2.1. Main Principles

2.1.1. Input Data

The epidemiology module is based on three main data sources:

2.1.2. Modelling framework

The model simulates disease pathway (incidence, fatality and remission) through events at the individual level. The epidemiology module is based on two main models: one for cancers (Section 2.2.2) and one for all the other chronic diseases (generic disease model see Section 2.2.1). The latter is largely derived from the framework of DisMod-MR 2.0 1 , a tool developed for GBD analyses ([Flaxman, Vos and Murray, 2015 [19]]) (Section 2.2.1). Other models are variations from those two models. See Modelling specific diseases for details.

2.1.2.1. Fatality and residual mortality

The cause-specific mortality is removed from total mortality to compute residual mortality using the following formula:

\[\begin{eqnarray} \lambda_r = \lambda - \sum_{i \in disease}P_i \cdot f_i - \sum_{j \in cancers}m_j \end{eqnarray}\]

Where \(\lambda\) (resp. \(\lambda_r\)) is the hazard ratio for the total (resp. residual) mortality, \(f_i\) and \(P_i\), the fatality and the prevalence of disease i (except cancers) and \(m_j\) the mortality of cancer j.

2.1.2.2. Disability weights

Disability weights are used to measure the overall disease burden. Every disease is attached to a single disability weight shown below in Table 2.1. Methodology is described below.

Table 2.1 Disability weights for diseases in the OECD microsimulation model

Condition

DW

Diabetes

(see Section 2.3.1)

Myocardial infarction

0.01

Stroke

0.432

COPD

(see Section 2.3.4)

Chronic Kidney Disease (stage IV)

0.15

Chronic Kidney Disease (stage V)

0.591

Depression

0.219

Dementia

(see Section 2.3.10)

Back pain

0.126

Rheumatoid Arthritis

0.247

Cirrhosis

0.178

Lower respiratory infections

0.0633

Atrial fibrillation

0.224

Cancers

(see Section 2.2.2)

Road injuries

0.113

Interpersonal violence

0.083

Self-harm

0.12

Disease specific disability weights

The OECD-SPHeP model uses the methodology described in the technical appendix of [GBD 2016 Disease and Injury Incidence and Prevalence Collaborators et al., 2017 [72]]. Every disease coincides with one or multiple health states which are associated with a disability weight (DW). These were taken from WHO [Mathers et al., 2017 [39]].

Where multiple health states were associated with a single disease, a severity split was used to weight the different DWs into a single measure. The summary disability weight table is shown in Table 2.1 and Section 2.3 provides the calculation of DWs for specific diseases. Where DWs with and without treatment were provided, “with treatment” was used.

Long-term disability weight

For acute diseases (e.g. stroke, injuries), another DW was used for the longer, post-recovery phase, reflecting the long-term disability associated with the disease. It applies until the death of the individual. These weights are described as ‘long-term’ DWs and shown in Table 2.2. (Conversely, classic disability weights are described as ‘short-term’).

For chronic conditions, there is no distinction between short and long term DWs (e.g. osteoarthritis). If the individual is afflicted by the disease, the DW will be applied to their health status. If the individual recovers, the DW no longer applies.

Table 2.2 Long term disability weights associated with diseases sequelae in the OECD microsimulation model

Condition

Long-Term DW

Myocardial infarction

0.08

Stroke

0.316

Road injuries

0.141

Interpersonal violence

0.11

Self-harm

0.018

Individual disability weight

The \(DL\) is a yearly measure which represents the disability-adjusted quality of life of an individual. It is computed along the life of an individual and takes into account changes in health status (incidence, recovery, etc…). In case of multi-comorbidities, it is assumed that DWs interact in a multiplicative fashion. More formally, the following formula is used:

(2.1)\[DL = 1 - \prod_d (1 - DW_d)\]

Where \(DL\) is the disability-adjusted quality of life of a person with a given set of comorbidities, d is one of the comorbidities affecting that person and \(DW_d\) is the disability weight for a comorbidity d.

1

DisMod-MR 2.0 is a Bayesian mixed-effects meta-regression modelling tool developed for GBD analyses. It provides estimates for incidence, remission, fatality and prevalence rate for over 300 conditions in 195 countries and territories from 1990 to 2016. This tool is used to generate the IHME database (Epi Visualization | Viz Hub) available at https://vizhub.healthdata.org/epi/