Prevalent Multimorbidity Combinations Among Middle-Aged and Older Adults Seen in Community Health Centers.

BACKGROUND: Multimorbidity (≥ 2 chronic diseases) is associated with greater disability and higher treatment burden, as well as difficulty coordinating self-management tasks for adults with complex multimorbidity patterns. Comparatively little work has focused on assessing multimorbidity patterns among patients seeking care in community health centers (CHCs). OBJECTIVE: To identify and characterize prevalent multimorbidity patterns in a multi-state network of CHCs over a 5-year period. DESIGN: A cohort study of the 2014-2019 ADVANCE multi-state CHC clinical data network. We identified the most prevalent multimorbidity combination patterns and assessed the frequency of patterns throughout a 5-year period as well as the demographic characteristics of patient panels by prevalent patterns. PARTICIPANTS: The study included data from 838,642 patients aged ≥ 45 years who were seen in 337 CHCs across 22 states between 2014 and 2019. MAIN MEASURES: Prevalent multimorbidity patterns of somatic, mental health, and mental-somatic combinations of 22 chronic diseases based on the U.S. Department of Health and Human Services Multiple Chronic Conditions framework: anxiety, arthritis, asthma, autism, cancer, cardiac arrhythmia, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), congestive heart failure, coronary artery disease, dementia, depression, diabetes, hepatitis, human immunodeficiency virus (HIV), hyperlipidemia, hypertension, osteoporosis, post-traumatic stress disorder (PTSD), schizophrenia, substance use disorder, and stroke. KEY RESULTS: Multimorbidity is common among middle-aged and older patients seen in CHCs: 40% have somatic, 6% have mental health, and 24% have mental-somatic multimorbidity patterns. The most frequently occurring pattern across all years is hyperlipidemia-hypertension. The three most frequent patterns are various iterations of hyperlipidemia, hypertension, and diabetes and are consistent in rank of occurrence across all years. CKD-hyperlipidemia-hypertension and anxiety-depression are both more frequent in later study years. CONCLUSIONS: CHCs are increasingly seeing more complex multimorbidity patterns over time; these most often involve mental health morbidity and advanced cardiometabolic-renal morbidity.

The Affordable Care Act improved health insurance coverage and cardiovascular-related screening rates for cancer survivors seen in community health centers.

BACKGROUND: This study assessed the impact of Affordable Care Act (ACA) Medicaid expansion on health insurance rates and receipt of cardiovascular-related preventive screenings (body mass index, glycated hemoglobin [HbA1c], low-density lipoproteins, and blood pressure) for cancer survivors seen in community health centers (CHCs). METHODS: This study identified cancer survivors aged 19 to 64 years with at least 3 CHC visits in 13 states from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE). Via inverse probability of treatment weighting multilevel multinomial modeling, insurance rates before and after the ACA were estimated by whether a patient lived in a state that expanded Medicaid, and changes between a pre-ACA time period and 2 post-ACA time periods were assessed. RESULTS: The weighted estimated sample size included 409 cancer survivors in nonexpansion states and 2650 in expansion states. In expansion states, the proportion of uninsured cancer survivors decreased significantly from 20.3% in 2012-2013 to 4.5%in 2016-2017, and the proportion of those with Medicaid coverage increased significantly from 38.8% to 55.6%. In nonexpansion states, there was a small decrease in uninsurance rates (from 33.6% in 2012-2013 to 22.5% in 2016-2017). Cardiovascular-related preventive screening rates increased over time in both expansion and nonexpansion states: HbA1c rates nearly doubled from the pre-ACA period (2012-2013) to the post-ACA period (2016-2017) in expansion states (from 7.2% to 12.8%) and nonexpansion states (from 9.3% to 16.8%). CONCLUSIONS: This study found a substantial decline in uninsured visits among cancer survivors in Medicaid expansion states. Yet, 1 in 5 cancer survivors living in a state that did not expand Medicaid remained uninsured. Several ACA provisions likely worked together to increase cardiovascular-related preventive screening rates for cancer survivors seen in CHCs.

Impact of Health Insurance Patterns on Chronic Health Conditions Among Older Patients.

BACKGROUND: Patients have varying levels of chronic conditions and health insurance patterns as they become Medicare age-eligible. Understanding these dynamics will inform policies and reforms that direct capacity and resources for primary care clinics to care for these aging patients. This study 1) determined changes in chronic condition rates following Medicare age eligibility among patients with different insurance patterns and 2) estimated the number of chronically ill patients who remain inadequately insured post-Medicare eligibility among patients receiving care in community health centers. METHOD: We used retrospective electronic health record data from 45,527 patients aged 62 to 68 from 990 community health centers in 25 states in 2014 to 2019. Insurance patterns (continuously insured, continuously uninsured, uninsured/discontinuously insured who gained insurance after age 65, lost insurance after age 65, discontinuously insured) and diagnosis of chronic conditions were defined at each visit pre- and post-Medicare eligibility. Difference-in-differences Poisson GEE models estimated changes of chronic condition rates by insurance groups pre- to post-Medicare age eligibility. RESULTS: Post-Medicare eligibility, 72% patients were continuously insured, 14% gained insurance; and 14% were uninsured or discontinuously insured. The prevalence of multimorbidity (≥2 chronic conditions) was 77%. Those who gained insurance had a significantly larger increase in the rate of documented chronic conditions from pre- to post-Medicare (DID: 1.06, 95%CI:1.05-1.07) compared with the continuously insured group. CONCLUSIONS: Post-Medicare age eligibility, a significant proportion of patients were diagnosed with new conditions leading to high burden of disease. One in 4 older adults continue to have inadequate health care coverage in their older age.

Trajectories of Chronic Disease and Multimorbidity Among Middle-aged and Older Patients at Community Health Centers.

Importance: Health-related consequences of multimorbidity (≥2 chronic diseases) are well documented. However, the extent and rate of accumulation of chronic diseases among US patients seeking care in safety-net clinics are not well understood. These insights are needed to enable clinicians, administrators, and policy makers to mobilize resources for prevention of disease escalations in this population. Objectives: To identify the patterns and rate of chronic disease accumulation among middle-aged and older patients seeking care in community health centers, as well as any sociodemographic differences. Design, Setting, and Participants: This cohort study used electronic health record data from January 1, 2012, to December 31, 2019, on 725 107 adults aged 45 years or older with 2 or more ambulatory care visits in 2 or more distinct years at 657 primary care clinics in the Advancing Data Value Across a National Community Health Center network in 26 US states. Statistical analysis was performed from September 2021 to February 2023. Exposures: Race and ethnicity, age, insurance coverage, and federal poverty level (FPL). Main Outcomes and Measures: Patient-level chronic disease burden, operationalized as the sum of 22 chronic diseases recommended by the Multiple Chronic Conditions Framework. Linear mixed models with patient-level random effects adjusted for demographic characteristics and ambulatory visit frequency with time interactions were estimated to compare accrual by race and ethnicity, age, income, and insurance coverage. Results: The analytic sample included 725 107 patients (417 067 women [57.5%]; 359 255 [49.5%] aged 45-54 years, 242 571 [33.5%] aged 55-64 years, and 123 281 [17.0%] aged ≥65 years). On average, patients started with a mean (SD) of 1.7 (1.7) morbidities and ended with 2.6 (2.0) morbidities over a mean (SD) of 4.2 (2.0) years of follow-up. Compared with non-Hispanic White patients, patients in racial and ethnic minoritized groups had marginally lower adjusted annual rates of accrual of conditions (-0.03 [95% CI, -0.03 to -0.03] for Spanish-preferring Hispanic patients; -0.02 [95% CI, -0.02 to -0.01] for English-preferring Hispanic patients; -0.01 [95% CI, -0.01 to -0.01] for non-Hispanic Black patients; and -0.04 [95% CI, -0.05 to -0.04] for non-Hispanic Asian patients). Older patients accrued conditions at higher annual rates compared with patients 45 to 50 years of age (0.03 [95% CI, 0.02-0.03] for 50-55 years; 0.03 [95% CI, 0.03-0.04] for 55-60 years; 0.04 [95% CI, 0.04-0.04] for 60-65 years; and 0.05 [95% CI, 0.05-0.05] for ≥65 years). Compared with those with higher income (always ≥138% of the FPL), patients with income less than 138% of the FPL (0.04 [95% CI, 0.04-0.05]), mixed income (0.01 [95% CI, 0.01-0.01]), or unknown income levels (0.04 [95% CI, 0.04-0.04]) had higher annual accrual rates. Compared with continuously insured patients, continuously uninsured and discontinuously insured patients had lower annual accrual rates (continuously uninsured, -0.003 [95% CI, -0.005 to -0.001]; discontinuously insured, -0.004 [95% CI, -0.005 to -0.003]). Conclusions and Relevance: This cohort study of middle-aged patients seeking care in community health centers suggests that disease accrued at high rates for patients' chronological age. Targeted efforts for chronic disease prevention are needed for patients near or below the poverty line.

Comparing ascertainment of chronic condition status with problem lists versus encounter diagnoses from electronic health records.

OBJECTIVE: To assess and compare electronic health record (EHR) documentation of chronic disease in problem lists and encounter diagnosis records among Community Health Center (CHC) patients. MATERIALS AND METHODS: We assessed patient EHR data in a large clinical research network during 2012-2019. We included CHCs who provided outpatient, older adult primary care to patients age ≥45 years, with ≥2 office visits during the study. Our study sample included 1 180 290 patients from 545 CHCs across 22 states. We used diagnosis codes from 39 Chronic Condition Warehouse algorithms to identify chronic conditions from encounter diagnoses only and compared against problem list records. We measured correspondence including agreement, kappa, prevalence index, bias index, and prevalence-adjusted bias-adjusted kappa. RESULTS: Overlap of encounter diagnosis and problem list ascertainment was 59.4% among chronic conditions identified, with 12.2% of conditions identified only in encounters and 28.4% identified only in problem lists. Rates of coidentification varied by condition from 7.1% to 84.4%. Greatest agreement was found in diabetes (84.4%), HIV (78.1%), and hypertension (74.7%). Sixteen conditions had <50% agreement, including cancers and substance use disorders. Overlap for mental health conditions ranged from 47.4% for anxiety to 59.8% for depression. DISCUSSION: Agreement between the 2 sources varied substantially. Conditions requiring regular management in primary care settings may have a higher agreement than those diagnosed and treated in specialty care. CONCLUSION: Relying on EHR encounter data to identify chronic conditions without reference to patient problem lists may under-capture conditions among CHC patients in the United States.

Key Data Gaps Regarding the Public Health Issues Associated with Opioid Analgesics.

Most pharmaceutical opioids are used to treat pain, and they have been demonstrated to be effective medications for many. Their abuse and misuse pose significant public health concerns in the USA. Research has provided much insight into the prevalence, scope, and drivers of opioid abuse, but a holistic understanding is limited by a lack of available data regarding key aspects of this public health problem. Twelve data gaps were revealed during the creation of a systems-level computer model of medical use, diversion, nonmedical use, and the adverse outcomes associated with opioid analgesics in the USA. Data specific to these gaps would enhance the validity and real-world applications of systems-level models of this public health problem and would increase understanding of the complex system in which use and abuse occur. This paper provides an overview of these gaps, argues for the importance of closing them, and provides specific recommendations for future data collection efforts.

Modeling the impact of simulated educational interventions on the use and abuse of pharmaceutical opioids in the United States: a report on initial efforts.

Three educational interventions were simulated in a system dynamics model of the medical use, trafficking, and nonmedical use of pharmaceutical opioids. The study relied on secondary data obtained in the literature for the period of 1995 to 2008 as well as expert panel recommendations regarding model parameters and structure. The behavior of the resulting systems-level model was tested for fit against reference behavior data. After the base model was tested, logic to represent three educational interventions was added and the impact of each intervention on simulated overdose deaths was evaluated over a 7-year evaluation period, 2008 to 2015. Principal findings were that a prescriber education intervention not only reduced total overdose deaths in the model but also reduced the total number of persons who receive opioid analgesic therapy, medical user education not only reduced overdose deaths among medical users but also resulted in increased deaths from nonmedical use, and a "popularity" intervention sharply reduced overdose deaths among nonmedical users while having no effect on medical use. System dynamics modeling shows promise for evaluating potential interventions to ameliorate the adverse outcomes associated with the complex system surrounding the use of opioid analgesics to treat pain.