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
0
Percentage of total deaths due to NCDserror_outline
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%
×
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:
Percentage of total deaths due to NCDs overall and percentage of total deaths due to: 1) cardiovascular diseases, 2) cancer 3) chronic respiratory diseases, 4) diabetes (including deaths from chronic kidney disease due to diabetes), 5) other NCDs, 6) injuries and 7) communicable, maternal, perinatal and nutritional conditions.
Estimation method:
Source: WHO Global Health Estimates (GHE). Detailed methods are available online (https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5) and summarized below.
For countries with a high-quality vital registration system including information on cause of death, the vital registration that Member States submit to the WHO Mortality Database were used, with adjustments where necessary, e.g. for under-reporting of deaths, unknown age and sex, and ill-defined causes of deaths. For countries without high-quality death registration data, cause of death estimates are calculated using other data, including household surveys with verbal autopsy, sample or sentinel registration systems, special studies. These estimates represent the best estimates of WHO, computed using standard categories, definitions and methods to ensure cross-country comparability, and may not be the same as official national estimates. Due to changes in input data and methods, the revisions of GHE are not comparable to previously published WHO estimates.
Total NCD deathserror_outline
Total NCD deaths
Total NCD deaths
Latest year: 2019
42 019 667
×
Total NCD deaths
Latest data available: 2019
Title:
Total NCD deaths
Definition:
Number of deaths due to all noncommunicable diseases.
Estimation method:
Source: WHO Global Health Estimates (GHE). Detailed methods are available online (https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5) and summarized below.
For countries with a high-quality vital registration system including information on cause of death, the vital registration that Member States submit to the WHO Mortality Database were used, with adjustments where necessary, e.g. for under-reporting of deaths, unknown age and sex, and ill-defined causes of deaths. For countries without high-quality death registration data, cause of death estimates are calculated using other data, including household surveys with verbal autopsy, sample or sentinel registration systems, special studies. These estimates represent the best estimates of WHO, computed using standard categories, definitions and methods to ensure cross-country comparability, and may not be the same as official national estimates. Due to changes in input data and methods, the revisions of GHE are not comparable to previously published WHO estimates.
Probability of premature mortality from NCDserror_outline
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%
×
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:
Percentage of 30-year-old-people who would die before their 70th birthday from any of cardiovascular diseases, cancer, diabetes or chronic respiratory diseases, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g. injuries or HIV/AIDS).
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 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:
Percentage of 30-year-old-people who would die before their 70th birthday from any of cardiovascular diseases, cancer, diabetes or chronic respiratory diseases, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g. injuries or HIV/AIDS).
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 death
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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
Percentage of total deaths due to NCDserror_outline
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
Full description
74 %
×
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:
Percentage of total deaths due to NCDs overall and percentage of total deaths due to: 1) cardiovascular diseases, 2) cancer 3) chronic respiratory diseases, 4) diabetes (including deaths from chronic kidney disease due to diabetes), 5) other NCDs, 6) injuries and 7) communicable, maternal, perinatal and nutritional conditions.
Estimation method:
Source: WHO Global Health Estimates (GHE). Detailed methods are available online (https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5) and summarized below.
For countries with a high-quality vital registration system including information on cause of death, the vital registration that Member States submit to the WHO Mortality Database were used, with adjustments where necessary, e.g. for under-reporting of deaths, unknown age and sex, and ill-defined causes of deaths. For countries without high-quality death registration data, cause of death estimates are calculated using other data, including household surveys with verbal autopsy, sample or sentinel registration systems, special studies. These estimates represent the best estimates of WHO, computed using standard categories, definitions and methods to ensure cross-country comparability, and may not be the same as official national estimates. Due to changes in input data and methods, the revisions of GHE are not comparable to previously published WHO estimates.
% of all deaths
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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
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
41 %
×
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:
Source: WHO Global Health Estimates (GHE). Detailed methods are available online (https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5) and summarized below.
For countries with a high-quality vital registration system including information on cause of death, the vital registration that Member States submit to the WHO Mortality Database were used, with adjustments where necessary, e.g. for under-reporting of deaths, unknown age and sex, and ill-defined causes of deaths. For countries without high-quality death registration data, cause of death estimates are calculated using other data, including household surveys with verbal autopsy, sample or sentinel registration systems, special studies. These estimates represent the best estimates of WHO, computed using standard categories, definitions and methods to ensure cross-country comparability, and may not be the same as official national estimates. Due to changes in input data and methods, the revisions of GHE are not comparable to previously published WHO estimates.
% of NCD deaths
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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
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
485 per 100 000 population
×
NCD age-standardized death rate (per 100 000 population)
Latest data available: 2019
Title:
Age-standardized death rate for NCDs
Definition:
Age-standardized death rate (per 100 000 population) for noncommunicable diseases.
Estimation method:
Source: WHO Global Health Estimates (GHE). Detailed methods are available online (https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5) and summarized below.
For countries with a high-quality vital registration system including information on cause of death, the vital registration that Member States submit to the WHO Mortality Database were used, with adjustments where necessary, e.g. for under-reporting of deaths, unknown age and sex, and ill-defined causes of deaths. For countries without high-quality death registration data, cause of death estimates are calculated using other data, including household surveys with verbal autopsy, sample or sentinel registration systems, special studies. These estimates represent the best estimates of WHO, computed using standard categories, definitions and methods to ensure cross-country comparability, and may not be the same as official national estimates. Due to changes in input data and methods, the revisions of GHE are not comparable to previously published WHO estimates.
Deaths per 100 000 population
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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
Total alcohol per capita consumptionerror_outline
Total alcohol per capita consumption litres
Total alcohol per capita (15+) consumption (in litres of pure alcohol) (SDG Indicator 3.5.2)
Latest year: 2019
Full description
5.5 litres
×
Total alcohol per capita consumption (litres)
Latest data available: 2019
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 Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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
Current tobacco use, adults aged 15+error_outline
Current tobacco use, adults aged 15+
Prevalence of current tobacco use among adults aged 15+ years (age-standardized estimate) (%)
Latest year: 2022
Full description
21 %
×
Current tobacco use, adults aged 15+
Latest data available: 2022
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 Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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
Mean population salt intake, adults aged 25+error_outline
Mean population salt intake, adults aged 25+ g/day
Mean daily salt intake among adults aged 25+ years (g/day)
Latest year: 2019
Full description
11 g/day
×
Mean population salt intake, adults aged 25+ (g/day)
Latest data available: 2019
Title:
Mean daily salt intake among adults aged 25+ years (g/day)
Definition:
Mean daily population salt intake, in grams/day, among adults aged 25+ years.
Estimation method:
Estimates for mean population sodium intake were calculated by the Institute for Health Metrics and Evaluation (IHME). More information available on their website: https://www.healthdata.org/results/gbd_summaries/2019/diet-high-sodium-level-3-risk
Estimates were converted to salt intake by multiplying by 2.5.
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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
Physical inactivity, adults aged 18+error_outline
Physical inactivity, adults aged 18+
Prevalence of insufficient physical activity among adults aged 18+ years (age-standardized estimate) (%)
Latest year: 2022
Full description
31 %
×
Physical inactivity, adults aged 18+
Latest data available: 2022
Title:
Prevalence of insufficient physical activity among adults aged 18+ years (age-standardized estimate) (%)
Definition:
Percentage of defined population attaining less than 150 minutes of moderate-intensity physical activity per week, or less than 75 minutes of vigorous-intensity physical activity per week, or equivalent.
Estimation method:
A Bayesian hierarchical model was used to produce estimates for each country or territory, age, sex and year. Full details of methods are available in: Strain T, Flaxman SR, Guthold R, Semenova E, et al. National, regional and global trends in insufficient physical activity among adults from 2000 to 2022: a pooled analysis of 507 surveys with 5.7 million participants. Lancet Global Health, 2024.. The estimates are based on self-reported physical activity captured using the GPAQ (Global Physical Activity Questionnaire), the IPAQ (International Physical Activity Questionnaire) or a similar questionnaire covering activity at work/in the household, for transport, and during leisure time. Where necessary, adjustments were made for the reported definition (in case it was different to the indicator definition), for known over-reporting of activity in questionnaires such as the IPAQ, and for survey coverage (in case a survey only covered urban areas). Age-standardized estimates are produced by applying the crude estimates to the WHO Standard Population.
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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.
Prevalence of insufficient physical activity among school-going adolescents aged 11-17 (crude estimate) (%)
Latest year: 2016
Full description
81 %
×
Physical inactivity, adolescents aged 11-17
Latest data available: 2016
Title:
Prevalence of insufficient physical activity among school-going adolescents aged 11-17 (crude estimate) (%)
Definition:
Percentage of school going adolescents not meeting WHO recommendations on Physical Activity for Health, i.e. doing less than 60 minutes of moderate- to vigorous-intensity physical activity daily.
Estimation method:
Full details of methods are available in: Guthold R et al. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolesc Health. 2020 Jan;4(1):23-35. (https://doi.org/10.1016/S2352-4642(19)30323-2). The estimates are based on self-reported physical activity using questionnaires. Main data sources included the Global School-based Student Health Survey (GSHS), the Health Behaviour in School aged Children (HBSC), and some other national surveys. Where necessary, adjustments were made for the reported definition (in case it was different to the indicator definition), and for survey coverage (in case a survey only covered urban areas). No estimates were produced for countries with no data.
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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, adults aged 30–79error_outline
Hypertension, adults aged 30–79
Prevalence of hypertension among adults aged 30–79 years (age-standardized estimate) (%)
Latest year: 2019
Full description
33 %
×
Hypertension, adults aged 30–79
Latest data available: 2019
Title:
Prevalence of hypertension among adults aged 30–79 years (age-standardized estimate) (%)
Definition:
Prevalence of hypertension (defined as having systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or taking medication for hypertension) among adults aged 30–79.
Estimation method:
Full details of input and data methods are available at: NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. The Lancet S0140-6736(21)01330-1. A total of 1,201 population-based studies that included measured blood pressure and data on blood pressure treatment in 104 million individuals aged 30–79 years were used to estimate trends in hypertension and hypertension diagnosis, treatment and control from 1990 to 2019. Age-standardized estimates are produced by applying the crude estimates to the WHO Standard Population.
Hypertension, adults aged 30–79
Diagnosed hypertension, adults aged 30–79 with hypertension
Treated hypertension, adults aged 30–79 with hypertension
Controlled hypertension, adults aged 30–79 with hypertension
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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
error_outline
30-79 have hypertension
(% of people aged 30-79)
Obesity, adults aged 18+error_outline
Obesity, adults aged 18+
Prevalence of obesity among adults (age-standardized estimate) (%)
Latest year: 2022
Full description
16 %
×
Obesity, adults aged 18+
Latest data available: 2022
Title:
Prevalence of obesity among adults (age-standardized estimate) (%)
Definition:
Percentage of adults aged 18+ years with a body mass index (BMI) of 30 kg/m2 or higher.
Estimation method:
Based on measured height and weight. Input data and methods are described here: NCD-RisC. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet 2024. DOI: https://doi.org/10.1016/S0140-6736(23)02750-2. Age-standardized estimates are produced by applying the crude estimates to the WHO Standard Population.
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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
Obesity, adolescents aged 10–19error_outline
Obesity, adolescents aged 10–19
Prevalence of obesity among adolescents aged 10–19 years (crude estimate) (%)
Latest year: 2022
Full description
7 %
×
Obesity, adolescents aged 10–19
Latest data available: 2022
Title:
Prevalence of obesity among adolescents aged 10–19 years (crude estimate) (%)
Definition:
Percentage of defined age group with a body mass index (BMI) greater than 2 standard deviation above the median, according to the WHO references for school-age children and adolescents.
Estimation method:
Based on measured height and weight. Input data and methods are described here: NCD-RisC. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet 2024. DOI: https://doi.org/10.1016/S0140-6736(23)02750-2.
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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.
Raised fasting blood glucose (≥7.0 mmol/l or on medication)(age-standardized estimate)
Latest year: 2014
Full description
9 %
×
Raised fasting blood glucose, adults aged 18+
Latest data available: 2014
Title:
Raised fasting blood glucose (≥7.0 mmol/l or on medication)(age-standardized estimate)
Definition:
Percentage of adults aged 18+ years with fasting glucose ≥ 7.0 mmol/l (126 mg/dl) or history of diagnosis with diabetes or use of insulin or oral hypoglycaemic drugs.
Estimation method:
Input data and methods are described here: NCD-RisC. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. The Lancet; 2016. (https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)00618-8/fulltext)
For producing comparable national estimates, data observations based on mean FPG, oral glucose tolerance test (OGTT), HbA1c or combinations of these are all converted to mean FPG. A Bayesian hierarchical model is then fitted to these data to calculate age-sex-year-country specific prevalences, which accounts for national versus subnational data sources and urban versus rural data sources, and allows for variation in prevalence across age and sex. Age-standardized estimates are then produced by applying the crude estimates to the WHO Standard Population.
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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
Mean total cholesterol, adults aged 18+error_outline
Mean total cholesterol, adults aged 18+ mmol/l
Mean total cholesterol (age-standardized estimate)
Latest year: 2018
Full description
4.5 mmol/l
×
Mean total cholesterol, adults aged 18+ (mmol/l)
Latest data available: 2018
Title:
Mean total cholesterol (age-standardized estimate)
Definition:
Mean total cholesterol of defined population in mmol/l.
Estimation method:
Full details of input and data methods are available in: NCD Risk Factor Collaboration (NCD-RisC). Repositioning of the global epicentre of non-optimal cholesterol. Nature 582, 73–77 (2020). A total of 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older were used to estimate mean total trends of HDL and non-HDL cholesterol from 1980 to 2018. Most studies in the analysis measured total cholesterol and HDL cholesterol, from which non-HDL cholesterol can be calculated through subtraction. Age-standardized estimates are produced by applying the crude estimates to the WHO Standard Population.
Range Slider
2000
2005
2010
2015
2020
2025
2030
Total
Males
Females
Past trends
Projected Trendserror_outline
Estimated projections for NCD indicators
Projections of NCD mortality and risk factor data
Latest year: 0
Full description
×
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:
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.