Multimorbidity and healthcare utilisation among high-cost patients in the US Veterans Affairs Health Care System

Neurocognitive function and medical care utilization in Veterans treated for substance use disorder

BACKGROUND

Veterans with substance use disorder (SUD) are at high risk for cognitive problems due to neurotoxic effects of chronic drug and alcohol use coupled in many cases with histories of traumatic brain injury (TBI). These problems may in turn result in proneness to SUD relapse and reduced adherence to medical self-care regimens and therefore reliance on health care systems. However, the direct relationship between cognitive function and utilization of Veterans Health Administration (VHA) SUD and other VHA health care services has not been evaluated. We sought initial evidence as to whether neurocognitive performance relates to repeated health care engagement in Veterans as indexed by estimated VHA care costs.

METHODS

Neurocognitive performance in 76 Veterans being treated for SUD was assessed using CNS-Vital Signs, a commercial computerized cognitive testing battery, and related to histories of outpatient and inpatient/residential care costs as estimated by the VHA Health Economics Resource Center.

RESULTS

After controlling for age, an aggregate metric of overall neurocognitive performance (Neurocognition Index) correlated negatively with total VHA health care costs, particularly with SUD-related outpatient care costs but also with non-mental health-related care costs. Barratt Impulsiveness Scale scores also correlated positively with total VHA care costs.

CONCLUSIONS

In Veterans receiving SUD care, higher impulsivity and lower cognitive performance were associated with greater health care utilization within the VHA system. This suggests that veterans with SUD who show lower neurocognitive performance are at greater risk for continued health problems that require healthcare engagement. Cognitive rehabilitation programs developed for brain injury and other neurological conditions could be tried in Veterans with SUD to improve their health outcomes.

An innovative approach to aligning healthcare with what matters most to patients: A hybrid type 1 trial protocol of patient priorities care for older adults with multiple chronic conditions

BACKGROUND

Providing healthcare for older adults with multiple chronic conditions (MCC) is challenging. Polypharmacy and complex treatment plans can lead to high treatment burden and risk for adverse events. For clinicians, managing the complexities of patients with MCC leaves little room to identify what matters and align care options with patients' health priorities. New care approaches are needed to navigate these challenges. In this clinical trial, we evaluate implementation and effectiveness outcomes of an innovative, structured, patient-centered care approach (Patient Priorities Care; PPC) for reducing treatment burden and aligning health care decisions with the health priorities of older adults with MCC.

METHODS

This is a multisite, assessor-blind, two-arm, parallel hybrid type 1 randomized controlled trial. We are enrolling 396 older (65+) Veterans with MCC who receive primary care at the Veterans Affairs Medical Center. Veterans are randomly assigned to either PPC or usual care. In the PPC arm, Veterans have a brief telephone call with a study facilitator to identify their personal health priorities. Then, primary care providers use this information to align healthcare with Veteran priorities during their established clinic appointments. Data are collected at baseline and 4-month follow up to assess for changes in treatment burden and use of home and community services. Formative and summative evaluations are also collected to assess for implementation outcomes according to Proctor's implementation framework.

CONCLUSIONS

This work has the potential to significantly improve the standard of care by personalizing healthcare and helping patients achieve what is most important to them.

Impact of Referring High-Risk Patients to Intensive Outpatient Primary Care Services: A Propensity Score-Matched Analysis

BACKGROUND

Many healthcare systems have implemented intensive outpatient primary care programs with the hopes of reducing healthcare costs.

OBJECTIVE

The Veterans Health Administration (VHA) piloted primary care intensive management (PIM) for patients at high risk for hospitalization or death, or "high-risk." We evaluated whether a referral model would decrease high-risk patient costs.

DESIGN

Retrospective cohort study using a quasi-experimental design comparing 456 high-risk patients referred to PIM from October 2017 to September 2018 to 415 high-risk patients matched on propensity score.

PARTICIPANTS

Veterans in the top 10th percentile of risk for 90-day hospitalization or death and recent hospitalization or emergency department (ED) visit.

INTERVENTION

PIM consisted of interdisciplinary teams that performed comprehensive assessments, intensive case management, and care coordination services.

MAIN OUTCOMES AND MEASURES

Change in VHA and non-VHA outpatient utilization, inpatient admissions, and costs 12 months pre- and post-index date.

KEY RESULTS

Of the 456 patients referred to PIM, 301 (66%) enrolled. High-risk patients referred to PIM had a marginal reduction in ED visits (- 0.7; [95% CI - 1.50 to 0.08]; p = 0.08) compared to propensity-matched high-risk patients; overall outpatient costs were similar. High-risk patients referred to PIM had similar number of medical/surgical hospitalizations (- 0.2; [95% CI, - 0.6 to 0.16]; p = 0.2), significant increases in length of stay (6.36; [CI, - 0.01 to 12.72]; p = 0.05), and higher inpatient costs ($22,628, [CI, $3587 to $41,669]; p = 0.02) than those not referred to PIM.

CONCLUSIONS AND RELEVANCE

VHA intensive outpatient primary care was associated with higher costs. Referral to intensive case management programs targets the most complex patients and may lead to increased utilization and costs, particularly in an integrated healthcare setting with robust patient-centered medical homes.

TRIAL REGISTRATION

PIM 2.0: Patient Aligned Care Team (PACT) Intensive Management (PIM) Project (PIM2). NCT04521816. https://clinicaltrials.gov/study/NCT04521816.

Implementing evidence-based practices to improve primary care for high-risk patients: study protocol for the VA high-RIsk VETerans (RIVET) type III effectiveness-implementation trial

BACKGROUND

Patients with significant multimorbidity and other factors that make healthcare challenging to access and coordinate are at high risk for poor health outcomes. Although most (93%) of Veterans' Health Administration (VHA) patients at high risk for hospitalization or death ("high-risk Veterans") are primarily managed by primary care teams, few of these teams have implemented evidence-based practices (EBPs) known to improve outcomes for the high-risk patient population's complex healthcare issues. Effective implementation strategies could increase adoption of these EBPs in primary care; however, the most effective implementation strategies to increase evidence-based care for high-risk patients are unknown. The high-RIsk VETerans (RIVET) Quality Enhancement Research Initiative (QUERI) will compare two variants of Evidence-Based Quality Improvement (EBQI) strategies to implement two distinct EBPs for high-risk Veterans: individual coaching (EBQI-IC; tailored training with individual implementation sites to meet site-specific needs) versus learning collaborative (EBQI-LC; implementation sites trained in groups to encourage collaboration among sites). One EBP, Comprehensive Assessment and Care Planning (CACP), guides teams in addressing patients' cognitive, functional, and social needs through a comprehensive care plan. The other EBP, Medication Adherence Assessment (MAA), addresses common challenges to medication adherence using a patient-centered approach.

METHODS

We will recruit and randomize 16 sites to either EBQI-IC or EBQI-LC to implement one of the EBPs, chosen by the site. Each site will have a site champion (front-line staff) who will participate in 18 months of EBQI facilitation.

ANALYSIS

We will use a mixed-methods type 3 hybrid Effectiveness-Implementation trial to test EBQI-IC versus EBQI-LC versus usual care using a Concurrent Stepped Wedge design. We will use the Practical, Robust Implementation and Sustainability Model (PRISM) framework to compare and evaluate Reach, Effectiveness, Adoption, Implementation, and costs. We will then assess the maintenance/sustainment and spread of both EBPs in primary care after the 18-month implementation period. Our primary outcome will be Reach, measured by the percentage of eligible high-risk patients who received the EBP.

DISCUSSION

Our study will identify which implementation strategy is most effective overall, and under various contexts, accounting for unique barriers, facilitators, EBP characteristics, and adaptations. Ultimately this study will identify ways for primary care clinics and teams to choose implementation strategies that can improve care and outcomes for patients with complex healthcare needs.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT05050643. Registered September 9th, 2021, https://clinicaltrials.gov/study/NCT05050643 PROTOCOL VERSION: This protocol is Version 1.0 which was created on 6/3/2020.

Concentration of High-Cost Head and Neck Cancer Surgical Patients

OBJECTIVE

Health care costs are disproportionately concentrated among a small number of patients. We sought to identify variables associated with high-cost patients and high hospital concentration of high-cost patients and to examine associations with short-term outcomes in head and neck cancer (HNCA) surgery.

STUDY DESIGN

The Nationwide Inpatient Sample was used to identify 170,577 patients who underwent HNCA surgery in 2001-2011. High-cost patients were defined as patients whose costs of care were in the top decile, and high-concentration hospitals were defined as those whose percentage of high-cost patients was in the top decile.

METHODS

Multivariable regression was used to evaluate associations between cost and patient and hospital variables, postoperative complications, and in-hospital mortality.

RESULTS

Costs associated with high-cost patients were 4.47-fold greater than the remaining 90% of patients. High-concentration hospitals treated 36% of all high-cost patients. High-cost patients were more likely to be non-white (OR = 2.08 [1.45-2.97]), have oral cavity cancer (OR = 1.21 [1.05-1.39]), advanced comorbidity (OR = 1.53 [1.31-1.77]), Medicaid (OR = 1.93 [1.62-2.31]) or self-pay payor status (OR = 1.72 [1.38-2.14]), income>50th percentile (OR = 1.25 [1.05-1.51]), undergo major procedures (OR = 3.52 [3.07-4.05]) and have non-routine discharge (OR = 7.50 [6.01-9.35]). High-concentration hospitals were more likely to be teaching hospitals (OR = 3.14 [1.64-6.05]) and less likely to be urban (OR = 0.20 [0.04-0.93]). After controlling for all other variables, high-cost patients were associated with an increased odds of mortality (OR = 8.00 [5.89-10.85]) and postoperative complications (OR = 5.88 [5.18-6.68]). High-concentration hospitals were associated with an increased odds of postoperative complications (OR = 1.31 [1.08-1.61]) but were not associated with increased mortality (OR = 0.98 [0.67-1.44]).

CONCLUSIONS

High-cost HNCA surgical patients are associated with increased postoperative morbidity and mortality, and are disproportionately concentrated at teaching hospitals.

LEVEL OF EVIDENCE

4 Laryngoscope, 2024.

Predictive modeling for identification of older adults with high utilization of health and social services

AIM

Machine learning techniques have demonstrated success in predictive modeling across various clinical cases. However, few studies have considered predicting the use of multisectoral health and social services among older adults. This research aims to utilize machine learning models to detect high-risk groups of excessive health and social services utilization at early stage, facilitating the implementation of preventive interventions.

METHODS

We used pseudonymized data covering a four-year period and including information on a total of 33,374 senior citizens from Southern Finland. The endpoint was defined based on the occurrence of unplanned healthcare visits and the total number of different services used. Input features included individual's basic demographics, health status and past usage of healthcare resources. Logistic regression and eXtreme Gradient Boosting (XGBoost) methods were used for binary classification, with the dataset split into 70% training and 30% testing sets.

RESULTS

Subgroup-based results mirrored trends observed in the full cohort, with age and certain health issues, e.g. mental health, emerging as positive predictors for high service utilization. Conversely, hospital stay and urban residence were associated with decreased risk. The models achieved a classification performance (AUC) of 0.61 for the full cohort and varying in the range of 0.55-0.62 for the subgroups.

CONCLUSIONS

Predictive models offer potential for predicting future high service utilization in the older adult population. Achieving high classification performance remains challenging due to diverse contributing factors. We anticipate that classification performance could be increased by including features based on additional data categories such as socio-economic data.

Primary Care Telehealth Initiation and Engagement Among Veterans at High Risk, 2019-2022

Importance

During the COVID-19 pandemic, the Veterans Health Administration (VHA) expanded telehealth infrastructure. Understanding telehealth initiation and sustained engagement could inform future resource allocation for high-need populations.

Objective

To describe and examine primary care use, including initiation, use, and engagement factors, of telehealth modalities (telephone, video visits, and secure messaging) from 2020 to 2022.

Design, Setting, and Participants

This cohort study was conducted among 1 383 070 patients in the 75th or higher percentile for 90-day risk of hospitalization or mortality (using previously validated Care Assessment Need scores) engaged in VHA primary care from March 11, 2019, to March 10, 2022.

Exposures

Patient sociodemographic characteristics (age, sex, race and ethnicity, and marital and housing status), health characteristics (chronic condition count, military service disability, serious mental illness, or substance use disorder diagnoses), geographic characteristics (driving distance to clinic and rural or urban location), and Federal Communications Commission-reported broadband speed among subgroups of patients at high risk categorized by telehealth use from 2020 to 2022.

Main Outcomes and Measures

Primary care utilization by modality.

Results

A total of 1 383 070 patients at high risk were engaged in VHA primary care in March 2020 (median age, 73.0 years [IQR, 65-80 years]; 92.4% male; 77.7% regular telehealth users in 2019). With the onset of the COVID-19 pandemic from March 2020 to March 2021, 92.7% of patients at high risk (1 158 804 of 1 250 438 retained in care) became regular telehealth users. The following year, most patients continued as telehealth users (83.4% [942 151 of 1 129 683 retained]), including 38.2% retention of users at high risk newly engaged in 2020. Between 2019 and 2022 among those living and engaged in VHA primary care, adjusted exploratory multinomial logit models estimated that new telehealth users in 2020 (both sustained or only transiently engaged) were more often Black non-Hispanic individuals with greater comorbidity burdens than those who never engaged in telehealth use (Black non-Hispanic with new persistent telehealth use: adjusted relative risk ratio [ARR], 1.18 [95% CI, 1.16-1.20]; Black non-Hispanic with transient telehealth use: ARR, 1.11 [95% CI, 1.08-1.13]; ≥5 chronic conditions with new persistent telehealth use: ARR, 1.92 [95% CI, 1.88-1.96]; ≥5 chronic conditions with transient telehealth use: ARR, 1.43 [95% CI, 1.40-1.46]).

Conclusions and Relevance

This cohort study suggests that primary care telehealth initiation, use and sustained engagement differed among subgroups of patients at high risk throughout the COVID-19 pandemic. Those never or only transiently engaged with telehealth had lower illness burdens and were less likely to identify as members of racial or ethnic minority groups. Variation in telehealth use among subgroups of patients at high risk during this period could inform future resource allocation.

Identifying multi-resolution clusters of diseases in ten million patients with multimorbidity in primary care in England

BACKGROUND

Identifying clusters of diseases may aid understanding of shared aetiology, management of co-morbidities, and the discovery of new disease associations. Our study aims to identify disease clusters using a large set of long-term conditions and comparing methods that use the co-occurrence of diseases versus methods that use the sequence of disease development in a person over time.

METHODS

We use electronic health records from over ten million people with multimorbidity registered to primary care in England. First, we extract data-driven representations of 212 diseases from patient records employing (i) co-occurrence-based methods and (ii) sequence-based natural language processing methods. Second, we apply the graph-based Markov Multiscale Community Detection (MMCD) to identify clusters based on disease similarity at multiple resolutions. We evaluate the representations and clusters using a clinically curated set of 253 known disease association pairs, and qualitatively assess the interpretability of the clusters.

RESULTS

Both co-occurrence and sequence-based algorithms generate interpretable disease representations, with the best performance from the skip-gram algorithm. MMCD outperforms k-means and hierarchical clustering in explaining known disease associations. We find that diseases display an almost-hierarchical structure across resolutions from closely to more loosely similar co-occurrence patterns and identify interpretable clusters corresponding to both established and novel patterns.

CONCLUSIONS

Our method provides a tool for clustering diseases at different levels of resolution from co-occurrence patterns in high-dimensional electronic health records, which could be used to facilitate discovery of associations between diseases in the future.

14-Year Epidemiologic study of Pseudomonas aeruginosa bloodstream infection incidence and resistance in the Veterans Health Administration system, 2009-2022

Background

Multidrug resistant Pseudomonas aeruginosa (PA) represents a serious threat to hospitalized patients. Characterizing the incidence of PA infection and degree of resistance can inform empiric treatment and preventative measures.

Objectives

We sought to describe trends in incidence and resistance characteristics of PA bloodstream infections (BSI) observed within the Veterans Health Administration (VHA) system and identify factors contributing to higher observed mortality within this population.

Methods

We characterized demographic and clinical features of unique patients among the VHA population presenting with their first episode of PA-BSI between 2009 and 2022 and summarized trends related to mortality and resistance phenotype based on year and geographical location. We additionally used logistic regression analysis to identify predictors of 30-day mortality among this cohort.

Results

We identified 8039 PA-BSIs during the study period, 32.7% of which were hospital onset. Annual PA-BSI cases decreased by 35.8%, and resistance among all antimicrobial classes decreased during the study period, while the proportion of patients receiving early active treatment based on susceptibility testing results increased. Average 30-day mortality rate was 23.3%. Higher Charlson Comorbidity Index, higher mAPACHE score, VHA facility complexity 1b and hospital-onset cases were associated with higher mortality, and early active treatment was associated with lower mortality.

Conclusions

PA-BSI resistance decreased across the VHA system during the study period. Further investigation of antimicrobial stewardship measures possibly contributing to the observed decreased resistance in this cohort and identification of measures to improve on the high mortality associated with PA-BSI in the VHA population is warranted.

Adverse childhood experiences and trajectories of multimorbidity in individuals aged over 50: Evidence from the English Longitudinal Study of Ageing

BACKGROUND

Adverse childhood experiences (ACE) are important for chronic diseases yet their association with multimorbidity remains understudied. Few studies consider the complexity of multimorbidity or observe multimorbidity development over time.

OBJECTIVE

We investigated whether ACE were associated with multimorbidity at baseline and over a 12-year follow-up period.

PARTICIPANTS AND SETTING

5326 participants aged over 50 were obtained from the English Longitudinal Study of Ageing (ELSA).

METHODS

An ACE summary score was derived using eight ACE items measuring abuse, social care, and household dysfunction. From repeated measurements of 29 chronic conditions over a 12-year period (2008-2019) we derived two multimorbidity measures: number of chronic diseases and number of chronic disease categories. We used multinomial logistic regression to assess associations between ACE and both measures. Mixed effects models were estimated to examine trajectories of multimorbidity by ACE over time.

RESULTS

Graded associations between ACE and multimorbidity were observed. Compared to those without ACE, participants with ≥3 ACE had three times the risk of having ≥3 chronic diseases (RRR 3.06, 95 % CI 1.85-5.05) and falling into ≥3 chronic disease categories (RRR 2·93 95 % CI 1·74-4·95). Graded associations persisted during 12-year follow-up, though differences in multimorbidity between those with ≥3 ACE and those without ACE remained constant (B 0.02, 95 % CI 0·01-0·03, and B -0·01, 95 % CI -0·02-0·00, number of chronic conditions and chronic condition categories respectively).

CONCLUSION

ACE are associated with multimorbidity risk and complexity, associations arising before the age of 50. Early intervention amongst those with ACE could attenuate this association.

Disease patterns in high-cost individuals with multimorbidity: a retrospective cross-sectional study in primary care

BACKGROUND

'High-cost' individuals with multimorbidity account for a disproportionately large share of healthcare costs and are at most risk of poor quality of care and health outcomes.

AIM

To compare high-cost with lower-cost individuals with multimorbidity and assess whether these populations can be clustered based on similar disease patterns.

DESIGN AND SETTING

A cross-sectional study based on 2019/2020 electronic medical records from adults registered to primary care practices (n = 41) in a London borough.

METHOD

Multimorbidity is defined as having ≥2 long-term conditions (LTCs). Primary care costs reflected consultations, which were costed based on provider and consultation types. High cost was defined as the top 20% of individuals in the cost distribution. Descriptive analyses identified combinations of 32 LTCs and their contribution to costs. Latent class analysis explored clustering patterns.

RESULTS

Of 386 238 individuals, 101 498 (26%) had multimorbidity. The high-cost group (n = 20 304) incurred 53% of total costs and had 6833 unique disease combinations, about three times the diversity of the lower-cost group (n = 81 194). The trio of anxiety, chronic pain, and depression represented the highest share of costs (5%). High-cost individuals were best grouped into five clusters, but no cluster was dominated by a single LTC combination. In three of five clusters, mental health conditions were the most prevalent.

CONCLUSION

High-cost individuals with multimorbidity have extensive heterogeneity in LTCs, with no single LTC combination dominating their primary care costs. The frequent presence of mental health conditions in this population supports the need to enhance coordination of mental and physical health care to improve outcomes and reduce costs.

Patterns of comorbidities differentially affect long-term functional evolution and disease activity in patients with 'difficult to treat' rheumatoid arthritis

BACKGROUND

Characterisation of the long-term outcome of patients with 'difficult to treat' (D2T) rheumatoid arthritis and factors contributing to its evolution are unknown. Herein, we explored the heterogeneity and contributing factors of D2T long-term outcome.

METHODS

Patients included from a prospective single centre cohort study. The EULAR definition of D2T was applied. Longitudinal clustering of functional status (modified Health Assessment Questionnaire (mHAQ)) and disease activity (Disease Activity Score-28 (DAS28)) were assessed using latent-class trajectory analysis. Multiple linear mixed models were used to examine the impact of comorbidities and their clusters on the long-term outcome.

RESULTS

251 out of 1264 patients (19.9%) were identified as D2T. Younger age, fibromyalgia, osteoarthritis, DAS28-erythrocyte sedimentation rate (ESR) at first biological or targeted synthetic disease-modifying antirheumatic drug (b/ts-DMARD) initiation and failure to reduce DAS28-ESR scores within the first 6 months of b/ts-DMARD therapy were significant predictors of patients becoming D2T. Long-term follow-up (total of 5872 person-years) revealed four groups of functional status evolution: 18.2% had stable, mildly compromised mHAQ (mean 0.41), 39.9% had gradual improvement (1.21-0.87) and two groups had either slow deterioration or stable significant functional impairment (HAQ>1). Similarly, four distinct groups of disease activity evolution were identified. Among the different clusters of comorbidities assessed, presence of 'mental-health and pain-related illnesses' or 'metabolic diseases' had significant contribution to mHAQ worsening (p<0.0001 for both) and DAS28 evolution (p<0.0001 and p=0.018, respectively).

CONCLUSION

D2T patients represent a heterogeneous group in terms of long-term disease course. Mental-health/pain-related illnesses as well as metabolic diseases contribute to long-term adverse outcomes and should be targeted in order to optimise the prognosis of this subset of rheumatoid arthritis.

Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions

Background

Identifying clusters of co-occurring diseases may help characterise distinct phenotypes of Multiple Long-Term Conditions (MLTC). Understanding the associations of disease clusters with health-related outcomes requires a strategy to assign clusters to people, but it is unclear how the performance of strategies compare.

Aims

First, to compare the performance of methods of assigning disease clusters to people at explaining mortality, emergency department attendances and hospital admissions over one year. Second, to identify the extent of variation in the associations with each outcome between and within clusters.

Methods

We conducted a cohort study of primary care electronic health records in England, including adults with MLTC. Seven strategies were tested to assign patients to fifteen disease clusters representing 212 LTCs, identified from our previous work. We tested the performance of each strategy at explaining associations with the three outcomes over 1 year using logistic regression and compared to a strategy using the individual LTCs.

Results

6,286,233 patients with MLTC were included. Of the seven strategies tested, a strategy assigning the count of conditions within each cluster performed best at explaining all three outcomes but was inferior to using information on the individual LTCs. There was a larger range of effect sizes for the individual LTCs within the same cluster than there was between the clusters.

Conclusion

Strategies of assigning clusters of co-occurring diseases to people were less effective at explaining health-related outcomes than a person's individual diseases. Furthermore, clusters did not represent consistent relationships of the LTCs within them, which might limit their application in clinical research.

Siddha Self-Inquiry for Flow, Bliss, and Uvagai: Mindfulness for Intuitive Lifestyle in Complex Cardiac Disease

Cardiovascular disease (CVD) and mental health disorders contribute to significant healthcare expenses. Lifestyle approaches that empower and enable patients to participate in their recovery are needed with the increasing complexity of cardiac patients. Traditional Tamil medical practice of Siddha self-inquiry meditation targets holistic health through intuitive lifestyle transformation. We describe 4 complex cardiac patients who explored Siddha based Hunger Gratitude Experience (HUGE) mindful eating and reported elevated levels of optimism and deeper experience of life as outlined by the 5000-year-old secular Siddha medical tradition. We cannot exclude the role of suggestion and placebo effect in descriptive series. However, the simultaneous improvement in physical health and emotional wellbeing along with demonstrated resilience against unforeseen adversities suggests this is Uvagai, the true essence of Siddha higher consciousness. Uvagai is extreme happiness and may be accessible universally with little formal training and targets positive psychology to improve wellbeing. While flow and bliss states are transient transcendental experiences, Uvagai may be more profound and therapeutic in CVD despite age and comorbidities. Seeking Uvagai can potentially overcome health disparities, including rural, minority, and underprivileged populations for better health. HUGE allows CVD patients to safely engage in Uvagai, experience higher consciousness and intuitively sustain lifestyle transformation.

Frontline perspectives on adoption and non-adoption of care management tools for high-risk patients in primary care

BACKGROUND

Population health management tools (PHMTs) embedded within electronic health records (EHR) could improve management of high-risk patients and reduce costs associated with potentially avoidable emergency department visits or hospitalizations. Adoption of PHMTs across the Veterans Health Administration (VA) has been variable and previous research suggests that understaffed primary care (PC) teams might not be using the tools.

METHODS

We conducted a retrospective content analysis of open-text responses (n = 1804) from the VA's 2018 national primary care personnel survey to, 1) identify system-level and individual-level factors associated with why clinicians are not using the tools, and 2) to document clinicians' recommendations to improve tool adoption.

RESULTS

We found three themes pertaining to low adoption and/or tool use: 1) IT burden and administrative tasks (e.g., manually mailing letters to patients), 2) staffing shortages (e.g., nurses covering multiple teams), and 3) no training or difficulty using the tools (e.g., not knowing how to access the tools or use the data). Frontline clinician recommendations included automating some tasks, reconfiguring team roles to shift administrative work away from providers and nurses, consolidating PHMTs into a centralized, easily accessible repository, and providing training.

CONCLUSIONS

Healthcare system-level factors (staffing) and individual-level factors (lack of training) can limit adoption of PHMTs that could be useful for reducing costs and improving patient outcomes. Future research, including qualitative interviews with clinicians who use/don't use the tools, could help develop interventions to address barriers to adoption.

IMPLICATIONS

Shifting more administrative tasks to clerical staff would free up clinician time for population health management but may not be possible for understaffed PC teams. Additionally, healthcare systems may be able to increase PHMT use by making them more easily accessible through the electronic health record and providing training in their use.

Multimorbidity is associated with lower total 24-hour movement activity among US adults

Objective

Having chronic conditions may result in reduced physical and cognitive function but less is known about multimorbidity with daily movement. We examined the association of multimorbidity and device-measured total daily movement in a nationally representative sample of US adults aged ≥ 30 years from the 2011-2014 National Health and Nutrition Examination Surveys.

Methods

Any multimorbidity (≥2 conditions) and complex multimorbidity (≥3 conditions across ≥ 3 body systems) were quantified using 16 chronic conditions via self-report and/or clinical thresholds. Total movement over 24-hours (Monitor-Independent Movement Summary units [MIMS-units]) was measured using a wrist-worn device (ActiGraph GT3X). Multivariable linear regression examined the association of 1) each chronic condition, 2) number of conditions, 3) any multimorbidity, and 4) complex multimorbidity with total movement. Covariates included age, gender, race/ethnicity, educational attainment, and smoking status.

Results

Among US adults (N = 7304, mean age: 53.2 ± 0.34 years, 53.2% female, 69.4% Non-Hispanic White), 62.2% had any multimorbidity with 34.2% having complex multimorbidity. After adjustment, a higher number of chronic conditions was associated with incrementally lower total movement (β MIMS-units [95% CI] compared to those with no chronic conditions; one: -419 [-772, -66], two: -605 [-933, -278], three: -1201 [-1506, -895], four: -1908 [-2351, -1465], 5+: -2972 [-3384, -2560]). Complex multimorbidity presence was associated with -1709 (95% CI: -2062, -1357) and -1269 (-1620, -918) lower total movement compared to those without multimorbidity and multimorbidity but not complex, respectively.

Conclusions

Multimorbidity was associated with lower 24-h movement among US adults and may be helpful for identifying adults at risk for low movement.

Transitions in health care settings for frequent and infrequent users of emergency departments: a population-based retrospective cohort study

BACKGROUND

Efforts to reduce emergency department (ED) volumes often target frequent users. We examined transitions in care across ED, hospital, and community settings, and in-hospital death, for high system users (HSUs) compared to controls.

METHODS

Population-based databases provided ED visits and hospitalizations in Alberta and Ontario, Canada. The retrospective cohort included the top 10% of all the ED users during 2015/2016 (termed HSUs) and a random sample of controls (4 per each HSU) from the bottom 90% per province. Rates of transitions among ED, hospitalization, community settings, and in-hospital mortality were adjusted for sociodemographic and ED variables in a multistate statistical model.

RESULTS

There were 2,684,924 patients and 579,230 (21.6%) were HSUs. Patient characteristics associated with shorter community to ED transition times for HSUs included Alberta residence (ratio of hazard ratio [RHR] = 1.11, 95% confidence interval [CI] 1.11,1.12), living in areas in the lower income quintile (RHR = 1.06, 95%CI 1.06,1.06), and Ontario residents without a primary health care provider (RHR = 1.13, 95%CI 1.13,1.14). Once at the ED, characteristics associated with shorter ED to hospital transition times for HSUs included higher acuity (e.g., RHR = 1.70, 95% CI 1.61, 1.81 for emergent), and for many diagnoses including chest pain (RHR = 1.71, 95%CI 1.65,1.76) and gastrointestinal (RHR = 1.66, 95%CI 1.62,1.71). Once admitted to hospital, HSUs did not necessarily have longer stays except for conditions such as chest pain (RHR = 0.90, 95% CI 0.86, 0.95). HSUs had shorter times to death in the ED if they presented for cancer (RHR = 2.51), congestive heart failure (RHR = 1.93), myocardial infarction (RHR = 1.53), and stroke (RHR = 1.84), and shorter times to death in-hospital if they presented with cancer (RHR = 1.29).

CONCLUSIONS

Differences between HSUs and controls in predictors of transitions among care settings were identified. Co-morbidities and limitations in access to primary care are associated with more rapid transitions from community to ED and hospital among HSUs. Interventions targeting these challenges may better serve patients across health systems..

TRIAL REGISTRATION

Not applicable.

Types of Engagement Strategies to Engage High-Risk Patients in VA

BACKGROUND

Many healthcare systems seek to improve care for complex high-risk patients, but engaging such patients to actively participate in their healthcare can be challenging.

OBJECTIVE

To identify and describe types of patient engagement strategies reported as successfully deployed by providers/teams and experienced by patients in a Veterans Health Administration (VA) intensive primary care (IPC) pilot program.

METHODS

We conducted semi-structured qualitative telephone interviews with 29 VA IPC staff (e.g., physicians, nurses, psychologists) and 51 patients who had at least four IPC team encounters. Interviews were recorded, transcribed, and analyzed thematically using a combination a priori/inductive approach.

RESULTS

The engagement strategies successfully deployed by the IPC providers/teams could be considered either more "facilitative," i.e., facilitated by and dependent on staff actions, or more "self-sustaining," i.e., taught to patients, thus cultivating their ongoing patient self-care. Facilitative strategies revolved around enhancing patient access and coordination of care, trust-building, and addressing social determinants of health. Self-sustaining strategies were oriented around patient empowerment and education, caregiver and/or community support, and boundaries and responsibilities. When patients described their experiences with the "facilitative" strategies, many discussed positive proximal outcomes (e.g., increased access to healthcare providers). Self-sustaining strategies led to positive (self-reported) longer-term clinical outcomes, such as behavior change.

CONCLUSION

We identified two categories of strategies for successfully engaging complex, high-risk patients: facilitative and self-sustaining. Intensive primary care program leaders may consider thoughtfully building "self-sustaining" engagement strategies into program development. Future research can confirm their effectiveness in improving health outcomes.

Racial differences in healthcare expenditures for prevalent multimorbidity combinations in the USA: a cross-sectional study

BACKGROUND

We aimed to model total charges for the most prevalent multimorbidity combinations in the USA and assess model accuracy across Asian/Pacific Islander, African American, Biracial, Caucasian, Hispanic, and Native American populations.

METHODS

We used Cerner HealthFacts data from 2016 to 2017 to model the cost of previously identified prevalent multimorbidity combinations among 38 major diagnostic categories for cohorts stratified by age (45-64 and 65 +). Examples of prevalent multimorbidity combinations include lipedema with hypertension or hypertension with diabetes. We applied generalized linear models (GLM) with gamma distribution and log link function to total charges for all cohorts and assessed model accuracy using residual analysis. In addition to 38 major diagnostic categories, our adjusted model incorporated demographic, BMI, hospital, and census division information.

RESULTS

The mean ages were 55 (45-64 cohort, N = 333,094) and 75 (65 + cohort, N = 327,260), respectively. We found actual total charges to be highest for African Americans (means $78,544 [45-64], $176,274 [65 +]) and lowest for Hispanics (means $29,597 [45-64], $66,911 [65 +]). African American race was strongly predictive of higher costs (p < 0.05 [45-64]; p < 0.05 [65 +]). Each total charge model had a good fit. With African American as the index race, only Asian/Pacific Islander and Biracial were non-significant in the 45-64 cohort and Biracial in the 65 + cohort. Mean residuals were lowest for Hispanics in both cohorts, highest in African Americans for the 45-64 cohort, and highest in Caucasians for the 65 + cohort. Model accuracy varied substantially by race when multimorbidity grouping was considered. For example, costs were markedly overestimated for 65 + Caucasians with multimorbidity combinations that included heart disease (e.g., hypertension + heart disease and lipidemia + hypertension + heart disease). Additionally, model residuals varied by age/obesity status. For instance, model estimates for Hispanic patients were highly underestimated for most multimorbidity combinations in the 65 + with obesity cohort compared with other age/obesity status groupings.

CONCLUSIONS

Our finding demonstrates the need for more robust models to ensure the healthcare system can better serve all populations. Future cost modeling efforts will likely benefit from factoring in multimorbidity type stratified by race/ethnicity and age/obesity status.

Multimorbidity patterns in the working age population with the top 10% medical cost from exhaustive insurance claims data of Japan Health Insurance Association

Although the economic burden of multimorbidity is a growing global challenge, the contribution of multimorbidity in patients with high medical expenses remains unclear. We aimed to clarify multimorbidity patterns that have a large impact on medical costs in the Japanese population. We conducted a cross-sectional study using health insurance claims data provided by the Japan Health Insurance Association. Latent class analysis (LCA) was used to identify multimorbidity patterns in 1,698,902 patients who had the top 10% of total medical costs in 2015. The present parameters of the LCA model included 68 disease labels that were frequent among this population. Moreover, subgroup analysis was performed using a generalized linear model (GLM) to assess the factors influencing annual medical cost and 5-year mortality. As a result of obtaining 30 latent classes, the kidney disease class required the most expensive cost per capita, while the highest portion (28.6%) of the total medical cost was spent on metabolic syndrome (MetS) classes, which were characterized by hypertension, dyslipidemia, and type 2 diabetes. GLM applied to patients with MetS classes showed that cardiovascular diseases or complex conditions, including malignancies, were powerful determinants of medical cost and mortality. MetS was classified into 7 classes based on real-world data and accounts for a large portion of the total medical costs. MetS classes with cardiovascular diseases or complex conditions, including malignancies, have a significant impact on medical costs and mortality.

Unhealthy alcohol use and brief intervention rates among high and low complexity veterans seeking primary care services in the Veterans Health Administration

INTRODUCTION

Brief intervention (BI) is recommended for all primary care (PC) patients who screen positive for unhealthy alcohol use; however, patients with multiple chronic health conditions who are at high-risk of hospitalization (i.e., "high complexity" patients) may face disparities in receiving BIs in PC. The current study investigated whether high complexity and low complexity patients in the Veterans Health Administration (VHA) differed regarding screening positive for unhealthy alcohol use, alcohol-use severity, and receipt of BI for those with unhealthy alcohol use.

METHODS

Patients were veterans receiving PC services at the VHA in a mid-Atlantic region of the United States. The study extracted VHA administrative and clinical data for a total of 282,242 patients who had ≥1 PC visits between 1/1/2014 and 12/31/2014, during which they were screened for unhealthy alcohol use by the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C). The study defined high complexity patients as those within and above the 90th percentile of risk for hospitalization per the VHA's Care Assessment Need Score. Logistic regression models assessed if being a high complexity patient was associated with screening positive for unhealthy alcohol use (AUDIT-C ≥ 5), severity of unhealthy alcohol use in those who screened positive (AUDIT-C score range 5-12), and receipt of BI in those who screened positive.

RESULTS

Our sample was 94.5% male, 83% White, 13% Black, 4% other race, and 1.7% Hispanic. A total of 10,813 (3.8%) patients screened positive for unhealthy alcohol use from which we identified 569 (5.3%) high complexity and 10,128 (93.6%) low complexity patients (n = 116 removed due to missing complexity data). Relative to low complexity patients, high complexity patients were less likely to screen positive for unhealthy alcohol use (3.3% vs. 4.1%, AOR = 0.59, p < .001); however, in patients who screened positive, high complexity patients had higher AUDIT-C scores (Mean AUDIT-C = 7.75 vs. 6.87, AOR = 1.46, p < .001) and were less likely to receive a BI (78.0% vs. 92.6%, AOR = 0.42, p < .001).

CONCLUSIONS

Disparities in BI exist for highly complex patients despite having more severe unhealthy alcohol use. Future research should examine the specific patient- and/or clinic-level factors impeding BI delivery for complex patients.

Sleep disorders in veterans with serious mental illnesses: prevalence in Veterans Affairs health record data

STUDY OBJECTIVES

This study aimed to estimate the 12-month prevalence of diagnosed sleep disorders among veterans with and without serious mental illnesses (SMI) in Veterans Affairs health record data in 2019. We also examined diagnosed sleep disorders across a 9-year period and explored associations with demographic and health factors.

METHODS

This study used health record data from VISN 4 of the Veterans Health Administration from 2011 to 2019. SMI diagnoses included schizophrenia and bipolar spectrum diagnoses as well as major depression with psychosis. Sleep diagnoses included insomnias, hypersomnias, sleep-related breathing disorders, circadian rhythm sleep-wake disorders, and sleep-related movement disorders. Demographic and health-related factors were also collected from the record.

RESULTS

In 2019, 21.8% of veterans with SMI were diagnosed with a sleep disorder. This is a significantly higher proportion than for veterans without SMI, 15.1% of whom were diagnosed with a sleep disorder. Sleep disorder rates were highest in veterans with a chart diagnosis of major depression with psychosis. From 2011 to 2019, the overall prevalence of sleep disorders in veterans with SMI more than doubled (10.2%-21.8%), suggesting improvements in the detection and diagnosis of sleep concerns for this group.

CONCLUSIONS

Our findings suggest that identification and diagnosis of sleep disorders for veterans with SMI has improved over the past decade, though diagnoses still likely underrepresent actual prevalence of clinically relevant sleep concerns. Sleep concerns may be at particularly high risk of going untreated in veterans with schizophrenia-spectrum disorders.

CITATION

Bonfils KA, Longenecker JM, Soreca I, et al. Sleep disorders in veterans with serious mental illnesses: prevalence in Veterans Affairs health record data. J Clin Sleep Med. 2023;19(9):1651-1660.

Virtual Management of Chronic Conditions During the COVID-19 Pandemic: Insights From Primary Care Providers and Clinical Pharmacists

INTRODUCTION

The abrupt change in care delivery caused by the coronavirus disease 2019 pandemic may have left some patients, particularly those with chronic conditions, unable to receive timely and appropriate routine care. Understanding the effect of the pandemic and the switch to virtual care for patients with chronic conditions requires in-depth qualitative feedback from providers who care for these patients.

MATERIALS AND METHODS

We interviewed 13 primary care providers and clinical pharmacists from the Veterans Health Administration. Interviews elicited experiences managing patients with chronic conditions, specifically diabetes and hypertension, during the coronavirus disease 2019 pandemic. We employed a rapid analytic approach for data analysis.

RESULTS

In general, interview participants maintained that most patients' chronic conditions could be managed remotely without significant disruption. However, patients who lack familiarity with technology and/or reliable broadband access, patients not compliant with recommended self-assessments, and older patients with hearing loss or cognitive disorders may be more difficult to manage virtually. Although providers reported minimal disruptions to care because of the pandemic, they did note that the closure of labs and experiences of social isolation may have negatively impacted patients. Providers suggested optimizing virtual management through more robust patient instruction on virtual care technology, increased use of Veterans Affairs home health services, and removing institutional barriers that may de-incentivize virtual care modalities.

CONCLUSIONS

For many patients with chronic conditions, virtual care is a promising approach to provide ongoing management in primary care. However, more tailored strategies may be needed to care for sicker, more vulnerable patients.

Co-occurring Medical Multimorbidity, Mental Illness, and Substance Use Disorders Among Older Criminal Legal System-Involved Veterans

BACKGROUND

Older veterans involved in the criminal legal system (CLS) may have patterns of multimorbidity that place them at risk for poor health outcomes.

OBJECTIVES

To estimate the prevalence of medical multimorbidity (≥2 chronic medical diseases), substance use disorders (SUDs), and mental illness among CLS-involved veterans aged 50 and older.

RESEARCH DESIGN

Using Veterans Health Administration health records, we estimated the prevalence of mental illness, SUD, medical multimorbidity, and the co-occurrence of these conditions among veterans by CLS involvement as indicated by Veterans Justice Programs encounters. Multivariable logistic regression models assessed the association between CLS involvement, the odds for each condition, and the co-occurrence of conditions.

SUBJECTS

Veterans aged 50 and older who received services at Veterans Health Administration facilities in 2019 (n=4,669,447).

METHODS

Mental illness, SUD, medical multimorbidity.

RESULTS

An estimated 0.5% (n=24,973) of veterans aged 50 and older had CLS involvement. For individual conditions, veterans with CLS involvement had a lower prevalence of medical multimorbidity compared with veterans without but had a higher prevalence of all mental illnesses and SUDs. After adjusting for demographic factors, CLS involvement remained associated with concurrent mental illness and SUD (adjusted odds ratio [aOR] 5.52, 95% CI=5.35-5.69), SUD and medical multimorbidity (aOR=2.09, 95% CI=2.04-2.15), mental illness and medical multimorbidity (aOR=1.04, 95% CI=1.01-1.06), and having all 3 simultaneously (aOR=2.42, 95% CI=2.35-2.49).

CONCLUSIONS

Older veterans involved in the CLS are at high risk for co-occurring mental illness, SUDs, and medical multimorbidity, all of which require appropriate care and treatment. Integrated care rather than disease-specific care is imperative for this population.

Home-Based Primary Care for Older Adults: Matched Case-Control Evaluation of Program’s Impact on Healthcare Utilization

Home-based Primary Care (HBPC) is an alternative model for homebound older adults. Healthcare utilization in HBPC was evaluated with a matched case-control design. Medical providers and social workers provided in-home visits. Enrolled patients were matched to controls on age, prior hospitalizations, and frailty risk. Difference-in-differences in utilization of primary care, specialty, and hospital services between 1-year pre- and 1-year post-enrollment were evaluated with hierarchical linear models. Analyses included 117 HBPC cases and 328 controls. HBPC cases had a significant increase in primary care visits compared to controls (7.8 ± 0.6; p < .0001) but significantly decreased utilization of in-clinic primary care visits (−3.2 ± 0.6; p < .0001). In-clinic specialty visits and hospital services did not differ between cases and controls (all p’s ≥ .75). Homebound patients accessed high-touch primary and social care, which is typically unachievable in clinic settings; however, additional work is needed to optimize services and target in-home care to impact healthcare utilization.

Intensive care management for high-risk veterans in a patient-centered medical home - do some veterans benefit more than others?

BACKGROUND

Primary care intensive management programs utilize interdisciplinary care teams to comprehensively meet the complex care needs of patients at high risk for hospitalization. The mixed evidence on the effectiveness of these programs focuses on average treatment effects that may mask heterogeneous treatment effects (HTEs) among subgroups of patients. We test for HTEs by patients' demographic, economic, and social characteristics.

METHODS

Retrospective analysis of a VA randomized quality improvement trial. 3995 primary care patients at high risk for hospitalization were randomized to primary care intensive management (n = 1761) or usual primary care (n = 1731). We estimated HTEs on ED and hospital utilization one year after randomization using model-based recursive partitioning and a pre-versus post-with control group framework. Splitting variables included administratively collected demographic characteristics, travel distance, copay exemption, risk score for future hospitalizations, history of hospital discharge against medical advice, homelessness, and multiple residence ZIP codes.

RESULTS

There were no average or heterogeneous treatment effects of intensive management one year after enrollment. The recursive partitioning algorithm identified variation in effects by risk score, homelessness, and whether the patient had multiple residences in a year. Within each distinct subgroup, the effect of intensive management was not statistically significant.

CONCLUSIONS

Primary care intensive management did not affect acute care use of high-risk patients on average or differentially for patients defined by various demographic, economic, and social characteristics.

IMPLICATIONS

Reducing acute care use for high-risk patients is complex, and more work is required to identify patients positioned to benefit from intensive management programs.

Gender differences in social support for diabetes self-management: A qualitative study among veterans

OBJECTIVE

Describe the role of social support in veterans' diabetes self-management and examine gender differences.

METHODS

We conducted semi-structured interviews among veterans with diabetes from one Veterans Health Administration Health Care System. Participants described how support persons influenced their diabetes self-management and perspectives on a proposed self-management program incorporating a support person. We used thematic analysis to identify salient themes and examine gender differences.

RESULTS

Among 18 women and 18 men, we identified four themes: 1) women felt responsible for their health and the care of others; 2) men shared responsibility for managing their diabetes, with support persons often attempting to correct behaviors (social control); 3) whereas both men and women described receiving instrumental and informational social support, primarily women described emotional support; and 4) some women's self-management efforts were hindered by support persons. Regarding programs incorporating a support person, some participants endorsed including family/friends and some preferred programs including other individuals with diabetes.

CONCLUSIONS

Notable gender differences in social support for self-management were observed, with women assuming responsibility for their diabetes and their family's needs and experiencing interpersonal barriers.

PRACTICE IMPLICATIONS

Gender differences in the role of support persons in diabetes self-management should inform support-based self-management programs.

Integrating data analytics, peer support, and whole health coaching to improve the health outcomes of homeless veterans: Study protocol for an effectiveness-implementation trial

BACKGROUND

Homelessness is a strong determinant of acute care service utilization (inpatient hospitalization, emergency department visits) among US adults. Data analytics, peer support, and patient-centered approaches can collectively offer high-quality care for homeless patients who frequently utilize acute care ("super utilizers"). However, few outpatient programs have integrated these components and tested their effectiveness for this patient population.

OBJECTIVE

To test the effectiveness and implementation potential of a novel intervention that integrates data analytics with peers trained in whole health coaching ("Peer Whole Health") to reduce use of acute care among homeless adults.

METHODS

Using a randomized controlled trial design at two US Veterans Health Administration Medical Centers, we plan to enroll 220 veterans in primary care on VHA's Homeless Registry who are flagged on a super-utilizer clinical dashboard. Participants will complete a baseline interview, be randomized to Enhanced Usual Care (EUC; primary care and data analytics) or EUC plus 18 sessions of Peer Whole Health over 6 months, and be re-interviewed at 3, 6, and 9 months. Qualitative interviews with primary care staff and patients will identify facilitators and barriers to more widespread implementation of the intervention.

DISCUSSION

The primary hypothesis is that those who receive the intervention will have fewer total days of all-cause hospitalization. If confirmed, the findings can provide healthcare systems that serve homeless super-utilizers with a high-value approach to care that can be integrated into primary care services and reduce overall costs for these patients.

CLINICAL TRIAL REGISTRATION

The study is registered with ClinicalTrials.gov (NCT05176977).

Geospatial assessment of the convergence of communicable and non-communicable diseases in South Africa

Introduction

Several low-and middle-income countries are undergoing rapid epidemiological transition with a rising burden of non-communicable diseases (NCDs). South Africa (SA) is a country with one of the largest HIV epidemics worldwide and a growing burden of NCDs where the collision of these epidemics poses a major public health challenge.

Methods

Using data from a large nationally representative survey, the South Africa Demographic and Health Survey (SADHS 2016), we conducted a geospatial analysis of several diseases including HIV, tuberculosis (TB), cardiovascular, respiratory, and metabolic diseases to identify areas with a high burden of co-morbidity within the country. We explored the spatial structure of each disease and associations between diseases using different spatial and visual data methodologies. We also assessed the individual level co-occurrence of HIV and the other diseases included in the analysis.

Results

The spatial distribution for HIV prevalence showed that this epidemic is most intense in the eastern region of the country, mostly within the Gauteng, Mpumalanga, and Kwazulu-Natal provinces. In contrast, chronic diseases had their highest prevalence rates the southern region of the country, particularly in the Eastern and Western Cape provinces. Individual-level analyses were consistent with the spatial correlations and found no statistically significant associations between HIV infection and the presence of any NCDs.

Conclusions

We found no evidence of geospatial overlap between the HIV epidemic and NCDs in SA. These results evidence the complex epidemiological landscape of the country, characterized by geographically distinct areas exhibiting different health burdens. The detailed description of the heterogenous prevalence of HIV and NCDs in SA reported in this study could be a useful tool to inform and direct policies to enhance targeted health service delivery according to the local health needs of each community.

The unique association of posttraumatic stress disorder with hypertension among veterans: A replication of Kibler et al. (2009) using Bayesian estimation and data from the United States-Veteran Microbiome Project

OBJECTIVE

Kibler et al. (2009) reported that hypertension was related to PTSD independent of depression. These two conditions have significant diagnostic overlap. The present study sought to conceptually replicate this work with a veteran sample, using Bayesian estimation to directly update past results, as well as examine symptom severity scores in relation to hypertension.

METHOD

This was a secondary analysis of data obtained from the United States-Veteran Microbiome Project. Lifetime diagnoses of PTSD and major depressive disorder (MDD) were obtained from a structured clinical interview and hypertension diagnoses were extracted from electronic medical records. PTSD and depressive symptom severity were obtained from self-report measures. Logistic regressions with Bayesian estimation were used to estimate the associations between hypertension and (a) psychiatric diagnostic history and (b) symptom severity scores.

RESULTS

Compared with veterans without lifetime diagnoses of either disorder, the PTSD-only group was estimated to have a 29% increase in hypertension risk, and the PTSD + MDD group was estimated to have a 66% increase in hypertension risk. Additionally, higher levels of PTSD symptom severity were associated with a higher risk of hypertension.

CONCLUSION

PTSD diagnosis and symptom severity are uniquely associated with hypertension, independent of MDD or depressive symptom severity. These results support previous findings that PTSD might be a modifiable risk factor for the prevention and treatment of hypertension. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Factors Associated With Self-reported Use of Web and Mobile Health Apps Among US Military Veterans: Cross-sectional Survey

BACKGROUND

Despite their prevalence and reported patient interest in their use, uptake of health-related apps is limited. The Veterans Health Administration (VHA) has developed a variety of apps to support veterans; however, uptake remains low nationally.

OBJECTIVE

We examined the prevalence of VHA health-related app use and how veterans learned about these apps in order to identify factors associated with their use.

METHODS

As part of a VHA quality improvement initiative, we recruited a national cohort of veterans to obtain feedback on their use of technology for health and collected data from them via a cross-sectional survey. The survey data were supplemented with VHA administrative data. We used descriptive statistics to examine demographic and health characteristics, health-related technology use, and how veterans learned about apps. We assessed factors associated with app use using bivariate analyses and multiple logistic regression models.

RESULTS

We had complete data on 1259 veterans. A majority of the sample was male (1069/1259, 84.9%), aged older than 65 years (740/1259, 58.8%), White (1086/1259, 86.3%), and non-Hispanic (1218/1259, 96.7%). Most respondents (1125/1259, 89.4%) reported being very comfortable and confident using computers, over half (675/1259, 53.6%) reported being an early adopter of technology, and almost half (595/1259, 47.3%) reported having used a VHA health-related app. Just over one-third (435/1259, 34.6%) reported that their VHA care team members encouraged them to use health-related apps. Respondents reported learning about available VHA health-related apps by reading about them on the VHA's patient portal (468/1259, 37.2%), being told about them by their VHA health care team (316/1259, 25.1%), and reading about them on the VHA's website (139/1259, 11%). Veterans who self-reported having used VHA health-related apps were more likely to receive care at the VHA (OR [odds ratio] 1.3, 95% CI 1.0-1.7), be in worse health (as assessed by Hierarchical Condition Community score; OR 1.1, 95% CI 1.0-1.2), report owning a desktop or laptop computer (OR 1.8, 95% CI 1.1-3.1), have posttraumatic stress disorder (OR 1.4, 95% CI 1.1-1.9), and report having VHA health care team members encourage them to use the apps (OR 2.7, 95% CI 2.1-3.4).

CONCLUSIONS

We found strong associations between self-reported use by veterans of VHA health-related apps and multiple variables in our survey. The strongest association was observed between a veteran self-reporting app use and having received encouragement from their VHA health care team to use the apps. Veterans who reported receiving encouragement from their VHA care team members had nearly 3 times higher odds of using VHA apps than veterans who did not report receiving such encouragement. Our results add to growing evidence suggesting that endorsement of apps by a health care system or health care team can positively impact patient uptake and use.

Preferences of Older Adult Veterans With Heart Failure for Engaging With Mobile Health Technology to Support Self-care: Qualitative Interview Study Among Patients With Heart Failure and Content Analysis

BACKGROUND

Heart failure (HF) affects approximately 6.5 million adults in the United States, disproportionately afflicting older adults. Mobile health (mHealth) has emerged as a promising tool to empower older adults in HF self-care. However, little is known about the use of this approach among older adult veterans.

OBJECTIVE

The goal of this study was to explore which features of an app were prioritized for older adult veterans with HF.

METHODS

Between January and July 2021, we conducted semistructured interviews with patients with heart failure aged 65 years and older at a single facility in an integrated health care system (the Veterans Health Administration). We performed content analysis and derived themes based on the middle-range theory of chronic illness, generating findings both deductively and inductively. The qualitative questions captured data on the 3 key themes of the theory: self-care maintenance, self-care monitoring, and self-care management. Qualitative responses were analyzed using a qualitative data management platform, and descriptive statistics were used to analyze demographic data.

RESULTS

Among patients interviewed (n=9), most agreed that a smartphone app for supporting HF self-care was desirable. In addition to 3 a priori themes, we identified 7 subthemes: education on daily HF care, how often to get education on HF, support of medication adherence, dietary restriction support, goal setting for exercises, stress reduction strategies, and prompts of when to call a provider. In addition, we identified 3 inductive themes related to veteran preferences for app components: simplicity, ability to share data with caregivers, and positive framing of HF language.

CONCLUSIONS

We identified educational and tracking app features that can guide the development of HF self-care for an older adult veteran population. Future research needs to be done to extend these findings and assess the feasibility of and test an app with these features.

Peer support and whole health coaching to address the healthcare needs of homeless veterans: a pilot study

BACKGROUND

Homelessness is a robust social determinant of acute care service utilization among veterans. Although intensive outpatient programs have been developed for homeless veterans who are high utilizers of acute care ("super utilizers"), few scalable programs have been implemented to address their needs.

OBJECTIVE

Describe the development and pilot testing of a novel intervention that integrates the roles of a peer and whole health coach ("Peer-WHC") in coordination with primary care teams to reduce homeless veterans' frequent use of acute care.

DESIGN

Single-arm trial in three outpatient primary care clinics at a Veterans Health Administration (VHA) medical center; pre/post design using mixed-methods.

PARTICIPANTS

Twenty veterans from VHA's homeless registry who were super-utilizers of acute care and enrolled in primary care.

INTERVENTION

Weekly health coaching sessions with a peer over 12 weeks, including discussions of patients' health care utilization patterns and coordination with primary care.

MAIN MEASURES

Rates of session attendance and intervention fidelity, patient-reported satisfaction and changes in patient engagement and perceptions of health, pre/post utilization of acute and supportive care services, and qualitative interviews with multiple stakeholders to identify barriers and facilitators to implementation.

KEY RESULTS

On average, patients attended 6.35 sessions (SD = 3.5, Median = 7). Satisfaction scores (M = 28.75 out of 32; SD = 2.79) exceeded a priori benchmarks. Patients' perceptions of health improved from pre to post [t(df)=-2.26(14), p = 0.04]. In the 3-months pre/post, 45% (n = 9) and 15% (n = 3) of patients, respectively, were hospitalized. Qualitative feedback from patients, providers, and peers and fidelity metrics suggested value in increasing the length of the intervention to facilitate goal-setting with patients and coordination with primary care.

CONCLUSION

Findings support the feasibility, acceptability, and utility of Peer-WHC to address the healthcare needs of homeless veterans. A future trial is warranted to test the impact of Peer-WHC on reducing these patients' frequent use of acute care.

Prevalence of Multimorbidity in the Middle East: A Systematic Review of Observational Studies

BACKGROUND

There has not been a review that evaluated the prevalence of multimorbidity in the Middle East. This review aims to measure the prevalence, demographic factors, and consequences of multimorbidity in the Middle East region.

STUDY DESIGN

A quantitative systematic review includes cross-sectional and longitudinal cohort studies.

METHODS

The prevalence systematic review approach from the Joanna Briggs Institute was applied. We searched PsychINFO, MEDLINE, EMCARE, CINAHL, Scopus, Science Direct, and the Cochrane Central Register of Controlled Trials. Data were extracted methodically in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards. Studies written in English and released between 2012 and March 2022 were included. For the meta-analysis, a random-effects model was applied. PROSPERO registration number: CRD42022335534.

RESULTS

The final sample consisted of eight cohort and observational studies. The number of participants varied from 354 to 796,427. Multimorbidity was present in all populations with a prevalence of 21.8% (95% confidence interval (CI): 21.7-21.8%).

CONCLUSION

Multimorbidity affects a significant section of the world's population. A uniform operationalization of multimorbidity is required in the Middle East in order to enable reliable estimates of illness burden, effective disease management, and resource distribution.

My Goals Are Not Their Goals: Barriers and Facilitators to Delivery of Patient-Centered Care for Patients with Multimorbidity

BACKGROUND

Patient-centered care reflecting patient preferences and needs is integral to high-quality care. Individualized care is important for psychosocially complex or high-risk patients with multiple chronic conditions (i.e., multimorbidity), given greater potential risks of interventions and reduced benefits. These patients are increasingly prevalent in primary care. Few studies have examined provision of patient-centered care from the clinician perspective, particularly from primary care physicians serving in integrated, patient-centered medical home settings within the US Veterans Health Administration.

OBJECTIVE

We sought to clarify facilitators and barriers perceived by primary care physicians in the Veterans Health Administration to delivering patient-centered care for high-risk or complex patients with multimorbidity.

DESIGN

We conducted semi-structured telephone interviews from April to July 2020 among physicians across 20 clinical sites. Findings were analyzed with deductive content analysis based on conceptual models of patient-centeredness and hierarchical factors affecting care delivery.

PARTICIPANTS

Of 23 physicians interviewed, most were female (n = 14/23, 61%), serving in hospital-affiliated outpatient clinics (n = 14/23, 61%). Participants had a mean of 21 (SD = 11.3) years of experience.

KEY RESULTS

Facilitators included the following: effective physician-patient communication to individualize care, prioritize among multiple needs, and elicit goals to improve patient engagement; access to care, enabled by interdisciplinary teams, and dictating personalized care planning; effortful but worthwhile care coordination and continuity; meeting complex needs through effective teamwork; and integrating medical and non-medical care aspects in recognition of patients' psychosocial contexts. Barriers included the following: intra- and interpersonal (e.g., perceived patient reluctance to engage in care); organizational (e.g., limited encounter time); and community or policy impediments (e.g., state decisional capacity laws) to patient-centered care.

CONCLUSIONS

Physicians perceived individual physician-patient interactions were the greatest facilitators or barriers to patient-centered care. Efforts to increase primary care patient-centeredness for complex or high-risk patients with multimorbidity could focus on targeting physician-patient communication and reducing interpersonal conflict.

Association between service scope of primary care facilities and prevalence of high-cost population: a retrospective study in rural Guizhou, China

BACKGROUND

High-cost (HC) patients, defined as the small percentage of the population that accounts for a high proportion of health care expenditures, are a concern worldwide. Previous studies have found that the occurrence of HC population is partially preventable by providing a greater scope of primary health care services. However, no study has examined the association between the service scope of primary care facilities and the prevalence of HC populations. Therefore, this study aimed to investigate the association between the service scope of primary care facilities (PCFs) and the prevalence of HC populations within the same communities.

METHODS

A multistage, stratified, clustered sampling method was used to identify the service scope of PCFs as of 2017 in rural Guizhou, China. The claims data of 299,633 patients were obtained from the local information system of the New Rural Cooperation Medical Scheme. Patients were sorted by per capita inpatient medical expenditures in descending order, and the top 1%, top 5% and top 10% of patients who had incurred the highest costs were defined as the HC population. Logistic regression models were used to assess the association between the service scope of PCFs and the prevalence of the HC population.

RESULTS

Compared with those in the 95% of the sample deemed as the general population, those in the top 5% of the sample deemed as the HC population were more likely to be over the age of 30 (P <  0.001), to be female (P = 0.014) and to be referred to high-level hospitals (P <  0.001). After controlling for other covariates, patients who lived in the communities serviced by the PCFs with the smallest service scope were more likely to be in the top 1%, top 5% and top 10% of the HC population.

CONCLUSION

A greater PCF service scope was associated with a reduction in the prevalence of the HC population, which would mean that providing a broader PCF service scope could reduce some preventable costs, thus reducing the prevalence of the HC population. Future policy efforts should focus on expanding the service scope of primary care providers to achieve better patient outcomes.

Primary Care Physician Perspectives on the Influence of Patient Values, Health Priorities, and Preferences on Clinical Decision-Making for Complex Patients with Multimorbidity: A Qualitative Study

Introduction

The prevalence of patients with multimorbidity (ie, multiple chronic conditions) is increasing. Clinical decision-making guided by patients' values, health priorities and goals, and treatment preferences is particularly important in the context of interacting diseases and psychosocial needs. Physicians face challenges incorporating patient perspectives into care plans. We examined primary care physician (PCP) views on the influence of patients' values, health priorities and goals, and preferences on clinical decisions for patients with multimorbidity and increased psychosocial complexity.

Methods

We conducted semi-structured telephone interviews with 23 PCPs within patient-centered medical home teams in a nationally integrated health system in the United States between May and July 2020. Data were analyzed via thematic analysis with deductive and inductive coding.

Results

Three major themes emerged: 1. Patient personal values were rarely explicitly discussed in routine clinical encounters but informed more commonly discussed concepts of patient priorities, goals, and preferences; 2. Patient values, health priorities and goals, and preferences were sources of divergent views about care plans between healthcare teams, patients, and families; 3. Physicians used explicit strategies to communicate and negotiate about patient values, health priorities and goals, and preferences when developing care plans, including trust-building; devoting extra effort to individualizing care; connecting patient values to healthcare recommendations; deliberate elicitation and acknowledgement of patient concerns; providing "space" for patient perspectives; incorporating family into care planning; pairing physician to patient priorities; and collaborative teamwork.

Conclusion

Primary care physicians perceive patient values, health priorities and goals, and preferences as influential during clinical decision-making for complex patients with multimorbidity. Participants used concrete strategies to negotiate alignment of these aspects when physician-patient divergence occurred. While rarely discussed directly in clinical encounters, personal values affected patient health priorities, goals, and preferences during care planning, suggesting a clinical role for more deliberate elicitation and discussion of patient values for this population.

Information and Communication Technologies (ICTs) enabling integrated primary care for complex patients: a protocol for a scoping review

INTRODUCTION

An increasing number of individuals are living with multiple chronic conditions, often combined with psychosocial complexities. For these patients with complex conditions, an integrated primary care model that provides care coordination and a team-based approach can help manage their multiple needs. Information and communication technologies (ICTs) are recognized as a critical enabler of integrated primary care. A better understanding of the use of ICTs in an integrated care setting and how ICTs are being leveraged would be beneficial to identify knowledge gaps and could lead to successful implementation for ICT-based interventions.

OBJECTIVE

This study will systematically scope the literature on the topic of ICT-enabled integrated healthcare delivery models for patients with complex care needs to identify which technologies have been used in integrated primary care settings.

METHOD

This study protocol outlines a scoping review of the peer-reviewed literature, using Arksey and O'Malley's (enhanced by Levac et al.) scoping review methodology. Peer-reviewed literature will be identified using a multi-database search strategy. The results of the search will be screened, abstracted, and charted in duplicate by six research team members.

DISCUSSION

The key findings of the study will be thematically analyzed to describe the implemented ICTs aimed for complex patients within the integrated primary care model. The finding will highlight what types of ICTs are being put in place to support these models, and how these ICTs are enabling care integration. This review will be the first step to formally identify how ICT is used to support integrated primary health care models. The results will be disseminated through peer-reviewed publications, conference presentations, and special interest groups.

Clinical Outcome and Utilization Profiles Among Latent Groups of High-Risk Patients: Moving from Segmentation Towards Intervention

BACKGROUND

The ability of latent class models to identify clinically distinct groups among high-risk patients has been demonstrated, but it is unclear how healthcare data can inform group-specific intervention design.

OBJECTIVE

Examine how utilization patterns across latent groups of high-risk patients provide actionable information to guide group-specific intervention design.

DESIGN

Cohort study using data from 2012 to 2015.

PATIENTS

Participants were 934,787 patients receiving primary care in the Veterans Health Administration, with predicted probability of 12-month hospitalization in the top 10^th percentile during 2014.

MAIN MEASURES

Patients were assigned to latent groups via mixture-item response theory models based on 28 chronic conditions. We modeled odds of all-cause mortality, hospitalizations, and 30-day re-hospitalizations by group membership. Detailed outpatient and inpatient utilization patterns were compared between groups.

KEY RESULTS

A total of 764,257 (81.8%) of patients were matched with a comorbidity group. Groups were characterized by substance use disorders (14.0% of patients assigned), cardiometabolic conditions (25.7%), mental health conditions (17.6%), pain/arthritis (19.1%), cancer (15.3%), and liver disease (8.3%). One-year mortality ranged from 2.7% in the Mental Health group to 14.9% in the Cancer group, compared to 8.5% overall. In adjusted models, group assignment predicted significantly different odds of each outcome. Groups differed in their utilization of multiple types of care. For example, patients in the Pain group had the highest utilization of in-person primary care, with a mean (SD) of 5.3 (5.0) visits in the year of follow-up, while the Substance Use Disorder group had the lowest, with 3.9 (4.1) visits. The Substance Use Disorder group also had the highest rates of using services for housing instability (25.1%), followed by the Liver group (10.1%).

CONCLUSIONS

Latent groups of high-risk patients had distinct hospitalization and utilization profiles, despite having comparable levels of predicted baseline risk. Utilization profiles pointed towards system-specific care needs that could inform tailored interventions.

Psychological distress, multimorbidity and health services among older adults in rural South Australia

OBJECTIVE

Psychological distress may relate to higher health services use. However, data on psychological distress and health services use among rural older adults are limited. This study investigates psychological distress in older adults (aged ≥60) and evaluates the relationship between psychological distress, multimorbidity and health services utilization.

DESIGN

A cross-sectional design was adopted using data on older adults (≥60) (n = 5920) from the South Australia's 2013-2017 population health survey. The Modified Monash Model MM2-7 was used to designate rural areas. The dataset provides information on reported physical health conditions, psychological distress, and patterns of health services use. The Kessler Psychological Distress Scale (K10) was used to compute scores for reported mental health disorders in this population.

RESULTS

The mean (SD) age of the study participants was 72.1 (8.1) years. Women constituted 58.8% of the sample. The mean (SD) score for psychological distress was 12.5 (3.6). One-fourth (33.7%) report one-chronic condition, 20.4% reported 2 chronic conditions and 13% had more than 3 chronic conditions. High psychological distress was associated with female gender (χ² = 14.4, p < 0.001), <80 years (χ² = 11.7, p = 0.019), lower education (χ² = 10.9, p = 0.027). Similarly, multimorbidity was associated with female gender (χ² = 51.1, p < 001), increasing age (χ² = 173.6, p < 0.001) and lower education (χ² = 28.8 p < 0.001). Psychological distress and multimorbidity were independently associated with health service use. High psychological distress was associated with general practitioner (GP) visit (odds ratio 3.6 (95% CI 2.6-5.1), p < 0.001), emergency department (ED) visit (odds ratio 2.5 (95% CI 1.2-5.0), p < 0.001) and hospital admission (odds ratio 2.3 (95% CI 1.3-4.3), p < 0.001). Multimorbidity was associated with general practitioner (GP) visit (odds ratio 6.8 (95% CI 5.6-8.3), p < 0.001), emergency department (ED) visit (odds ratio 2.5 (95% CI 1.4-4.3), p < 0.001) and hospital admission (odds ratio 3.1 (95% CI 1.9-5.1), p < 0.001). Model included age, gender, education, number of chronic condition and psychological distress.

CONCLUSION/IMPLICATION

Psychological distress and multimorbidity were independently associated with health services use. Thus, psychological distress, particularly in the presence of multimorbidity, presents an opportunity for intervention by clinicians that may reduce the demand on rural health services.

Age, multimorbidity and dementia with health care costs in older people in Alberta: a population-based retrospective cohort study

BACKGROUND

The growing burden associated with population aging, dementia and multimorbidity poses potential challenges for the sustainability of health systems worldwide. We sought to examine how the intersection among age, dementia and greater multimorbidity is associated with health care costs.

METHODS

We did a retrospective population-based cohort study in Alberta, Canada, with adults aged 65 years and older between April 2003 and March 2017. We identified 31 morbidities using algorithms (30 algorithms were validated), which were applied to administrative health data, and assessed costs associated with hospital admission, provider billing, ambulatory care, medications and long-term care (LTC). Actual costs were used for provider billing and medications; estimated costs for inpatient and ambulatory patients were based on the Canadian Institute for Health Information's resource intensive weights and Alberta's cost of a standard hospital stay. Costs for LTC were based on an estimated average daily cost.

RESULTS

There were 827 947 people in the cohort. Dementia was associated with higher mean annual total costs and individual mean component costs for almost all age categories and number of comorbidities categories (differences in total costs ranged from $27 598 to $54 171). Similarly, increasing number of morbidities was associated with higher mean total costs and component costs (differences in total costs ranged from $4597 to $10 655 per morbidity). Increasing age was associated with higher total costs for people with and without dementia, driven by increasing LTC costs (differences in LTC costs ranged from $115 to $9304 per age category). However, there were no consistent trends between age and non-LTC costs among people with dementia. When costs attributable to LTC were excluded, older age tended to be associated with lower costs among people with dementia (differences in non-LTC costs ranged from -$857 to -$7365 per age category).

INTERPRETATION

Multimorbidity, older age and dementia were all associated with increased use of LTC and thus health care costs, but some costs among people with dementia decreased at older ages. These findings illustrate the complexity of projecting the economic consequences of the aging population, which must account for the interplay between multimorbidity and dementia.

Factors Associated With Patient-Centered Medical Home Teams' Use of Resources for Identifying and Approaches for Managing Patients With Complex Needs

Using data from a Veterans Health Administration national primary care survey, this study identified the most highly rated tools and care approaches for patients with complex needs and how preferences varied by professional role, staffing, and training. Nurses were significantly more likely to rate most tools as very important as compared with primary care providers. Having a fully staffed team was also significantly associated with a very important rating on all tools. Nurses and fully staffed teams reported a greater likeliness to use most care approaches, and those with perceived need for training reporting a lower likeliness to use.

Unpacking complex interventions that manage care for high-need, high-cost patients: a realist review

OBJECTIVE

Payers, providers and policymakers in the USA are interested in developing interventions that reduce preventable or modifiable healthcare use among high-need, high-cost (HNHC) patients. This study seeks to describe how and why complex interventions for HNHC patients lead to more appropriate use of healthcare services.

DESIGN

A realist review which develops programme theories from causal explanations generated and articulated through the creation of context-mechanism-outcome configurations.

METHODS

Electronic databases (including PubMed and Embase) and gray literature from January 2000 to March 2021 were searched. All study designs were included if the article provided data to develop our programme theories. Included studies were conducted in the USA and focused on interventions for adult, HNHC patients.

RESULTS

Data were synthesised from 48 studies. Identifying HNHC patients for inclusion in interventions requires capturing a combination of characteristics including their prior use of healthcare services, complexity of chronic disease(s) profile, clinician judgment and willingness to participate. Once enrolled, engaging HNHC patients in interventions requires intervention care providers and patients to build a trusting relationship. Tailored, individualised assistance for medical and non-medical needs, emotional support and self-management education empowers patients to increase their participation in managing their own care. Engagement of care providers in interventions to expand support of HNHC patients is facilitated by targeted outreach, adequate staffing support with shared values and regular and open communication.

CONCLUSIONS

Building relationships with HNHC patients and gaining their trust is a key component for interventions to successfully change HNHC patients' behaviors. Identifying HNHC patients for an intervention can be best achieved through a multipronged strategy that accounts for their clinical and psychosocial complexity and prior experiences with the healthcare system. Successful interventions recognise that relationships with HNHC patients require the sustained engagement of care providers. To succeed, providers need ongoing emotional, financial, logistical and practical resources.

PROSPERO REGISTRATION NUMBER

CRD42020161179.

Trajectories in Outpatient Care for People with Multimorbidity: A Population-Based Register Study in Denmark

Background

Multimorbidity is a global health challenge. Individuals with multimorbidity are frequent users of healthcare services, and many experience fragmented healthcare. We assessed the number of outpatient trajectories and contacts with hospital outpatient clinics for individuals with multimorbidity and explored different time intervals for the occurrence of concurrent outpatient trajectories.

Methods

A population-based cohort of 1.3 million residents, ≥18 years, with multimorbidity was identified through Danish national health registries. Multimorbidity was defined as having two or more of 39 specific chronic conditions. Nine disease system categories were used to categorize outpatient contacts in 2018 into outpatient trajectories and trajectory-related contacts. We defined an "outpatient trajectory" as two contacts within 12 consecutive months for the same medical condition. All outpatient contacts and trajectories with related contacts were counted for 2018. The impact of different time intervals on the number of concurrent trajectories was analyzed.

Results

On 1 January 2019, 29% of the adult Danish population was classified as multimorbid. During 2018, 68% of them had ≥1 outpatient contact (median: 2 (IQI: 0-4)). Twenty-six percent had ≥1 outpatient trajectory. The median number of trajectory contacts was 3 (IQI: 2-5). The 4% of individuals with ≥2 outpatient trajectories accounted for 28% of trajectory contacts. During the 6-week period from the latest outpatient contact, 33% of all patients with ≥2 trajectories in 2018 experienced concurrent trajectories with outpatient contact.

Conclusion

Two-thirds of adult Danes with multimorbidity attended an outpatient clinic in 2018, and one-fourth had at least one outpatient trajectory. Individuals with two or more trajectories represented 4% and comprised 28% of the trajectory contacts; 33% had concurrent trajectories within a 6-week period. It appears that a small proportion place demands on outpatient clinics because of frequent attendance. A more uniform way of organizing outpatient trajectories for these patients merits consideration.

Multimorbidity and Employment Outcomes Among Middle-Aged US Cancer Survivors

OBJECTIVE

The objective of this study was to evaluate the relationship between multimorbidity and subsequent 2-year employment outcomes among middle-aged United States (US) cancer survivors. In addition, we examined whether the relationship differed by survivor characteristics.

METHODS

Data of 633 cancer survivors (ages 51 to 64) from the 2014 Health and Retirement Study were used to identify multimorbidity profiles and evaluate associations between multimorbidity and prolonged unemployment during follow-up.

RESULTS

Approximately 64% of cancer survivors met the criteria for multimorbidity. Latent class analysis revealed three distinct multimorbidity profiles distinguished by the presence or absence of psychiatric disorders. We observed a significant association between high psychiatric multimorbidity and prolonged unemployment after 2-year follow-up (relative risk = 2.78, 95% Confidence Interval = 1.28 to 6.00), with the effect more pronounced among low-income survivors.

CONCLUSIONS

Psychiatric multimorbidity was associated with prolonged unemployment among middle-aged cancer survivors, particularly among low-income survivors.

One in five South Africans are multimorbid: An analysis of the 2016 demographic and health survey

Multimorbidity is a global research priority, yet relatively little is known about it in low and middle income countries. South Africa has the largest burden of HIV worldwide but also has a growing burden of non-communicable diseases; potentially leading to uncommon disease combinations. Information about the prevalence of multimorbidity and factors associated with it can assist in healthcare planning and targeting groups of people for interventions. This study aimed to determine the prevalence of multimorbidity by age and sex, as well as factors associated with multimorbidity in people 15 years and older. This study analyses the nationally representative 2016 South African Demographic Health Survey. The sample included 10 336 people who participated in the Adult Health questionnaire and approximately 7 961 people who provided biomarkers. Multivariate logistic regression was used to measure the association of multimorbidity with age, sex, living in an urban or rural area, education level, wealth level, employment status, body mass index, current alcohol or tobacco use. All analyses were conducted using STATA 15. Multimorbidity was present in 20.7% (95% CI: 19.5%- 21.9%) of participants; in 14.8% (95% CI: 13.4% - 16.3%) of males and 26.2% (95% CI: 24.7-27.7%) of females. Multimorbidity increased with age; with the highest odds in the 55-64 years old age group (OR: 24.910, 95% CI: 14.901-41.641, p < 0.001) compared to those aged 15-24 years. The odds of multimorbidity was also higher in young females compared to young males (OR: 2.734, 95% CI: 1.50-4.99, p = 0.001). Possessing tertiary education (OR: 0.722, 95% CI: 0.537-0.97, p = 0.031), being employed (OR: 0.813, 95% CI: 0.675-0.979, p = 0.029) or currently using alcohol (OR: 0.815, 95% CI: 0.686-0.968, p = 0.02) was protective against multimorbidity. Multimorbidity is prevalent within the South African population, with females and older adults being most affected. However, multimorbidity is also observed in younger adults and most likely driven by the high prevalence of HIV and hypertension.

Financial Costs of Emergency Department Presentations for Australian Patients With Heart Disease in the Last 3 Years of Life

Aims:

This study described emergency department (ED) resource use patterns and associated costs among patients with heart disease in their last 3 years of life in a high-income country.

Methods:

This study used linked data from ED and death registry databases in Australia. A random sample of 1000 patients who died due to any cause in 2017, and who had been living with heart disease for at least the prior 10-years were included. The outcomes of interest were number of ED presentations over each of the last 3 years prior to death and relative cost contributions of ED-related items.

Results:

The number of patients needing ED care and number of ED presentations per patient increased as patients were closer to death, with 85% experiencing at least one ED presentation in their last year of life. Mean per patient ED presentation cost increased with each year closer to death. Costs related to labor, pathology, patient travel, and goods and services contributed more than 85% of the total cost in each of the 3 years.

Conclusion:

The increase in cost burden as patients neared death was attributable to more frequent ED presentations per person rather than more expensive ED presentations. The scope of this study was limited to ED presentations, and may not be representative of heart-disease-related end-of-life care more broadly.

Empiric segmentation of high-risk patients: a structured literature review

OBJECTIVES

Empiric segmentation is a rapidly growing, learning health system approach that uses large health care system data sets to identify groups of high-risk patients who may benefit from similar interventions. We aimed to review studies that used data-driven approaches to segment high-risk patient populations and describe how their designs and findings can inform health care leaders who are interested in applying similar techniques to their patient populations.

STUDY DESIGN

Structured literature review.

METHODS

We searched for original research articles published since 2000 that identified high-risk adult patient populations and applied data-driven analyses to segment the population. Two reviewers independently extracted study population source and criteria for high-risk designation, segmentation method, data types included, model selection criteria, and model results from the identified studies.

RESULTS

Our search identified 224 articles, 12 of which met criteria for full review. Of these, 8 segmented high-risk patients and 4 segmented diagnoses without assigning patients to unique groups. Studies segmenting patients more often had clinically interpretable results. Common groups were defined by high prevalence of diabetes, cardiovascular disease, psychiatric conditions including substance use disorders, and neurologic disease (eg, stroke). Few studies incorporated patients' functional or social factors. Resulting patient and diagnosis clusters varied in ways closely linked to the model inputs, patient population inclusion criteria, and health care system context.

CONCLUSIONS

Empiric segmentation can yield clinically relevant groups of patients with complex medical needs. Segmentation results are context dependent, suggesting the need for careful design and interpretation of segmentation models to ensure that results can inform clinical care and program design in the target setting.

Predictors and Cost Comparison of Subsequent Urinary Stone Care at Index versus Non-Index Hospitals

OBJECTIVE

To examine the effects of care fragmentation, or the engagement of different health care systems along the continuum of care, on patients with urinary stone disease.

METHODS

All-payer data from the 2016 Healthcare Cost and Utilization Project (HCUP) State Databases from Florida (FL) and New York (NY) were used to identify a cohort of adult patients with an emergency department visit for a diagnosis of urolithiasis, who subsequently re-presented to an index or non-index hospital for renal colic and/or urological intervention. Patient demographics, regional data, and procedural information were collected and 30-day episode-based costs were calculated. Multivariable logistic and gamma generalized linear regression were utilized to identify predictors of receiving subsequent care at an index hospital and associated costs, respectively.

RESULTS

Of the 33,863 patients who experienced a subsequent encounter related to nephrolithiasis, 9,593 (28.3%) received care at a non-index hospital. Receiving subsequent care at the index hospital was associated with fewer acute care encounters prior to surgery (2.5 vs. 2.7; p <0.001) and less days to surgery (29 vs. 42; p < 0.001). Total episode-based costs were higher in the non-index setting, with a mean difference of $783 (Non-index: $13,672, 95% CI $13,292 - $14,053; Index: $12,889, 95% CI $12,677 - $13,102; p < 0.001).

CONCLUSIONS

Re-presentation to a unique healthcare facility following an initial diagnosis of urolithiasis is associated with a greater number of episode-related health encounters, longer time to definitive surgery, and increased costs.