Using "big data" to capture overall health status: properties and predictive value of a claims-based health risk score

Dementia Risk Scores and Their Role in the Implementation of Risk Reduction Guidelines

Dementia prevention is a global health priority. In 2019, the World Health Organisation published its first evidence-based guidelines on dementia risk reduction. We are now at the stage where we need effective tools and resources to assess dementia risk and implement these guidelines into policy and practice. In this paper we review dementia risk scores as a means to facilitate this process. Specifically, we (a) discuss the rationale for dementia risk assessment, (b) outline some conceptual and methodological issues to consider when reviewing risk scores, (c) evaluate some dementia risk scores that are currently in use, and (d) provide some comments about future directions. A dementia risk score is a weighted composite of risk factors that reflects the likelihood of an individual developing dementia. In general, dementia risks scores have a wide range of implementations and benefits including providing early identification of individuals at high risk, improving risk perception for patients and physicians, and helping health professionals recommend targeted interventions to improve lifestyle habits to decrease dementia risk. A number of risk scores for dementia have been published, and some are widely used in research and clinical trials e.g., CAIDE, ANU-ADRI, and LIBRA. However, there are some methodological concerns and limitations associated with the use of these risk scores and more research is needed to increase their effectiveness and applicability. Overall, we conclude that, while further refinement of risk scores is underway, there is adequate evidence to use these assessments to implement guidelines on dementia risk reduction.

Characterizing Long-Term Trajectories of Work and Disability Leave: The Role of Occupational Exposures, Health, and Personal Demographics

OBJECTIVE

This article characterizes trajectories of work and disability leave across the tenure of a cohort of 49,595 employees in a large American manufacturing firm.

METHODS

We employ sequence and cluster analysis to group workers who share similar trajectories of work and disability leave. We then use multinomial logistic regression models to describe the demographic, health, and job-specific correlates of these trajectories.

RESULTS

All workers were clustered into one of eight trajectories. Female workers (RR 1.3 to 2.1), those experiencing musculoskeletal disease (RR 1.3 to 1.5), and those whose jobs entailed exposure to high levels of air pollution (total particulate matter; RR 1.9 to 2.4) were more likely to experience at least one disability episode.

CONCLUSIONS

These trajectories and their correlates provide insight into disability processes and their relationship to demographic characteristics, health, and working conditions of employees.

Association of Neighborhood Disadvantage With Cardiovascular Risk Factors and Events Among Refugees in Denmark

IMPORTANCE

Refugees are among the most disadvantaged individuals in society, and they often have elevated risks of cardiovascular risk factors and events. Evidence is limited regarding factors that may worsen cardiovascular health among this vulnerable group.

OBJECTIVE

To test the hypothesis that refugee placement in socioeconomically disadvantaged neighborhoods is associated with increased cardiovascular risk.

DESIGN, SETTING, AND PARTICIPANTS

The study population of this quasi-experimental, registry-based cohort study included 49 305 adults 18 years and older who came to Denmark as refugees from other countries during the years of Denmark's refugee dispersal policy from 1986 to 1998. Refugees were dispersed to neighborhoods with varying degrees of socioeconomic disadvantage in an arbitrary manner conditional on observed characteristics. The association of neighborhood disadvantage on arrival with several cardiovascular outcomes in subsequent decades was evaluated using regression models that adjusted for individual, family, and municipal characteristics. Health outcomes were abstracted from the inpatient register, outpatient specialty clinic register, and prescription drug register through 2016. Data analysis was conducted from May 2018 to July 2019.

EXPOSURES

A composite index of neighborhood disadvantage was constructed using 8 neighborhood-level socioeconomic characteristics derived from Danish population register data.

MAIN OUTCOMES AND MEASURES

Primary study outcomes included hypertension, hyperlipidemia, type 2 diabetes, myocardial infarction, and stroke. Before data analysis commenced, it was hypothesized that higher levels of neighborhood disadvantage were associated with an increased risk of cardiovascular risk factors and events.

RESULTS

A total of 49 305 participants were included (median [interquartile range] age, 30.5 [24.9-39.8] years; 43.3% women). Participant region of origin included 6318 from Africa (12.8%), 7253 from Asia (14.7%), 3446 from Eastern Europe (7.0%), 5416 from Iraq (11.0%), 6206 from Iran (12.6%), 5558 from Palestine (via Lebanon, Israel, Occupied Palestinian Territories; 11.3%), and 15 108 from Yugoslavia (30.6%). Adjusted models revealed an association between placement in disadvantaged neighborhoods and increased risk of hypertension (0.71 [95% CI, 0.30-1.13] percentage points per unit of disadvantage index; P < .01), hyperlipidemia (0.44 [95% CI, 0.06-0.83] percentage points; P = .01), diabetes (0.45 [95% CI, 0.09-0.81] percentage points; P = .01), and myocardial infarction (0.14 [95% CI, 0.03-0.25] percentage points; P = .01). No association was found for stroke. Individuals who arrived in Denmark before age 35 years had an increased risk of hyperlipidemia (1.16 [95% CI, 0.41-1.92] percentage points; P < .01), and there were no differences by sex.

CONCLUSIONS AND RELEVANCE

In this quasi-experimental cohort study, neighborhood disadvantage was associated with increased cardiovascular risk in a relatively young population of refugees. Neighborhood characteristics may be an important consideration when refugees are placed by resettlement agencies and host countries. Future work should examine additional health outcomes as well as potential mediating pathways to target future interventions (eg, neighborhood ease of walking, employment opportunities).

Intraseason Waning of Influenza Vaccine Effectiveness

BACKGROUND

In the United States, it is recommended that healthcare providers offer influenza vaccination by October, if possible. However, if the vaccine's effectiveness soon begins to wane, the optimal time for vaccination may be somewhat later. We examined whether the effectiveness of influenza vaccine wanes during the influenza season with increasing time since vaccination.

METHODS

We identified persons who were vaccinated with inactivated influenza vaccine from 1 September 2010 to 31 March 2017 and who were subsequently tested for influenza and respiratory syncytial virus (RSV) by a polymerase chain reaction test. Test-confirmed influenza was the primary outcome and days-since-vaccination was the predictor of interest in conditional logistic regression. Models were adjusted for age and conditioned on calendar day and geographic area. RSV was used as a negative-control outcome.

RESULTS

Compared with persons vaccinated 14 to 41 days prior to being tested, persons vaccinated 42 to 69 days prior to being tested had 1.32 (95% confidence interval [CI], 1.11 to 1.55) times the odds of testing positive for any influenza. The odds ratio (OR) increased linearly by approximately 16% for each additional 28 days since vaccination. The OR was 2.06 (95% CI, 1.69 to 2.51) for persons vaccinated 154 or more days prior to being tested. No evidence of waning was found for RSV.

CONCLUSIONS

Our results suggest that effectiveness of inactivated influenza vaccine wanes during the course of a single season. These results may lead to reconsideration of the optimal timing of seasonal influenza vaccination.

Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance

Technological advances in big data (large amounts of highly varied data from many different sources that may be processed rapidly), data sciences and artificial intelligence can improve health-system functions and promote personalized care and public good. However, these technologies will not replace the fundamental components of the health system, such as ethical leadership and governance, or avoid the need for a robust ethical and regulatory environment. In this paper, we discuss what a robust ethical and regulatory environment might look like for big data analytics in health insurance, and describe examples of safeguards and participatory mechanisms that should be established. First, a clear and effective data governance framework is critical. Legal standards need to be enacted and insurers should be encouraged and given incentives to adopt a human-centred approach in the design and use of big data analytics and artificial intelligence. Second, a clear and accountable process is necessary to explain what information can be used and how it can be used. Third, people whose data may be used should be empowered through their active involvement in determining how their personal data may be managed and governed. Fourth, insurers and governance bodies, including regulators and policy-makers, need to work together to ensure that the big data analytics based on artificial intelligence that are developed are transparent and accurate. Unless an enabling ethical environment is in place, the use of such analytics will likely contribute to the proliferation of unconnected data systems, worsen existing inequalities, and erode trustworthiness and trust.

The effect of the look-back period for estimating incidence using administrative data

Background

The look-back period is needed to define baseline population for estimating incidence. However, short look-back period is known to overestimate incidence of diseases misclassifying prevalent cases to incident cases. The purpose of this study is to evaluate the impact of the various length of look-back period on the observed incidences of uterine leiomyoma, endometriosis and adenomyosis, and to estimate true incidences considering the misclassification errors in the longitudinal administrative data in Korea.

Methods

A total of 319,608 women between 15 to 54 years of age in 2002 were selected from Korea National Health Insurance Services (KNHIS) cohort database. In order to minimize misclassification bias incurred when applying various length of look-back period, we used 11 years of claim data to estimate the incidence by equally setting the look-back period to 11 years for each year using prediction model. The association between the year of diagnosis and the number of prevalent cases with the misclassification rates by each look-back period was investigated. Based on the findings, prediction models on the proportion of misclassified incident cases were developed using multiple linear regression.

Results

The proportion of misclassified incident cases of uterine leiomyoma, endometriosis and adenomyosis were 32.8, 10.4 and 13.6% respectively for the one-year look-back period in 2003. These numbers decreased to 6.3% in uterine leiomyoma and − 0.8% in both endometriosis and adenomyosis using all available look-back periods (11 years) in 2013.

Conclusion

This study demonstrates approaches for estimating incidences considering the different proportion of misclassified cases for various length of look-back period. Although the prediction model used for estimation showed strong R-squared values, follow-up studies are required for validation of the study results.

Accelerated lung function decline in an aluminium manufacturing industry cohort exposed to PM2.5: an application of the parametric g-formula

OBJECTIVE

Occupational dust exposure has been associated with accelerated lung function decline, which in turn is associated with overall morbidity and mortality. In the current study, we assess potential benefits on lung function of hypothetical interventions that would reduce occupational exposure to fine particulate matter (PM2.5) while adjusting for the healthy worker survivor effect.

METHODS

Analyses were performed in a cohort of 6485 hourly male workers in an aluminium manufacturing company in the USA, followed between 1996 and 2013. We used the parametric g-formula to assess lung function decline over time under hypothetical interventions while also addressing time-varying confounding by underlying health status, using a composite risk score based on health insurance claims.

RESULTS

A counterfactual scenario envisioning a limit on exposure equivalent to the 10th percentile of the observed exposure distribution of 0.05 mg/m3 was associated with an improvement in forced expiratory volume in one second (FEV1) equivalent to 37.6 mL (95% CI 13.6 to 61.6) after 10 years of follow-up when compared with the observed. Assuming a linear decrease and (from NHANES reference values), a 20 mL decrease per year for a 1.8 m-tall man as they age, this 37.6 mL FEV1 loss over 10 years associated with observed exposure would translate to approximately a 19% increase to the already expected loss per year from age alone.

CONCLUSIONS

Our results indicate that occupational PM2.5 exposure in the aluminium industry accelerates lung function decline over age. Reduction in exposure may mitigate accelerated loss of lung function over time in the industry.

Association Between Clinical Practice Group Adherence to Quality Measures and Adverse Outcomes Among Adult Patients With Diabetes

IMPORTANCE

Clinical practice group performance on quality measures associated with chronic disease management has become central to reimbursement. Therefore, it is important to determine whether commonly used process and disease control measures for chronic conditions correlate with utilization-based outcomes, as they do in acute disease.

OBJECTIVE

To examine the associations among clinical practice group performance on diabetes quality measures, including process measures, disease control measures, and utilization-based outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective, cross-sectional analysis examined commercial claims data from a national health insurance plan. A cohort of eligible beneficiaries with diabetes aged 18 to 65 years who were enrolled for at least 12 months from January 1, 2010, through December 31, 2014, was defined. Eligible beneficiaries were attributed to a clinical practice group based on the plurality of their primary care or endocrinology office visits. Data were analyzed from October 1, 2018, through April 30, 2019.

MAIN OUTCOMES AND MEASURES

For each clinical practice group, performance on current diabetes quality measures included 3 process measures (2 testing measures [hemoglobin A1c {HbA1c} and low-density lipoprotein {LDL} testing] and 1 drug use measure [statin use]) and 2 disease control measures (HbA1c <8% and LDL level <100 mg/dL). The rates of utilization-based outcomes, including hospitalization for diabetes and major adverse cardiovascular events (MACEs), were also measured.

RESULTS

In this cohort of 652 258 beneficiaries with diabetes from 886 clinical practice groups, 42.9% were aged 51 to 60 years, and 52.6% were men. Beneficiaries lived in areas that were predominantly white (68.1%). At the clinical practice group level, except for high correlation between the 2 testing measures, correlations among different quality measures were weak (r range, 0.010-0.244). Rate of HbA1c of less than 8% had the strongest correlation with hospitalization for MACE (r = -0.046; P = .03) and diabetes (r = -0.109; P < .001). Rates of HbA1c control at the clinical practice group level were not significantly associated with likelihood of hospitalization at the individual level. Performance on the process and disease control measures together explained 3.9% of the variation in the likelihood of hospitalization for a MACE or diabetes at the individual level.

CONCLUSIONS AND RELEVANCE

In this study, performance on utilization-based measures-intended to reflect the quality of chronic disease management-was only weakly associated with direct measures of chronic disease management, namely, disease control measures. This correlation should be considered when determining the degree of financial emphasis to place on hospitalization rates as a measure of quality in treatment of chronic diseases.

The Future of Observational Epidemiology: Improving Data and Design to Align With Population Health

Improvements in data resources and computational power provide important opportunities to ensure the continued relevance and growth of observational epidemiology. To achieve that promise, rigorous statistical analyses are important but not sufficient. We must prioritize articulating relevant research questions and developing strong study designs. Relevance depends on designing observational research so it delivers actionable clinical or population health evidence. Expanding data sources, including administrative records and data from emerging technologies such as sensors, can potentially be leveraged to improve study design, statistical power, measurement, and availability of evidence on diverse populations. With these advantages, particularly evidence on the heterogeneity of treatment effects, observational research can better guide design of randomized trials. Evidence on the heterogeneity of treatment effects is also essential to extend the evidence from randomized trials beyond the narrow range of settings and populations for which trials have been conducted. Machine learning tools will likely grow in importance in observational epidemiology in coming years, although we need careful attention to the appropriate uses of prediction models. Despite the potential of these innovations, they will only be useful if embedded in theoretical frameworks motivated by applied clinical and population health questions.

Big Data in occupational medicine: the convergence of -omics sciences, participatory research and e-health

Background:

New occupational hazards and risks are emerging in our progressively globalized society, in which ageing, migration, wild urbanization and rapid economic growth have led to unprecedented biological, chemical and physical exposures, linked to novel technologies, products and duty cycles. A focus shift from worker health to worker/citizen and community health is crucial. One of the major revolutions of the last decades is the computerization and digitization of the work process, the so-called “work 4.0”, and of the workplace.

Objectives:

To explore the roles and implications of Big Data in the new occupational medicine settings.

Methods:

Comprehensive literature search.

Results:

Big Data are characterized by volume, variety, veracity, velocity, and value. They come both from wet-lab techniques (“molecular Big Data”) and computational infrastructures, including databases, sensors and smart devices (“computational Big Data” and “digital Big Data”).

Conclusions:

In the light of novel hazards and thanks to new analytical approaches, molecular and digital underpinnings become extremely important in occupational medicine. Computational and digital tools can enable us to uncover new relationships between exposures and work-related diseases; to monitor the public reaction to novel risk factors associated to occupational diseases; to identify exposure-related changes in disease natural history; and to evaluate preventive workplace practices and legislative measures adopted for workplace health and safety.

Night and rotational work exposure within the last 12 months and risk of incident hypertension

Objectives Shift work, such as alternating day and nights, causes chronobiologic disruptions which may cause an increase in hypertension risk. However, the relative contributions of the components of shift work ‒ such as shift type (eg, night work) and rotations (ie, switching of shift times; day to night) ‒ on this association are not clear. To address this question, we constructed novel definitions of night work and rotational work and assessed their associations with risk of incident hypertension. Methods A cohort of 2151 workers at eight aluminum manufacturing facilities previously studied for cardiovascular disease was followed from 2003 through 2013 for incident hypertension, as defined by ICD-9 insurance claims codes. Detailed time-registry data was used to classify each worker's history of rotational and night work. The associations between recent rotational work and night work in the last 12 months and incident hypertension were estimated using adjusted Cox proportional hazards models. Results Elevated hazard ratios (HR) were observed for all levels of recent night work (>0-5, >5-50, >50-95, >95-100%) compared with non-night workers, and among all levels of rotational work (<1, 1-10, >10-20, >20-30, and >30%) compared with those working <1% rotational work. In models for considering the combination of night and rotational work, workers with mostly night work and frequent rotations (≥50% night and ≥10% rotation) had the highest risk of hypertension compared to non-night workers [HR 4.00, 95% confidence interval (CI )1.69-9.52]. Conclusions Our results suggest recent night and rotational work may both be associated with higher rates of incident hypertension.

National Rates of Initiation and Intensification of Antidiabetic Therapy Among Patients With Commercial Insurance

OBJECTIVE

Prompt initiation and intensification of antidiabetic therapy can delay or prevent complications from diabetes. We sought to understand the rates of and factors associated with the initiation and intensification of antidiabetic therapy among commercially insured patients in the U.S.

RESEARCH DESIGN AND METHODS

Using 2008-2015 commercial claims linked with laboratory and pharmacy data, we created an initiation cohort with no prior antidiabetic drug use and an HbA_1c ≥8% (64 mmol/mol) and an intensification cohort of patients with an HbA_1c ≥8% (64 mmol/mol) who were on a stable dose of one noninsulin diabetes drug. Using multivariable logistic regression, we determined the rates of and factors associated with initiation and intensification. In addition, we determined the percent of variation in treatment patterns explained by measurable patient factors.

RESULTS

In the initiation cohort (n = 9,799), 63% of patients received an antidiabetic drug within 6 months of the elevated HbA_1c test. In the intensification cohort (n = 10,941), 82% had their existing antidiabetic therapy intensified within 6 months of the elevated HbA_1c test. Higher HbA_1c levels, lower generic drug copayments, and more frequent office visits were associated with higher rates of both initiation and intensification. Better patient adherence prior to the elevated HbA_1c level, existing therapy with a second-generation antidiabetic drug, and lower doses of existing therapy were also associated with intensification. Patient factors explained 7.96% of the variation in initiation and 7.35% of the variation in intensification.

CONCLUSIONS

Approximately two-thirds of patients were newly initiated on antidiabetic therapy, and four-fifths of those already receiving antidiabetic therapy had it intensified within 6 months of an elevated HbA_1c in a commercially insured population. Patient factors explain 7-8% of the variation in diabetes treatment patterns.

Social, Psychological, And Physical Aspects Of The Work Environment Could Contribute To Hypertension Prevalence

Studies on the physical and social characteristics of the workplace have begun to provide evidence for the role of specific workplace factors on health. However, the overall contribution of the workplace to health has not been considered. Estimates of the influences on health across domains of the work environment are a critical first step toward understanding what level of priority the workplace should take as the target for public policies to improve health. The influences or contribution of these domains on health in the work environment are particularly useful to study since they are potentially modifiable through changes in policies and environment. Our analysis used detailed data from blue-collar industrial workers at two dozen Alcoa plants. It includes work environmental measures of psychological hazards, physical hazards, and the workplace social environment, to estimate the overall importance of the workplace environment for hypertension. Our findings suggest that social, psychological, and physical aspects of the work environment could contribute to a substantial proportion of hypertension prevalence. These attributes of the workplace could thus be a useful target for improving workforce health.

PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT

Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients. Most of these models are hypothesis driven and repetitively assess the predictive abilities of the same set of biomarkers as predictive features. In this manuscript, we discuss our attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities. We have assessed a large repertoire of variables from electronic medical records of heart failure patients in a single center. The cohort included 1,068 patients with 178 patients were readmitted within a 30-day interval (16.66% readmission rate). A total of 4,205 variables were extracted from EMR including diagnosis codes (n=1,763), medications (n=1,028), laboratory measurements (n=846), surgical procedures (n=564) and vital signs (n=4). We designed a multistep modeling strategy using the Naïve Bayes algorithm. In the first step, we created individual models to classify the cases (readmitted) and controls (non-readmitted). In the second step, features contributing to predictive risk from independent models were combined into a composite model using a correlation-based feature selection (CFS) method. All models were trained and tested using a 5-fold cross-validation method, with 70% of the cohort used for training and the remaining 30% for testing. Compared to existing predictive models for HF readmission rates (AUCs in the range of 0.6-0.7), results from our EMR-wide predictive model (AUC=0.78; Accuracy=83.19%) and phenome-wide feature selection strategies are encouraging and reveal the utility of such datadriven machine learning. Fine tuning of the model, replication using multi-center cohorts and prospective clinical trial to evaluate the clinical utility would help the adoption of the model as a clinical decision system for evaluating readmission status.

The high price of depression: Family members' health conditions and health care costs

OBJECTIVE

To compare the health conditions and health care costs of family members of patients diagnosed with a Major Depressive Disorder (MDD) to family members of patients without an MDD diagnosis.

METHODS

Using electronic health record data, we identified family members (n=201,914) of adult index patients (n=92,399) diagnosed with MDD between 2009 and 2014 and family members (n=187,011) of matched patients without MDD. Diagnoses, health care utilization and costs were extracted for each family member. Logistic regression and multivariate models were used to compare diagnosed health conditions, health services cost, and utilization of MDD and non-MDD family members. Analyses covered the 5years before and after the index patient's MDD diagnosis.

RESULTS

MDD family members were more likely than non-MDD family members to be diagnosed with mood disorders, anxiety, substance use disorder, and numerous other conditions. MDD family members had higher health care costs than non-MDD family members in every period analyzed, with the highest difference being in the year before the index patient's MDD diagnosis.

CONCLUSIONS

Family members of patients with MDD are more likely to have a number of health conditions compared to non-MDD family members, and to have higher health care cost and utilization.

Cohort Research in "Omics" and Preventive Medicine

Cohort studies are observational studies in which the investigator determines the exposure status of subjects and then follows them for subsequent outcomes. The incidence of outcomes is observed in the exposed group and compared with that in a nonexposed group. Recently, new epidemiologic strategies have encouraged cohort research information exchange and cooperation to improve the cognition of disease etiology, such as case-cohort design and nested case-control study, which is available for "omics" data. Meanwhile, large-scale cohort studies using a prospective multiple design and long follow-ups have explored some of the challenges in preventive medicine. Cohort study can bridge the gap between the micro and macro research.This chapter is divided into three parts: 1. Basic knowledge of cohort study, which included the definition of cohort study and different types of cohort study, how to design the cohort study, data analysis for the cohort study, sources of bias in cohort studies, tools and software for cohort studies, and strengths and limitations of cohort study 2. Cohort study for "omics" data analysis, which introduced three related methodologically distinct study designs, case-cohort design for genomic cohort study, nested case-control design for transcriptomics cohort data, and population-based design for integrative "omics" cohort 3. Perspectives on cohort study including data-driven medicine and cohort research, cohort research for healthcare medicine, and cohort research for preventive medicine.

Predicting later life health status and mortality using state-level socioeconomic characteristics in early life

Studies extending across multiple life stages promote an understanding of factors influencing health across the life span. Existing work has largely focused on individual-level rather than area-level early life determinants of health. In this study, we linked multiple data sets to examine whether early life state-level characteristics were predictive of health and mortality decades later. The sample included 143,755 U.S. employees, for whom work life claims and administrative data were linked with early life state-of-residence and mortality. We first created a "state health risk score" (SHRS) and "state mortality risk score" (SMRS) by modeling state-level contextual characteristics with health status and mortality in a randomly selected 30% of the sample (the "training set"). We then examined the association of these scores with objective health status and mortality in later life in the remaining 70% of the sample (the "test set") using multivariate linear and Cox regressions, respectively. The association between the SHRS and adult health status was β=0.14 (95%CI: 0.084, 0.20), while the hazard ratio for the SMRS was 0.96 (95%CI: 0.93, 1.00). The association between the SHRS and health was not statistically significant in older age groups at a p-level of 0.05, and there was a statistically significantly different association for health status among movers compared to stayers. This study uses a life course perspective and supports the idea of "sensitive periods" in early life that have enduring impacts on health. It adds to the literature examining populations in the U.S. where large linked data sets are infrequently available.

Ischemic Heart Disease Incidence in Relation to Fine versus Total Particulate Matter Exposure in a U.S. Aluminum Industry Cohort

Ischemic heart disease (IHD) has been linked to exposures to airborne particles with an aerodynamic diameter <2.5 μm (PM_2.5) in the ambient environment and in occupational settings. Routine industrial exposure monitoring, however, has traditionally focused on total particulate matter (TPM). To assess potential benefits of PM_2.5 monitoring, we compared the exposure-response relationships between both PM_2.5 and TPM and incidence of IHD in a cohort of active aluminum industry workers. To account for the presence of time varying confounding by health status we applied marginal structural Cox models in a cohort followed with medical claims data for IHD incidence from 1998 to 2012. Analyses were stratified by work process into smelters (n = 6,579) and fabrication (n = 7,432). Binary exposure was defined by the 10^th-percentile cut-off from the respective TPM and PM_2.5 exposure distributions for each work process. Hazard Ratios (HR) comparing always exposed above the exposure cut-off to always exposed below the cut-off were higher for PM_2.5, with HRs of 1.70 (95% confidence interval (CI): 1.11–2.60) and 1.48 (95% CI: 1.02–2.13) in smelters and fabrication, respectively. For TPM, the HRs were 1.25 (95% CI: 0.89–1.77) and 1.25 (95% CI: 0.88–1.77) for smelters and fabrication respectively. Although TPM and PM_2.5 were highly correlated in this work environment, results indicate that, consistent with biologic plausibility, PM_2.5 is a stronger predictor of IHD risk than TPM. Cardiovascular risk management in the aluminum industry, and other similar work environments, could be better guided by exposure surveillance programs monitoring PM_2.5.

The Effects of Job Insecurity on Health Care Utilization: Findings from a Panel of U.S. Workers

OBJECTIVE

To examine the impacts of job insecurity during the recession of 2007-2009 on health care utilization among a panel of U.S. employees.

DATA SOURCES/STUDY SETTING

Linked administrative and claims datasets on a panel of continuously employed, continuously insured individuals at a large multisite manufacturing firm that experienced widespread layoffs (N = 9,486).

STUDY DESIGN

We employed segmented regressions to examine temporal discontinuities in utilization during 2006-2012. To assess the effects of job insecurity, we compared individuals at high- and low-layoff plants. Because the dataset includes multiple observations for each individual, we included individual-level fixed effects.

PRINCIPAL FINDINGS

We found discontinuous increases in outpatient (3.5 visits/month/10,000 individuals, p = .002) and emergency (0.4 visits/month/10,000 individuals, p = .05) utilization in the panel of all employees. Compared with individuals at low-layoff plants, individuals at high-layoff plants decreased outpatient utilization (-4.0 visits/month/10,000 individuals, p = .008), suggesting foregone preventive care, with a marginally significant increase in emergency utilization (0.4 visits/month/10,000 individuals, p = .08).

CONCLUSIONS

These results suggest changes in health care utilization and potentially adverse impacts on employee health in response to job insecurity during the latest recession. This study contributes to our understanding of the impacts of economic crises on the health of the U.S. working population.

Incident Ischemic Heart Disease After Long-Term Occupational Exposure to Fine Particulate Matter: Accounting for 2 Forms of Survivor Bias

Little is known about the heart disease risks associated with occupational, rather than traffic-related, exposure to particulate matter with aerodynamic diameter of 2.5 µm or less (PM2.5). We examined long-term exposure to PM2.5 in cohorts of aluminum smelters and fabrication workers in the United States who were followed for incident ischemic heart disease from 1998 to 2012, and we addressed 2 forms of survivor bias. Left truncation bias was addressed by restricting analyses to the subcohort hired after the start of follow up. Healthy worker survivor bias, which is characterized by time-varying confounding that is affected by prior exposure, was documented only in the smelters and required the use of marginal structural Cox models. When comparing always-exposed participants above the 10th percentile of annual exposure with those below, the hazard ratios were 1.67 (95% confidence interval (CI): 1.11, 2.52) and 3.95 (95% CI: 0.87, 18.00) in the full and restricted subcohorts of smelter workers, respectively. In the fabrication stratum, hazard ratios based on conditional Cox models were 0.98 (95% CI: 0.94, 1.02) and 1.17 (95% CI: 1.00, 1.37) per 1 mg/m(3)-year in the full and restricted subcohorts, respectively. Long-term exposure to occupational PM2.5 was associated with a higher risk of ischemic heart disease among aluminum manufacturing workers, particularly in smelters, after adjustment for survivor bias.

The impact of retirement on health: quasi-experimental methods using administrative data

Background

Is retirement good or bad for health? Disentangling causality is difficult. Much of the previous quasi-experimental research on the effect of health on retirement used self-reported health and relied upon discontinuities in public retirement incentives across Europe. The current study investigated the effect of retirement on health by exploiting discontinuities in private retirement incentives to test the effect of retirement on health using a quasi-experimental study design.

Methods

Secondary data (1997–2009) on a cohort of male manufacturing workers in a United States setting. Health status was determined using claims data from private insurance and Medicare. Analyses used employer-based administrative and claims data and claim data from Medicare.

Results

Widely used selection on observables models overstate the negative impact of retirement due to the endogeneity of the decision to retire. In addition, health status as measured by administrative claims data provide some advantages over the more commonly used survey items. Using an instrument and administrative health records, we find null to positive effects from retirement on all fronts, with a possible exception of increased risk for diabetes.

Conclusions

This study provides evidence that retirement is not detrimental and may be beneficial to health for a sample of manufacturing workers. In addition, it supports previous research indicating that quasi-experimental methodologies are necessary to evaluate the relationship between retirement and health, as any selection on observable model will overstate the negative relationship of retirement on health. Further, it provides a model for how such research could be implemented in countries like the United States that do not have a strong public pension program. Finally, it demonstrates that such research need-not rely upon survey data, which has certain shortcomings and is not always available for homogenous samples.

Linking individual medicare health claims data with work-life claims and other administrative data

Background

Researchers investigating health outcomes for populations over age 65 can utilize Medicare claims data, but these data include no direct information about individuals’ health prior to age 65 and are not typically linkable to files containing data on exposures and behaviors during their worklives. The current paper is a proof-of-concept, of merging employers’ administrative data and private, employment-based health claims with Medicare data. Characteristics of the linked data, including sensitivity and specificity, are evaluated with an eye toward potential uses of such linked data. This paper uses a sample of former manufacturing workers from an industrial cohort as a test case. The dataset created by this integration could be useful to research in areas such as social epidemiology and occupational health.

Methods

Medicare and employment administrative data were linked for a large cohort of manufacturing workers (employed at some point during 1996–2008) who transitioned onto Medicare between 2001–2009. Data on work-life health, including biometric indicators, were used to predict health at age 65 and to investigate the concordance of employment-based insurance claims with subsequent Medicare insurance claims.

Results

Chronic diseases were found to have relatively high levels of concordance between employment-based private insurance and subsequent Medicare insurance. Information about patient health prior to receipt of Medicare, including biometric indicators, were found to predict health at age 65.

Conclusions

Combining these data allows for evaluation of continuous health trajectories, as well as modeling later-life health as a function of work-life behaviors and exposures. It also provides a potential endpoint for occupational health research. This is the first harmonization of its kind, providing a proof-of-concept. The dataset created by this integration could be useful for research in areas such as social epidemiology and occupational health.