Are Changes in Heart Rate Variability in Middle-Aged and Older People Normative or Caused by Pathological Conditions? Findings From a Large Population-Based Longitudinal Cohort Study.

BACKGROUND: No study to date has investigated longitudinal trajectories of cardiac autonomic modulation changes with aging; therefore, we lack evidence showing whether these changes occur naturally or are secondary to disease or medication use. This study tested whether heart rate variability (HRV) trajectories from middle to older age are largely normative or caused by pathological changes with aging in a large prospective cohort. We further assessed whether HRV changes were modified by socioeconomic status, ethnicity, or habitual physical activity. METHODS AND RESULTS: This study involved 3176 men and 1238 women initially aged 44 to 69 years (1997-1999) from the UK Whitehall II population-based cohort. We evaluated time- and frequency-domain HRV measures of short-term recordings at 3 time points over a 10-year period. Random mixed models with time-varying covariates were applied. Cross-sectionally, HRV measures were lower for men than for women, for participants with cardiometabolic conditions, and for participants reporting use of medications other than beta blockers. Longitudinally, HRV measures decreased significantly with aging in both sexes, with faster decline in younger age groups. HRV trajectories were not explained by increased prevalence of cardiometabolic problems and/or medication use. In women, cardiometabolic problems were associated with faster decline in the standard deviation of all intervals between R waves with normal-to-normal conduction, in low-frequency HRV, and in low-frequency HRV in normalized units. Socioeconomic status, ethnicity, and habitual physical activity did not have significant effects on HRV trajectories. CONCLUSIONS: Our investigation showed a general pattern and timing of changes in indices of cardiac autonomic modulation from middle to older age. These changes seem likely to reflect the normal aging process rather than being secondary to cardiometabolic problems and medication use

ASSOCIATION BETWEEN WARFARIN ADHERENCE TRAJECTORIES, HOSPITALIZATION RISK, AND HEALTHCARE UTILIZATION AMONG MEDICARE PATIENTS WITH ATRIAL FIBRILLATION: A GROUP-BASED TRAJECTORY MODELLING APPROACH

Introduction: Warfarin is the most commonly prescribed drug for stroke prevention among Atrial Fibrillation (AF) patients, especially in older adult populations, but medication nonadherence reduces its effectiveness in clinical practice. Group Based Trajectory Models (GBTM) have been used to identify distinct patterns of adherence behavior related to various medications and understand the patient characteristics associated with each trajectory. The objectives of the study were: 1) Describe trajectories of warfarin adherence among Medicare AF patients, 2) Assess impact of adherence trajectories on AF-related hospitalization, 3) Estimate the AF-related direct costs for each adherence trajectory group. Methods: We identified elderly AF patients initiating warfarin treatment during 2008-2010 using data from a random sample of Medicare beneficiaries. The study’s first aim is to classify patients into different trajectory groups based on their monthly adherence patterns using a Group-Based Trajectory Model (GBTM). A multinomial regression model was used to assess associations between baseline characteristics and adherence trajectories. The second aim is to evaluate the association between adherence trajectories and time to first hospitalization related to stroke or bleeding event. Hospitalization events due to bleeding or stroke were identified using corresponding ICD-9 codes, and a Cox proportional hazard model was performed. The third aim of the study is to calculate AF-related direct medical costs associated with each trajectory group. SASv9.4 was used for analysis. Results: Among 3,246 beneficiaries who met inclusion criteria, six adherence trajectories were identified: 1) rapid-decline non-adherence group (11.5%), 2) moderate non-adherence group (24%), 3) rapid-decline then increasing adherence group (6.8%), 4) moderate-decline non-adherence group (8.2%), 5) slow-decline non-adherence group (24.3%), and 6) perfect adherence group (25.3%). Even though no statistical significances were found in the hazard of hospitalization among the adherence groups, there were higher odds of hospitalization among the lower adherence groups compared to perfect adherence group. Outpatient and monitoring costs were significantly higher in the lower adherence trajectories compared to perfect adherence group. Conclusion:The GBTM is considered an innovative methodological approach that can be applied to longitudinal medication adherence data and account for the dynamic nature of adherence behavior in a better way than traditional adherence measures

Understanding Society: design overview

Understanding Society, the UK Household Longitudinal Study, builds on the success of the British Household Panel Survey (BHPS). This paper describes some of the key elements of the design and conduct of the study and suggests how Understanding Society is distinctive as a longitudinal survey. Its large sample size offers new opportunities to study sub-groups that may be too small for separate analysis on other studies. The new content included in Understanding Society, not least the bio-measures, provides exciting prospects for interdisciplinary research across the social and medical sciences. The Innovation Panel is already proving to be an invaluable resource for research in longitudinal survey methodology. Finally, the inclusion of the BHPS sample within Understanding Society enables this long running panel to continue into the future, opening up inter-generational research and the opportunity to look at very long-term trajectories of change. This paper also describes the four sample components: the general population sample, ethnic minority boost sample, the Innovation Panel, and participants from the BHPS. Each component has a multistage sample designs, mostly with stratification and clustering. A complex weighting strategy is being developed to support varied analyses. This overview also describes the instruments, methods of data collection, and the timetable for data collection. A summary of the survey content?s is provided. With the data becoming available the user community is beginning to benefit from this investment in longitudinal studies