Do Medical Homes Improve Quality of Care for Persons with Multiple Chronic Conditions?

Multimorbidity and adverse longitudinal outcomes among patients attending chronic outpatient medical care in Bahir Dar, Northwest Ethiopia

Background

Multimorbidity is becoming more prevalent in low-and middle-income countries (LMICs). However, the evidence base on the burden and its longitudinal outcomes are limited. This study aimed to determine the longitudinal outcomes of patients with multimorbidity among a sample of individuals attending chronic outpatient non communicable diseases (NCDs) care in Bahir Dar, northwest Ethiopia.

Methods

A facility-based longitudinal study was conducted among 1,123 participants aged 40+ attending care for single NCD (n = 491) or multimorbidity (n = 633). Data were collected both at baseline and after 1 year through standardized interviews and record reviews. Data were analyzed using Stata V.16. Descriptive statistics and longitudinal panel data analyzes were run to describe independent variables and identify factors predicting outcomes. Statistical significance was considered at p-value <0.05.

Results

The magnitude of multimorbidity has increased from 54.8% at baseline to 56.8% at 1 year. Four percent (n = 44) of patients were diagnosed with one or more NCDs and those having multimorbidity at baseline were more likely than those without multimorbidity to develop new NCDs. In addition, 106 (9.4%) and 22 (2%) individuals, respectively were hospitalized and died during the follow up period. In this study, about one-third of the participants had higher quality of life (QoL), and those having higher high activation status were more likely to be in the higher versus the combined moderate and lower QoL [AOR1 = 2.35, 95%CI: (1.93, 2.87)] and in the combined higher and moderate versus lower level of QoL [AOR2 = 1.53, 95%CI: (1.25, 1.88)].

Conclusion

Developing new NCDs is a frequent occurrence and the prevalence of multimorbidity is high. Living with multimorbidity was associated with poor progress, hospitalization and mortality. Patients having a higher activation level were more likely than those with low activation to have better QoL. If health systems are to meet the needs of the people with chronic conditions and multimorbidity, it is essential to understand diseases trajectories and of impact of multimorbidity on QoL, and determinants and individual capacities, and to increase their activation levels for better health improve outcomes through education and activation.

Predicting Poor Outcomes Among Individuals Seeking Care for Major Depressive Disorder

Objective

To develop and validate algorithms to identify individuals with major depressive disorder (MDD) at elevated risk for suicidality or for an acute care event.

Methods

We conducted a retrospective cohort analysis among adults with MDD diagnosed between January 1, 2018 and February 28, 2019. Generalized estimating equation models were developed to predict emergency department (ED) visit, inpatient hospitalization, acute care visit (ED or inpatient), partial-day hospitalization, and suicidality in the year following diagnosis. Outcomes (per 1000 patients per month, PkPPM) were categorized as all-cause, psychiatric, or MDD-specific and combined into composite measures. Predictors included demographics, medical and pharmacy utilization, social determinants of health, and comorbid diagnoses as well as features indicative of clinically relevant changes in psychiatric health. Models were trained on data from 1.7M individuals, with sensitivity, positive predictive value, and area-under-the-curve (AUC) derived from a validation dataset of 0.7M.

Results

Event rates were 124.0 PkPPM (any outcome), 21.2 PkPPM (psychiatric utilization), and 7.6 PkPPM (suicidality). Among the composite models, the model predicting suicidality had the highest AUC (0.916) followed by any psychiatric acute care visit (0.891) and all-cause ED visit (0.790). Event-specific models all achieved an AUC >0.87, with the highest AUC noted for partial-day hospitalization (AUC = 0.938). Select predictors of all three outcomes included younger age, Medicaid insurance, past psychiatric ED visits, past suicidal ideation, and alcohol use disorder diagnoses, among others.

Conclusions

Analytical models derived from clinically-relevant features identify individuals with MDD at risk for poor outcomes and can be a practical tool for health care organizations to divert high-risk populations into comprehensive care models.

Geographic proximity to primary care providers as a risk-assessment criterion for quality performance measures

IMPORTANCE

Previous studies have found a mixed association between Patient-Centered Medical Home (PCMH) designation and improvements in primary care quality indicators, including avoidable pediatric emergency department (ED) encounters. Whether these associations persist after accounting for the geographic locations of providers relative to where patients reside is unknown.

OBJECTIVE

To examine the association between geographic proximity to primary care providers versus hospitals and risk of avoidable and potentially avoidable ED visits among children with pre-existing diagnosis of attention-deficit/hyperactivity disorder or asthma.

METHODS

Retrospective cohort study of a panel of pediatric Medicaid claims data from the South Carolina from 2016-2018 for 2,959 beneficiaries having a pre-existing diagnosis of attention-deficit/hyperactivity disorder (ADD, ages 6-12) and 6,390 beneficiaries with asthma (MMA, ages 5-18), as defined using Healthcare Effectiveness Data and Information Set (HEDIS) performance measures. We calculated differences in avoidable and potentially avoidable ED visits by the beneficiary's PCMH attribution type and in relation to differences in proximity to their primary care providers versus hospitals. Outcomes were defined using the New York University Emergency Department Algorithm (NYU-EDA). Differences in ED visit risk were assessed using generalized estimation equations and compared using marginal effects models.

RESULTS

The 2.4 percentage point reduction in risk of avoidable ED visits among children in the ADD cohort who attended a PCMH versus those who did not increased to 3.9 to 7.2 percentage points as relative proximity to primary care providers versus hospitals improved (p < 0.01). Children in the ADD and MMA cohorts that were enrolled in a medical home, but did not attend one for primary care services exhibited a 5.4 and 3.0 percentage point increase in avoidable ED visit compared to children who were unenrolled and did not attend medical homes (p < 0.05), but these differences were only observed when geographic proximity to hospitals was more convenient than primary care providers. Mixed findings were observed for potentially avoidable visits.

CONCLUSIONS

In several health care performance evaluations, patient-centered medical homes have not been found to reduce differences in hospital utilization for conditions that are treatable in primary care settings among children with chronic illnesses. Analytical approaches that also consider geographic proximity to health care services can identify performance benefits of medical homes. Expanding risk-adjustment models to also include geographic data would benefit ongoing quality improvement initiatives.

Multimorbidity of chronic non-communicable diseases: burden, care provision and outcomes over time among patients attending chronic outpatient medical care in Bahir Dar, Ethiopia-a mixed methods study protocol

INTRODUCTION

Multimorbidity refers to the presence of two or more chronic non-communicable diseases (NCDs) in a given individual. It is associated with premature mortality, lower quality of life (QoL) and greater use of healthcare resources. The burden of multimorbidity could be huge in the low and middle-income countries (LMICs), including Ethiopia. However, there is limited evidence on the magnitude of multimorbidity, associated risk factors and its effect on QoL and functionality. In addition, the evidence base on the way health systems are organised to manage patients with multimorbidity is sparse. The knowledge gleaned from this study could have a timely and significant impact on the prevention, management and survival of patients with NCD multimorbidity in Ethiopia and in LMICs at large.

METHODS AND ANALYSIS

This study has three phases: (1) a cross-sectional quantitative study to determine the magnitude of NCD multimorbidity and its effect on QoL and functionality, (2) a qualitative study to explore organisation of care for patients with multimorbidity, and (3) a longitudinal quantitative study to investigate disease progression and patient outcomes over time. A total of 1440 patients (≥40 years) on chronic care follow-up will be enrolled from different facilities for the quantitative studies. The quantitative data will be collected from multiple sources using the KoBo Toolbox software and analysed by STATA V.16. Multiple case study designs will be employed to collect the qualitative data. The qualitative data will be coded and analysed by Open Code software thematically.

ETHICS AND DISSEMINATION

Ethical clearance has been obtained from the College of Medicine and Health Sciences, Bahir Dar University (protocol number 003/2021). Subjects who provide written consent will be recruited in the study. Confidentiality of data will be strictly maintained. Findings will be disseminated through publications in peer-reviewed journals and conference presentations.

Individual-Level and Clinic-Level Factors Associated With Achieving Glycemic Control in a Large Cohort of People With HIV in Care-Washington, DC

BACKGROUND

Optimal management of noncommunicable diseases, including diabetes mellitus (DM), is crucially important as people with HIV (PWH) live longer with antiretroviral therapy. Our objective was to assess patient-level and clinic-level factors associated with achieving hemoglobin A1c (HbA1c) ≤7.0% among PWH and DM.

SETTING

The DC Cohort, an observational clinical cohort of PWH, followed from 2011 to 2019 at 12 sites in Washington, DC.

METHODS

Among PWH with diagnosed DM and elevated HbA1c (>7.0%), we examined the association between achieving HbA1c ≤7.0% and demographic and clinical factors, including time-updated medication data, and clinic-level factors related to services and structure. A multilevel marginal extended Cox regression model was generated to identify factors associated with time to HbA1c ≤7.0%.

RESULTS

Over half (52.3%) of 419 participants achieved HbA1c ≤7.0%. Individual-level factors associated with HbA1c ≤7.0% included a diagnosis of DM after enrollment and a longer time since HIV diagnosis [hazard ratio (HR) = 2.65 and 1.13, P < 0.05 for both]. Attending a clinic with an endocrinologist was associated with the outcome [adjusted HR (aHR) = 1.41 95% confidence interval (CI): (1.01 to 1.97)]. In addition, comparing clinics that treat everyone, refer everyone or have a mix of treating and referring, showed an association between attending a clinic that treats everyone [aHR = 1.52 95% CI: (1.21 to 1.90)] or a clinic that refers everyone [aHR = 2.24 95% CI: (1.63 to 3.07)] compared with clinics with a mix in achieving glycemic control.

CONCLUSION

Multiple factors are associated with achieving glycemic control in an urban cohort of PWH. Determining if specific services or structures improve DM outcomes may improve health outcomes for PWH and DM.

Model Homes: Evaluating Approaches to Patient-centered Medical Home Implementation

BACKGROUND

The patient-centered medical home (PCMH) model has been widely adopted, but the evidence on its effectiveness remains mixed. One potential explanation for these mixed findings is variation in how the model is implemented by practices.

OBJECTIVE

To identify the impact of different approaches to PCMH adoption on health care utilization in a long-term, geographically diverse sample of patients.

DESIGN

Difference-in-differences evaluation of PCMH impact on cost and utilization.

SUBJECTS

A total of 5,314,284 patient-year observations from the HealthCore Integrated Research Database, and 5943 practices which adopted the PCMH model in 14 states between 2011 and 2015.

INTERVENTION

PCMH adoption, as defined by the National Committee for Quality Assurance.

MEASUREMENTS

Six claims-based utilization measures, plus total health care expenditures. We employ hierarchical clustering to organize practices into groups based on their PCMH capabilities, then use generalized difference-in-differences models with practice or patient fixed effects to estimate the effect of PCMH recognition (overall and separately by the groups identified by the clustering algorithm) on utilization.

RESULTS

PCMH adoption was associated with a >8% reduction in total expenditures. We find significant reductions in emergency department utilization and outpatient care, and both lab and imaging services. In our by-group results we find that while the reduction in outpatient care is significant across all 3 groups, the reduction in emergency department utilization is driven entirely by 1 group with enhanced electronic communications.

CONCLUSION

The PCMH model has significant impact on patterns of health care utilization, especially when heterogeneity in implementation is accounted for in program evaluation.

Strong and sustainable primary healthcare is associated with a lower risk of hospitalization in high risk patients

In 2004, Germany introduced a program based on voluntary contracting to strengthen the role of general practice care in the healthcare system. Key components include structured management of chronic diseases, coordinated access to secondary care, data-driven quality improvement, computerized clinical decision-support, and capitation-based reimbursement. Our aim was to determine the long-term effects of this program on the risk of hospitalization of specific categories of high-risk patients. Based on insurance claims data, we conducted a longitudinal observational study from 2011 to 2018 in Baden-Wuerttemberg, Germany. Patients were assigned to one or more of four open cohorts (in 2011, elderly, n = 575,363; diabetes mellitus, n = 163,709; chronic heart failure, n = 82,513; coronary heart disease, n = 125,758). Adjusted for key patient characteristics, logistic regression models were used to compare the hospitalization risk of the enrolled patients (intervention group) with patients receiving usual primary care (control group). At the start of the study and throughout long-term follow-up, enrolled patients in the four cohorts had a lower risk of all-cause hospitalization and ambulatory, care-sensitive hospitalization. Among patients with chronic heart failure and coronary heart disease, the program was associated with significantly reduced risk of cardiovascular-related hospitalizations across the eight observed years. The effect of the program also increased over time. Over the longer term, the results indicate that strengthening primary care could be associated with a substantial reduction in hospital utilization among high-risk patients.

Applying Care Coordination Principles to Reduce Cardiovascular Disease Risk Factors in People With Serious Mental Illness: A Case Study Approach

People with serious mental illness (SMI) have a 2-3-fold higher mortality than the general population, much of which is driven by largely preventable cardiovascular disease. One contributory factor is the disconnect between the behavioral and physical health care systems. New care models have sought to integrate physical health care into primary mental health care settings. However, few examples of successful care coordination interventions to improve health outcomes with the SMI population exist. In this paper, we examine challenges faced in coordinating care for people with SMI and explore pragmatic, multi-disciplinary strategies for overcoming these challenges used in a cardiovascular risk reduction intervention shown to be effective in a clinical trial.

Association of the Patient-Centered Medical Home Implementation with Chronic Disease Quality in Patients with Multimorbidity

BACKGROUND

The patient-centered medical home (PCMH) was established in part to improve chronic disease management, yet evidence is limited for effects on patients with multimorbidity.

OBJECTIVE

To examine the association of Patient-Aligned Care Team (PACT) implementation, the Veterans Health Administration (VA) PCMH model, and care quality for multimorbid patients enrolled in VA primary care from 2012 to 2014.

DESIGN

Retrospective cohort.

PATIENTS

318,764 multimorbid (> 3 chronic diseases) patients receiving care in 917 clinics.

MAIN MEASURES

PCMH implementation was measured using the PACT Implementation Progress Index (PI²) for clinics in 2012. The PI² is a validated composite measure of administrative and survey data with higher scores associated with greater care quality. Quality outcomes from 2013 to 2014 were assessed from External Peer Review Program (EPRP) metrics. Outcomes included preventative care, chronic disease management, and mental health and substance use metrics. We used generalized estimating equations to model associations adjusting for patient and clinic characteristics. We also examined associations for a subgroup with > 5 chronic diseases.

KEY RESULTS

For one-third of metrics (5/15), greater implementation of PACT in 2012 was associated with higher predicted probability of meeting the quality metric in 2013-2014. This association persisted for only two metrics (diabetic glycemic control, P < 0.001; lipid control in ischemic heart disease, P = 0.02) among patients with > 5 chronic diseases.

CONCLUSIONS

Multimorbid patients engaged in care from clinics with higher PCMH implementation received higher quality care across several quality domains, but this association was reduced in patients with > 5 chronic diseases.

Multimorbidity of chronic non-communicable diseases and its models of care in low- and middle-income countries: a scoping review protocol

INTRODUCTION

Multimorbidity is the coexistence of two or more chronic non-communicable diseases (NCDs) in a given individual. Multimorbidity is increasing in low- and middle-income countries (LMICs) and challenging health systems. Individuals with multimorbidity are facing the risk of premature mortality, lower quality of life and greater use of healthcare services. However, despite the huge challenge multimorbidity brings in LMICs, gaps remain in mapping and synthesising the available knowledge on the issue. The focus of this scoping review will be to synthesise the extent, range and nature of studies on the epidemiology and models of multimorbidity care in LMICs.

METHODS

PubMed (MEDLINE) will be the main database to be searched. For articles that are not indexed in the PubMed, Scopus, PsycINFO and Cochrane databases will be searched. Grey literature databases will also be explored. There will be no restrictions on study setting or year of publication. Articles will be searched using key terms, including comorbidity, co-morbidity, multimorbidity, multiple chronic conditions and model of care. Relevant articles will be screened by two independent reviewers and data will be charted accordingly. The result of this scoping review will be presented using the Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist and reporting guideline.

ETHICS AND DISSEMINATION

This scoping review does not require ethical approval. Findings will be published in peer-reviewed journal and presented at scientific conferences.

Do Medical Homes Improve Quality of Care for Persons with Multiple Chronic Conditions?

OBJECTIVE

To examine the association between medical home enrollment and receipt of recommended care for Medicaid beneficiaries with multiple chronic conditions (MCC).

DATA SOURCES/STUDY SETTING

Secondary claims data from fiscal years 2008-2010. The sample included nonelderly Medicaid beneficiaries with at least two of eight target conditions (asthma, chronic obstructive pulmonary disease, diabetes, hypertension, hyperlipidemia, seizure disorder, major depressive disorder, and schizophrenia).

STUDY DESIGN

We used linear probability models with person- and year-level fixed effects to examine the association between patient-centered medical home (PCMH) enrollment and nine disease-specific quality-of-care metrics, controlling for selection bias and time-invariant differences between enrollees.

DATA COLLECTION METHODS

This study uses a dataset that links Medicaid claims with other administrative data sources.

PRINCIPAL FINDINGS

Patient-centered medical home enrollment was associated with an increased likelihood of receiving eight recommended mental and physical health services, including A1C testing for persons with diabetes, lipid profiles for persons with diabetes and/or hyperlipidemia, and psychotherapy for persons with major depression and persons with schizophrenia. PCMH enrollment was associated with overuse of short-acting β-agonists among beneficiaries with asthma.

CONCLUSIONS

The PCMH model can improve quality of care for patients with multiple chronic conditions.