Does Chronic Illness Affect the Adequacy of Health Insurance Coverage?

Although chronically ill individuals need protection against high medical expenses, they often have difficulty obtaining adequate insurance coverage due to medical underwriting practices used to classify and price risks and to define and limit coverage for individuals and groups. Using data from healthy and chronically ill individuals in Indiana, we found that illness decreased the probability of having adequate insurance, particularly among single individuals. Chronic illness decreased the probability of having adequate coverage by about 10 percentage points among all individuals and by about 25 percentage points among single individuals. Preexisting condition exclusions were a major source of inadequate insurance. Our results emphasize the impact of enforcing the Health Insurance Portability and Accountability Act (HIPAA) of 1997, which limits preexisting condition exclusions

Measurement invariance of the kidney disease and quality of life instrument (KDQOL-SF) across Veterans and non-Veterans

<p>Abstract</p> <p>Background</p> <p>Studies have demonstrated that perceived health-related quality of life (HRQOL) of patients receiving hemodialysis is significantly impaired. Since HRQOL outcome data are often used to compare groups to determine health care effectiveness it is imperative that measures of HRQOL are valid. However, valid HRQOL comparisons between groups can only be made if instrument invariance is demonstrated. The Kidney Disease Quality of Life-Short Form (KDQOL-SF) is a widely used HRQOL measure for patients with chronic kidney disease (CKD) however, it has not been validated in the Veteran population. Therefore, the purpose of this study was to examine the measurement invariance of the KDQOL-SF across Veterans and non-Veterans with CKD.</p> <p>Methods</p> <p>Data for this study were from two large prospective observational studies of patients receiving hemodialysis: 1) Veteran End-Stage Renal Disease Study (VETERAN) (N = 314) and 2) Dialysis Outcomes and Practice Patterns Study (DOPPS) (N = 3,300). Health-related quality of life was measured with the KDQOL-SF, which consists of the SF-36 and the Kidney Disease Component Summary (KDCS). Single-group confirmatory factor analysis was used to evaluate the goodness-of-fit of the hypothesized measurement model for responses to the subscales of the KDCS and SF-36 instruments when analyzed together; and given acceptable goodness-of-fit in each group, multigroup CFA was used to compare the structure of this factor model in the two samples. Pattern of factor loadings (configural invariance), the magnitude of factor loadings (metric invariance), and the magnitude of item intercepts (scalar invariance) were assessed as well as the degree to which factors have the same variances, covariances, and means across groups (structural invariance).</p> <p>Results</p> <p>CFA demonstrated that the hypothesized two-factor model (KDCS and SF-36) fit the data of both the Veteran and DOPPS samples well, supporting configural invariance. Multigroup CFA results concerning metric and scalar invariance suggested partial strict invariance for the SF-36, but only weak invariance for the KDCS. Structural invariance was not supported.</p> <p>Conclusions</p> <p>Results suggest that Veterans may interpret the KDQOL-SF differently than non-Veterans. Further evaluation of measurement invariance of the KDQOL-SF between Veterans and non-Veterans is needed using large, randomly selected samples before comparisons between these two groups using the KDQOL-SF can be done reliably.</p

Chronic Illness and Health Insurance-Related Job Lock

We examine job duration patterns for evidence of health insurance-related job lock among chronically ill workers or workers with a chronically ill family member. Using Cox proportional hazard models, we allow for more general insurance effects than in the existing literature to indicate the impact of health insurance and health status on workers\u27 job durations. We use data for workers in Indiana predating the Health Insurance Portability and Accountability Act (HIPAA) to examine the potential impact of HIPAA on job mobility. Chronic illness reduced job mobility by about 40 percent among the workers in our sample who relied on their employers for coverage as compared to otherwise similar workers who did not rely on their employers for coverage. Our results identify previously underappreciated job lock among chronically ill workers and workers with a chronically ill family member, clarify how one best researches job lock, and indicate the potential impact of policies aimed at alleviating job lock and promoting inter-employer worker mobility. This paper was revised August 2000

Does Chronic Illness Affect the Adequacy of Health Insurance Coverage?

Although chronically ill individuals need protection against high medical expenses, they often have difficulty obtaining adequate insurance coverage due to medical underwriting practices used to classify and price risks and to define and limit coverage for individuals and groups. Using data from healthy and chronically ill individuals in Indiana, we found that illness decreased the probability of having adequate insurance, particularly among single individuals. Chronic illness decreased the probability of having adequate coverage by about 10 percentage points among all individuals and by about 25 percentage points among single individuals. Pre-existing condition exclusions were a major source of inadequate insurance. Our results emphasize the impact of enforcing the Health Insurance Portability and Accountability Act (HIPAA) of 1997, which limits pre-existing condition exclusions

Does Chronic Illness Affect the Adequacy of Health Insurance Coverage?

Up-to-date information about CPR’s research projects and other activities is available from our World Wide Web site at www-cpr.maxwell.syr.edu. All recent working papers and Policy Briefs can be read and/or printed from there as well. Although chronically ill individuals need protection against high medical expenses, they often have difficulty obtaining adequate insurance coverage due to medical underwriting practices used to classify and price risks and to define and limit coverage for individuals and groups. Using data from healthy and chronically ill individuals in Indiana, we found that illness decreased the probability of having adequate insurance, particularly among single individuals. Chronic illness decreased the probability of having adequate coverage by about 10 percentage points among all individuals and by about 25 percentage points among single individuals. Preexisting condition exclusions were a major source of inadequate insurance. Our results emphasize the impact of enforcing the Health Insurance Portability and Accountability Act (HIPAA) of 1997, which limits preexisting condition exclusions

Patient prioritization of comorbid chronic conditions in the Veteran population: Implications for patient-centered care

OBJECTIVE: Patients with comorbid chronic conditions may prioritize some conditions over others; however, our understanding of factors influencing those prioritizations is limited. In this study, we sought to identify and elaborate a range of factors that influence how and why patients with comorbid chronic conditions prioritize their conditions. METHODS: We conducted semi-structured, one-on-one interviews with 33 patients with comorbidities recruited from a single Veterans Health Administration Medical Center. FINDINGS: The diverse factors influencing condition prioritization reflected three overarching themes: (1) the perceived role of a condition in the body, (2) self-management tasks, and (3) pain. In addition to these themes, participants described the rankings that they believed their healthcare providers would assign to their conditions as an influencing factor, although few reported having shared their priorities or explicitly talking with providers about the importance of their conditions. CONCLUSION: Studies that advance understanding of how and why patients prioritize their various conditions are essential to providing care that is patient-centered, reflecting what matters most to the individual while improving their health. This analysis informs guideline development efforts for the care of patients with comorbid chronic conditions as well as the creation of tools to promote patient-provider communication regarding the importance placed on different conditions

How Do Patients with Mental Health Diagnoses Use Online Patient Portals? An Observational Analysis from the Veterans Health Administration

Online patient portals may be effective for engaging patients with mental health conditions in their own health care. This retrospective database analysis reports patient portal use among Veterans with mental health diagnoses. Unadjusted and adjusted odds of portal feature use was calculated using logistic regressions. Having experienced military sexual trauma or having an anxiety disorder, post-traumatic stress disorder, or depression were associated with increased odds of portal use; bipolar, substance use, psychotic and adjustment disorders were associated with decreased odds. Future research should examine factors that influence portal use to understand diagnosis-level differences and improve engagement with such tools

Alerting Doctors About Patient Life Challenges: A Randomized Control Trial of a Previsit Inventory of Contextual Factors

Objective. Effective care attends to relevant patient life context. We tested whether a patient-completed inventory helps providers contextualize care and increases patients’ perception of patient-centered care (PCC). Method. The inventory listed six red flags (e.g., emergency room visits) and if the patient checked any, prompted for related contextual factors (e.g., transportation difficulties). Patients were randomized to complete the inventory or watch health videos prior to their visit. Patients presented their inventory results to providers during audio-recorded encounters. Audios were coded for physician probing and incorporating context in care plans. Patients completed the Consultation and Relational Empathy (CARE) instrument after the encounter. Results. A total of 272 Veterans were randomized. Adjusting for covariates and clustering within providers, inventory patients rated visits as more patient-centered (44.5; standard error = 1.1) than controls (42.7, standard error = 1.1, P = 0.04, CARE range = 10–50). Providers were more likely to probe red flags (odds ratio = 1.54; confidence interval = 1.07–2.22; P = 0.02) when receiving the inventory, but not incorporating context into care planning. Conclusion. A previsit inventory of life context increased perceptions of PCC and providers’ likelihood of exploring context but not contextualizing care. Information about patients’ life challenges is not sufficient to assure that context informs provider decision making even when provided at the point of care by patients themselves

Transition to the new race/ethnicity data collection standards in the Department of Veterans Affairs

BACKGROUND: Patient race in the Department of Veterans Affairs (VA) information system was previously recorded based on an administrative or clinical employee's observation. Since 2003, the VA started to collect self-reported race in compliance with a new federal guideline. We investigated the implications of this transition for using race/ethnicity data in multi-year trends in the VA and in other healthcare data systems that make the transition. METHODS: All unique users of VA healthcare services with self-reported race/ethnicity data in 2004 were compared with their prior observer-recorded race/ethnicity data from 1997 – 2002 (N = 988,277). RESULTS: In 2004, only about 39% of all VA healthcare users reported race/ethnicity values other than "unknown" or "declined." Females reported race/ethnicity at a lower rate than males (27% vs. 40%; p < 0.001). Over 95% of observer-recorded data agreed with self-reported data. Compared with the patient self-reported data, the observer-recorded White and African American races were accurate for 98% (kappa = 0.89) and 94% (kappa = 0.93) individuals, respectively. Accuracy of observer-recorded races was much worse for other minority groups with kappa coefficients ranging between 0.38 for American Indian or Alaskan Natives and 0.79 for Hispanic Whites. When observer-recorded race/ethnicity values were reclassified into non-African American groups, they agreed with the self-reported data for 98% of all individuals (kappa = 0.93). CONCLUSION: For overall VA healthcare users, the agreement between observer-recorded and self-reported race/ethnicity was excellent and observer-recorded and self-reported data can be used together for multi-year trends without creating serious bias. However, this study also showed that observation was not a reliable method of race/ethnicity data collection for non-African American minorities and racial disparity might be underestimated if observer-recorded data are used due to systematic patterns of inaccurate race/ethnicity assignments