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Method Article

A GBD 2021-Based Analytical Workflow to Estimate the Global Burden of Neoplasms Attributable to Low Physical Activity

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DOI:

10.3791/70495

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August 7th, 2026

 ,  , 
1Department of Gastroenterology, Changshu Hospital of Traditional Chinese Medicine, 2The First School of Clinical Medicine, Nanjing University of Chinese Medicine, 3Department of Traditional Chinese Medicine, Inner Mongolia Medical University Affiliated Hospital, 4Department of Gastroenterology, Jiangsu Second Chinese Medicine Hospital

In This Article

Summary

This protocol demonstrates a reproducible workflow for extracting, processing, analyzing, and visualizing GBD 2021 estimates of neoplasms attributable to low physical activity. The workflow enables researchers to evaluate burden indicators, temporal trends, decomposition components, socioeconomic inequalities, and model-based projections using standardized population-level data.

Abstract

This protocol describes a reproducible analytical workflow for estimating the global burden of neoplasms attributable to low physical activity using Global Burden of Disease 2021 data. The workflow includes data extraction from the GBD Results Tool, definition of eligible outcomes and exposure categories, estimation of deaths, disability-adjusted life years, and age-standardized rates, temporal trend analysis using estimated annual percentage change and joinpoint regression, decomposition analysis, socioeconomic inequality assessment, and Bayesian age-period-cohort projection. The protocol also describes how to interpret uncertainty intervals, decomposition components, and inequality metrics in the context of modeled population-level estimates. As a representative application, the workflow was applied to colorectal cancer and female breast cancer attributable to low physical activity across 204 countries and territories from 1990 to 2021, with projections to 2045. This protocol can be adapted for other GBD risk-outcome pairs when researchers need a structured approach for risk-attributable burden estimation, trend characterization, inequality assessment, and projection analysis.

Introduction

Neoplasms represent a significant category of noncommunicable diseases (NCDs) contributing to global mortality and disability, posing a critical public health challenge worldwide. Globocan 2022 data revealed 19.96 million new neoplasm cases and 9.74 million deaths worldwide, with lung, breast (female), and colorectal cancers constituting the predominant malignancies. Projected cancer incidence will reach 35 million annually by 2050 (77% increase from 2022), with nations scoring low on the Human Development Index (HDI) projected to experience a 142% surge, driven by demographic transitions. Demographic expansion, population aging, deficient screening systems, and constrained treatment access in low-HDI regions drive these trends, creating an imperative for equitable allocation of global oncology resources1.

Low physical activity (LPA) is an established modifiable determinant for major NCDs, spanning malignancies, metabolic disorders, and neuropsychiatric conditions2,3,4. LPA constitutes a significant global health burden, demonstrating a 6.6% age-standardized prevalence rise since 2000 and contributing to 7.2% of global all-cause mortality in 20225,6. Moderate physical activity (PA) confers significant protection against breast (HR 0.90; 95% CI: 0.87–0.93), colon (HR 0.84; 95% CI: 0.77–0.91), and rectal cancers (HR 0.87; 95% CI: 0.80–0.95), with parallel reductions in site-specific mortality7. To promote essential PA worldwide, numerous countries and regions have developed national PA guidelines tailored to their specific contexts (e.g., Canada, the UK, and the US), which have been widely disseminated through organizations such as the World Health Organization (WHO) and the European Union. Regrettably, only 72.5% of adults globally meet the minimum recommended PA levels outlined in public health guidelines8.

Mitigating low physical activity-attributable neoplasm burden is pivotal to achieving the UN Sustainable Development Goal (SDG) 2030 target: one-third reduction in premature NCDs mortality via prevention and care9. Generating robust evidence to elucidate the relationship between LPA and neoplasms is essential for guiding future healthcare policies. The Global Burden of Diseases, Injuries, and Risk Factors Study 2021 (GBD 2021) provides comparable population-level estimates of mortality, disability-adjusted life years (DALYs), and risk-attributable burden across 204 countries and territories from 1990 to 2021 using standardized epidemiological approaches. In GBD 2021, disease and injury estimates cover 371 diseases and injuries, while the risk factor analysis estimates exposure, relative risk, population attributable fractions, and attributable burden for 88 risk factors10. However, fewer studies have provided a stepwise and reproducible workflow for extracting GBD 2021 low physical activity-attributable neoplasm data and applying trend, decomposition, inequality, and projection analyses within a single protocol. This manuscript addresses this methodological gap by detailing the data extraction, variable definition, statistical modeling, and interpretation procedures for this specific GBD risk-outcome pairing. This protocol describes a reproducible secondary analysis of GBD 2021 estimates for neoplasms attributable to low physical activity across 204 countries and territories from 1990 to 2021, with projections to 2045. The objectives were to quantify deaths, DALYs, age-standardized rates, temporal trends, decomposition components, socioeconomic inequalities, and projected burden, without inferring individual-level causality or direct intervention effects.

This workflow was chosen because it integrates several complementary analytical components into a single reproducible framework. Compared with studies that report only descriptive GBD estimates or single trend metrics, this protocol combines burden estimation, EAPC, joinpoint regression, decomposition analysis, inequality assessment, and BAPC projection, allowing users to examine not only the magnitude of burden but also temporal changes, demographic drivers, socioeconomic gradients, and future trends. The protocol is applicable to researchers conducting secondary analyses of GBD risk-attributable estimates, particularly when the aim is to compare burden patterns across time, sex, age groups, countries, regions, or SDI categories.

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Protocol

This study used publicly available, de-identified GBD 2021 population-level estimates. No individual-level human participant data were accessed. The GBD 2021 study received a waiver of informed consent from the University of Washington Institutional Review Board; therefore, no additional institutional ethical approval was required for this secondary analysis.

NOTE: This protocol describes a reproducible workflow for conducting a secondary analysis of GBD 2021 estimates for neoplasms11 attributable to low physical activity. The workflow includes data extraction, variable definition, burden estimation, temporal trend analysis, decomposition analysis, inequality assessment, and Bayesian age-period-cohort projection. The results are presented to illustrate the application of the protocol rather than to establish a primary causal relationship. The analytical workflow consisted of six sequential steps: extraction of GBD 2021 estimates, definition of exposure, disease outcomes, and SDI strata, estimation of deaths, DALYs, age-standardized mortality rate (ASMR), and age-standardized DALYs rate (ASDR), temporal trend analysis using estimated annual percentage change (EAPC) and joinpoint regression, decomposition and inequality analyses, and Bayesian age-period-cohort projection through 2045. Each result and figure was generated from one of these linked analytical steps.

1. Data source identification and access

  1. Access the Global Burden of Disease (GBD) 2021 Results Tool.
    1. Open a web browser and navigate to the Global Burden of Disease Results Tool at https://vizhub.healthdata.org/gbd-results/.
    2. Select GBD 2021 as the study version from the available dataset options.
    3. Select Risk Factors from the cause hierarchy menu.
  2. Define disease outcomes and risk factors.
    1. Select Neoplasms as the Disease Category.
    2. Select Low Physical Activity as the Risk Factor.
    3. Select Deaths and Disability-Adjusted Life Years (DALYs) as Outcome Measures.
  3. Specify population, geography, and time period.
    1. Select Both sexes, Male, and Female separately for sex-specific analyses.
    2. Select all available age groups ≥25 years.
    3. Select all 204 countries and territories.
    4. Set the analysis period from 1990 to 2021.
  4. Extract age-standardized outcome measures.
    1. Enable age standardization using the GBD 2021 standard population.
    2. Select age-standardized mortality rate (ASMR) and age-standardized DALYs rate (ASDR) as output variables.
    3. Include 95% uncertainty intervals (UIs) for all extracted estimates.
    4. Export the extracted data in CSV format for downstream analysis.
    5. For reproducibility, use the following GBD Results Tool selections: study version: GBD 2021; context: Risk factors; risk: Low physical activity; cause: Neoplasms; measures: Deaths and DALYs; metrics: Number and Rate; ages: Age-standardized and all available 5-year age groups aged ≥ 25 years; sexes: Both, Male, and Female; locations: Global, 21 GBD regions, SDI regions, and 204 countries and territories; years: 1990–2021.
    6. Export the mean estimate, lower uncertainty interval, and upper uncertainty interval for each stratum in CSV format.
  5. Obtain sociodemographic index (SDI)-stratified data.
    1. Enable stratification by Sociodemographic Index (SDI).
    2. Extract SDI-specific ASMR and ASDR data for comparative analyses.
  6. Access underlying data sources.
    1. Navigate to the GBD 2021 Data Input Sources Tool at https://ghdx.healthdata.org/gbd-2021.
    2. Review publicly available documentation describing primary data sources, including vital registration systems, population surveys, medical records, and disease registries.

2. Definition of key variables and classification criteria

  1. Define neoplasms.
    1. Define neoplasms as malignant tumors according to the Global Burden of Disease (GBD) 2021 cause hierarchy.
    2. Include only malignant neoplasms characterized by uncontrolled cellular proliferation resulting from genetic and epigenetic dysregulation.
    3. Exclude benign neoplasms from all analyses.
  2. Define low physical activity.
    1. Define physical activity intensity using metabolic equivalent of task (MET) values, where 1 MET equals an oxygen consumption of 3.5 mL O₂/kg/min at rest.
    2. Calculate weekly physical activity levels using total MET-minutes per week.
    3. Classify physical activity levels into four categories12,13:
      Less than 600 MET-minutes per week.
      600 to 3,999 MET-minutes per week.
      4,000 to 7,999 MET-minutes per week.
      8,000 or more MET-minutes per week.
    4. Define low physical activity as less than 4,000 MET-minutes per week, consistent with GBD 2021 risk attribution methodology.
      NOTE: This cutoff was adopted from the GBD 2021 risk attribution definition and was not selected de novo in the present study. It was used to maintain consistency with the GBD comparative risk assessment framework and comparability with other GBD-based estimates.
  3. Define SDI.
    1. Define the SDI as a composite indicator ranging from 0 to 1.
    2. Calculate SDI using the geometric mean of standardized per capita income, educational attainment, and fertility rate in individuals younger than 25 years.
    3. Classify countries and regions into five SDI categories14: Low, Low-Middle, Middle, High-Middle, and High.
    4. Assign countries to one of 21 GBD regions based on SDI stratification.
  4. Define neoplasms attributable to low physical activity.
    1. Identify neoplasms attributable to low physical activity using the GBD 2021 comparative risk assessment framework.
      1. Within this framework, operationalize low physical activity-attributable burden by combining exposure distributions, risk-outcome relative risks, and the theoretical minimum risk exposure level to estimate population attributable fractions. Then, apply this to deaths and DALYs for eligible neoplasm outcomes.
    2. Include colorectal cancer and female breast cancer as neoplasm outcomes attributable to low physical activity exposure.
      NOTE: Thus, in this study, neoplasms attributable to low physical activity refer only to colorectal cancer and female breast cancer as defined in the GBD 2021 comparative risk assessment framework, rather than all malignant tumors.

3. Estimation of global burden of neoplasms attributable to low physical activity.

  1. Extract disease burden indicators.
    1. Import deaths and disability-adjusted life years (DALYs) data for neoplasms attributable to low physical activity from the Global Burden of Disease (GBD) 2021 Results Tool, following the standard GBD comparative risk assessment framework.
    2. Extract age-standardized mortality rates (ASMR) and age-standardized DALYs rates (ASDR) per 100,000 population using the GBD 2021 standard population.
    3. Retain corresponding 95% uncertainty intervals for all extracted estimates as provided by the GBD 2021 methodology.
  2. Calculate disability-adjusted life years.
    1. Define disability-adjusted life years (DALYs) as the sum of years of life lost (YLLs) and years lived with disability (YLDs), following standard GBD definitions.
    2. Calculate YLLs by multiplying age-specific mortality by the reference life expectancy specified in the GBD 2021 life table.
    3. Calculate YLDs by multiplying incident cases by disease-specific disability weights and disease duration, as described in the GBD methodology.
    4. Verify that calculated DALY values correspond to GBD 2021 reported estimates.

4. Temporal trend analysis

  1. Estimate annual percentage change15,16.
    1. Import ASMR and ASDR data into R software (version 4.4.2).
    2. Apply natural logarithmic transformation to age-standardized rates.
    3. Fit linear regression models with calendar year as the independent variable to assess temporal trends, following established epidemiological trend analysis methods.
    4. Calculate the estimated annual percentage change (EAPC) from the regression slope using the standard exponential transformation.
    5. Classify trends as increasing, decreasing, or stable based on whether the 95% confidence interval of the EAPC is above, below, or includes zero.
  2. Perform joinpoint regression analysis.
    1. Open the Joinpoint Regression Program (version 5.3.0; National Cancer Institute).
    2. Import ASMR and ASDR datasets into the software.
    3. Select a log-linear regression model for trend analysis, consistent with Joinpoint methodological guidelines17.
    4. Specify a minimum of 0 and a maximum of 6 joinpoints.
    5. Select the Data-Driven Bayesian Information Criterion (BIC) method with weighted BIC optimization to determine optimal joinpoints.
      1. Select the maximum of six joinpoints according to the length of the annual time series from 1990 to 2021 and the default recommendations of the Joinpoint Regression Program.
      2. Determine the final number of joinpoints by the data-driven BIC.
    6. Run the analysis to calculate annual percentage change (APC) and average annual percentage change (AAPC) with 95% confidence intervals. For strata with sparse data or unstable annual estimates, segment-specific APCs were interpreted cautiously and were not emphasized unless the fitted trend was supported by corresponding uncertainty intervals and was epidemiologically plausible.
    7. Export APC and AAPC results for downstream analysis.
      NOTE: EAPC and joinpoint regression were used for different purposes. EAPC provided a single summary estimate of the average log-linear trend across 1990–2021, whereas joinpoint regression identified calendar periods in which the rate of change shifted and estimated segment-specific APCs. Therefore, EAPC was used for overall comparison across strata, while joinpoint regression was used to describe temporal inflection patterns.

5. Decomposition analysis

  1. Prepare decomposition inputs.
    1. Stratify mortality and DALY data by age group, calendar year, and geographic region.
    2. Import stratified datasets into JD_GBDR software (version 2.37.1).
  2. Perform decomposition analysis.
    1. Decompose changes in mortality and DALYs between selected time points into aging, population growth, and epidemiological change components using established decomposition methods18.
    2. Quantify the contribution of each component to overall changes in disease burden.
      1. Interpret percentage contributions as the proportional contribution of aging, population growth, and epidemiological change to the net change in deaths or DALYs between two time points.
      2. Values exceeding 100% or negative values can occur when one component increases burden while another offsets it; interpret such values as mathematical decomposition effects rather than direct biological effects.
    3. Export decomposition results for global, SDI-level, and GBD regional analyses.

6. Health inequality analysis

  1. Prepare data for inequality assessment.
    1. Rank all countries and territories according to their SDI values from lowest to highest.
    2. Import ASMR and ASDR into R software (version 4.4.2).
  2. Calculate the slope index of inequality.
    1. Assign a relative socioeconomic rank to each country based on cumulative population distribution across SDI strata.
    2. Fit a weighted linear regression model with ASMR or ASDR as the dependent variable and relative SDI rank as the independent variable.
    3. Calculate the slope index of inequality (SII) as the absolute difference in disease burden between the highest and lowest ends of the SDI gradient, following established inequality analysis methodology.
  3. Calculate the concentration index.
    1. Construct concentration curves by plotting cumulative proportions of ASMR or ASDR against cumulative population proportions ranked by SDI.
    2. Calculate the concentration index (CII) as twice the area between the concentration curve and the line of equality.
    3. Interpret CII values close to zero as low inequality and values approaching −1 or +1 as increasing relative inequality, consistent with standard health economics methods19.
  4. Estimate uncertainty for inequality metrics.
    1. For SII, derive 95% confidence intervals from the weighted regression model using country-level population weights.
    2. For CII, estimate 95% confidence intervals using bootstrap resampling of countries and territories with replacement.
    3. When posterior draws are available, repeat SII and CII calculations across draws and report the 2.5th and 97.5th percentiles as uncertainty intervals.

7. Bayesian age-period-cohort modeling

  1. Prepare input data for projection.
    1. Organize age-specific deaths, DALYs, and corresponding population data by 5-year age group and calendar year from 1990 to 2021.
    2. Ensure consistency of age-group definitions and time intervals across the full observation period.
  2. Apply the Bayesian age-period-cohort model20.
    1. Import the age-specific burden and population datasets into R using a Bayesian age-period-cohort modeling framework.
    2. Specify age, period, and cohort effects as structured random effects, with second-order random-walk priors used to smooth adjacent age groups, calendar periods, and birth cohorts.
    3. Generate posterior estimates of age-specific rates and then calculate projected ASMR and ASDR using the GBD 2021 standard population.
    4. Assess model fit by comparing fitted estimates with observed estimates during the historical period.
    5. Conduct internal validation by withholding the most recent observed years, refitting the model, and comparing predicted estimates with observed values.
  3. Generate future projections.
    1. Forecast age-standardized mortality rates and age-standardized DALYs rates for neoplasms attributable to low physical activity from 2022 to 2045.
    2. Extract posterior mean estimates and corresponding 95% uncertainty intervals for all projected outcomes. Evaluate projection uncertainty using posterior 95% uncertainty intervals, and interpret estimates farther from the observed period with increasing caution.
    3. Export projection results for visualization and reporting.

8. Statistical analysis

  1. Perform general statistical analysis.
    1. Import all cleaned datasets into R software (version 4.4.2).
    2. Use R software to conduct all statistical analyses and generate figures and tables.
    3. Set statistical significance at a two-sided p-value of less than 0.05 for all analyses.
  2. Conduct specialized GBD analyses.
    1. Import GBD-formatted datasets into JD_GBDR software (version 2.37.1).
    2. Use the JD_GBDR software to perform disease burden calculations, decomposition analyses, and standardized rate visualizations according to GBD analytical conventions.
  3. Perform joinpoint regression analysis.
    1. Download and install the Joinpoint Regression Program (version 5.3.0) from the National Cancer Institute website (https://surveillance.cancer.gov/joinpoint/download).
    2. Import age-standardized mortality rate and age-standardized DALYs rate datasets into the Joinpoint software.
    3. Select a log-linear regression model and execute joinpoint regression analysis to evaluate temporal trends, following the Joinpoint Regression Program methodology.
    4. Export trend estimates, including annual percentage change and average annual percentage change, with corresponding 95% confidence intervals.
      NOTE: All analyses were conducted using R software version 4.4.2, Joinpoint Regression Program version 5.3.0, and JD_GBDR software version 2.37.1. The main analytical settings included log-linear EAPC models, joinpoint regression with 0–6 joinpoints selected by the data-driven Bayesian Information Criterion, decomposition into aging, population growth, and epidemiological change components, weighted regression for SII, bootstrap resampling or posterior draws for CII uncertainty, and Bayesian age-period-cohort projection to 2045.

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Results

Successful implementation of this protocol should generate a complete set of harmonized GBD-derived estimates across predefined locations, years, sexes, age groups, measures, and metrics. The expected outputs include age-standardized and age-specific burden estimates with 95% uncertainty intervals, EAPC and joinpoint trend estimates, decomposition components, SII and CII inequality metrics, and BAPC-based projections with posterior uncertainty intervals. A suboptimal implementation is indicated by missing strata, inconsi...

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Discussion

This GBD 2021 analysis shows two contrasting patterns in low physical activity-attributable neoplasm burden. While ASMR and ASDR for neoplasms attributable to LPA significantly declined from 1990 to 2021 (ΔASMR = -19.72%; ΔASDR = -18.51%), absolute deaths and DALYs increased by 95.73% and 84.79%, respectively. The increase in absolute burden despite declining age-standardized rates was mainly explained by population growth and population aging. Declining ASRs may reflect multiple factors, including improvements...

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Disclosures

The authors have nothing to disclose.

Acknowledgements

We acknowledge the Global Burden of Disease Study (GBD 2021) collaborators for their contributions to neoplasms attributable to LPA epidemiology, facilitated by the Institute for Health Metrics and Evaluation's (IHME) open-access data. This work received support from Jingding Medical Tech (providing JD_GBDR software authorization and pivotal data processing), with language editing services by Home for Researchers (www.home-for-researchers.com). This work was supported by the Jiangsu Provincial Traditional Chinese Medicine Science and Technology Development Program Project (QN202513), and the Internal Research Fund Project of the Second Affiliated Hospital of Nanjing University of Chinese Medicine (SEZRC202504, SEZJY202505).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Bayesian Age-Period-Cohort (BAPC) R PackageR Foundation / CRAN Repository https://cran.r-project.org/Used for Bayesian age-period-cohort projection analyses. 
CSV Data FilesInstitute for Health Metrics and Evaluation (IHME)N/AExported burden datasets from GBD 2021 Results Tool for downstream analysis.
dplyr R PackageR Foundation / CRAN Repositoryhttps://cran.r-project.org/package=dplyrUsed for data manipulation and cleaning in R.
GBD 2021 Data Input Sources ToolInstitute for Health Metrics and Evaluation (IHME)https://ghdx.healthdata.org/gbd-2021Public repository for GBD source documentation.
ggplot2 R PackageR Foundation / CRAN Repositoryhttps://cran.r-project.org/package=ggplot2Used for graphical visualization of temporal trends and inequality analyses. 
Global Burden of Disease (GBD) 2021 Results ToolInstitute for Health Metrics and Evaluation (IHME)https://vizhub.healthdata.org/gbd-results/Public database used for extraction of deaths, DALYs, ASMR, and ASDR data. 
INLA R PackageR-INLA Projecthttps://www.r-inla.org/Used for Bayesian inference within age-period-cohort modeling. 
JD_GBDR Software Jingding Medical Technology Co., Ltd.Version 2.37.1Used for GBD burden estimation, decomposition analysis, and standardized rate visualization.
Joinpoint Regression Program (Version 5.3.0)National Cancer Institute (NCI)https://surveillance.cancer.gov/joinpoint/downloadUsed for joinpoint regression analysis, APC, and AAPC estimation. 
Microsoft ExcelMicrosoft Corporationhttps://www.microsoft.com/excelUsed for organizing and cleaning extracted CSV datasets prior to analysis. 
Personal Computer WorkstationAny standard computing workstationN/ARecommended minimum: 8 GB RAM, Windows 10/11 or macOS for statistical analyses and projections.
R Statistical Software (Version 4.4.2)R Foundation for Statistical Computinghttps://www.r-project.org/Used for statistical analysis, EAPC calculation, inequality analysis, visualization, and Bayesian age-period-cohort modeling. 
RStudio DesktopPosit Software, PBChttps://posit.co/download/rstudio-desktop/Integrated development environment for R analyses.  

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