scholarly journals Different approaches to quantify years of life lost from COVID-19

2021 ◽  
Author(s):  
Tamás Ferenci
Keyword(s):  
Years Of Life Lost ◽  
Expected Number ◽  
Long Term Conditions ◽  
Developed Country ◽  
Individual Patient Level ◽  
Level Data ◽  
Parametric Survival Model ◽  
Epidemiologic Risk ◽  
Parametric Survival

AbstractThe burden of an epidemic is often characterized by death counts, but this can be misleading as it fails to acknowledge the age of the deceased patients. Years of life lost is therefore widely used as a more relevant metric, however, such calculations in the context of COVID-19 are all biased upwards: patients dying from COVID-19 are typically multimorbid, having far worse life expectation than the general population. These questions are quantitatively investigated using a unique Hungarian dataset that contains individual patient level data on comorbidities for all COVID-19 deaths in the country. To account for the comorbidities of the patients, a parametric survival model using 11 important long-term conditions was used to estimate a more realistic years of life lost. As of 12 May, 2021, Hungary reported a total of 27,837 deaths from COVID-19 in patients above 50 years of age. The usual calculation indicates 10.5 years of life lost for each death, which decreases to 9.2 years per death after adjusting for 11 comorbidities. The expected number of years lost implied by the life table, reflecting the mortality of a developed country just before the pandemic is 11.1 years. The years of life lost due to COVID-19 in Hungary is therefore 12% or 1.3 years per death lower when accounting for the comorbidities and is below its expected value, but how this should be interpreted is still a matter of debate. Further research is warranted on how to optimally integrate this information into epidemiologic risk assessments during a pandemic.

scholarly journals Self-Reported Incident Hypertension and Long-Term Kidney Function in Living Kidney Donors Compared with Healthy Nondonors

2019 ◽  
Vol 14 (10) ◽  
pp. 1493-1499 ◽  
Author(s):  
Courtenay M. Holscher ◽  
Christine E. Haugen ◽  
Kyle R. Jackson ◽  
Jacqueline M. Garonzik Wang ◽  
Madeleine M. Waldram ◽  
...  
Keyword(s):  
Kidney Donation ◽  
Living Kidney Donation ◽  
Kidney Donors ◽  
Incident Hypertension ◽  
Living Kidney Donors ◽  
Parametric Survival Model ◽  
Yearly Change ◽  
Parametric Survival ◽  
Multilevel Linear Regression

Background and objectivesThe risk of hypertension attributable to living kidney donation remains unknown as does the effect of developing postdonation hypertension on subsequent eGFR. We sought to understand the association between living kidney donation, hypertension, and long-term eGFR by comparing donors with a cohort of healthy nondonors.Design, setting, participants, & measurementsWe compared 1295 living kidney donors with median 6 years of follow-up with a weighted cohort of 8233 healthy nondonors. We quantified the risk of self-reported hypertension using a parametric survival model. We examined the association of hypertension with yearly change in eGFR using multilevel linear regression and clustering by participant, with an interaction term for race.ResultsKidney donation was independently associated with a 19% higher risk of hypertension (adjusted hazard ratio, 1.19; 95% confidence interval, 1.01 to 1.41; P=0.04); this association did not vary by race (interaction P=0.60). For white and black nondonors, there was a mean decline in eGFR (−0.4 and −0.3 ml/min per year, respectively) that steepened after incident hypertension (−0.8 and −0.9 ml/min per year, respectively; both P<0.001). For white and black kidney donors, there was a mean increase in eGFR after donation (+0.4 and +0.6 ml/min per year, respectively) that plateaued after incident hypertension (0 and −0.2 ml/min per year, respectively; P=0.07 and P=0.01, respectively, after hypertension).ConclusionsKidney donors are at higher risk of hypertension than similar healthy nondonors, regardless of race. Donors who developed hypertension had a plateau in the usual postdonation increase of eGFR.

scholarly journals COVID-19 – exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study

2021 ◽  
Vol 5 ◽  
pp. 75
Author(s):  
Peter Hanlon ◽  
Fergus Chadwick ◽  
Anoop Shah ◽  
Rachael Wood ◽  
Jon Minton ◽  
...  
Keyword(s):  
Care Home ◽  
Life Tables ◽  
Years Of Life Lost ◽  
Limited Data ◽  
Long Term Conditions ◽  
Healthcare Data ◽  
Estimate Life Expectancy ◽  
Before And After ◽  
Wide Variability

Background: COVID-19 is responsible for increasing deaths globally. As most people dying with COVID-19 are older with underlying long-term conditions (LTCs), some speculate that YLL are low. We aim to estimate YLL attributable to COVID-19, before and after adjustment for number/type of LTCs, using the limited data available early in the pandemic. Methods: We first estimated YLL from COVID-19 using WHO life tables, based on published age/sex data from COVID-19 deaths in Italy. We then used aggregate data on number/type of LTCs in a Bayesian model to estimate likely combinations of LTCs among people dying with COVID-19. We used routine UK healthcare data from Scotland and Wales to estimate life expectancy based on age/sex/these combinations of LTCs using Gompertz models from which we then estimate YLL. Results: Using the standard WHO life tables, YLL per COVID-19 death was 14 for men and 12 for women. After adjustment for number and type of LTCs, the mean YLL was slightly lower, but remained high (11.6 and 9.4 years for men and women, respectively). The number and type of LTCs led to wide variability in the estimated YLL at a given age (e.g. at ≥80 years, YLL was >10 years for people with 0 LTCs, and <3 years for people with ≥6). Conclusions: Deaths from COVID-19 represent a substantial burden in terms of per-person YLL, more than a decade, even after adjusting for the typical number and type of LTCs found in people dying of COVID-19. The extent of multimorbidity heavily influences the estimated YLL at a given age. More comprehensive and standardised collection of data (including LTC type, severity, and potential confounders such as socioeconomic-deprivation and care-home status) is needed to optimise YLL estimates for specific populations, and to understand the global burden of COVID-19, and guide policy-making and interventions.

scholarly journals COVID-19 – exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study

2020 ◽  
Vol 5 ◽  
pp. 75 ◽  
Author(s):  
Peter Hanlon ◽  
Fergus Chadwick ◽  
Anoop Shah ◽  
Rachael Wood ◽  
Jon Minton ◽  
...  
Keyword(s):  
Policy Making ◽  
Life Tables ◽  
Years Of Life Lost ◽  
Long Term Conditions ◽  
Healthcare Data ◽  
Estimate Life Expectancy ◽  
Before And After ◽  
Wide Variability ◽  
The Mean

Background: The COVID-19 pandemic is responsible for increasing deaths globally. Most estimates have focused on numbers of deaths, with little direct quantification of years of life lost (YLL) through COVID-19.  As most people dying with COVID-19 are older with underlying long-term conditions (LTCs), some have speculated that YLL are low. We aim to estimate YLL attributable to COVID-19, before and after adjustment for number/type of LTCs. Methods: We first estimated YLL from COVID-19 using standard WHO life tables, based on published age/sex data from COVID-19 deaths in Italy. We then used aggregate data on number/type of LTCs to model likely combinations of LTCs among people dying with COVID-19. From these, we used routine UK healthcare data to estimate life expectancy based on age/sex/different combinations of LTCs. We then calculated YLL based on age, sex and type of LTCs and multimorbidity count. Results: Using the standard WHO life tables, YLL per COVID-19 death was 14 for men and 12 for women. After adjustment for number and type of LTCs, the mean YLL was slightly lower, but remained high (13 and 11 years for men and women, respectively). The number and type of LTCs led to wide variability in the estimated YLL at a given age (e.g. at ≥80 years, YLL was >10 years for people with 0 LTCs, and <3 years for people with ≥6). Conclusions: Deaths from COVID-19 represent a substantial burden in terms of per-person YLL, more than a decade, even after adjusting for the typical number and type of LTCs found in people dying of COVID-19. The extent of multimorbidity heavily influences the estimated YLL at a given age. More comprehensive and standardised collection of data on LTCs is needed to better understand and quantify the global burden of COVID-19 and to guide policy-making and interventions.

scholarly journals COVID-19 – exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study

2021 ◽  
Vol 5 ◽  
pp. 75
Author(s):  
Peter Hanlon ◽  
Fergus Chadwick ◽  
Anoop Shah ◽  
Rachael Wood ◽  
Jon Minton ◽  
...  
Keyword(s):  
Life Tables ◽  
Years Of Life Lost ◽  
Long Term Conditions ◽  
Healthcare Data ◽  
Estimate Life Expectancy ◽  
Sex Type ◽  
Before And After ◽  
Wide Variability ◽  
The Mean

Background: COVID-19 is responsible for increasing deaths globally. Estimates focused on numbers of deaths, do not quantify potential years of life lost (YLL) through COVID-19.  As most people dying with COVID-19 are older with underlying long-term conditions (LTCs), some speculate that YLL are low. We aim to estimate YLL attributable to COVID-19, before and after adjustment for number/type of LTCs. Methods: We first estimated YLL from COVID-19 using WHO life tables, based on published age/sex data from COVID-19 deaths in Italy. We then used aggregate data on number/type of LTCs inform a Bayesian model for likely combinations of LTCs among people dying with COVID-19. From these, we used routine UK healthcare data from Scotland and Wales to estimate life expectancy based on age/sex/ combinations of LTCs using Gompertz models. We then calculated YLL based on age, sex, type of LTCs and multimorbidity count. Results: Using the standard WHO life tables, YLL per COVID-19 death was 14 for men and 12 for women. After adjustment for number and type of LTCs, the mean YLL was slightly lower, but remained high (11.6 and 9.4 years for man and women, respectively). The number and type of LTCs led to wide variability in the estimated YLL at a given age (e.g. at ≥80 years, YLL was >10 years for people with 0 LTCs, and <3 years for people with ≥6). Conclusions: Deaths from COVID-19 represent a substantial burden in terms of per-person YLL, more than a decade, even after adjusting for the typical number and type of LTCs found in people dying of COVID-19. The extent of multimorbidity heavily influences the estimated YLL at a given age. More comprehensive and standardised collection of data on LTCs is needed to better understand and quantify the global burden of COVID-19 and to guide policy-making and interventions.

Estimating long-term survival of previously untreated patients with EGFR mutation-positive (EGFRm) advanced non-small cell lung cancer (NSCLC) who received osimertinib in the FLAURA study.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e20560-e20560
Author(s):  
Matthew Dyer ◽  
Matthew Green ◽  
simon jones ◽  
Rachel Hodge
Keyword(s):  
Advanced Nsclc ◽  
Survival Rates ◽  
Survival Model ◽  
Survival Models ◽  
Term Survival ◽  
Parametric Survival Model ◽  
Best Fitting ◽  
Parametric Survival

e20560 Background: In the Phase III FLAURA trial (NCT02296125), osimertinib, a third-generation EGFR-TKI, provided clinically and statistically significantly longer progression-free survival versus gefitinib/erlotinib as first-line treatment for patients with EGFRm advanced NSCLC. At the time of analysis, data on overall survival (OS) were immature (25% maturity). To better understand the long-term survival potential of osimertinib beyond the observed trial follow-up period, mathematical parametric survival models were used to estimate clinically plausible survival rates up to 5 years from FLAURA. Methods: Following published best-practice guidelines, candidate parametric survival models were evaluated based on both statistical and visual goodness-of-fit to the observed FLAURA OS data. Two modeling approaches were considered: single models with treatment included as a covariate; and separate models fitted to the osimertinib and gefitinib/erlotinib arms. Point estimates of 5-year survival rates with 95% confidence intervals (CIs) are reported for the best fitting model. Results: The best fitting parametric survival model to the FLAURA OS data was the Weibull model with treatment included as a covariate. Based on this model, estimated median OS was longer with osimertinib than with gefitinib/erlotinib (41.4 months vs 30.6 months). The estimated 3- and 5-year survival rates with osimertinib were 57.3% (95% CI 46.6%, 69.2%) and 31.1% (95% CI 23.7%, 41.8%), respectively. In comparison, the estimated 3- and 5-year survival rates with gefitinib/erlotinib were 41.1% (95% CI 31.9%, 52.9%) and 15.5% (95% CI 11.6%, 22.1%), respectively. Conclusions: Based on the best fitting parametric survival model to FLAURA OS data, the estimated 5-year survival rate with osimertinib was double that with gefitinib/erlotinib (31.1% vs 15.5%) in patients with EGFRm advanced NSCLC. Long-term follow-up data from FLAURA (60% OS maturity) will further validate this finding. Clinical trial information: NCT02296125.

Enhancing community welfare through improved water quality: a case for applying staggered pollution charges in the river Musi sub-basin of Andhra Pradesh, India

Water Policy ◽  
2005 ◽  
Vol 7 (5) ◽  
pp. 469-483
Author(s):  
Tishya Chatterjee
Keyword(s):  
Andhra Pradesh ◽  
Relevant Literature ◽  
Pollution Prevention ◽  
Economic Benefits ◽  
Environmental Damage ◽  
Marginal Costs ◽  
Community Acceptance ◽  
Level Data ◽  
Over Time

In conditions of severe water-pollution and dormant community acceptance of accumulating environmental damage, the regulator's role goes beyond pollution prevention and more towards remediation and solutions based on the community's long-term expectations of economic benefits from clean water. This paper suggests a method to enable these benefits to become perceptible progressively, through participatory clean-up operations, supported by staggered pollution charges. It analyses the relevant literature on pollution prevention and applies a cost-based “willingness to pay” model, using primary basin-level data of total marginal costs. It develops a replicable demand-side approach imposing charge-standard targets over time in urban-industrial basins of developing countries.

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