Payer approval and rejection of oral anticoagulant prescriptions and prescription abandonment patterns among patients with venous thromboembolism.

BACKGROUND: Venous thromboembolism (VTE) is a major public health condition that renders patients at risk of recurrent events, which significantly increases their morbidity, mortality, and health care costs. Apart from warfarin, direct oral anticoagulants, such as apixaban, dabigatran, or rivaroxaban, are approved for VTE treatment. Cardiovascular drugs are largely impacted by formulary restrictions; however, the impact on oral anticoagulants (including warfarin and direct oral anticoagulants) in VTE has not been well studied. OBJECTIVE: To describe the extent of payer-rejected claims for oral anticoagulants for VTE and the factors associated with rejected claims. Prescription abandonment of oral anticoagulants and the time to an eventual fill for oral anticoagulant after rejection or abandonment were also evaluated. METHODS: A retrospective cohort study was conducted among patients with VTE newly prescribed an oral anticoagulant (first claim was the index) between October 2016 and October 2021. Descriptive statistics were used to describe the proportion of patients with paid (ie, filled), rejected, or abandoned index oral anticoagulant prescription and journey to paid prescription among those with initial rejection. Multivariable logistic regression was used to identify factors associated with initial rejection. RESULTS: Among the overall sample (N = 297,312), 74.3% had initial oral anticoagulant prescriptions approved, 9.1% had them rejected, and 16.7% abandoned them. Of the patients with initial rejection, 82.1% eventually filled their oral anticoagulant prescriptions; however, for 14.2% of these patients, the first fill was for an oral anticoagulant other than that initially prescribed. The mean time to a first fill for an oral anticoagulant after an initial rejection was 18.3 days. More than half of the patients with an initial rejected oral anticoagulant claim had at least 1 additional rejection during the follow-up period. Of the patients who abandoned their initial oral anticoagulant prescription, 83.9% filled an oral anticoagulant prescription during follow-up; the mean time to fill for the index oral anticoagulant was 15.6 days. Oral anticoagulant type, Medicare payer coverage, prescribing physician specialty, and VTE diagnosis setting of care were significantly associated with index oral anticoagulant claim rejection (P < 0.05). CONCLUSIONS: Rejection and abandonment may delay access to oral anticoagulant treatment. Factors contributing to these scenarios should be understood and addressed for proper VTE management.

Association between Financial Barriers to Healthcare Access and Mental Health Outcomes in Tennessee.

OBJECTIVES: A large number of people cannot afford healthcare services in the United States. Researchers have studied the impact of lack of affordability of health care on the outcomes of various physical conditions. Mental health disorders have emerged as a major public health challenge during the past decade. The lack of affordability of health care also may contribute to the burden of mental health. This research focuses on the association between financial barriers to health care and mental health outcomes in the US state of Tennessee. METHODS: We used cross-sectional data contained in the 2019 US Behavioral Risk Factor Surveillance System (BRFSS). We extracted data for the state of Tennessee, which included 6242 adults aged 18 years or older. Multinomial regression analyses were conducted to test the association between not being able to see a doctor with the number of mentally unhealthy days during the past month. We coded the outcome as a three-level variable, ≥20 past-month mentally unhealthy days, 1 to 20 past-month mentally unhealthy days, and 0 past-month mentally unhealthy days. The covariates examined included self-reported alcohol use, self-reported marijuana use, and other demographic variables. RESULTS: Overall, 11.0% of participants reported ≥20 past-month mentally unhealthy days and 24.0% reported 1 to 20 past-month mentally unhealthy days. More than 13% of study participants reported they could not see a doctor because of the cost in the past 12 months. The inability to see a doctor because of the cost of care was associated with a higher risk of ≥20 past-month mentally unhealthy days (relative risk ratio 3.18; 95% confidence interval 2.57-3.92, P < 0.001) and 1 to 19 past-month mentally unhealthy days (relative risk ratio 1.94; 95% confidence interval 1.63-2.32, P < 0.001). CONCLUSIONS: Statistically significant associations were observed between the inability to see a doctor when needed because of cost and increased days of poorer mental health outcomes. This research has potential policy implications in the postcoronavirus disease 2019 era with healthcare transformation and significant financial impact.

Feeling Not Wanted/Loved and Depression: Does Gender Matter?

Depression affects over 17 million American adults yearly and has been identified as the leading cause of disability in people between the ages of 15 and 44 years. There is evidence that feeling neglect or a lack of parental attachment during childhood is associated with depression. One construct that has been overlooked is love from a parent. The purpose of this study was to analyze the association between individuals who felt not wanted/loved during adolescence and lifetime depression and to examine this association by gender. We examined 5114 participants aged 24-32 years at Wave IV of the National Longitudinal Study of Adolescent Health (Add Health) public use dataset. We used logistic regression analysis to determine the association between an individual feeling not wanted/loved by their parent/caregiver prior to age 18, covariates, and lifetime depression. We then stratified by gender and ran logistic models for both men and women separately. Overall, 16.2% (n = 827) reported lifetime depression diagnosis, while 16.5% of the participants reported feeling not wanted/loved "often," while 29.8% reported it as "sometimes." Feeling not wanted/loved "often" was associated with higher odds of depression (AOR = 3.00; 95% CI, 2.45-3.66; p < .001) versus "sometimes" (AOR = 1.59; 95% CI, 1.31-1.90; p < .001). When stratified by gender, feeling not loved/wanted was associated with depression among both men (AOR = 3.70; 95% CI, 2.60-5.25; p < .001) and women (AOR = 2.73; 95% CI, 2.13-3.48; p < .001). Feeling not loved or wanted by a parent/caregiver during adolescence has serious implications, for both men and women. Future studies should further examine this construct and identify family-based interventions that focus on parent/caregiver and child relationships.

Early Age of Cannabis Initiation and Its Association With Suicidal Behaviors.

Background: Suicide rates in the U.S. have continued to rise over the last 2 decades. The increased availability and broader legalization of cannabis is a public health concern, particularly among adolescents. The objective of this study was to examine the association between the age of cannabis initiation and lifetime suicidal ideations and attempts in a sample of adults aged 18 or older. Methods: Data are from the Collaborative Psychiatric Epidemiology Surveys (CPES), 2001 to 2003 (N = 15 238). The primary objective of the CPES was to collect data about the prevalence of mental disorders, impairments associated with these disorders, and their treatment patterns from representative samples of majority and minority adult populations in the U.S. Logistic regression analyses were conducted to test the association between cannabis initiation age (early ⩽14 years old; later >14 years old) and outcomes of lifetime suicide ideation and attempts. Cigarette use, cannabis use, gender, income, race, education, and age were controlled for the analyses. Results: Overall, 12.5% of participants reported suicide ideation, while 4.2% reported attempt. Early cannabis use was associated with a higher risk of suicide ideation (AOR = 3.32, 95% CI [2.75, 3.80]) than later cannabis use (AOR = 2.15, 95% CI [1.92, 2.39]). Early cannabis use was associated with a higher risk of suicide attempt (AOR = 4.38, 95% CI [3.48, 5.52]) than later cannabis use (AOR = 2.56, 95% CI [2.14, 3.06]). Wald chi-squared tests revealed significant differences between the early and late initiation for both ideation (χ2 = 26.99; P < .001) and attempts (χ2 = 26.02; P < .001). Conclusions: Significant associations were found between early initiation of cannabis and suicide behaviors. As suicide rates continue to rise, it is recommended that clinicians, treatment providers, and other professionals consider the use of cannabis at an early age as a risk for subsequent suicide behaviors.

The association of prescription opioid use with suicide attempts: An analysis of statewide medical claims data.

BACKGROUND: Suicides and opioid overdose deaths are among the most pressing public health concerns in the US. However direct evidence for the association between opioid use and suicidal behavior is limited. The objective of this article is to examine the association between frequency and dose of prescription opioid use and subsequent suicide attempts. METHODS AND FINDINGS: This retrospective cohort study analyzed 4 years of statewide medical claims data from the Connecticut All-Payer Claims Database. Commercially insured adult patients in Connecticut (n = 842,773) who had any medical claims beginning in January 2012 were followed through December 2015. The primary outcome was suicide attempt identified using International Classification of Diseases (ICD 9) diagnosis codes. Primary predictor variables included frequency of opioid use, which was defined as the number of months with claims for prescription opioids per year, and strength of opioid dose, which was standardized using morphine milligram equivalent (MME) units. We also controlled for psychiatric and medical comorbidities using ICD 9 codes. We used Cox proportional hazards regression to examine the association between frequency, dose, and suicide attempts, adjusting for medical and psychiatric comorbid conditions. Interactions among measures of opioid use and comorbid conditions were analyzed. In this cohort study with follow-up time up to 4 years (range = 2-48 months, median = 46 months), the hazard ratios (HR) from the time-to-event analysis indicated that patients prescribed opioid medications for at least 6 months during the past year and at 20-50 MME levels or higher had 4.44 (95% CI: [3.71, 5.32]) to 7.23 (95% CI: [6.22, 8.41]) times the risk of attempted suicide compared to those not prescribed opioids. Risk of suicide attempt was sharply elevated among patients with psychiatric conditions other than anxiety who were prescribed more frequent and higher opioid doses. In contrast, more frequent and higher doses of prescription opioids were associated with lower risk of suicide attempts among patients with medical conditions necessitating pain management. This study is limited by its exclusive focus on commercially insured patients and does not include patients covered by public insurance. It is also limited to patients' receipt of prescription opioids and does not take into account opioids obtained through other means, nor does it include measures of actual patient opioid use. CONCLUSIONS: This analysis provides evidence of a complex relationship among prescription opioids, mental health, pain and other medical comorbidities, and suicide risk. Findings indicate the need for proactive suicide surveillance among individuals diagnosed with affective or psychotic disorders who are receiving frequent and high doses of opioids. However, appropriate opioid treatment may have significant value in reducing suicide risk for those without psychiatric comorbidities.

Reconciling Statistical and Clinicians' Predictions of Suicide Risk.

Statistical models, including those based on electronic health records, can accurately identify patients at high risk for a suicide attempt or death, leading to implementation of risk prediction models for population-based suicide prevention in health systems. However, some have questioned whether statistical predictions can really inform clinical decisions. Appropriately reconciling statistical algorithms with traditional clinician assessment depends on whether predictions from these two methods are competing, complementary, or merely duplicative. In June 2019, the National Institute of Mental Health convened a meeting, "Identifying Research Priorities for Risk Algorithms Applications in Healthcare Settings to Improve Suicide Prevention." Here, participants of this meeting summarize key issues regarding the potential clinical application of suicide prediction models. The authors attempt to clarify the key conceptual and technical differences between traditional risk prediction by clinicians and predictions from statistical models, review the limited evidence regarding both the accuracy of and the concordance between these alternative methods of prediction, present a conceptual framework for understanding agreement and disagreement between statistical and clinician predictions, identify priorities for improving data regarding suicide risk, and propose priority questions for future research. Future suicide risk assessment will likely combine statistical prediction with traditional clinician assessment, but research is needed to determine the optimal combination of these two methods.

Machine learning for suicide risk prediction in children and adolescents with electronic health records.

Accurate prediction of suicide risk among children and adolescents within an actionable time frame is an important but challenging task. Very few studies have comprehensively considered the clinical risk factors available to produce quantifiable risk scores for estimation of short- and long-term suicide risk for pediatric population. In this paper, we built machine learning models for predicting suicidal behavior among children and adolescents based on their longitudinal clinical records, and determining short- and long-term risk factors. This retrospective study used deidentified structured electronic health records (EHR) from the Connecticut Children's Medical Center covering the period from 1 October 2011 to 30 September 2016. Clinical records of 41,721 young patients (10-18 years old) were included for analysis. Candidate predictors included demographics, diagnosis, laboratory tests, and medications. Different prediction windows ranging from 0 to 365 days were adopted. For each prediction window, candidate predictors were first screened by univariate statistical tests, and then a predictive model was built via a sequential forward feature selection procedure. We grouped the selected predictors and estimated their contributions to risk prediction at different prediction window lengths. The developed predictive models predicted suicidal behavior across all prediction windows with AUCs varying from 0.81 to 0.86. For all prediction windows, the models detected 53-62% of suicide-positive subjects with 90% specificity. The models performed better with shorter prediction windows and predictor importance varied across prediction windows, illustrating short- and long-term risks. Our findings demonstrated that routinely collected EHRs can be used to create accurate predictive models for suicide risk among children and adolescents.

Identifying risk factors for mortality among patients previously hospitalized for a suicide attempt.

Age-adjusted suicide rates in the US have increased over the past two decades across all age groups. The ability to identify risk factors for suicidal behavior is critical to selected and indicated prevention efforts among those at elevated risk of suicide. We used widely available statewide hospitalization data to identify and test the joint predictive power of clinical risk factors associated with death by suicide for patients previously hospitalized for a suicide attempt (N = 19,057). Twenty-eight clinical factors from the prior suicide attempt were found to be significantly associated with the hazard of subsequent suicide mortality. These risk factors and their two-way interactions were used to build a joint predictive model via stepwise regression, in which the predicted individual survival probability was found to be a valid measure of risk for later suicide death. A high-risk group with a four-fold increase in suicide mortality risk was identified based on the out-of-sample predicted survival probabilities. This study demonstrates that the combination of state-level hospital discharge and mortality data can be used to identify suicide attempters who are at high risk of subsequent suicide death.

Age Differences in Racial/Ethnic Disparities in Preventable Hospitalizations for Heart Failure in Connecticut, 2009-2015: A Population-Based Longitudinal Study.

OBJECTIVE: Preventable hospitalizations for heart failure result in a large proportion of hospitalizations. The primary objective of this study was to describe longitudinal trends in the association of race/ethnicity with preventable hospitalizations for heart failure in Connecticut and differences in disparities by age. METHODS: We analyzed data on hospitalizations in all civilian acute-care hospitals in Connecticut during a 7-year period, 2009 through 2015. We used raking methodology to weight the nonhospitalized population to create a reference population representative of the state's general population. Multivariate regression models examined racial/ethnic disparities among adults aged 35-64, controlling for age, sex, and type of health insurance. For adults aged ≥65, regression models controlled for age and sex. RESULTS: After controlling for age and sex, the non-Hispanic black to non-Hispanic white odds ratio for preventable hospitalizations for heart failure ranged from 5.2-6.4 during the study period among adults aged 35-64. Among adults aged ≥65, non-Hispanic black adults had significantly higher odds (range, 1.2-1.8) of preventable hospitalizations than non-Hispanic white adults. Rates among Hispanic adults were significantly higher than rates among non-Hispanic adults after controlling for age and sex among adults aged ≥65 in 2014 and 2015. CONCLUSIONS: This research provides information for clinical and population-based interventions targeting racial/ethnic gaps in heart failure hospitalizations. Demonstrating the persistent black-white disparity and age differences in racial/ethnic disparities, this study emphasizes the need for focused prevention among vulnerable populations. Raking methodology is an innovative approach to eliminating selection bias in hospital discharge data.

Synchronous and asynchronous video observed therapy (VOT) for tuberculosis treatment adherence monitoring and support.

Directly observed therapy (DOT) for monitoring tuberculosis (TB) treatment is intended to reduce disease transmission, mortality and acquired drug resistance by facilitating treatment adherence and support. Synchronous (S-VOT) and asynchronous (A-VOT) video observed therapy are mHealth solutions for remotely monitoring medication ingestion. This paper synthesizes literature through December 2018 to describe existing VOT approaches, summarize evidence, identify knowledge gaps, evaluate VOT strengths and weaknesses, and examine patient and provider factors influencing VOT feasibility and acceptability. High rates of adherence and patient acceptance were obtained using both VOT methods. VOT reduced travel time for TB program staff and/or patients, improving program efficiency compared to in-person DOT while maintaining high patient satisfaction. The impact of VOT on TB treatment outcomes, such as cure and relapse, require further study with longer follow-up. Individual patient, provider and program factors should be considered in selecting either or both VOT approaches for provision of patient-centered care.

Variation in patterns of health care before suicide: A population case-control study.

BACKGROUND: The United States has experienced a significant rise in suicide. As decision makers identify how to address this national concern, healthcare systems have been identified as an optimal location for prevention. OBJECTIVE: To compare variation in patterns of healthcare use, by health setting, between individuals who died by suicide and the general population. DESIGN: Case-Control Study. SETTING: Eight healthcare systems across the United States. PARTICIPANTS: 2674 individuals who died by suicide between 2000 and 2013 along with 267,400 individuals matched on time-period of health plan membership and health system affiliation. MEASUREMENTS: Healthcare use in the emergency room, inpatient hospital, primary care, and outpatient specialty setting measured using electronic health record data during the 7-, 30-, 60-, 90-, 180-, and 365-day time periods before suicide and matched index date for controls. RESULTS: Healthcare use was more common across all healthcare settings for individuals who died by suicide. Nearly 30% of individuals had a healthcare visit in the 7-days before suicide (6.5% emergency, 16.3% outpatient specialty, and 9.5% primary care), over half within 30 days, and >90% within 365 days. Those who died by suicide averaged 16.7 healthcare visits during the year. The relative risk of suicide was greatest for individuals who received care in the inpatient setting (aOR = 6.23). There was both a large relative risk (aOR = 3.08) and absolute utilization rate (43.8%) in the emergency room before suicide. LIMITATIONS: Participant race/ethnicity was not available. The sample did not include uninsured individuals. CONCLUSIONS: This study provides important data about how care utilization differs for those who die by suicide compared to the general population and can inform decision makers on targeting of suicide prevention activities within health systems.

Interventions to Improve Management of Chronic Conditions Among Racial and Ethnic Minorities.

Digital and mhealth interventions can be effective in improving health outcomes among minority patients with diabetes, congestive heart failure, and chronic respiratory diseases. A number of electronic and digital approaches to individual and population-level interventions involving telephones, internet and web-based resources, and mobile platforms have been deployed to improve chronic disease outcomes. This paper summarizes the evidence supporting the efficacy of various behavioral and digital interventions targeting intermediate outcomes and hospitalizations with particular emphasis on studies examining the effects of these interventions on racial and ethnic minority population.

Tuberculosis control, and the where and why of artificial intelligence.

Countries aiming to reduce their tuberculosis (TB) burden by 2035 to the levels envisaged by the World Health Organization End TB Strategy need to innovate, with approaches such as digital health (electronic and mobile health) in support of patient care, surveillance, programme management, training and communication. Alongside the large-scale roll-out required for such interventions to make a significant impact, products must stay abreast of advancing technology over time. The integration of artificial intelligence into new software promises to make processes more effective and efficient, endowing them with a potential hitherto unimaginable. Users can benefit from artificial intelligence-enabled pattern recognition software for tasks ranging from reading radiographs to adverse event monitoring, sifting through vast datasets to personalise a patient's care plan or to customise training materials. Many experts forecast the imminent transformation of the delivery of healthcare services. We discuss how artificial intelligence and machine learning could revolutionise the management of TB.

Racial and Ethnic Disparities in Preventable Hospitalizations for Chronic Disease: Prevalence and Risk Factors.

BACKGROUND: Hospitalizations due to ambulatory care sensitive conditions (ACSCs) result in high morbidity and economic burden on the American healthcare system. Admissions due to chronic ACSCs, in particular, cost the American healthcare system over 30 billion dollars annually. OBJECTIVES AND METHODS: This paper presents the current research on racial and ethnic disparities in the burden of hospitalizations due to chronic ACSCs. For this narrative review, we evaluated over 800 abstracts from MEDLINE and Google Scholar and cited 62 articles. RESULTS: Since 1998, racial and ethnic disparities in hospitalizations from chronic ACSCs have increased resulting in over 430,000 excess hospitalizations among non-Hispanic Blacks compared to non-Hispanic Whites. CONCLUSIONS: Racial disparities in chronic ACSCs hospitalizations are pervasive in the USA. There is need for more research on the pathways through which an individual's race modifies the risk for hospitalizations due to chronic ACSCs.

Impact of electronic personal health record use on engagement and intermediate health outcomes among cardiac patients: a quasi-experimental study.

OBJECTIVES: To determine the impact of tethered personal health record (PHR) use on patient engagement and intermediate health outcomes among patients with coronary artery disease (CAD). METHODS: Adult CAD patients (N = 200) were enrolled in this prospective, quasi-experimental observational study. Each patient received a PHR account and training on its use. PHRs were populated with information from patient electronic medical records, hosted by a Health Information Exchange. Intermediate health outcomes including blood pressure, body mass index, and hemoglobin A1c (HbA1c) were evaluated through electronic medical record review or laboratory tests. Trends in patient activation measure® (PAM) were determined through three surveys conducted at baseline, 6 and 12 months. Frequency of PHR use data was collected and used to classify participants into groups for analysis: Low, Active, and Super users. RESULTS: There was no statistically significant improvement in patient engagement as measured by PAM scores during the study period. HbA1c levels improved significantly in the Active and Super user groups at 6 months; however, no other health outcome measures improved significantly. Higher PAM scores were associated with lower body mass index and lower HbA1c, but there was no association between changes in PAM scores and changes in health outcomes. Use of the PHR health diary increased significantly following PHR education offered at the 6-month study visit and an elective group refresher course. CONCLUSIONS: The study findings show that PHR use had minimal impact on intermediate health outcomes and no significant impact on patient engagement among CAD patients.

Pathways of economic inequalities in maternal and child health in urban India: a decomposition analysis.

BACKGROUND/OBJECTIVE: Children and women comprise vulnerable populations in terms of health and are gravely affected by the impact of economic inequalities through multi-dimensional channels. Urban areas are believed to have better socioeconomic and maternal and child health indicators than rural areas. This perception leads to the implementation of health policies ignorant of intra-urban health inequalities. Therefore, the objective of this study is to explain the pathways of economic inequalities in maternal and child health indicators among the urban population of India. METHODS: Using data from the third wave of the National Family Health Survey (NFHS, 2005-06), this study calculated relative contribution of socioeconomic factors to inequalities in key maternal and child health indicators such as antenatal check-ups (ANCs), institutional deliveries, proportion of children with complete immunization, proportion of underweight children, and Infant Mortality Rate (IMR). Along with regular CI estimates, this study applied widely used regression-based Inequality Decomposition model proposed by Wagstaff and colleagues. RESULTS: The CI estimates show considerable economic inequalities in women with less than 3 ANCs (CI = -0.3501), institutional delivery (CI = -0.3214), children without fully immunization (CI = -0.18340), underweight children (CI = -0.19420), and infant deaths (CI = -0.15596). Results of the decomposition model reveal that illiteracy among women and her partner, poor economic status, and mass media exposure are the critical factors contributing to economic inequalities in maternal and child health indicators. The residuals in all the decomposition models are very less; this implies that the above mentioned factors explained maximum inequalities in maternal and child health of urban population in India. CONCLUSION: Findings suggest that illiteracy among women and her partner, poor economic status, and mass media exposure are the critical pathways through which economic factors operate on inequalities in maternal and child health outcomes in urban India.

Detection of previously undetected leprosy cases in a defined rural and urban area of Maharashtra, Western India.

INTRODUCTION: Leprosy has been a major public health problem in India for centuries. In India, between 2001 and 2005, the prevalence of leprosy was reduced by 80%. This sharp decline in the prevalence of leprosy alongside the cessation of active surveillance for detection of leprosy cases has raised a sense of alarm in the scientific community. MATERIALS AND METHODS: This is a total population survey aiming to estimate the prevalence of undetected active cases of leprosy in the community in defined rural (Panvel Taluka, Raigad District) and urban (M-East Ward, Mumbai) areas by health workers from Kushth Nivaran Samiti (Panvel) and Lok Seva Sangam (Mumbai). Those provisionally diagnosed with leprosy were subjected to an independent verification using clinical, bacteriological and histopathological investigations at the Foundation for Medical Research. FINDINGS: A population of 196,694 and 600,247 was covered in defined rural and urban areas respectively. In the rural area on examining 178,646 individuals, 120 provisionally diagnosed leprosy cases were detected, of which 65 were paucibacillary (PB) and 55 were multibacillary (MB) based on the WHO operational classification used by health workers at field level. In the urban area, of the 512,434 individuals who were examined, 134 provisionally diagnosed leprosy cases were detected with 92 PB and 42 MB cases. Among the clinically confirmed cases, 35.6% (32/90) and 34.9% (36/109) in rural and urban areas respectively were children. CONCLUSIONS: There are large numbers of undetected leprosy cases in the community with a high proportion of MB patients and children among them. This indicates active transmission pointing to the need for a paradigm shift in leprosy care services and control programme.