Population Health Metrics最新文献

{"title":"War and Population Health: there is no metric for the horror.","authors":"Jonathan M Samet","doi":"10.1186/s12963-023-00321-x","DOIUrl":"10.1186/s12963-023-00321-x","url":null,"abstract":"","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":"22 1","pages":"1"},"PeriodicalIF":3.2,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10826054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139643324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collider and reporting biases involved in the analyses of cause of death associations in death certificates: an illustration with cancer and suicide.","authors":"Moussa Laanani, Vivian Viallon, Joël Coste, Grégoire Rey","doi":"10.1186/s12963-023-00320-y","DOIUrl":"10.1186/s12963-023-00320-y","url":null,"abstract":"<p><strong>Background: </strong>Mortality data obtained from death certificates have been studied to explore causal associations between diseases. However, these analyses are subject to collider and reporting biases (selection and information biases, respectively). We aimed to assess to what extent associations of causes of death estimated from individual mortality data can be extrapolated as associations of disease states in the general population.</p><p><strong>Methods: </strong>We used a multistate model to generate populations of individuals and simulate their health states up to death from national health statistics and artificially replicate collider bias. Associations between health states can then be estimated from such simulated deaths by logistic regression and the magnitude of collider bias assessed. Reporting bias can be approximated by comparing the estimates obtained from the observed death certificates (subject to collider and reporting biases) with those obtained from the simulated deaths (subject to collider bias only). As an illustrative example, we estimated the association between cancer and suicide in French death certificates and found that cancer was negatively associated with suicide. Collider bias, due to conditioning inclusion in the study population on death, increasingly downwarded the associations with cancer site lethality. Reporting bias was much stronger than collider bias and depended on the cancer site, but not prognosis.</p><p><strong>Results: </strong>The magnitude of the biases ranged from 1.7 to 9.3 for collider bias, and from 4.7 to 64 for reporting bias.</p><p><strong>Conclusions: </strong>These results argue for an assessment of the magnitude of both collider and reporting biases before performing analyses of cause of death associations exclusively from mortality data. If these biases cannot be corrected, results from these analyses should not be extrapolated to the general population.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":"21 1","pages":"21"},"PeriodicalIF":3.2,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10722743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138810375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics of hemoglobin distributions in preschool children and non-pregnant women of reproductive age and their implications for establishing quality control criteria for hemoglobin data in field surveys: evidence from 483 surveys conducted in refugee settings worldwide.","authors":"Oleg Bilukha, Behzad Kianian, Kaitlyn L I Samson","doi":"10.1186/s12963-023-00315-9","DOIUrl":"10.1186/s12963-023-00315-9","url":null,"abstract":"<p><strong>Background: </strong>Currently, there is a lack of clear guidance on hemoglobin (Hb) data quality parameters and plausible flagging ranges for population-representative surveys. There is a need to determine which properties of Hb data indicate lower data quality and increased measurement error and which represent intrinsic statistical properties of Hb distributions rather than quality problems.</p><p><strong>Methods: </strong>We explored statistical characteristics of Hb distributions and plausible exclusion ranges in population-representative surveys of non-pregnant women of reproductive age (WRA) (15-49 years, n = 401 surveys) and children (6-59 months, n = 461 surveys) conducted in refugee settings by the United Nations High Commissioner for Refugees (UNHCR). Hb distribution characteristics [standard deviation (SD), skewness and kurtosis] were compared to those from Demographic and Health Surveys (DHS).</p><p><strong>Results: </strong>Overall, 0.08% of child and 0.14% of WRA Hb values were outside of the previously proposed 4.0-18.0 g/dL plausible range. Surveys conducted in Uganda tended to have unusually high SD compared with surveys from other settings, possibly an indication of problematic measurement quality. We therefore used summary results on SD, skewness and kurtosis excluding surveys from Uganda when comparing with DHS results or proposing plausible ranges. Both WRA and child Hb distributions tended to be left-skewed and had excess positive kurtosis. Mean survey-level SD was greater, mean skewness more negative, and mean kurtosis more positive in WRA surveys compared to child surveys. All these findings were broadly similar to those from DHS surveys. Mean SD in DHS surveys was higher than that in our data for both children (1.48 vs. 1.34) and WRA (1.58 vs. 1.43).</p><p><strong>Conclusions: </strong>We observed several statistical characteristics of Hb distributions that may not necessarily be indicative of data quality problems and bear strong similarities with the characteristics found in DHS surveys. Hb distributions tended to be negatively skewed and positively kurtotic, and SD in many surveys exceeded 1.5 (previously proposed upper plausible range). Based on our empirical evidence, surveys with skewness above + 0.2 and kurtosis below -0.5 or Hb SD outside the range of 1.1-1.55 g/dL for children (6-59 mo) or 1.1-1.65 g/dL for non-pregnant WRA (15-49 y) may require further quality investigation.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":"21 1","pages":"20"},"PeriodicalIF":3.2,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10633933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71523283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing synthetic populations in the age of big data.","authors":"Mioara A Nicolaie, Koen Füssenich, Caroline Ameling, Hendriek C Boshuizen","doi":"10.1186/s12963-023-00319-5","DOIUrl":"10.1186/s12963-023-00319-5","url":null,"abstract":"<p><strong>Background: </strong>To develop public health intervention models using micro-simulations, extensive personal information about inhabitants is needed, such as socio-demographic, economic and health figures. Confidentiality is an essential characteristic of such data, while the data should reflect realistic scenarios. Collection of such data is possible only in secured environments and not directly available for open-source micro-simulation models. The aim of this paper is to illustrate a method of construction of synthetic data by predicting individual features through models based on confidential data on health and socio-economic determinants of the entire Dutch population.</p><p><strong>Methods: </strong>Administrative records and health registry data were linked to socio-economic characteristics and self-reported lifestyle factors. For the entire Dutch population (n = 16,778,708), all socio-demographic information except lifestyle factors was available. Lifestyle factors were available from the 2012 Dutch Health Monitor (n = 370,835). Regression model was used to sequentially predict individual features.</p><p><strong>Results: </strong>The synthetic population resembles the original confidential population. Features predicted in the first stages of the sequential procedure are virtually similar to those in the original population, while those predicted in later stages of the sequential procedure carry the accumulation of limitations furthered by data quality and previously modelled features.</p><p><strong>Conclusions: </strong>By combining socio-demographic, economic, health and lifestyle related data at individual level on a large scale, our method provides us with a powerful tool to construct a synthetic population of good quality and with no confidentiality issues.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":"21 1","pages":"19"},"PeriodicalIF":3.2,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10617102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71428981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel comorbidity index in Italy based on diseases detected by the surveillance system PASSI and the Global Burden of Diseases disability weights.","authors":"Angela Andreella, Lorenzo Monasta, Stefano Campostrini","doi":"10.1186/s12963-023-00317-7","DOIUrl":"10.1186/s12963-023-00317-7","url":null,"abstract":"<p><strong>Background: </strong>Understanding comorbidity and its burden characteristics is essential for policymakers and healthcare providers to allocate resources accordingly. However, several definitions of comorbidity burden can be found in the literature. The main reason for these differences lies in the available information about the analyzed diseases (i.e., the target population studied), how to define the burden of diseases, and how to aggregate the occurrence of the detected health conditions.</p><p><strong>Methods: </strong>In this manuscript, we focus on data from the Italian surveillance system PASSI, proposing an index of comorbidity burden based on the disability weights from the Global Burden of Disease (GBD) project. We then analyzed the co-presence of ten non-communicable diseases, weighting their burden thanks to the GBD disability weights extracted by a multi-step procedure. The first step selects a set of GBD weights for each disease detected in PASSI using text mining. The second step utilizes an additional variable from PASSI (i.e., the perceived health variable) to associate a single disability weight for each disease detected in PASSI. Finally, the disability weights are combined to form the comorbidity burden index using three approaches common in the literature.</p><p><strong>Results: </strong>The comorbidity index (i.e., combined disability weights) proposed allows an exploration of the magnitude of the comorbidity burden in several Italian sub-populations characterized by different socioeconomic characteristics. Thanks to that, we noted that the level of comorbidity burden is greater in the sub-population characterized by low educational qualifications and economic difficulties than in the rich sub-population characterized by a high level of education. In addition, we found no substantial differences in terms of predictive values of comorbidity burden adopting different approaches in combining the disability weights (i.e., additive, maximum, and multiplicative approaches), making the Italian comorbidity index proposed quite robust and general.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":"21 1","pages":"18"},"PeriodicalIF":3.2,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10617130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71415235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring unequal distribution of pandemic severity across census years, variants of concern and interventions.","authors":"Quang Dang Nguyen, Sheryl L Chang, Christina M Jamerlan, Mikhail Prokopenko","doi":"10.1186/s12963-023-00318-6","DOIUrl":"10.1186/s12963-023-00318-6","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic stressed public health systems worldwide due to emergence of several highly transmissible variants of concern. Diverse and complex intervention policies deployed over the last years have shown varied effectiveness in controlling the pandemic. However, a systematic analysis and modelling of the combined effects of different viral lineages and complex intervention policies remains a challenge due to the lack of suitable measures of pandemic inequality and nonlinear effects.</p><p><strong>Methods: </strong>Using large-scale agent-based modelling and a high-resolution computational simulation matching census-based demographics of Australia, we carried out a systematic comparative analysis of several COVID-19 pandemic scenarios. The scenarios covered two most recent Australian census years (2016 and 2021), three variants of concern (ancestral, Delta and Omicron), and five representative intervention policies. We introduced pandemic Lorenz curves measuring an unequal distribution of the pandemic severity across local areas. We also quantified pandemic biomodality, distinguishing between urban and regional waves, and measured bifurcations in the effectiveness of interventions.</p><p><strong>Results: </strong>We quantified nonlinear effects of population heterogeneity on the pandemic severity, highlighting that (i) the population growth amplifies pandemic peaks, (ii) the changes in population size amplify the peak incidence more than the changes in density, and (iii) the pandemic severity is distributed unequally across local areas. We also examined and delineated the effects of urbanisation on the incidence bimodality, distinguishing between urban and regional pandemic waves. Finally, we quantified and examined the impact of school closures, complemented by partial interventions, and identified the conditions when inclusion of school closures may decisively control the transmission.</p><p><strong>Conclusions: </strong>Public health response to long-lasting pandemics must be frequently reviewed and adapted to demographic changes. To control recurrent waves, mass-vaccination rollouts need to be complemented by partial NPIs. Healthcare and vaccination resources need to be prioritised towards the localities and regions with high population growth and/or high density.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":"21 1","pages":"17"},"PeriodicalIF":3.2,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71415236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical reference framework to analyze non-COVID-19 events.","authors":"María Del Pilar Villamil, Nubia Velasco, David Barrera, Andrés Segura-Tinoco, Oscar Bernal, José Tiberio Hernández","doi":"10.1186/s12963-023-00316-8","DOIUrl":"10.1186/s12963-023-00316-8","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic has disrupted the healthcare system, leading to delays in detection of other non-COVID-19 diseases. This paper presents ANE Framework (Analytics for Non-COVID-19 Events), a reliable and user-friendly analytical forecasting framework designed to predict the number of patients with non-COVID-19 diseases. Prior to 2020, there were analytical models focused on specific illnesses and contexts. Then, most models have focused on understanding COVID-19 behavior. There is a lack of analytical frameworks that enable disease forecasting for non-COVID-19 diseases.</p><p><strong>Methods: </strong>The ANE Framework utilizes time series analysis to generate forecasting models. The framework leverages daily data from official government sources and employs SARIMA models to forecast the number of non-COVID-19 cases, such as tuberculosis and suicide attempts.</p><p><strong>Results: </strong>The framework was tested on five different non-COVID-19 events. The framework performs well across all events, including tuberculosis and suicide attempts, with a Mean Absolute Percentage Error (MAPE) of up to 20% and the consistency remains independent of the behavior of each event. Moreover, a pairwise comparison of averages can lead to over or underestimation of the impact. The disruption caused by the pandemic resulted in a 17% gap (2383 cases) between expected and reported tuberculosis cases, and a 19% gap (2464 cases) for suicide attempts. These gaps varied between 20 and 64% across different cities and regions. The ANE Framework has proven to be reliable for analyzing several diseases and exhibits the flexibility to incorporate new data from various sources. Regular updates and the inclusion of new associated data enhance the framework's effectiveness.</p><p><strong>Conclusions: </strong>Current pandemic shows the necessity of developing flexible models to be adapted to different illness data. The framework developed proved to be reliable for the different diseases analyzed, presenting enough flexibility to update with new data or even include new data from different databases. To keep updated on the result of the project allows the inclusion of new data associated with it. Similarly, the proposed strategy in the ANE framework allows for improving the quality of the obtained results with news events.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":"21 1","pages":"16"},"PeriodicalIF":3.2,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49684844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence of unmet health care need in older adults in 83 countries: measuring progressing towards universal health coverage in the context of global population ageing.","authors":"Paul Kowal, Barbara Corso, Kanya Anindya, Flavia C D Andrade, Thanh Long Giang, Maria Teresa Calzada Guitierrez, Wiraporn Pothisiri, Nekehia T Quashie, Herney Alonso Rengifo Reina, Megumi Rosenberg, Andy Towers, Paolo Miguel Manalang Vicerra, Nadia Minicuci, Nawi Ng, Julie Byles","doi":"10.1186/s12963-023-00308-8","DOIUrl":"10.1186/s12963-023-00308-8","url":null,"abstract":"<p><p>Current measures for monitoring progress towards universal health coverage (UHC) do not adequately account for populations that do not have the same level of access to quality care services and/or financial protection to cover health expenses for when care is accessed. This gap in accounting for unmet health care needs may contribute to underutilization of needed services or widening inequalities. Asking people whether or not their needs for health care have been met, as part of a household survey, is a pragmatic way of capturing this information. This analysis examined responses to self-reported questions about unmet need asked as part of 17 health, social and economic surveys conducted between 2001 and 2019, representing 83 low-, middle- and high-income countries. Noting the large variation in questions and response categories, the results point to low levels (less than 2%) of unmet need reported in adults aged 60+ years in countries like Andorra, Qatar, Republic of Korea, Slovenia, Thailand and Viet Nam to rates of over 50% in Georgia, Haiti, Morocco, Rwanda, and Zimbabwe. While unique, these estimates are likely underestimates, and do not begin to address issues of poor quality of care as a barrier or contributing to unmet need in those who were able to access care. Monitoring progress towards UHC will need to incorporate estimates of unmet need if we are to reach universality and reduce health inequalities in older populations.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":"21 1","pages":"15"},"PeriodicalIF":3.3,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10650909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The fraction of life years lost after diagnosis (FLYLAD): a person-centred measure of cancer burden.","authors":"David Banham, Jonathan Karnon, Alex Brown, David Roder, John Lynch","doi":"10.1186/s12963-023-00314-w","DOIUrl":"10.1186/s12963-023-00314-w","url":null,"abstract":"<p><strong>Background: </strong>Cancer control initiatives are informed by quantifying the capacity to reduce cancer burden through effective interventions. Burden measures using health administrative data are a sustainable way to support monitoring and evaluating of outcomes among patients and populations. The Fraction of Life Years Lost After Diagnosis (FLYLAD) is one such burden measure. We use data on Aboriginal and non-Aboriginal South Australians from 1990 to 2010 to show how FLYLAD quantifies disparities in cancer burden: between populations; between sub-population cohorts where stage at diagnosis is available; and when follow-up is constrained to 24-months after diagnosis.</p><p><strong>Method: </strong>FLYLAD_cancer is the fraction of years of life expectancy lost due to cancer (YLL_cancer) to life expectancy years at risk at time of cancer diagnosis (LYAR) for each person. The Global Burden of Disease standard life table provides referent life expectancies. FLYLAD_cancer was estimated for the population of cancer cases diagnosed in South Australia from 1990 to 2010. Cancer stage at diagnosis was also available for cancers diagnosed in Aboriginal people and a cohort of non-Aboriginal people matched by sex, year of birth, primary cancer site and year of diagnosis.</p><p><strong>Results: </strong>Cancers diagnoses (N = 144,891) included 777 among Aboriginal people. Cancer burden described by FLYLAD_cancer was higher among Aboriginal than non-Aboriginal (0.55, 95% CIs 0.52-0.59 versus 0.39, 95% CIs 0.39-0.40). Diagnoses at younger ages among Aboriginal people, 7 year higher LYAR (31.0, 95% CIs 30.0-32.0 versus 24.1, 95% CIs 24.1-24.2) and higher premature cancer mortality (YLL_cancer = 16.3, 95% CIs 15.1-17.5 versus YLL_cancer = 8.2, 95% CIs 8.2-8.3) influenced this. Disparities in cancer burden between the matched Aboriginal and non-Aboriginal cohorts manifested 24-months after diagnosis with FLYLAD_cancer 0.44, 95% CIs 0.40-0.47 and 0.28, 95% CIs 0.25-0.31 respectively.</p><p><strong>Conclusion: </strong>FLYLAD described disproportionately higher cancer burden among Aboriginal people in comparisons involving: all people diagnosed with cancer; the matched cohorts; and, within groups diagnosed with same staged disease. The extent of disparities were evident 24-months after diagnosis. This is evidence of Aboriginal peoples' substantial capacity to benefit from cancer control initiatives, particularly those leading to earlier detection and treatment of cancers. FLYLAD's use of readily available, person-level administrative records can help evaluate health care initiatives addressing this need.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":"21 1","pages":"14"},"PeriodicalIF":3.3,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10262474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flexible parametric methods for calculating life expectancy in small populations.","authors":"Freya Tyrer, Yogini V Chudasama, Paul C Lambert, Mark J Rutherford","doi":"10.1186/s12963-023-00313-x","DOIUrl":"10.1186/s12963-023-00313-x","url":null,"abstract":"<p><strong>Background: </strong>Life expectancy is a simple measure of assessing health differences between two or more populations but current life expectancy calculations are not reliable for small populations. A potential solution to this is to borrow strength from larger populations from the same source, but this has not formally been investigated.</p><p><strong>Methods: </strong>Using data on 451,222 individuals from the Clinical Practice Research Datalink on the presence/absence of intellectual disability and type 2 diabetes mellitus, we compared stratified and combined flexible parametric models, and Chiang's methods, for calculating life expectancy. Confidence intervals were calculated using the Delta method, Chiang's adjusted life table approach and bootstrapping.</p><p><strong>Results: </strong>The flexible parametric models allowed calculation of life expectancy by exact age and beyond traditional life expectancy age thresholds. The combined model that fit age interaction effects as a spline term provided less bias and greater statistical precision for small covariate subgroups by borrowing strength from the larger subgroups. However, careful consideration of the distribution of events in the smallest group was needed.</p><p><strong>Conclusions: </strong>Life expectancy is a simple measure to compare health differences between populations. The use of combined flexible parametric methods to calculate life expectancy in small samples has shown promising results by allowing life expectancy to be modelled by exact age, greater statistical precision, less bias and prediction of different covariate patterns without stratification. We recommend further investigation of their application for both policymakers and researchers.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":"21 1","pages":"13"},"PeriodicalIF":3.3,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10588595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}