Global

Proportional mortality

Percentage of total deaths due to NCDs 1) Cardiovascular diseases (CVDs) 2) Cancers 3) Chronic respiratory diseases (CRDs) 4) Diabetes 5) Other NCDs 6) Injuries and 7) Communicable, maternal, perinatal and nutritional conditions.

Latest year: 2019

NCDs are estimated to account for

74% of all deaths.

Total population

7 811 000 000

Percentage of total deaths due to NCDs

Percentage of total deaths due to NCDs overall and percentage of total deaths due to 7 main categories (5 different NCD categories plus 2 non-NCD categories which together total all deaths)

Latest year: 2019

74%

Total NCD deaths

Latest year: 2019

42 019 667

Probability of premature mortality from NCDs

Probability (%) of dying between age 30 and exact age 70 from any of cardiovascular diseases, cancer, diabetes, or chronic respiratory diseases (SDG indicator 3.4.1)

Latest year: 2019

18%

Diseases & Risk Factors

Noncommunicable Diseases (NCDs)

Mortality

18 %

Probability (%) of premature death

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

74 %

% of all deaths

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

41 %

% of NCD deaths

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

485 per 100 000 population

Deaths per 100 000 population

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

Risk factors

5.5 litres

Title:

Total alcohol per capita (15+) consumption (in litres of pure alcohol) (SDG Indicator 3.5.2)

Definition:

Total APC is defined as the total (sum of three-year average recorded and three-year average unrecorded APC, adjusted for three-year average tourist consumption) amount of alcohol consumed per adult (15+ years) over a calendar year, in litres of pure alcohol. Recorded alcohol consumption refers to official statistics (production, import, export, and sales or taxation data), while the unrecorded alcohol consumption refers to alcohol which is not taxed and is outside the usual system of governmental control. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries. Positive figures denote alcohol consumption of outbound tourists being greater than alcohol consumption by inbound tourists, negative numbers the opposite. Tourist consumption is based on UN tourist statistics.

Estimation method:

Recorded alcohol per capita (15+) consumption of pure alcohol is calculated as the sum of beverage-specific alcohol consumption of pure alcohol (beer, wine, spirits, other) from different sources: the first priority in the decision tree is given to government statistics; second are country-specific alcohol industry statistics in the public domain based on interviews or field work (GlobalData (formerly Canadean), IWSR-International Wine and Spirit Research, Wine Institute, historically World Drink Trends), or data from the International Organisation of Vine and Wine (OIV); third is the Food and Agriculture Organization of the United Nations' statistical database (FAOSTAT); and fourth is data from alcohol industry statistics in the public domain based on desk review. For countries, where the data source is FAOSTAT the unrecorded consumption may be included in the recorded consumption. As from the introduction of the "Other" beverage-specific category, beer includes malt beers, wine includes wine made from grapes, spirits include all distilled beverages, and other includes one or several other alcoholic beverages, such as fermented beverages made from sorghum, maize, millet, rice, or cider, fruit wine, fortified wine.

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

21 %

Title:

Prevalence of current tobacco use among adults aged 15+ years (age-standardized estimate) (%)

Definition:

The percentage of the population aged 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”.

Estimation method:

A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015). Once the age-and-sex-specific prevalence rates from national surveys were compiled into a dataset, the model was fit to calculate trend estimates from the year 2000 to 2025. The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. Estimates for countries with irregular surveys or many data gaps will have large uncertainty ranges, and such results should be interpreted with caution.

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

11 g/day

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

31 %

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

81 %

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

26 %

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

33 %

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

Hypertension diagnosis, treatment and control

Total

Males

Females

30-79 have hypertension

(% of people aged 30-79)

16 %

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

7 %

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

14 %

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

4.5 mmol/l

Range Slider

2000
2005
2010
2015
2020
2025
2030

Total

Males

Females

Past trends

Projected Trends

Estimated projections for NCD indicators

Projections of NCD mortality and risk factor data

Latest year: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Projections were generated based on the methods used for the projections of the WHO Global Programme of Work (GPW13) indicators. For glucose, mortality, alcohol and salt projections, eight models were considered: random walk with trend (RW2), autoregressive (AR1), exponential smoothing, Holt’s linear trend, Holt’s linear trend (damped), flat extrapolation, linear extrapolation and annual average rate change extrapolation. As described in further detail in the GPW13 methodology document, models were tested using existing data, with part of these serving as test data. To determine which model performed best, the following statistical metrics were used: RMSE (root mean squared error), MAE (mean absolute error), MdAE (median absolute error), MASE (median absolute scaled error) and CBA (confidence bound accuracy: percentage of test points that lie within the predicted confidence intervals). Using all of these metrics, the best model was selected independently for each indicator and then used to project data to 2025 or 2030. For tobacco use, a Bayesian negative binomial meta-regression was used to fit trendlines and project to 2025. Projections are predominantly based on data from before the COVID-19 pandemic and are not adjusted for any possible impact on trends the pandemic may have had. Adult prevalence of obesity, prevalence of insufficient activity were projected assuming trends since 2010 continue. To do so, a regression was fit for each Bayesian model iteration, country and sex, with the probit of age-standardized prevalence as the dependent variable, and year as the independent variable. The regression was used to predict age-standardized prevalence for each model iteration, country, sex and projection year. The projected values shown are the mean of all Bayesian iterations for the country, sex and projection year. For obesity, on-track status was assessed based on the proportion of iterations for which the regression coefficient was less than or equal to zero, which is the posterior probability that the trend in age-standardized prevalence was flat or decreasing. Each country-sex was considered on track if the posterior probability of a flat or decreasing trend was greater than 0.5. For insufficient physical activity, each sex and country were considered on-track if the projected value for 2030 was less than 85% of the estimated value for 2010. For both obesity and insufficient physical activity, countries were further categorized by the likelihood that past trends were sufficient to meet the target. Raised blood pressure was projecting using a similar method, with the exception that regressions were fit for each country and sex.

Global Target

Publications

Invisible numbers

21 September 2022

World mental health report: Transforming mental health for all

16 June 2022

Toolkit for developing a multisectoral action plan for noncommunicable diseases: overview

19 April 2022

Saving lives, spending less: the case for investing in noncommunicable diseases

13 December 2021

Tackling NCDs: 'best buys' and other recommended interventions for the prevention and control of noncommunicable diseases

26 October 2017

Global Action Plan for the Prevention and Control of NCDs 2013-2020

14 November 2013

Percentage of total deaths due to NCDs

Percentage of total deaths due to NCDs

Latest data available: 2019

Title:

Percentage of total deaths due to NCDs overall and percentage of total deaths due to 7 main categories (5 different NCD categories plus 2 non-NCD categories which together total all deaths)

Definition:

Estimation method:

Total NCD deaths

Total NCD deaths

Latest data available: 2019

Title:

Total NCD deaths

Definition:

Number of deaths due to all noncommunicable diseases.

Estimation method:

Probability of premature mortality from NCDs

Probability of premature mortality from NCDs

Latest data available: 2019

Title:

Probability (%) of dying between age 30 and exact age 70 from any of cardiovascular diseases, cancer, diabetes, or chronic respiratory diseases (SDG indicator 3.4.1)

Definition:

Estimation method:

Probability of death between exact age 30 and exact age 70 was calculated using cause-specific mortality rates in each 5-year age group and standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE) (https://www.who.int/data/global-health-estimates).

Probability of premature mortality from NCDs

Probability of premature mortality from NCDs

Latest data available: 2019

Title:

Probability (%) of dying between age 30 and exact age 70 from any of cardiovascular diseases, cancer, diabetes, or chronic respiratory diseases (SDG indicator 3.4.1)

Definition:

Estimation method:

Probability of death between exact age 30 and exact age 70 was calculated using cause-specific mortality rates in each 5-year age group and standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE) (https://www.who.int/data/global-health-estimates).

Range Slider

Estimated projections for NCD indicators

Estimated projections for NCD indicators

Latest data available: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Percentage of total deaths due to NCDs

Percentage of total deaths due to NCDs

Latest data available: 2019

Title:

Percentage of total deaths due to NCDs overall and percentage of total deaths due to 7 main categories (5 different NCD categories plus 2 non-NCD categories which together total all deaths)

Definition:

Estimation method:

Range Slider

Estimated projections for NCD indicators

Estimated projections for NCD indicators

Latest data available: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

Percentage of NCD deaths occurring under 70 yearserror_outline Percentage of NCD deaths occurring under 70 years Premature deaths due to noncommunicable diseases (NCD) as a proportion of all NCD deaths (%) Latest year: 2019 Full description

Percentage of NCD deaths occurring under 70 years

Percentage of NCD deaths occurring under 70 years

Latest data available: 2019

Title:

Premature deaths due to noncommunicable diseases (NCD) as a proportion of all NCD deaths (%)

Definition:

Deaths due to noncommunicable diseases (NCDs) among people aged below 70 years, as a percentage of NCD deaths among all ages.

Estimation method:

Range Slider

Estimated projections for NCD indicators

Estimated projections for NCD indicators

Latest data available: 0

Title:

Projections of NCD mortality and risk factor data

Definition:

Projections of NCD mortality and risk factor indicators with time-series data for estimating future country attainment of targets.

Estimation method:

NCD age-standardized death rateerror_outline NCD age-standardized death rate per 100 000 population Age-standardized death rate for NCDs Latest year: 2019 Full description

NCD age-standardized death rate per 100 000 population

NCD age-standardized death rate (per 100 000 population)

Latest data available: 2019

Title:

Age-standardized death rate for NCDs