Adjusting expected deaths for mortality displacement during the COVID-19 pandemic: a model based counterfactual approach at the level of individuals
Abstract Background Near-real time surveillance of excess mortality has been an essential tool during the COVID-19 pandemic. It remains critical for monitoring mortality as the pandemic wanes, to detect fluctuations in the death rate associated both with the longer-term impact of the pandemic (e.g....
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12874-023-01984-8 |
| _version_ | 1797452152822562816 |
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| author | Richard James Holleyman Sharmani Barnard Clarissa Bauer-Staeb Andrew Hughes Samantha Dunn Sebastian Fox John N. Newton Justine Fitzpatrick Zachary Waller David John Deehan Andre Charlett Celia L. Gregson Rebecca Wilson Paul Fryers Peter Goldblatt Paul Burton |
| author_facet | Richard James Holleyman Sharmani Barnard Clarissa Bauer-Staeb Andrew Hughes Samantha Dunn Sebastian Fox John N. Newton Justine Fitzpatrick Zachary Waller David John Deehan Andre Charlett Celia L. Gregson Rebecca Wilson Paul Fryers Peter Goldblatt Paul Burton |
| author_sort | Richard James Holleyman |
| collection | DOAJ |
| description | Abstract Background Near-real time surveillance of excess mortality has been an essential tool during the COVID-19 pandemic. It remains critical for monitoring mortality as the pandemic wanes, to detect fluctuations in the death rate associated both with the longer-term impact of the pandemic (e.g. infection, containment measures and reduced service provision by the health and other systems) and the responses that followed (e.g. curtailment of containment measures, vaccination and the response of health and other systems to backlogs). Following the relaxing of social distancing regimes and reduction in the availability of testing, across many countries, it becomes critical to measure the impact of COVID-19 infection. However, prolonged periods of mortality in excess of the expected across entire populations has raised doubts over the validity of using unadjusted historic estimates of mortality to calculate the expected numbers of deaths that form the baseline for computing numbers of excess deaths because many individuals died earlier than they would otherwise have done: i.e. their mortality was displaced earlier in time to occur during the pandemic rather than when historic rates predicted. This is also often termed “harvesting” in the literature. Methods We present a novel Cox-regression-based methodology using time-dependent covariates to estimate the profile of the increased risk of death across time in individuals who contracted COVID-19 among a population of hip fracture patients in England (N = 98,365). We use these hazards to simulate a distribution of survival times, in the presence of a COVID-19 positive test, and then calculate survival times based on hazard rates without a positive test and use the difference between the medians of these distributions to estimate the number of days a death has been displaced. This methodology is applied at the individual level, rather than the population level to provide a better understanding of the impact of a positive COVID-19 test on the mortality of groups with different vulnerabilities conferred by sociodemographic and health characteristics. Finally, we apply the mortality displacement estimates to adjust estimates of excess mortality using a “ball and urn” model. Results Among the exemplar population we present an end-to-end application of our methodology to estimate the extent of mortality displacement. A greater proportion of older, male and frailer individuals were subject to significant displacement while the magnitude of displacement was higher in younger females and in individuals with lower frailty: groups who, in the absence of COVID-19, should have had a substantial life expectancy. Conclusion Our results indicate that calculating the expected number of deaths following the first wave of the pandemic in England based solely on historical trends results in an overestimate, and excess mortality will therefore be underestimated. Our findings, using this exemplar dataset are conditional on having experienced a hip fracture, which is not generalisable to the general population. Fractures that impede mobility in the weeks that follow the accident/surgery considerably shorten life expectancy and are in themselves markers of significant frailty. It is therefore important to apply these novel methods to the general population, among whom we anticipate strong patterns in mortality displacement – both in its length and prevalence – by age, sex, frailty and types of comorbidities. This counterfactual method may also be used to investigate a wider range of disruptive population health events. This has important implications for public health monitoring and the interpretation of public health data in England and globally. |
| first_indexed | 2024-03-09T15:04:39Z |
| format | Article |
| id | doaj.art-23802162e5fe4969894986590520d583 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-03-09T15:04:39Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-23802162e5fe4969894986590520d5832023-11-26T13:42:44ZengBMCBMC Medical Research Methodology1471-22882023-10-0123112010.1186/s12874-023-01984-8Adjusting expected deaths for mortality displacement during the COVID-19 pandemic: a model based counterfactual approach at the level of individualsRichard James Holleyman0Sharmani Barnard1Clarissa Bauer-Staeb2Andrew Hughes3Samantha Dunn4Sebastian Fox5John N. Newton6Justine Fitzpatrick7Zachary Waller8David John Deehan9Andre Charlett10Celia L. Gregson11Rebecca Wilson12Paul Fryers13Peter Goldblatt14Paul Burton15UK Health Security AgencySchool of Population Health, Curtin UniversityOffice for Health Improvement and Disparities, Department of Health and Social CareOffice for Health Improvement and Disparities, Department of Health and Social CareOffice for Health Improvement and Disparities, Department of Health and Social CareOffice for Health Improvement and Disparities, Department of Health and Social CareOffice for Health Improvement and Disparities, Department of Health and Social CareOffice for Health Improvement and Disparities, Department of Health and Social CareOffice for Health Improvement and Disparities, Department of Health and Social CareNewcastle Upon Tyne Hospitals NHS Foundation TrustUK Health Security AgencyMusculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, University of BristolDepartment of Public Health, Policy and Systems, University of Liverpool Waterhouse BuildingOffice for Health Improvement and Disparities, Department of Health and Social CareOffice for Health Improvement and Disparities, Department of Health and Social CarePopulation Health Sciences Institute, Newcastle UniversityAbstract Background Near-real time surveillance of excess mortality has been an essential tool during the COVID-19 pandemic. It remains critical for monitoring mortality as the pandemic wanes, to detect fluctuations in the death rate associated both with the longer-term impact of the pandemic (e.g. infection, containment measures and reduced service provision by the health and other systems) and the responses that followed (e.g. curtailment of containment measures, vaccination and the response of health and other systems to backlogs). Following the relaxing of social distancing regimes and reduction in the availability of testing, across many countries, it becomes critical to measure the impact of COVID-19 infection. However, prolonged periods of mortality in excess of the expected across entire populations has raised doubts over the validity of using unadjusted historic estimates of mortality to calculate the expected numbers of deaths that form the baseline for computing numbers of excess deaths because many individuals died earlier than they would otherwise have done: i.e. their mortality was displaced earlier in time to occur during the pandemic rather than when historic rates predicted. This is also often termed “harvesting” in the literature. Methods We present a novel Cox-regression-based methodology using time-dependent covariates to estimate the profile of the increased risk of death across time in individuals who contracted COVID-19 among a population of hip fracture patients in England (N = 98,365). We use these hazards to simulate a distribution of survival times, in the presence of a COVID-19 positive test, and then calculate survival times based on hazard rates without a positive test and use the difference between the medians of these distributions to estimate the number of days a death has been displaced. This methodology is applied at the individual level, rather than the population level to provide a better understanding of the impact of a positive COVID-19 test on the mortality of groups with different vulnerabilities conferred by sociodemographic and health characteristics. Finally, we apply the mortality displacement estimates to adjust estimates of excess mortality using a “ball and urn” model. Results Among the exemplar population we present an end-to-end application of our methodology to estimate the extent of mortality displacement. A greater proportion of older, male and frailer individuals were subject to significant displacement while the magnitude of displacement was higher in younger females and in individuals with lower frailty: groups who, in the absence of COVID-19, should have had a substantial life expectancy. Conclusion Our results indicate that calculating the expected number of deaths following the first wave of the pandemic in England based solely on historical trends results in an overestimate, and excess mortality will therefore be underestimated. Our findings, using this exemplar dataset are conditional on having experienced a hip fracture, which is not generalisable to the general population. Fractures that impede mobility in the weeks that follow the accident/surgery considerably shorten life expectancy and are in themselves markers of significant frailty. It is therefore important to apply these novel methods to the general population, among whom we anticipate strong patterns in mortality displacement – both in its length and prevalence – by age, sex, frailty and types of comorbidities. This counterfactual method may also be used to investigate a wider range of disruptive population health events. This has important implications for public health monitoring and the interpretation of public health data in England and globally.https://doi.org/10.1186/s12874-023-01984-8DisplacementHarvestingCOVID-19CounterfactualExcess mortalityExcess death |
| spellingShingle | Richard James Holleyman Sharmani Barnard Clarissa Bauer-Staeb Andrew Hughes Samantha Dunn Sebastian Fox John N. Newton Justine Fitzpatrick Zachary Waller David John Deehan Andre Charlett Celia L. Gregson Rebecca Wilson Paul Fryers Peter Goldblatt Paul Burton Adjusting expected deaths for mortality displacement during the COVID-19 pandemic: a model based counterfactual approach at the level of individuals BMC Medical Research Methodology Displacement Harvesting COVID-19 Counterfactual Excess mortality Excess death |
| title | Adjusting expected deaths for mortality displacement during the COVID-19 pandemic: a model based counterfactual approach at the level of individuals |
| title_full | Adjusting expected deaths for mortality displacement during the COVID-19 pandemic: a model based counterfactual approach at the level of individuals |
| title_fullStr | Adjusting expected deaths for mortality displacement during the COVID-19 pandemic: a model based counterfactual approach at the level of individuals |
| title_full_unstemmed | Adjusting expected deaths for mortality displacement during the COVID-19 pandemic: a model based counterfactual approach at the level of individuals |
| title_short | Adjusting expected deaths for mortality displacement during the COVID-19 pandemic: a model based counterfactual approach at the level of individuals |
| title_sort | adjusting expected deaths for mortality displacement during the covid 19 pandemic a model based counterfactual approach at the level of individuals |
| topic | Displacement Harvesting COVID-19 Counterfactual Excess mortality Excess death |
| url | https://doi.org/10.1186/s12874-023-01984-8 |
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