Global Burden of Disease Study 2021 (GBD 2021) - Nonfatal Health Outcomes 2 (Analysis for the estimation of cause-specific YLDs by location, age, sex, and year for GBD 2021 - Atrial fibrillation & flutter)

Analysis for the estimation of cause-specific YLDs by location, age, sex, and year for GBD 2021

• 1. Data Adjustment
  • Add study-level covariates
  • Adjustment for multiple admissions in same individual
  • Adjustment for multiple outpatient visits per prevalent\incident case based on claims data
  • Adjustment for underreporting
  • Adjustment from primary code to all code based on claims data for causes with long duration
  • Age-sex splitting
  • Compute excess mortality prior from available incidence or prevalence & CSMR
  • Pre DisMod bias correction
• 2a. DisMod-MR 2.1 Estimation
  • DisMod-MR 2.1
• 2b. Alternative Disease Modeling Strategies
  • Cancer
  • Case fatality proportion and cause of death rate models
  • HIV\AIDS and TB
  • Malaria
  • Neonatal disorders
  • Seroprevalence to incidence models
• 2c. Injury Modeling strategy
  • Apply cause-nature of injury matrices
  • Determine most severe nature of injury category in any individual
  • Estimate duration of short-term disability
  • Generate cause-nature of injury matrices with neg. binomial models
  • Meta-analysis
  • Probability of long-term disability
  • DisMod-MR 2.1
• 3. Impairment and Underlying Cause Estimation
  • Apply etiology\severity proportions to disease\impairment morbidity estimates
  • Scale impairment prevalence by underlying cause or severity to envelope
  • Scale to 100%
• 4. Severity Distribution
  • Scale to 100%
  • DisMod analysis proportion by severity level
  • Map EQ5D to SF-12
  • Map SF-12 to GBD disability weights
  • Meta-analysis proportion by severity level
  • Regression to estimate disability weight by cause in survey resps controlling for comorbidity
• 6. Comorbidity
  • Comorbidity correction (COMO)
• 5. Disability Weights
  • Analysis of paired comparison & population health equivalence responses
• 7. YLDs
  • YLD to YLL ratio for 12 residual causes without primary data

---

Anemia

• Ensemble Weighting
  • MoM distribution fits for all single distributions, calculate prediction errors of mild, moderate, and severe anemia weighted by disability weight, and generate final ensemble weights
• Data Prep
  • Age- and sex-split data and crosswalk on pregnancy status
  • Make normal hemoglobin and custom covariates for modeling
• Variance estimation
  • Estimate unique distribution variance from mean hemoglobin and prevalence estimates
• Envelope ensemble modeling
  • Use mean and standard deviation estimates and ensemble weights to generate hemoglobin distribution for every location/year/age/sex
  • Use WHO thresholds, mean hemoglobin, and distributions to estimate anemia prevalence (area-under-curve) by severity
• ST-GPR modeling
  • ST-GPR models of mean hemoglobin and prevalence of anemia
• YLD estimation
  • Comorbidity Correction (COMO)
• Anemia Causal Attribution
  • Subtract clinical malaria from parasitemia model to get exclusive outputs
  • Save new malaria models
  • Causal Attribution for mild, moderate, and severe anemia for each input based on the envelope
  • Files reformatted and uncertainty added to draws
  • Save anemia causal attribution results
  • Post-processing to create proportions for HIV/PUD/etc.
  • Uploading post-processing proportions
  • Compile "normal" hemoglobin level for every demographic
  • Generate TMREL draws for iron deficiency, upload draws
  • Launch/Interpolate PAFs for iron deficiency

---

Anorexia nervosa

• Age-sex splitting
• MR-BRT Sex Ratio Analysis
• Comorbidity correction (COMO)
• Dismod-MR 2.1

---

Anxiety disorders

• MR-BRT analysis on change in prevalence during COVID-19 pandemic compared to before the pandemic
• Age-sex splitting
• MR-BRT Sex Ratio Analysis
• MR-BRT Bias Correction Analysis for Alternative Case Defintion / Method
• Comorbidity correction (COMO)
• Dismod-MR 2.1
• Mapping EQ5D to SF12
• Mapping SF-12 to GBD disability weight
• Regression to estimate disability weights by cause in survey respondents controlling for comorbidity

---

Appendicitis

• Model excess mortality in MR-BRT
• MR-BRT bias correction analysis for altenrative case definition/method
• Comorbidity correction (COMO)
• DisMod-MR 2.1
• Adjustment from primary code to all code for both inpatient and outpatient encounters based on claims data

---

Ascariasis

• Prepare data for ST-GPR
• Compute age-specific ratios; apply ratio to ST-GPR output
• Apply health state proportions to overall ascariasis envelope
• Compute proportion of cases that belong to health states
• Estimate prevalence of asymptomatic ascarasis
• ST-GPR
• Dismod-MR 2.1
• YLD Calculation

---

Asthma

• Age sex splitting
• Computing excess mortality
• Claims data 2000, other claims data
• DisMod-MR 2.1
• Severity splits
• Comorbidity correction (COMO)

---

Atopic dermatitis

• Format codes, Map to modeling causes
• Convert claims to prevalent cases, Apply age and sex restrictions, Aggregate and apply population denominator
• DisMod MR 2.1
• Severity splits
• Comorbidity correction (COMO)

---

Atrial fibrillation & flutter

• 1. CODEm
  • CODEm
  • Age-sex splitting
  • Garbage code redistribution
  • ICD Mapping
  • Noise reduction
  • Standardize input data
• 2. DisMod-MR 2.1
  • Age-sex splitting, bias adjustment
  • Dismod-MR 2.1
  • Adjustment from primary code to all code based on claims data
• 3. Excess Mortality Rate Calculation
  • Calculate EMR = CSMR\prevalence
  • Bayesian meta-regression of EMR with fixed effects
  • Select countries
• 4. DisMod-MR 2.1
  • Dismod-MR 2.1
  • COMO

---