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New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model.

ABSTRACT: BACKGROUND:Recently, we have shown that the age-specific prevalence of a disease can be related to the transition rates in the illness-death model via a partial differential equation (PDE). The transition rates are the incidence rate, the remission rate and mortality rates from the 'Healthy' and 'Ill' states. In case of a chronic disease, we now demonstrate that the PDE can be used to estimate the excess mortality from age-specific prevalence and incidence data. For the prevalence and incidence, aggregated data are sufficient - no individual subject data are needed, which allows application of the methods in contexts of strong data protection or where data from individual subjects is not accessible. METHODS:After developing novel estimators for the excess mortality derived from the PDE, we apply them to simulated data and compare the findings with the input values of the simulation aiming to evaluate the new approach. In a practical application to claims data from 35 million men insured by the German public health insurance funds, we estimate the population-wide excess mortality of men with diagnosed type 2 diabetes. RESULTS:In the simulation study, we find that the estimation of the excess mortality is feasible from prevalence and incidence data if the prevalence is given at two points in time. The accuracy of the method decreases as the temporal difference between these two points in time increases. In our setting, the relative error was 5% and below if the temporal difference was three years or less. Application of the new method to the claims data yields plausible findings for the excess mortality of type 2 diabetes in German men. CONCLUSIONS:The described approach is useful to estimate the excess mortality of a chronic condition from aggregated age-specific incidence and prevalence data. TRIAL REGISTRATION:The article does not report the results of any health care intervention.

SUBMITTER: Brinks R

PROVIDER: S-EPMC6599235 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model.

Brinks Ralph R Tönnies Thaddäus T Hoyer Annika A

BMC public health 20190628 1

<h4>Background</h4>Recently, we have shown that the age-specific prevalence of a disease can be related to the transition rates in the illness-death model via a partial differential equation (PDE). The transition rates are the incidence rate, the remission rate and mortality rates from the 'Healthy' and 'Ill' states. In case of a chronic disease, we now demonstrate that the PDE can be used to estimate the excess mortality from age-specific prevalence and incidence data. For the prevalence and in ...[more]

PMID: 31253126

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Project description:BackgroundExcess mortality in severe mental illness (defined here as schizophrenia, schizoaffective disorders, and bipolar affective disorders) is well described, but little is known about this inequality in ethnic minorities. We aimed to estimate excess mortality for people with severe mental illness for five ethnic groups (white British, black Caribbean, black African, south Asian, and Irish) and to assess the association of ethnicity with mortality risk.MethodsWe conducted a longitudinal cohort study of individuals with a valid diagnosis of severe mental illness between Jan 1, 2007, and Dec 31, 2014, from the case registry of the South London and Maudsley Trust (London, UK). We linked mortality data from the UK Office for National Statistics for the general population in England and Wales to our cohort, and determined all-cause and cause-specific mortality by ethnicity, standardised by age and sex to this population in 2011. We used Cox proportional hazards regression to estimate hazard ratios and a modified Cox regression, taking into account competing risks to derive sub-hazard ratios, for the association of ethnicity with all-cause and cause-specific mortality.FindingsWe identified 18 201 individuals with a valid diagnosis of severe mental illness (median follow-up 6·36 years, IQR 3·26-9·92), of whom 1767 died. Compared with the general population, age-and-sex-standardised mortality ratios (SMRs) in people with severe mental illness were increased for a range of causes, including suicides (7·65, 95% CI 6·43-9·04), non-suicide unnatural causes (4·01, 3·34-4·78), respiratory disease (3·38, 3·04-3·74), cardiovascular disease (2·65, 2·45-2·86), and cancers (1·45, 1·32-1·60). SMRs were broadly similar in different ethnic groups with severe mental illness, although the south Asian group had a reduced SMR for cancer mortality (0·49, 0·21-0·96). Within the cohort with severe mental illness, hazard ratios for all-cause mortality and sub-hazard ratios for natural-cause and unnatural-cause mortality were lower in most ethnic minority groups relative to the white British group.InterpretationPeople with severe mental illness have excess mortality relative to the general population irrespective of ethnicity. Among those with severe mental illness, some ethnic minorities have lower mortality than the white British group, for which the reasons deserve further investigation.FundingUK Health Foundation and UK Academy of Medical Sciences.

Dataset Information

New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model.

Publications

New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model.

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