Decreasing rates of cost-related medication non-adherence by age advancement among American generational cohorts 2004-2014: a longitudinal study

Factors Associated with Delaying and Forgoing Care Due to Cost among Long-term, Appalachian Cancer Survivors in Rural North Carolina

Background

Little research exists on delayed and forgone health and mental health care due to cost among rural cancer survivors.

Methods

We surveyed survivors in 7 primarily rural, Appalachian counties February to May 2020. Univariable analyses examined the distribution and prevalence of delayed/forgone care due to cost in the past year by independent variables. Chi-square or Fisher's tests examined bivariable differences. Logistic regressions assessed the odds of delayed/forgone care due to cost.

Results

Respondents (n=428), aged 68.6 years on average (SD: 12.0), were 96.3% non-Hispanic white and 49.8% female; 25.0% reported delayed/forgone care due to cost. The response rate was 18.5%. The proportion of delayed/forgone care for those aged 18-64 years was 46.7% and 15.0% for those aged 65+ years (P<0.0001). Females aged 65+ years (OR: 2.00; CI: 1.02-3.93) had double the odds of delayed/forgone care due to cost compared to males aged 65+ years.

Conclusion

About one in four rural cancer survivors reported delayed/forgone care due to cost, with rates approaching 50% in survivors aged <65 years.

Impact

Clinical implications indicate the need to: 1) ask about the impact of care costs, and 2) provide supportive services to mitigate effects of treatment costs, particularly for younger and female survivors.

Developing an Integrated Longitudinal Dataset for Patient-Centered Outcome Measures in Cost-Related Medication Nonadherence

BACKGROUND

Cost-related medication nonadherence (CRN) is an important patient-centered outcome measure. Longitudinal follow-up of CRN is rare.

OBJECTIVE

We propose to develop a novel integrated dataset to study CRN longitudinally.

RESEARCH DESIGN

A dataset of 2000 Medicare beneficiaries at high risk of hospitalization surveyed quarterly on CRN and followed up individually for 8 quarters between 2013 and 2018 was linked to Medicare files. A metric of CRN categorizing persistent, intermittent, and transient CRN during the 8 quarters was developed. An ordered logit model and a logit model were developed to assess the factors influencing CRN overall and persistent CRN, respectively.

RESULTS

A total of 1761 patients were included in the analysis, among whom 869 (49.3%) reported CRN at least once in the 8-quarter study period, 178 (10%) reported persistent CRN, 395 (22.4%) reported intermittent CRN, and 296 (16.8%) reported transient CRN. The conditional effect in the logit model for persistent CRN revealed that baseline dual eligibility was negatively associated (adjusted odds ratio = 0.45, P < 0.01) and depression positively associated (adjusted odds ratio = 1.55, P = 0.01) with persistent CRN. The marginal analysis in the ordered logit model revealed a clear pattern of higher probabilities of persistent and intermittent CRN at younger ages while transient CRN was flat. Among the 252 subjects who were deceased, 31 (12.3%) reported persistent CRN, compared with 147 (9.74%) who were alive (P = 0.21 by χ2 test).

CONCLUSIONS

A significant number of patients reported persistent CRN, including those who were at the end of life. Research is critically needed to understand behavioral patterns among the younger Medicare population.

In the wake of a crisis: Caught between housing and healthcare

Objective

To measure the association between housing insecurity and foregone medication due to cost among Medicare beneficiaries aged 65+ during the Recession.

Methods

Data came from Medicare beneficiaries aged 65+ years from the 2006-2012 waves of the Health and Retirement Study (HRS). Two-wave housing insecurity changes are evaluated as follows: (i) No insecurity, (ii) Persistent insecurity, (iii) Onset insecurity, and (iv) Onset security. We implemented a series of four weighted longitudinal General Estimating Equation (GEE) models, two minimally adjusted and two fully adjusted models, to estimate the probability of foregone medications due to cost between 2008 and 2012.

Results

Our study sample was restricted to non-proxy interviews of non-institutionalized Medicare beneficiaries aged 65+ in the 2006 wave (n = 9936) and their follow up visits (n = 8753; in 2008; n = 7464 in 2010; and n = 6594 in 2012). Results from our fully adjusted model indicated that the odds of foregone medication was 64% higher among individuals experiencing Onset insecurity versus No insecurity in 2008, and also generally larger for individuals experiencing Onset Insecurity versus Persistent Insecurity. Odds of foregone medication was also larger among females, minority versus non-Hispanic white adults, those reporting a chronic condition, those with higher medical expenditures, and those living in the South versus Northeast.

Conclusion

This study drew from nationally representative data to elucidate the disparate health and financial impacts of a crisis on Medicare beneficiaries who, despite health insurance coverage, displayed variability in foregone medication patterns. Our findings suggest that the onset of housing insecurity is most closely linked with unexpected acute economic shocks leading households with little time to adapt and forcing trade-offs in their prescription and other needs purchases. Both housing and healthcare policy implications exist from these findings including expansion of low-income housing units and rent relief post-recession as well as wider prescription drug coverage for Medicare adults.

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

BACKGROUND

Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.

METHODS

We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.

RESULTS

Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.

CONCLUSION

Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

Prevalence and persistence of cost-related medication non-adherence before and during the COVID-19 pandemic among medicare patients at high risk of hospitalization

OBJECTIVE

To study cost-related medication non-adherence (CRN) for a 30-month period before and during the COVID-19 pandemic using a sample of Medicare patients at high risk of hospitalization.

DESIGN

A novel data set of quarterly surveys of CRN was used to evaluate CRN before and during the COVID-19 pandemic. Generalized Estimating Equation (GEE) analyses were conducted to evaluate the adjusted coefficients of change in CRN behaviors controlling for socio-demographic and health characteristics.

PARTICIPANTS

Six hundred seventy-seven Medicare beneficiaries at high risk of hospitalization who were alive on January 1, 2020 and followed up through quarterly surveys on CRN for 30 months before and during the COVID-19 pandemic.

MAIN OUTCOMES AND MEASURES

Two metrics of prevalence and persistence of CRN and their adjusted coefficients in GEE with binomial family distribution and log link function controlling for socio-demographic and health characteristics.

RESULTS

A total of 5,990 quarterly surveys were completed by the 677 patients during the 30-month study period. Among the 677 patients, 250 (37%) were men, 591 (87%) were African American, and 288 (42%) were Medicare-Medicaid dual eligible. The unadjusted prevalence of CRN before and during the COVID-19 pandemic was 31.1% and 25.7% respectively (p = 0.02 by Chi-squared test), and persistent CRN rates were 12.1% and 9.7% respectively (p = 0.17 by Chi-squared test). The adjusted odds ratio of CRN prevalence during the pandemic compared to the pre-pandemic level was 0.75 (p<0.01), and 0.74 (p = 0.03) for persistent CRN in GEE estimations.

CONCLUSION AND RELEVANCE

There are coherent evidence of a reversal of CRN rates during the COVID-19 pandemic among this high-need, high-cost resource utilization Medicare population. Patients' CRN behaviors may be responsive to exogenous impacts, and the behaviors changed in the same direction with similar magnitude in terms of prevalence (the extensive margin) and persistence (the intensive margin). More research is needed to advance the understanding of the driving forces behind patients' behavioral changes and to identify factors that may be informative for reducing CRN in the long run.

Factors associated with the incidence and the expenditure of self-medication among middle-aged and older adults in China: A cross-sectional study

Background

With the accelerated ageing of population and the growing prevalence of various chronic diseases in China, self-medication plays an increasingly important role in complementing the health care system due to its convenience and economy.

Objective

This study aimed to investigate the incidence of self-medication and the amount of self-medication expenditure among middle-aged and older adults in China, and to explore factors associated with them.

Methods

A total of 10,841 respondents aged 45 years and older from the China Health and Retirement Longitudinal Study (CHARLS) wave 4 which conducted in 2018 were included as the sample of this study. The two-part model was adopted to identify the association between the incidence of self-medication and the amount of self-medication expenditure and specific factors, respectively.

Results

The incidence of self-medication among Chinese middle-aged and older adults was 62.30%, and the average total and out-of-pocket (OOP) pharmaceutical expenditure of self-medication of the self-medicated individuals were 290.50 and 264.38 Chinese yuan (CNY) respectively. Participants who took traditional Chinese medicine (TCM), self-reported fair, and poor health status, suffered from one and multiple chronic diseases had strongly higher incidence of self-medication. Older age and multiple chronic diseases were strongly associated with higher expenditure of self-medication. Those who took TCM had more self-medication expenditure, while those who drank alcohol had less.

Conclusion

Our study demonstrated the great prevalence of self-medication among middle-aged and older adults in China and the large pharmaceutical expenditure that come with it, especially in the high-risk groups of self-medication identified in this paper. These findings enhanced our understanding of self-medication behaviors among Chinese middle-aged and older adults and may contribute to the formulation of targeted public health policy.