Global Alliance for Chronic Disease researchers' statement on multimorbidity

Hurst, John R.; Dickhaus, Julia; Maulik, Pallab K.; Miranda, J. Jaime; Pastakia, Sonak D.; Soriano, Joan B.; Siddharthan, Trishul; Vedanthan, Rajesh; Agarwal, Gina; Ca, Alford; Bloomfield, GS; Nh, Chavannes; Daivadanam, Meena; Gómez-Olivé, F Xavier; Gorre, Celina E.; Levitt, Naomi; Lotrean, LM; Norris, S.A.; Owolabi, Mayowa; Parker, Gary; Postma, MJ; Sheikh, Asim A.; Siddiqi, Kamran; Stratev, Velin; Trofor, AC; Boven, JF van; Kleij, RM van der

doi:10.1016/s2214-109x(18)30391-7

Cited by 48 publications

(41 citation statements)

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“…[14][15][16][17] Multi morbidity was defined as the cooccurrence of two or more conditions in an indi vidual. 18 Given recent interest in the potential roles of angiotensinconverting enzyme (ACE) inhibitors 19 and non-steroidal inflammatory drugs (NSAIDs), 20 we also estimated prevalence of their use.…”

Section: Open National Portal For Definitions Of Underlying Conditionsmentioning

confidence: 99%

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study

et al. 2020

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Background The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. Methods In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK-CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1•5, 2•0, and 3•0 at differing infection rate scenarios, including full suppression (0•001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation. Findings We included 3 862 012 individuals (1 957 935 [50•7%] women and 1 904 077 [49•3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13•7% were older than 70 years and 6•3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4•46% (95% CI 4•41-4•51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1•5, four with an RR of 2•0, and seven with an RR of 3•0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1•5, 36 749 with an RR of 2•0, and 73 498 with an RR of 3•0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1•5, 293 991 with an RR of 2•0, and 587 982 with an RR of 3•0. Interpretation We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indir...

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Section: Open National Portal For Definitions Of Underlying Conditionsmentioning

confidence: 99%

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study

et al. 2020

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“…CVD was defined as the 12 most is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint per prior studies (18,19). Multimorbidity was defined as the co-occurrence of 2 or more conditions in an individual (20). Given the recent interest in the potential roles of angiotensinconverting enzyme inhibitors (ACEI) (21,22) and non-steroidal inflammatory drugs (NSAIDs) (23,24), we also estimated prevalence of use of these drugs.…”

Section: Study Populationmentioning

confidence: 99%

The impact of temperature and absolute humidity on the coronavirus disease 2019 (COVID-19) outbreak - evidence from China

Shi

Dong

Yan

et al. 2020

Preprint

160

170

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“…CVD was defined as the 12 most common symptomatic manifestations: chronic stable angina, unstable angina, myocardial infarction, unheralded death from coronary heart disease, heart failure, cardiac arrest/sudden coronary death, transient ischaemic attack, ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, peripheral arterial disease, and abdominal aortic aneurysm, as per prior studies (18,19). Multimorbidity was defined as the co-occurrence of 2 or more conditions in an individual (20). Given the recent interest in the potential roles of angiotensinconverting enzyme inhibitors (ACEI) (21,22) and non-steroidal inflammatory drugs (NSAIDs) (23,24), we also estimated prevalence of use of these drugs.…”

Section: Study Populationmentioning

confidence: 99%

Estimating excess 1- year mortality from COVID-19 according to underlying conditions and age in England: a rapid analysis using NHS health records in 3.8 million adults

Banerjee

Pasea

Harris

et al. 2020

Preprint

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Background:The medical, health service, societal and economic impact of the COVID-19 emergency has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom (to date at least) have underlying conditions. Models have not incorporated information on high risk conditions or their longer term background (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence rates and differing mortality impacts. Methods:Using population based linked primary and secondary care electronic health records in England (HDR UK -CALIBER), we report the prevalence of underlying conditions defined by UK Public Health England COVID-19 guidelines (16 March 2020) in 3,862,012 individuals aged ≥30 years from 1997-2017. We used previously validated phenotypes, openly available (https://caliberresearch.org/portal), for each condition using ICD-10 diagnosis, Read, procedure and medication codes. We estimated the 1-year mortality in each condition, and developed simple models of excess COVID-19-related deaths assuming relative risk (RR) of the impact of the emergency (compared to background mortality) of 1.2, 1.5 and 2.0. Rapid communication 22/3/20202 Findings: 20.0% of the population are at risk according to current PHE guidelines, of which; 13.7% were age>70 years and 6.3% aged ≤70 years with ≥1 underlying condition (cardiovascular disease (2.3%), diabetes (2.2%), steroid therapy (1.9%), severe obesity (0.9%), chronic kidney disease (0.6%) and chronic obstructive pulmonary disease, COPD (0.5%). Multimorbidity (co-occurrence of ≥2 conditions in an individual) was common (10.1%). The 1-year mortality in the at-risk population was 4.46%, and age and underlying conditions combine to influence background risk, varying markedly across conditions (5.9% in age>70 years, 8.6% for COPD and 13.1% in those with ≥3 or more conditions). In a suppression scenario (at SARS CoV2 rates of 0.001% of the UK population), there would be minimal excess deaths (3 and 7 excess deaths at relative risk, RR, 1.5 and 2.0 respectively).At SARS CoV2 rates of 10% of the UK population (mitigation) the model estimates the numbers of excess deaths as: 13791, 34479 and 68957 (at RR 1.2, 1.5 and 2.0 respectively).At SARS CoV2 rates of 80% in the UK population ("do-nothing"), the model estimates the number of excess deaths as 110332, 275,830 and 551,659 (at RR 1.2, 1.5 and 2.0) respectively. Interpretation:We provide the public, researchers and policy makers a simple model to estimate the excess mortality over 1 year from COVID-19, based on underlying conditions at different ages. If the relative mortality impact of COVID-19 were to be about 20% (similar magnitude as the established winter vs summer mortality excess), then the excess deaths would be 0 when 1 in 100 000 (suppression), 13791 when 1 in 10 (mitigation) and 110332 when 8 in 10 are infected ("do nothing") scenario. However, the relative impact of COVID-19 is unknown....

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Global Alliance for Chronic Disease researchers' statement on multimorbidity

Abstract: This statement reflects the perspectives of researchers from the GACD, but it does not necessarily reflect the perspective of the funding agencies. We declare no competing interests.

Cited by 48 publications

References 0 publications

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study

The impact of temperature and absolute humidity on the coronavirus disease 2019 (COVID-19) outbreak - evidence from China

Estimating excess 1- year mortality from COVID-19 according to underlying conditions and age in England: a rapid analysis using NHS health records in 3.8 million adults

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