Population Health Studies Research Articles

Association between depression symptom severity and total cost of care: Findings from a large, 2-year, claims-based, retrospective population health study

BackgroundDespite the substantial impact of depression on individuals and healthcare utilization, little is known about the specific relationship between depression severity and total cost of care (TCC). This study evaluates the association between depression symptom severity and TCC and how changes in severity affect TCC. MethodsThe analysis was conducted using insurance claims data and data from electronic health records between January 1, 2019 and December 31, 2020. Inclusion criteria comprised insured individuals with coverage during 2019 or 2020, aged one year or older, and identified as having depression in at least one year of the study. Depression symptom severity was assessed using the screening Identification and Stratification (IDS) framework and data available to the research team. The main outcome was TCC per member per month (PMPM) evaluated across the two-year period. ResultsAcross 2019 and 2020, 744,854 members met inclusion criteria. A total of 369,460 members were studied across both years. Greater depression symptom severity was associated with higher TCC across both years. Unchanged severity was associated with limited change in TCC from 2019 to 2020. Decrease in depression symptoms was associated with an average $41 reduction in PMPM spend, whereas increase in depression symptom severity was associated with an average $608 increase. LimitationsLimitations include fragmented data, retrospective design that limits causality, and the IDS framework design. ConclusionChanges in depression symptom severity were significantly associated with changes in TCC. Findings reveal financial and clinical opportunities associated with early identification and targeted management of depression.

The CanPath-HDRN Canada Collaboration: Enabling Multi-jurisdictional Research in Canada

ObjectiveTo highlight the partnership between the Canadian Partnership for Tomorrow’s Health (CanPath) and Health Data Research Network (HDRN Canada), which enables researchers to link CanPath’s health and lifestyle survey data to health records and related data across multiple regions. BackgroundCanPath is a population health study of over 350,000 Canadians from seven regional cohorts across all ten provinces, making it one of the world's largest population cohorts. HDRN Canada is a network of member organizations, including provincial, territorial, and pan-Canadian data centres. ApproachThis partnership leveraged collective expertise and resources to facilitate the linkage of CanPath’s harmonized data to multi-regional health and health-related administrative data held at HDRN Canada’ s data centres. Researchers can access these data for multi-regional projects through HDRN Canada’s Data Access Support Hub, which provides a single access portal. ResultsResearchers were able to successfully link CanPath’s survey data to provincial administrative health data. The collaboration’s streamlined process for data access enhances efficiency and facilitates pan-Canadian population health research. ConclusionThis collaboration demonstrates the feasibility and value of linking population health datasets while demonstrating the challenges and opportunities associated with accessing national administrative health data within a federated health data system. ImplicationsThe partnership between CanPath and HDRN Canada has significant implications for advancing population health research in Canada. By providing researchers with access to linked data from diverse sources, the partnership enables comprehensive investigations into health determinants, disease patterns, and clinical outcomes. This enhances the scope and depth of population health research in Canada, thereby leading to a better understanding of the most pressing health challenges.

Swipe to Inhale: Tobacco-Related Content on Social Media and Susceptibility to Tobacco Use

Research has linked youth exposure and engagement with tobacco-related content on social media to behavioral changes; however, there is a lack of studies exploring the source and types of such content and their impact on youth's susceptibility to tobacco use. This study examined the association between the type and source of content posted on social media and susceptibility to tobacco use, defined as curiosity or intention to use tobacco or e-cigarettes. Data were from the Population Assessment Tobacco and Health study, a nationally representative cohort study of U.S. youth (n=5,652). This analysis conducted in March 2024, focused on Wave 6 (2021), examining youth who used social media in the past month and did not use cigarettes or e-cigarettes. Multivariable logistic regression was performed to evaluate the associations. Of the youth who had used social media in the past month (88.7%), 61.4% had encountered tobacco-related content. Exposure to such content was associated with greater susceptibility to e-cigarette use (OR=1.49, 95% CI: 1.38-1.62) and cigarette use (OR=1.29, 95% CI: 1.17-1.43). Daily or near-daily exposure to tobacco-related content compared to non-exposed respondents was associated with greater odds of susceptibility to tobacco use (OR=1.53, 95% CI: 1.37-1.71). Only posts made by celebrities and influencers were associated with a greater susceptibility to tobacco use. Regular exposure to tobacco-related content on social media, particularly content shared by celebrities and social media influencers, was associated with susceptibility to tobacco use. These findings underscore the need for targeted interventions to mitigate the effects of social media influencers on youth.

Mendelian randomization supports causal effects of inflammatory biomarkers on myopic refractive errors.

To determine whether inflammatory biomarkers are causal risk factors for more myopic refractive errors. Northern Sweden Population Health Study (NSPHS), providing inflammatory biomarkers data; UK Biobank, providing refractive errors data. 95,619 European men and women aged 40 to 69 years with available information of refractive errors and inflammatory biomakers. Inflammatory biomarkers including ADA, CCL23, CCL25, CD6, CD40, CDCP-1, CST5, CXCL-5, CXCL-6, CXCL-10, IL-10RB, IL-12B, IL-15RA, IL-18R1, MCP-2, MMP-1, TGF-β1, TNF-β, TWEAK and VEGF-A were exposures, and spherical equivalent (SE) using the formula SE = sphere + (cylinder/2) was outcome. Mendelian randomization analyses showed that each unit increase in VEGF-A, CD6, MCP-2 were causally related to a more myopic refractive errors of 0.040 D/pg.mL-1 (95% confidence interval 0.019 to 0.062; P = 2.031 × 10-4), 0.042 D/pg.mL-1 (0.027 to 0.057; P = 7.361 × 10-8) and 0.016 D/pg.mL-1 (0.004 to 0.028; P = 0.009), and each unit increase in TWEAK was causally related to a less myopic refractive errors of 0.104 D/pg.mL-1 (-0.152 to -0.055; P = 2.878 × 10-5). Tested by the MR-Egger, weighted median, MR-PRESSO, Leave-one-out methods, our results were robust to horizontal pleiotropy and heterogeneity in VEGF-A, MCP-2, CD6, but not in TWEAK. Our Mendelian Randomization analysis supported the causal effects of VEGF-A, MCP-2, CD6 and TWEAK on myopic refractive errors. These findings are important for providing new indicators for early intervention of myopia to make myopic eyesight threatening consequences less inevitable.

The Impact of Delayed Processing of Chilled Whole Blood Specimens on the Measurement of Nutritional Biomarkers in the United Kingdom National Diet and Nutrition Survey Rolling Programme

BackgroundThe logistics of timely processing of blood specimens remains a barrier in population health studies to the generation of micronutrient status data. ObjectivesTo test a blood specimen processing protocol that includes overnight postage with cooling and its effect on nutritional biomarker concentrations. MethodsThis study was embedded within the United Kingdom National Diet and Nutrition Survey. Paired specimens were collected from 64 participants (16 y+). One set of specimens were processed within 2 h of collection [“field”] and paired samples were mailed in an insulated box with cold packs using an overnight postal service to a central laboratory [“postal”]. Specimen processing protocols were aligned across field sites and the central laboratory. Specimens were frozen and later analyzed using established methods for vitamins, minerals, lipids, ferritin, and C-reactive protein (CRP). Percent difference was calculated between protocols and compared with quality specifications determined from intra- and interindividual variation. ResultsIn the postal protocol, ferritin [geometric mean percent difference (95% confidence interval)] [6% (3, 8)] (P = 0.002) and zinc [4% (1, 6)] (P = 0.004) were higher compared with the field protocol. Retinol [−3% (−4, −1)] (P < 0.0001) and selenium [−3% (−5, −1)] (P = 0.003) concentrations were lower in the postal protocol, whereas total [2% (1, 3)] and HDL [4% (2, 5)] cholesterol were higher (P < 0.0001) than in the field protocol. Percent differences were within the optimum quality specification for the majority of biomarkers, but ferritin, zinc, and selenium fell outside of the optimum limits. Higher ferritin concentration in the postal protocol led to a decrease in the proportion of specimens with ferritin concentration <15 μg/L from 13% to 9%. ConclusionsThe majority of micronutrient biomarkers, serum lipids, and CRP were minimally affected by delayed processing when cooled. The study suggests acceptable stability of nutritional biomarkers within the described protocol, which can provide accurate data for nutritional biomarkers commonly measured in studies and surveys.

Modern Trends in Development of Educational and Scientific Discipline ‘Public Health and Healthcare’: Its Methodological Role and Integrative Function

INTRODUCTION: In modern conditions of social development, there is a change of emphasis in definitions concerning not only public, but also individual health. Preserving and strengthening the health of the population is a complex task that implies the interaction of various specialists. This integration, the complexity of interaction in issues concerning preserving and strengthening the health of the population, should undoubtedly be reflected in the training of specialists, and, first of all, in higher medical education. AIM: To determine the role of the discipline ‘Public Health and Healthcare’ (PHH) in teaching population health issues and in the integration of departments of various profiles in the study of individual, group and population health in modern conditions. The article presents a discussion about modern trends in the development of the educational and scientific PHH discipline based on the opinions of students, teachers and the experience of Sechenovskiy University. Thus, students of profiles ‘General Medicine’, ‘Pediatrics’, ‘Medical and Preventive Care’ think that studying public health issues is the task of doctors of all clinical specialties (69.4%–76.7%), of hygienists ( 60.0%), of epidemiologists ( 30.0%). Integration in the teaching of PHH issues, according to teachers of PHH departments, in such types of activities as academic work, educational work, general scientific projects, joint printed works, textbooks and teaching aids, joint innovative projects, is mainly carried out with teachers of preventive educational disciplines, to a much lesser extent with teachers of clinical and fundamental disciplines. CONCLUSION: An integrative approach to definitions, terms, and concepts is needed in the study of the health of individuals, population groups and population with a significant variety of disciplines and departments teaching them. The methodological role in teaching these issues is the prerogative of the PHH discipline.

Prevalence of tight glycemic control based on frailty status and associated factors in community-dwelling older adults.

Tight control of type 2 diabetes (T2DM) in frail older adults has shown to be associated with adverse outcomes. The objective of this study is to determine the prevalence of tight glycemic control based on underlying frailty status and its association with functional and cognitive measures in community-dwelling older adults. Ancillary study of the Singapore Population Health Studies on older adults aged ≥65years with T2DM. Tight glycemic control cut-offs were based on the 2019 Endocrine Society guideline using HbA1c target range based on a patient's overall health status measured by the FRAIL scale. Data on basic demographics, frailty, cognitive, and functional statuses were collected. Multivariable regression was used to assess potential factors associated with tight glycemic control. Of 172 community-dwelling older adults with diabetes mellitus and HbA1c done, frail (65%) and pre-frail (64.4%) participants were more likely to have tight glycemic control than robust participants (31.6%, P< 0.001). In multi-variate analysis, frailty (OR 6.43, 95% CI 1.08-38.1, P= 0.041), better cognition (OR 1.15, 95% CI 1.02-1.32, P= 0.028), and multi-morbidity (OR 7.36, 95% CI 1.07-50.4, P= 0.042) were found to be significantly associated with increased odds of tight glycemic control. Tight glycemic control was highly prevalent in frail and pre-frail older adults, especially in those with multi-morbidity and better cognition. Future prospective longitudinal studies are required to evaluate effectiveness of frailty screening in making treatment decisions and long-term outcomes. Key messages What is already known on this topic: There is growing recognition that glycemic targets should be adjusted based on health or frailty status. However, there is no consensus on how health status or frailty should be defined when determining glycemic control targets. What this study adds: Our study found that tight glycemic control was highly prevalent in frail and pre-frail older adults. Our findings highlight the importance of assessing for tight glycemic control based on frailty status and further work is needed to aid implementation of screening and intervention policies to avoid the attendant harms of tight glycemic control.

Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence.

To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app "listener" that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and artificial intelligence (AI) for processing unstructured text. Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across 5 healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs, (2) increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.

AI-based approach to enable proactive identification of early lung cancer: A retrospective population health study and economic model.

10536 Background: Approximately 48,500 new cases of lung cancer are diagnosed each year in the UK with a five-year survival rate which lags behind other European countries. Most patients are diagnosed at stage III or IV disease, which has a significantly worse prognosis. The integration of real-world data and AI techniques presents a promising avenue for improvement, using novel solutions not achievable through traditional care models. This study explores proactive patient identification at scale, aligning with the "left shift" paradigm in population health. The approach uses innovative technologies and novel data sources to identify patients with early-stage disease, enhancing both clinical and economic outcomes. Importantly, these interventions should be feasible on a large scale without overburdening already stretched clinical services. Methods: A retrospective study of real-world data for an urban population with high socioeconomic disadvantage and health needs was undertaken. Two endpoints were determined: detection potential and health economic impact. For detection potential (DP), patients aged 40 and above who underwent chest X-rays (CXR) at a large academic medical centre between 2016-2022 were included. Natural Language Processing (NLP) analysed unstructured free-text data from Electronic Health Records, combined with ICD-10 codes, for predictive model (PM) development using Machine Learning (ML) classification techniques. The PM's performance was assessed using the area under the receiver operating characteristic curve (AUCROC). Health economic impact was evaluated through a theoretical extrapolation of DP. Assuming a 9% left shift from late (stage III or IV) to early (stage I or II) disease, a health economic model (HEM) compared the impact of the current care model to a potential future state facilitated by the digital medicine care model (DMCM). Results: 75,342 patients were included, with 755 lung cancer diagnoses occurring within 6 months of CXR. The PM achieved a peak AUROC of 0.75, exceeding the performance of currently used risk prediction models (Q Cancer Risk, Risk Assessment Tools). A 9% left shift equated to 4.5 years mean increase in survival. The DMCM was associated with a net healthcare cost benefit of £6k per patient diagnosed with lung cancer, improved quality-adjusted life years of £21M for the year 1 cohort over 10 years and increased economic productivity (from additional years in workforce) of £2.4M. Conclusions: The study demonstrates the practical utility of AI-supported early lung cancer detection at population scale. Real-world evidence is needed to prospectively validate the model as cost-effective, scalable, and efficient. Future work will seek to improve the model's performance against a broader at-risk patient population and explore its applicability to other late-presenting tumours.

Alzheimer Disease Associated Loci: APOE Single Nucleotide Polymorphisms in Marmara Region.

Alzheimer's disease (AD) is a major global health challenge, especially among individuals aged 65 or older. According to population health studies, Turkey has the highest AD prevalence in the Middle East and Europe. To accurately determine the frequencies of common and rare APOE single nucleotide polymorphisms (SNPs) in the Turkish population residing in the Marmara Region, we conducted a retrospective study analyzing APOE variants in 588 individuals referred to the Bursa Uludag University Genetic Diseases Evaluation Center. Molecular genotyping, clinical exome sequencing, bioinformatics analysis, and statistical evaluation were employed to identify APOE polymorphisms and assess their distribution. The study revealed the frequencies of APOE alleles as follows: ε4 at 9.94%, ε2 at 9.18%, and ε3 at 80.68%. The gender-based analysis in our study uncovered a tendency for females to exhibit a higher prevalence of mutant genotypes across various SNPs. The most prevalent haplotype observed was ε3/ε3, while rare APOE SNPs were also identified. These findings align with global observations, underscoring the significance of genetic diversity and gender-specific characteristics in comprehending health disparities and formulating preventive strategies.

Family physicians’ preferences for education to support family caregivers: a sequential mixed methods study

BackgroundOne in four Canadians is a family caregiver. Family caregivers (carers, care-partners) are relatives or chosen family, friends, or neighbors who provide 75 to 90% of the care for people with physical or mental illness, disabilities, or frailty living in community homes and assist with 15 to 30% of the care in congregate care. However, a recent (2022) Statistics Canada population health study reports 44% of family caregivers are distressed. Family physicians and primary care teams are well-positioned to support family caregivers; yet, family caregiver needs assessments tend to be ad hoc and their most common needs remain unmet. Research recommends training healthcare professionals to enhance their knowledge and skills to support family caregivers.MethodsThe objective of this sequential mixed methods research, a survey followed by qualitative interviews, was to explore family physicians’ desire and preferences for education about supporting family caregivers. 85 family physicians completed the online survey and eight took part in the interviews. Results from the survey and interviews were compared, contrasted, and interpreted together.ResultsPrimary care physicians expressed a desire to be better equipped to assess and support FCGs’ needs. Even though most physicians (61%) were very/confident about addressing family caregivers’ needs, 72% were highly/interested in education to support family caregivers of their patients. Topics with the most interest were assessing family caregivers needs in an organized way, assisting family caregivers to access resources, and address system and practice barriers to support family caregivers. The overarching theme running through the interviews was physicians hope for education to help change the patient-focused culture to inclusion of FCGs. The three themes reflect physicians’ conviction about including family caregivers in patient care: We need to take care of their caregivers, Practice and system barriers thwart including family caregivers, and Practical education might help.ConclusionsThis study of family physicians’ preferences for education to support family caregivers will inform the development of education about supporting family caregivers for family physicians and trainees.

Female Reproductive Factors and Risk of Mild Cognitive Impairment and Dementia: The HUNT Study

BackgroundMore women are living with dementia than men worldwide and there is a need to investigate causes for this female preponderance. While reproductive factors have been investigated as risk factors, the results are conflicting. We aim to clarify this using a large cohort with a long observation time, adjusting for multiple health and lifestyle variables and encompassing a wider range of cognitive impairment.ObjectiveTo study the association between menopause age, menarche age and risk of and risk of mild cognitive impairment (MCI) and dementia.SettingThe Trøndelag Health study (HUNT), a longitudinal population health study in Norway (1984–2019).ParticipantsWomen who were ≥70 years in 2017–2019 were assessed for cognitive impairment.MeasurementsData on menopause age and menarche age were obtained from questionnaires. Diagnosis of MCI or dementia was set using a standardised procedure by a diagnostic group of nine physicians. Multinomial logistic regression was used to study the association between menopause age, menarche age and risk of MCI and dementia with adjustment for birth year, education, smoking, ApoE4, number of children, diabetes, body mass index, alcohol use and physical inactivity.ResultsWe evaluated 5314 women where 900 (16.9%) had dementia, and 1747 (32.8%) had MCI. Multiple adjusted relative risk ratio (RRR) and 95% confidence intervals (CI) for dementia were: 0.96(95%CI 0.95–0.98) (p<0.001) for menopause age, 0.97(95%CI 0.94–0.99) (p=0.007) for natural menopause age (excluding hysterectomy and/or oophorectomy<55 years) and 0.97(95%CI 0.95–0.99) (p<0.001) for reproductive span (menopause age minus menarche age). Menopause age <45years was associated with a 56% higher risk compared to mean menopause age 50 years. We found no significant associations between menarche age and dementia and no associations with MCI.ConclusionsOlder menopause age and longer reproductive span corresponding to longer oestrogen exposure were associated with a lower dementia risk. Future studies should explore therapeutical options to offset this risk in women.

Impact of non-participation bias due to psychiatric illness on mortality and cardiovascular event estimates: a Danish longitudinal population study

ObjectivesThe impact of non-participation due to psychiatric illness on study outcomes in general population studies is insufficiently investigated. Here, we investigate the mental health bias in a population study and...

Advancing Global Health Surveillance of Mycotoxin Exposures using Minimally Invasive Sampling Techniques: A State-of-the-Science Review.

Mycotoxins are a heterogeneous group of toxins produced by fungi that can grow in staple crops (e.g., maize, cereals), resulting in health risks due to widespread exposure from human consumption and inhalation. Dried blood spot (DBS), dried serum spot (DSS), and volumetric tip microsampling (VTS) assays were developed and validated for several important mycotoxins. This review summarizes studies that have developed these assays to monitor mycotoxin exposures in human biological samples and highlights future directions to facilitate minimally invasive sampling techniques as global public health tools. A systematic search of PubMed (MEDLINE), Embase (Elsevier), and CINAHL (EBSCO) was conducted. Key assay performance metrics were extracted to provide a critical review of the available methods. This search identified 11 published reports related to measuring mycotoxins (ochratoxins, aflatoxins, and fumonisins) using DBS/DSS and VTS assays. Multimycotoxin assays adapted for DBS/DSS and VTS have undergone sufficient laboratory validation for applications in large-scale population health and human biomonitoring studies. Future work should expand the number of mycotoxins that can be measured in multimycotoxin assays, continue to improve multimycotoxin assay sensitivities of several biomarkers with low detection rates, and validate multimycotoxin assays across diverse populations with varying exposure levels. Validated low-cost and ultrasensitive minimally invasive sampling methods should be deployed in human biomonitoring and public health surveillance studies to guide policy interventions to reduce inequities in global mycotoxin exposures.

Critical analyses of Latina mortality: disentangling the heterogeneity of ethnic origin, place, nativity, race, and socioeconomic status

Despite the significant body of research on social determinants of health (SDH) and mortality, limited knowledge is available on the epidemiology of aggregated Latino health overall, and by women and subgroups. In population health studies, U.S. Latinos often are considered a monolithic population and presented as an aggregate, obscuring the diversity and variations within and across Latino subgroups, contributing to missed opportunities to identify SDH of health outcomes, and limiting the understanding of health differences. Given diverse environmental, racial, class, and geographic factors, a specific focus on women facilitates a more in-depth view of health disparities. This paper provides a scoping review of current gaps in research that assesses the relationships between SDH and mortality rates for the five leading causes of chronic-disease related deaths among Latinas by ethnic origin, place, race, and SES. We analyzed 2020 national mortality statistics from the CDC WONDER Online database jointly with reviews of empirical articles on Latina health, employing the EBSCOhost MEDLINE databases. These findings challenge the phenomenon of the Hispanic paradox that identified Latinos as a relatively healthy population compared to non-Hispanic White populations despite their lower economic status. The findings confirm that prior research on Latino women had methodological limitations due to the exclusion of SDH and an overemphasis on culturalist perspectives, while overlooking the critical role of socioeconomic impacts on health. Findings indicate major knowledge gaps in Latina mortality by SDH and subgroups that may undermine surveillance efforts and treatment efficacy. We offer forward-looking recommendations to assure the inclusion of key SDH associated with Latina mortality by subgroup as essential to inform future studies, intervention programs, and health policy.

Prevalence Study of MASLD in Adolescent and Young Adult Pacific Islanders and Asians Living in Hawai'i.

Nonalcoholic fatty liver disease, renamed metabolic dysfunction-associated steatotic liver disease (MASLD), is the most common cause of chronic liver disease with an estimated worldwide prevalence of 30.1% while clinical practice observations reflect a disproportionately lower prevalence of 1.9%, indicating a condition that is underrecognized in clinical care settings. Screening for MASLD is rarely performed, and little is known about the prevalence in Hawai'i. This pilot aims to develop an understanding of the prevalence and factors associated with MASLD in Hawai'i's adolescent and young adult (AYA) population. Cross-sectional observational pilot study: We used Fibroscan®-liver ultrasonographic vibration-controlled transient elastography (VCTE) to identify MASLD based on controlled attenuation parameter (CAP) scores ≥238 (dB/m) and collected biometric, anthropometric, and Beverage Intake Questionnaire (sugar-sweetened beverage) survey data. The study took place at community clinics in Hawai'i on the island of O'ahu. One hundred individuals were evaluated, age 14 to 34 years. We used VCTE Fibroscan® with CAP scoring to identify the presence of hepatocyte steatosis (fatty liver). Overall MASLD prevalence in the sample was 44% (95% confidence interval: 34.1%-54.3%). In participants with MASLD, obese Native Hawaiian and other Pacific Islanders (62%) and nonobese Asians (43%) had the highest rates of MASLD. This pilot evaluation of the AYA NHOPI and Asian MASLD population in Hawai'i shows a higher rate of MASLD than those reported in other parts of the United States. Larger population health studies are indicated to expand our knowledge of MASLD in the Hawaiian Islands.

The costs of chronic pain-Long-term estimates.

Chronic pain is a condition with severe impact on many aspects of life, including work, functional ability and quality of life, thereby reducing physical, mental and social well-being. Despite the high prevalence and burden of chronic pain, it has received disproportionally little attention in research and public policy and the societal costs of chronic pain remain largely unknown. This study aimed to describe the long-term healthcare and work absence costs of individuals with and without self-identified chronic pain. The study population were participants in two Norwegian population health studies (HUNT3 and Tromsø6). Participants were defined as having chronic pain based on a self-reported answer to a question on chronic pain in the health studies in 2008. Individuals in the study population were linked to four national register databases on healthcare resource use and work absence. In our study, 36% (n = 63,782) self-reported to have chronic pain and the average years of age was 56.6. The accumulated difference in costs between those with and without chronic pain from 2010 to 2016 was €55,003 (CI: 54,414-55,592) per individual. Extrapolating this to the entire population suggests that chronic pain imposes a yearly burden of 4% of GDP. Eighty per cent of the costs were estimated to be productivity loss. Insights from this study can provide a greater understanding of the extent of healthcare use and productivity loss by those with chronic pain and serve as an important basis for improvements in rehabilitation and quality of care, and the education of the public on the burden of chronic pain. This was the first study to estimate the economic burden associated with chronic pain in the general population using linked individual-level administrative data and self-reported survey answers. We provide calculations showing that annual costs of chronic pain may be as high as €12 billion or 4% of GDP. Findings from this study highlight the need for a greater understanding of the substantial healthcare use and productivity losses among individuals with chronic pain.

BIAS AND GENERALIZABILITY IN MEDICARE-BASED ANALYSES ACROSS THREE POLICY PERIODS

Abstract Administrative Big Health data such as that drawn from the Medicare program is an invaluable resource for the study of population health. Although the Medicare program provides near universal health coverage for older U.S. adults age 65+, for most types of care data is collected only for individuals enrolled in traditional Medicare (TM) plans, with those enrolled in private Medicare Advantage (MA) plans being effectively censored in terms of the extent of available data. This presents two potential problems for health outcomes research: bias and generalizability. In this study we use Health and Retirement Study data (1998-2015) to assess the presence and magnitude of group-specific differences between MA and TM beneficiaries as well as plan switchers across three policy-specific periods (Balanced Budget Act; Medicare Modernization Act; Affordable Care Act). We found that the MA population was characterized by higher diversity, adverse behavioral habits, lower education, and economic disadvantage. Initially, lower morbidity levels converged with the TM average over time. Individuals, switching into MA after TM had much larger morbidity levels and mortality risk than any other Medicare group. We found significant loss of generalizability with respect to the Hispanic population; such estimates could be further biased to the extent that MA Hispanics differ from TM Hispanics. Of great concern is the combination of low education and low economic status in MA beneficiaries – a combination also found in many disadvantaged minorities. Care is needed in the design of TM-based studies, especially those focused on ethnic differences.

Moving towards the detection of frailty with biomarkers: A population health study.

Aging adults experience increased health vulnerability and compromised abilities to cope with stressors, which are the clinical manifestations of frailty. Frailty is complex, and efforts to identify biomarkers to detect frailty and pre-frailty in the clinical setting are rarely reproduced across cohorts. We developed a predictive model incorporating biological and clinical frailty measures to identify robust biomarkers across data sets. Data were from two large cohorts of older adults: "Invecchiare in Chianti (Aging in Chianti, InCHIANTI Study") (n = 1453) from two small towns in Tuscany, Italy, and replicated in the Atherosclerosis Risk in Communities Study (ARIC) (n = 6508) from four U.S. communities. A complex systems approach to biomarker selection with a tree-boosting machine learning (ML) technique for supervised learning analysis was used to examine biomarker population differences across both datasets. Our approach compared predictors with robust, pre-frail, and frail participants and examined the ability to detect frailty status by race. Unique biomarker features identified in the InCHIANTI study allowed us to predict frailty with a model accuracy of 0.72 (95% confidence interval (CI) 0.66-0.80). Replication models in ARIC maintained a model accuracy of 0.64 (95% CI 0.66-0.72). Frail and pre-frail Black participant models maintained a lower model accuracy. The predictive panel of biomarkers identified in this study may improve the ability to detect frailty as a complex aging syndrome in the clinical setting. We propose several concrete next steps to keep research moving toward detecting frailty with biomarker-based detection methods.

Population Health and Cancer Testing (PHACT) Study: A Model for Providing Cancer Gene Panel Testing in a Community-Recruited Multi-Ethnic Population

Population Health and Cancer Testing (PHACT) Study: A Model for Providing Cancer Gene Panel Testing in a Community-Recruited Multi-Ethnic Population