Gastritis and duodenitis
- 1. Nonfatal health outcome estimation
- Format codes, Map to modeling causes
- Convert claims to cases, Apply duration window, Apply age and sex restrictions, Aggregate
- Model correction factors
- Convert inpatient encounters to incident or prevalent cases (+/- Adjust: primary dx -> any dx), Apply age and sex restrictions, Aggregate
- Model excess mortality in MR-BRT
- DisMod-MR 2.1
- Comorbidity correction (COMO)
- 1. Nonfatal health outcomes estimation
- 2. Anemia estimation
- 3. Severity and anemia splits
Gastroesophageal reflux disease (GERD)
- MR-BRT Sex Ratio Analysis/Split both-sex data
- Study design bias analysis in MR-BRT/Adjust non-reference data
- Age-split data-points for age-range > 24y
- Meta-analysis of symptom frequency
- Meta-analysis of symptom severity
- Apply symptomatic and severity proportions
- DisMod-MR 2.1
- Comorbidity correction (COMO)
Genital Herpes due to HSV-2: Nonfatal health outcome estimation
- Severity Splits
- Data Adjustments: Sex-split, crosswalk, age-split
- Dismod MR-2.1
- Comorbidity correction (COMO)
Genital Prolapse: Nonfatal Health Outcome Estimation
- Age-split
- Data Adjustments: MR-BRT crosswalks
- Comorbidity correction (COMO)
- Dismod-MR 2.1
- Meta-analysis of % mild, moderate, severe prolapse
- Severity splits
- Adjustment from primary code to all code based on Claims data
Glaucoma
- MR-BRT Sex Ratio Analysis
- Age Pattern analysis
- MR-BRT bias correction analysis for Alternative Case Defintion / Method
- Age-sex splitting
- Split into moderate and severe vision loss
- Squeeze into severity-specific vision loss envelope
- Comorbidity correction (COMO)
- Dismod-MR 2.1
Gout
- Case definition crosswalk
- Age-sex splitting
- Comorbidity correction (COMO)
- DisMod-MR 2.1
- Severity splits
- Average of two studies reporting average duration
- Lognormal fit to distribution of ## attacks per year
Guillain-Barre syndrome (GBS)
- Age-sex splitting
- Meta-analysis of % GBS underlying etiologies
- Adjustment for case fatality rate via meta-analysis
- Identify and outlier extreme hospital data
- Etiology splits
- Dismod-MR 2.1
- Comorbidity correction (COMO)
Guinea Worm
- Apply sex split
- Estimate uncertainty for vetted case data
- Estimate incidence from Poisson model
- Apply Dismod Age
- Assign sequelea
- Dismod-MR 2.1 to generate age trend
HAT
- Age model
- Incident cases
- Prevalence estimation
- Splitting sleeping disorder\disfig
- Comorbidity correction (COMO)
Headaches
- Age-sex splitting and age splitting
- Squeeze definite and probable migraine and TTh to respective total
- Apply proportion of time episodic by probable/definite
- Meta-analysis proportion time symptomatic
- Meta-analysis for proportion of medication overuse headache due to migraine and tension-type headache
- Definite to both crosswalk
- Comorbidity correction (COMO)
- Dismod-MR 2.1
Hearing Impairment
- Adjust for hearing aid use
- Apply hearing aid coverage distribution by severity
- By severity, estimate proportion of people experiencing tinnitus
- Calculate congenital hearing loss as birth prevalence
- crosswalk
- Multiple hearing loss by proportion of hearing loss due to age-relate and other
- Proportionally split according to distribution of hearing loss envelope prevalences
- Proportionally split into mild\moderate hearing loss due to otitis via meta-analysis
- Proportionally squeeze causes to fit within each severity envelope
- Proportionally squeeze to 35+ envelope
- Proportionally squeeze to population
- Split into prevalence with\without tinnitus
- Comorbidity correction (COMO)
- Dismod-MR 2.1
Heart failure
- Age-sex splitting and bias adjustments
- Multiply squeezed proportions by overall prevalence of heart failure not including Chagas, degenerative mitral valve disease, and calcific aortic valve disease
- Proportion splits
- Subtract prevalence of heart failure due to Chagas, DMVD, and CAVD
- Cause of Death\EMR data proportions
- Comorbidity correction (COMO)
- Dismod-MR 2.1
- Meta-analysis of % mild, moderate, severe heart failure
- Meta-analysis of % mild, moderate, severe heart failure due to Chagas
- Regression of % mild, moderate, severe heart failure
- Severity splits
- Adjustment from primary code to all code based on Claims data
- Disability weights for each sequela
