The school response rate was 100%, the student response rate was 77%, and the overall response rate was 77%. Rest of Country: The school response rate was 100%, the student response rate was 78%, and the overall response rate was 78%.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

• Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
• Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Abstract

The Iraq GSHS was a school-based survey of students in Grades 1st, 2nd, and 3rd Intermediate.

The purpose of the GSHS is to provide data on health behaviors and protective factors among students to: - Help countries develop priorities, establish programs, and advocate for resources for school health and youth health programs and policies; - Allow international agencies, countries, and others to make comparisons across countries regarding the prevalence of health behaviors and protective factors; and - Establish trends in the prevalence of health behaviors and protective factors by country for use in evaluation of school health and youth health promotion.

Geographic coverage

National coverage

Analysis unit

Students aged 13-15 years

Kind of data

Sample survey data [ssd]

Sampling procedure

The Iraq GSHS was a school-based survey of students in Grades 1st, 2nd, and 3rd Intermediate. A two-stage cluster sample design was used to produce data representative of all students in Grades 1st, 2nd, and 3rd Intermediate in Iraq. At the first stage, schools were selected with probability proportional to enrollment size. At the second stage, classes were randomly selected and all students in selected classes were eligible to participate. A total of 2038 students participated in the Iraq GSHS.

Mode of data collection

Face-to-face [f2f]

Research instrument

The GSHS uses a standardized scientific sample selection process; common school-based methodology; and core questionnaire modules, core-expanded questions, and country-specific questions that are combined to form a self-administered questionnaire that can be administered during one regular class period.

The 10 core questionnaire modules address the leading causes of morbidity and mortality among children and adults worldwide. - Alcohol use - Dietary behaviors - Drug use - Hygiene - Mental health - Physical activity - Protective factors - Sexual behaviors that contribute to HIV infection, other sexually-transmitted infections, and unintended pregnancy - Tobacco use - Violence and unintentional injury

Cleaning operations

Students self-reported their responses to each question on a computer scannable answer sheet.

Response rate

The school response rate was 94%, the student response rate was 94%, and the overall response rate was 88%.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format. Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc. Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format. Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc. Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

The Voice of the People Survey Series is WIN/Gallup International Association's End of Year survey and is a global study that collects the public's view on the challenges that the world faces today. Ongoing since 1977, the purpose of WIN/Gallup International's End of Year survey is to provide a platform for respondents to speak out concerning government and corporate policies. The Voice of the People, End of Year Surveys for 2012, fielded June 2012 to February 2013, were conducted in 56 countries to solicit public opinion on social and political issues. Respondents were asked whether their country was governed by the will of the people, as well as their attitudes about their society. Additional questions addressed respondents' living conditions and feelings of safety around their living area, as well as personal happiness. Respondents' opinions were also gathered in relation to business development and their views on the effectiveness of the World Health Organization. Respondents were also surveyed on ownership and use of mobile devices. Demographic information includes sex, age, income, education level, employment status, and type of living area.

Abstract

The Bolivia GSHS was a school-based survey of students in 2nd, 3rd and 4th grade secondary.

The purpose of the GSHS is to provide data on health behaviors and protective factors among students to: - Help countries develop priorities, establish programs, and advocate for resources for school health and youth health programs and policies; - Allow international agencies, countries, and others to make comparisons across countries regarding the prevalence of health behaviors and protective factors; and - Establish trends in the prevalence of health behaviors and protective factors by country for use in evaluation of school health and youth health promotion.

Geographic coverage

National coverage

Analysis unit

Students aged 13-15 years

Kind of data

Sample survey data [ssd]

Sampling procedure

The Bolivia GSHS was a school-based survey of students in 2nd, 3rd and 4th grade secondary. A two-stage cluster sample design was used to produce data representative of all students in 2nd, 3rd and 4th grade secondary in Bolivia. At the first stage, schools were selected with probability proportional to enrollment size. At the second stage, classes were randomly selected and all students in selected classes were eligible to participate.

A total of 3696 students participated in the Bolivia GSHS. A total of 1441 students participated in the Bolivia Highland GSHS. A total of 1021 students participated in the Bolivia Plains GSHS. A total of 1234 students participated in the Bolivia Valley GSHS.

Mode of data collection

Face-to-face [f2f]

Research instrument

The GSHS uses a standardized scientific sample selection process; common school-based methodology; and core questionnaire modules, core-expanded questions, and country-specific questions that are combined to form a self-administered questionnaire that can be administered during one regular class period.

The 10 core questionnaire modules address the leading causes of morbidity and mortality among children and adults worldwide. - Alcohol use - Dietary behaviors - Drug use - Hygiene - Mental health - Physical activity - Protective factors - Sexual behaviors that contribute to HIV infection, other sexually-transmitted infections, and unintended pregnancy - Tobacco use - Violence and unintentional injury

Cleaning operations

Students self-reported their responses to each question on a computer-scannable answer sheet.

Response rate

National: The school response rate was 99%, the student response rate was 89%, and the overall response rate was 88%. Highland: The school response rate was 100%, the student response rate was 93%, and the overall response rate was 93%. Plains: The school response rate was 96%, the student response rate was 89%, and the overall response rate was 86%. Valley: The school response rate was 100%, the student response rate was 85%, and the overall response rate was 85%.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".