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OBJECTIVE: Given their association with varying health risks, lifestyle-related behaviors are essential to consider in population-level disease prevention. Health insurance claims are a key source of information for population health analytics, but the availability of lifestyle information within claims data is unknown. Our goal was to assess the availability and prevalence of data items that describe lifestyle behaviors across several domains within a large U.S. claims database. METHODS: We conducted a retrospective, descriptive analysis to determine the availability of the following claims-derived lifestyle domains: nutrition, eating habits, physical activity, weight status, emotional wellness, sleep, tobacco use, and substance use. To define these domains, we applied a serial review process with three physicians to identify relevant diagnosis and procedure codes within claims for each domain. We used enrollment files and medical claims from a large national U.S. health plan to identify lifestyle relevant codes filed between 2016 and 2020. We calculated the annual prevalence of each claims-derived lifestyle domain and the proportion of patients by count within each domain. RESULTS: Approximately half of all members within the sample had claims information that identified at least one lifestyle domain (2016 = 41.9%; 2017 = 46.1%; 2018 = 49.6%; 2019 = 52.5%; 2020 = 50.6% of patients). Most commonly identified domains were weight status (19.9-30.7% across years), nutrition (13.3-17.8%), and tobacco use (7.9-9.8%). CONCLUSION: Our study demonstrates the feasibility of using claims data to identify key lifestyle behaviors. Additional research is needed to confirm the accuracy and validity of our approach and determine its use in population-level disease prevention.
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Seguro Saúde , Estilo de Vida , Humanos , Estudos Retrospectivos , PrevalênciaRESUMO
We investigated the role of both individual-level social needs and community-level social determinants of health (SDOH) in explaining emergency department (ED) utilization rates. We also assessed the potential synergies between the two levels of analysis and their combined effect on patterns of ED visits. We extracted electronic health record (EHR) data between July 2016 and June 2020 for 1,308,598 unique Maryland residents who received care at Johns Hopkins Health System, of which 28,937 (2.2%) patients had at least one documented social need. There was a negative correlation between median household income in a neighborhood with having a social need such as financial resource strain, food insecurity, and residential instability (correlation coefficient: -0.05, -0.01, and - 0.06, p = 0, respectively). In a multilevel model with random effects after adjusting for other factors, living in a more disadvantaged neighborhood was found to be significantly associated with ED utilization statewide and within Baltimore City (OR: 1.005, 95% CI: 1.003-1.007 and 1.020, 95% CI: 1.017-1.022, respectively). However, individual-level social needs appeared to enhance the statewide effect of living in a more disadvantaged neighborhood with the OR for the interaction term between social needs and SDOH being larger, and more positive, than SDOH alone (OR: 1.012, 95% CI: 1.011-1.014). No such moderation was found in Baltimore City. To our knowledge, this study is one of the first attempts by a major academic healthcare system to assess the combined impact of patient-level social needs in association with community-level SDOH on healthcare utilization and can serve as a baseline for future studies using EHR data linked to population-level data to assess such synergistic association.
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Determinantes Sociais da Saúde , Fatores Sociais , Humanos , Aceitação pelo Paciente de Cuidados de Saúde , Serviço Hospitalar de Emergência , ConhecimentoRESUMO
Although digital health solutions are increasingly popular in clinical psychiatry, one application that has not been fully explored is the utilization of survey technology to monitor patients outside of the clinic. Supplementing routine care with digital information collected in the "clinical whitespace" between visits could improve care for patients with severe mental illness. This study evaluated the feasibility and validity of using online self-report questionnaires to supplement in-person clinical evaluations in persons with and without psychiatric diagnoses. We performed a rigorous in-person clinical diagnostic and assessment battery in 54 participants with schizophrenia (N = 23), depressive disorder (N = 14), and healthy controls (N = 17) using standard assessments for depressive and psychotic symptomatology. Participants were then asked to complete brief online assessments of depressive (Quick Inventory of Depressive Symptomatology) and psychotic (Community Assessment of Psychic Experiences) symptoms outside of the clinic for comparison with the ground-truth in-person assessments. We found that online self-report ratings of severity were significantly correlated with the clinical assessments for depression (two assessments used: R = 0.63, p < 0.001; R = 0.73, p < 0.001) and psychosis (R = 0.62, p < 0.001). Our results demonstrate the feasibility and validity of collecting psychiatric symptom ratings through online surveys. Surveillance of this kind may be especially useful in detecting acute mental health crises between patient visits and can generally contribute to more comprehensive psychiatric treatment.
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Depressão , Inquéritos Epidemiológicos , Internet , Transtornos Psicóticos , Autorrelato , Saúde Mental/normas , Intervenção Baseada em Internet , Inquéritos Epidemiológicos/métodos , Inquéritos Epidemiológicos/normas , Reprodutibilidade dos Testes , Depressão/diagnóstico , Depressão/psicologia , Humanos , Masculino , Feminino , Adulto Jovem , Adulto , Esquizofrenia/diagnóstico , Transtornos Psicóticos/diagnóstico , Transtornos Psicóticos/psicologiaRESUMO
This study aimed to assess the impact of coronavirus disease (COVID-19) prevalence in the United States in the week leading to the relaxation of the stay-at-home orders (SAH) on future prevalence across states that implemented different SAH policies. We used data on the number of confirmed COVID-19 cases as of August 21, 2020 on county level. We classified states into four groups based on the 7-day change in prevalence and the state's approach to SAH policy. The groups included: (1) High Change (19 states; 7-day prevalence change ≥50th percentile), (2) Low Change (19 states; 7-day prevalence change <50th percentile), (3) No SAH (11 states: did not adopt SAH order), and (4) No SAH End (2 states: did not relax SAH order). We performed regression modeling assessing the association between change in prevalence at the time of SAH order relaxation and COVID-19 prevalence days after the relaxation of SAH order for four selected groups. After adjusting for other factors, compared to the High Change group, counties in the Low Change group had 33.8 (per 100,000 population) fewer cases (standard error (SE): 19.8, p < 0.001) 7 days after the relaxation of SAH order and the difference was larger by time passing. On August 21, 2020, the No SAH End group had 383.1 fewer cases (per 100,000 population) than the High Change group (SE: 143.6, p < 0.01). A measured, evidence-based approach is required to safely relax the community mitigation strategies and practice phased-reopening of the country.
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COVID-19/epidemiologia , COVID-19/prevenção & controle , Saúde Pública/estatística & dados numéricos , Saúde Pública/tendências , Quarentena/estatística & dados numéricos , Quarentena/normas , Medição de Risco/estatística & dados numéricos , Previsões , Política de Saúde , Humanos , Prevalência , SARS-CoV-2 , Estados Unidos/epidemiologiaRESUMO
BACKGROUND: The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents' mobility across neighborhoods of different levels of socioeconomic disadvantage. METHODS: This was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately. RESULTS: On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation. CONCLUSIONS: Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.
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COVID-19 , Humanos , Distanciamento Físico , Políticas , Prevalência , SARS-CoV-2 , Classe Social , Estados UnidosRESUMO
BACKGROUND: Clozapine is the most effective antipsychotic for treatment-resistant schizophrenia. Although serum clozapine levels can help guide treatment, they are underutilized owing to requirements for frequent venous blood draws and lack of immediate results. METHODS: Clozapine levels measured with a novel immunoassay technology (which enables point-of-care development) were compared with those measured by standard liquid chromatography/tandem mass spectrometry (LC-MS/MS). Frozen serum aliquots of 117 samples (N = 48 patients with schizophrenia on clozapine; N = 24 patients with schizophrenia not on clozapine; N = 45 healthy controls) were sent to a national reference laboratory (NRL) for clozapine level determination by LC-MS/MS, and matching samples were subjected to novel immunoassay (3 runs). At a later date, another frozen aliquot from the same date was sent to the NRL for repeat testing. RESULTS: The NRL obtained 18 false-positive clozapine results (mean 42.39 ± 32.06, range 21-159 ng/mL) in participants not on clozapine (N = 3) and healthy controls (N = 15). The immunoassay showed no false-positive clozapine results. The clozapine levels were correlated between both assays (r = 0.84, P < 0.0001), despite 16% higher clozapine levels with immunoassay (482.08 ± 270.88 ng/mL immunoassay, 414.98 ± 186.29 ng/mL LC-MS/MS [P = 0.03]). Agreement analysis using concordance correlation coefficient (CCC) for LC-MS/MS of the 2 aliquots yielded CCC = 0.869; 95% confidence interval = 0.690-0.970, whereas higher agreement results were observed for the 3 runs of immunoassay (CCC = 0.99; 95% confidence interval = 0.979-0.997). CONCLUSIONS: The lack of false positives observed with immunoassay, higher repeat performance agreement, and good correlation with LC-MS/MS may indicate the more robust performance of immunoassay than that of LC-MS/MS clozapine-level determination.
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Cromatografia Líquida/métodos , Clozapina/sangue , Monitoramento de Medicamentos/métodos , Imunoensaio/métodos , Espectrometria de Massas em Tandem/métodos , Adulto , Antipsicóticos/sangue , Antipsicóticos/uso terapêutico , Clozapina/uso terapêutico , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Sistemas Automatizados de Assistência Junto ao Leito , Esquizofrenia/sangue , Esquizofrenia/tratamento farmacológico , Adulto JovemRESUMO
Oxidative stress is implicated in many diseases yet no simple, rapid, and robust measurement is available at the point-of-care to assist clinicians in detecting oxidative stress. Here, we report results from a discovery-based research approach in which a redox mediator is used to probe serum samples for chemical information relevant to oxidative stress. Specifically, we use an iridium salt (K2IrCl6) to probe serum for reducing activities that can transfer electrons to iridium and thus generate detectable optical and electrochemical signals. We show that this Ir-reducing assay can detect various biological reductants and is especially sensitive to glutathione (GSH) compared to alternative assays. We performed an initial clinical evaluation using serum from 10 people diagnosed with schizophrenia, a mental health disorder that is increasingly linked to oxidative stress. The measured Ir-reducing capacity was able to discriminate people with schizophrenia from healthy controls (p < 0.005), and correlations were observed between Ir-reducing capacity and independent measures of symptom severity.
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Cloretos/química , Irídio/química , Estresse Oxidativo , Área Sob a Curva , Técnicas Eletroquímicas , Glutationa/química , Humanos , Oxirredução , Curva ROC , Esquizofrenia/diagnóstico , Esquizofrenia/metabolismo , Compostos de Sulfidrila/sangue , Compostos de Sulfidrila/químicaRESUMO
People with schizophrenia are 3-4 times more likely to die from cardiovascular disease than the general population. Clozapine (CLZ) is the gold standard of treatment for refractory schizophrenia. It has been associated with tachycardia and recent evidence shows individuals prescribed CLZ may develop blood pressure (BP) elevation and hypertension. The purpose of this study was to examine the effects of CLZ on BP and heart rate (HR). This was a retrospective chart review of patients 18-75 years old with a DSM IV diagnosis of Schizophrenia or Schizoaffective disorder. Primary outcomes were systolic blood pressure (SBP), diastolic blood pressure (DBP), and HR measured 12 weeks before and 24 weeks during CLZ treatment. Eighteen patient records were included in this study. The mean stabilized CLZ dose was 441.7 ± 171.8 mg/day. DBP (t = 1.02, df = 79.5, = 2.00, 0.049) and HR (t = 1.32, df = 355 = -4.61, < 0.0001) were significantly higher after CLZ initiation. A trend was noted for increase in SBP (p = 0.071). 22 % of patients met criteria for hypertension before CLZ and 67 % during CLZ treatment (Chi Square = 6.25, df = 1, p = 0.0124). No significant changes in weight or renal function occured during CLZ treatment. No patients had evidence of cardiomyopathy. The data suggest CLZ may be associated with a rise in BP and HR. The results of this study support previous literature that found an increase in SBP/DBP regardless of CLZ dose, occurring early in treatment. Due to high risk of cardiovascular morbidity and mortality, more work is needed to determine risk factors and understand the mechanism of action that may cause this side effect.
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Antipsicóticos/efeitos adversos , Pressão Sanguínea/efeitos dos fármacos , Clozapina/efeitos adversos , Frequência Cardíaca/efeitos dos fármacos , Hipertensão/induzido quimicamente , Esquizofrenia/tratamento farmacológico , Adolescente , Adulto , Idoso , Antipsicóticos/administração & dosagem , Clozapina/administração & dosagem , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Taquicardia/induzido quimicamente , Adulto JovemRESUMO
BACKGROUND: Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients' medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied. AIM: Determine the face validity of a claims-based algorithm to prioritize patients who likely need CMM. METHOD: We used claims data to construct patient-level markers of "regimen complexity" and "high-risk for adverse effects," which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic. RESULTS: Most records identified as 'very likely' (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the 'very unlikely' group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6, p < 0.001). CONCLUSION: Claims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings.
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BACKGROUND: Social needs and social determinants of health (SDOH) significantly outrank medical care when considering the impact on a person's length and quality of life, resulting in poor health outcomes and worsening life expectancy. Integrating social needs and SDOH data along with clinical risk information within operational clinical decision support (CDS) systems built into electronic health records (EHRs) is an effective approach to addressing health-related social needs. To achieve this goal, applied research is needed to develop EHR-integrated CDS tools and closed-loop referral systems and implement and test them in the digital and clinical workflows at health care systems and collaborating community-based organizations (CBOs). OBJECTIVE: This study aims to describe the protocol for a mixed methods study including a randomized controlled trial and a qualitative phase assessing the feasibility, acceptability, and effectiveness of an EHR-integrated digital platform to identify patients with social needs and provide navigation services and closed-loop referrals to CBOs to address their social needs. METHODS: The randomized controlled trial will enroll and randomize adult patients living in socioeconomically challenged neighborhoods in Baltimore City receiving care at a single academic health care institution in the 3-month intervention (using the digital platform) or the 3-month control (standard-of-care assessment and addressing of social needs) arms (n=295 per arm). To evaluate the feasibility and acceptability of the digital platform and its impact on the clinical and digital workflow and patient care, we will conduct focus groups with the care teams in the health care system (eg, clinical providers, social workers, and care managers) and collaborating CBOs. The outcomes will be the acceptability, feasibility, and effectiveness of the CDS tool and closed-loop referral system. RESULTS: This clinical trial opened to enrollment in June 2023 and will be completed in March 2025. Initial results are expected to be published in spring 2025. We will report feasibility outcome measures as weekly use rates of the digital platform. The acceptability outcome measure will be the provider's and patient's responses to the truthfulness of a statement indicating a willingness to use the platform in the future. Effectiveness will be measured by tracking a 3-month change in identified social needs and provided navigation services as well as clinical outcomes such as hospitalization and emergency department visits. CONCLUSIONS: The results of this investigation are expected to contribute to our understanding of the use of digital interventions and the implementation of such interventions in digital and clinical workflows to enhance the health care system and CBO ability related to social needs assessment and intervention. These results may inform the construction of a future multi-institutional trial designed to test the effectiveness of this intervention across different health care systems and care settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT05574699; https://clinicaltrials.gov/study/NCT05574699. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57316.
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Sistemas de Apoio a Decisões Clínicas , Encaminhamento e Consulta , Humanos , Encaminhamento e Consulta/organização & administração , Projetos Piloto , Navegação de Pacientes/organização & administração , Adulto , Masculino , Feminino , Determinantes Sociais da Saúde , Avaliação das NecessidadesRESUMO
BACKGROUND: Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations. OBJECTIVE: We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs. METHODS: We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient. RESULTS: The study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs. CONCLUSIONS: Our model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.
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Objectives: To develop and test a scalable, performant, and rule-based model for identifying 3 major domains of social needs (residential instability, food insecurity, and transportation issues) from the unstructured data in electronic health records (EHRs). Materials and Methods: We included patients aged 18 years or older who received care at the Johns Hopkins Health System (JHHS) between July 2016 and June 2021 and had at least 1 unstructured (free-text) note in their EHR during the study period. We used a combination of manual lexicon curation and semiautomated lexicon creation for feature development. We developed an initial rules-based pipeline (Match Pipeline) using 2 keyword sets for each social needs domain. We performed rule-based keyword matching for distinct lexicons and tested the algorithm using an annotated dataset comprising 192 patients. Starting with a set of expert-identified keywords, we tested the adjustments by evaluating false positives and negatives identified in the labeled dataset. We assessed the performance of the algorithm using measures of precision, recall, and F1 score. Results: The algorithm for identifying residential instability had the best overall performance, with a weighted average for precision, recall, and F1 score of 0.92, 0.84, and 0.92 for identifying patients with homelessness and 0.84, 0.82, and 0.79 for identifying patients with housing insecurity. Metrics for the food insecurity algorithm were high but the transportation issues algorithm was the lowest overall performing metric. Discussion: The NLP algorithm in identifying social needs at JHHS performed relatively well and would provide the opportunity for implementation in a healthcare system. Conclusion: The NLP approach developed in this project could be adapted and potentially operationalized in the routine data processes of a healthcare system.
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OBJECTIVE: To explore demographic predictors of Emergency Department (ED) utilization among youth with a history of suicidality (i.e., ideation or behaviors). METHODS: Electronic health records were extracted from 2017 to 2021 for 3094 8-22 year-old patients with a history of suicidality at an urban academic medical center ED in the Mid-Atlantic. Logistic regression analyses were used to assess for demographic predictors of ED utilization frequency, timing of subsequent visits, and reasons for subsequent visits over a 24-month follow-up period. RESULTS: Black race (OR = 1.45, 95% CI = 1.11-1.92), Female sex (OR = 1.59, 95% CI = 1.26-2.03), and having Medicaid insurance (OR = 1.71, 95% CI = 1.37-2.14) were associated with increased utilization, while being under 18 was associated with lower utilization (<12: OR = 0.38, 95% CI = 0.26-0.56; 12-18: OR = 0.47, 95% CI = 0.35-0.63). These demographics were also associated with ED readmission within 90 days, while being under 18 was associated with a lower odds of readmission. CONCLUSIONS: Among patients with a history of suicidality, those who identify as Black, young adults, patients with Medicaid, and female patients were more likely to be frequent utilizers of the ED within the 2 years following their initial visit. This pattern may suggest inadequate health care access for these groups, and a need to develop better care coordination with an intersectional focus to facilitate utilization of other health services.
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Serviços Médicos de Emergência , Suicídio , Adulto Jovem , Estados Unidos/epidemiologia , Humanos , Feminino , Adolescente , Medicaid , Serviço Hospitalar de Emergência , Demografia , Estudos RetrospectivosRESUMO
Purpose: Patient vital signs are related to specific health risks and outcomes but are underutilized in the prediction of health-care utilization and cost. To measure the added value of electronic health record (EHR) extracted Body Mass Index (BMI) and blood pressure (BP) values in improving healthcare risk and utilization predictions. Patients and Methods: A sample of 12,820 adult outpatients from the Johns Hopkins Health System (JHHS) were identified between 2016 and 2017, having high data quality and recorded values for BMI and BP. We evaluated the added value of BMI and BP in predicting health-care utilization and cost through a retrospective cohort design. BMI, mean arterial pressure (MAP), systolic and diastolic BPs were summarized as annual aggregated values. Concurrent annual BMI and MAP changes were quantified as the difference between maximum and minimum recorded values. Model performance estimates consisted of repeated 10-fold cross validation, compared to base model point estimates for demographic and diagnostic, coded events: (1) patient age and sex, (2) age, sex, and the Charlson weighted index, (3) age, sex and the Johns Hopkins ACG system's DxPM risk score. Results: Both categorical BMI and BP were progressively indicative of disease comorbidity, but not uniformly related to health-care utilization or cost. Annual change in BMI and MAP improved predictions for most concurrent year outcomes when compared to base models. Conclusion: When a healthcare system lacks relevant diagnostic or risk assessment information for a patient, vital signs may be useful for a simple estimation of disease risk, cost and utilization.
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BACKGROUND: Patient effort to comply with complex medication instructions is known to be related to nonadherence and subsequent medical complications or health care costs. A widely used Medication Regimen Complexity Index (MRCI) has been used with electronic health records (EHRs) to identify patients who could benefit from pharmacist intervention. A similar claims-derived measure may be better suited for clinical decision support, since claims offer a more complete view of patient care and health utilization. OBJECTIVE: To define and validate a novel insurance claims-based medication complexity score (MCS) patterned after the widely used MRCI, derived from EHRs. METHODS: Insurance claims and EHR data were provided by HealthPartners (N = 54,988) (Bloomington, Minnesota) and The Johns Hopkins Health System (N = 28,589) (Baltimore, Maryland) for years 2013 and 2017, respectively. Yearly measures of medication complexity were developed for each patient and evaluated with one another using rank correlation within different clinical subgroupings. Indicators for the presence of individually complex prescriptions were also developed and assessed using exact agreement. Complexity measures were then correlated with select covariates to further validate the concordance between MCS and MRCI with respect to clinical metrics. These included demographic, comorbidity, and health care utilization markers. Prescribed medications in each system's EHR were coded using the previously validated MRCI weighting rules. Insurance claims for retail pharmacy medications were coded using our novel MCS, which closely followed MRCI scoring rules. RESULTS: EHR-based MRCI and claims-based MCS were significantly correlated with one another for most clinical subgroupings. Likewise, both measures were correlated with several covariates, including count of active medications and chronic conditions. The MCS was, in most cases, more associated with key health covariates than was MRCI, although both were consistently significant. We found that the highest correlation between MCS and MRCI is obtained with patients who have similar counts of pharmacy records between EHRs and claims (HealthPartners: P = 0.796; Johns Hopkins Health System: P = 0.779). CONCLUSIONS: The findings suggest good correspondence between MCS and MRCI and that claims data represent a useful resource for assessing medication complexity. Claims data also have major practical advantages, such as interoperability across health care systems, although they lack the detailed clinical context of EHRs. DISCLOSURES: The Johns Hopkins University holds the copyright to the Adjusted Clinical Groups (ACG) system and receives royalties from the global distribution of the ACG system. This revenue supports a portion of the authors' salary. No additional or external funding supported this work. The authors have no conflict of interest to disclose.
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Registros Eletrônicos de Saúde , Seguro , Comorbidade , Estudos Transversais , Humanos , PolimedicaçãoRESUMO
BACKGROUND: Three claims-based pharmacy markers (complex, costly and risky medications) were developed to help automatically identify patients for comprehensive medication management. OBJECTIVE: To evaluate the association between newly-developed markers and healthcare outcomes. METHODS: This was a two-year retrospective cohort study using PharMetrics Plus patient-level administrative claims in 2014 and 2015. We included all claims from 1,541,873 individuals with: (1) 24-month medical and pharmacy enrollment in 2014 and 2015, (2) aged between 18 and 63 in 2014, and (3) known gender. Independent/control variables came from 2014 while outcomes came from 2014 (concurrent analysis) and 2015 (prospective analysis). Three pharmacy markers, separately or together, were added to four base models to predict concurrent and prospective healthcare costs (total, medical, and pharmacy) and utilization (having any hospitalization, having any emergency department visit, and having any readmission). We applied linear regression for costs while logistic regression for utilization. Measures of model performances and coefficients were derived from a 5-fold cross-validation repeated 20 times. RESULTS: Individuals with 1+ complex, risky or costly medication markers had higher comorbidity, healthcare costs and utilization than their counterparts. Nine binary risky category markers performed the best among the three types of risky medication markers; the Medication Complexity Score and three-level complex category both outperformed a simpler complex medication indicator. Adding three novel pharmacy markers separately or together into the base models provided the greatest improvement in explaining pharmacy costs, compared with medical (non-medication) costs. These pharmacy markers also added value in explaining healthcare utilization among the simple base models. CONCLUSIONS: Three claims-based pharmacy indicators had positive associations with healthcare outcomes and added value in predicting them. This initial study suggested that these novel markers can be used by pharmacy case management programs to help identify potential high-risk patients most likely to benefit from clinical pharmacist review and other interventions.
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Assistência Farmacêutica , Farmácia , Adolescente , Adulto , Custos de Cuidados de Saúde , Humanos , Conduta do Tratamento Medicamentoso , Pessoa de Meia-Idade , Estudos Retrospectivos , Adulto JovemRESUMO
BACKGROUND: The COVID-19 pandemic has impacted communities differentially, with poorer and minority populations being more adversely affected. Prior rural health research suggests such disparities may be exacerbated during the pandemic and in remote parts of the U.S. OBJECTIVES: To understand the spread and impact of COVID-19 across the U.S., county level data for confirmed cases of COVID-19 were examined by Area Deprivation Index (ADI) and Metropolitan vs. Nonmetropolitan designations from the National Center for Health Statistics (NCHS). These designations were the basis for making comparisons between Urban and Rural jurisdictions. METHOD: Kendall's Tau-B was used to compare effect sizes between jurisdictions on select ADI composites and well researched social determinants of health (SDH). Spearman coefficients and stratified Poisson modeling was used to explore the association between ADI and COVID-19 prevalence in the context of county designation. RESULTS: Results show that the relationship between area deprivation and COVID-19 prevalence was positive and higher for rural counties, when compared to urban ones. Family income, property value and educational attainment were among the ADI component measures most correlated with prevalence, but this too differed between county type. CONCLUSIONS: Though most Americans live in Metropolitan Areas, rural communities were found to be associated with a stronger relationship between deprivation and COVID-19 prevalence. Models predicting COVID-19 prevalence by ADI and county type reinforced this observation and may inform health policy decisions.
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AIM: Brief screening and predictive modeling have garnered attention for utility at identifying individuals at risk of suicide. Although previous research has investigated these methods, little is known about how these methods compare against each other or work in combination in the pediatric population. METHODS: Patients were aged 8-18 years old who presented from January 1, 2017, to June 30, 2019, to a Pediatric Emergency Department (PED). All patients were screened with the Ask Suicide Questionnaire (ASQ) as part of a universal screening approach. For all models, we used 5-fold cross-validation. We compared four models: Model 1 only included the ASQ; Model 2 included the ASQ and EHR data gathered at the time of ED visit (EHR data); Model 3 only included EHR data; and Model 4 included EHR data and a single item from the ASQ that asked about a lifetime history of suicide attempt. The main outcome was subsequent PED visit with suicide-related presenting problem within a 3-month follow-up period. RESULTS: Of the N = 13,420 individuals, n = 141 had a subsequent suicide-related PED visit. Approximately 63% identified as Black. Results showed that a model based only on EHR data (Model 3) had an area under the curve (AUC) of 0.775 compared to the ASQ alone (Model 1), which had an AUC of 0.754. Combining screening and EHR data (Model 4) resulted in a 17.4% (absolute difference = 3.6%) improvement in sensitivity and 13.4% increase in AUC (absolute difference = 6.6%) compared to screening alone (Model 1). CONCLUSION: Our findings show that predictive modeling based on EHR data is helpful either in the absence or as an addition to brief suicide screening. This is the first study to compare brief suicide screening to EHR-based predictive modeling and adds to our understanding of how best to identify youth at risk of suicidal thoughts and behaviors in clinical care settings.
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Registros Eletrônicos de Saúde , Ideação Suicida , Adolescente , Criança , Serviço Hospitalar de Emergência , Humanos , Programas de Rastreamento/métodos , Tentativa de Suicídio/prevenção & controleRESUMO
BACKGROUND: Pharmacists optimize medication use and ensure the safe and effective delivery of pharmacotherapy to patients using comprehensive medication management (CMM). Identifying and prioritizing individual patients who will most likely benefit from CMM can be challenging. Health systems have far more candidates for CMM than there are clinical pharmacists to provide this service. Furthermore, current evidence lacks widely accepted standards or automated mechanisms for identifying patients who would likely benefit from a pharmacist consultation. Existing tools to prioritize patients for pharmacist review often require manual chart review by a pharmacist or other clinicians or data collection by patient survey. OBJECTIVES: To (1) create new medication risk markers for identifying and prioritizing patients within a population and (2) identify patients who met these new markers, assess their clinical characteristics, and compare them with criteria that are widely used for medication therapy management (MTM). METHODS: Along with published literature, a panel of subject matter experts informed the development of 3 medication risk markers. To assess the prevalence of markers developed, we used Multum, a medication database, for medication-level characteristics, and for patient-level characteristics, we used QuintilesIMS, an administrative claims database derived from health plans across the United States, with data for 1,541,873 eligible individuals from 2014-2015. We compared the health care costs, utilization, and medication gap among patients identified through MTM criteria (both broad and narrow, as these are provided as ranges) and our new medication management score markers. RESULTS: We developed 3 claims-derivable markers: (1) instances when a patient filled a medication with high complexity that could affect adherence, (2) instances where a patient filled a medication defined as costly within a therapeutic category that could affect access, and (3) instances when a patient filled a medication defined as risky that could increase incidence of adverse drug events. In the QuintilesIMS database, individuals with 2 new medication risk markers plus at least 3 conditions and more than $3,017 in medication costs when compared with individuals meeting narrow MTM eligibility criteria (≥ 8 medications, ≥ 3 conditions, and > $3,017 medication costs) had increased costs ($36,000 vs $26,100 total; $24,800 vs 21,400 medical; $11,300 vs $4,800 pharmacy); acute care utilization (0.328 vs 0.256 inpatient admissions and 0.627 vs 0.579 emergency department visits); and 1 or more gaps in medication adherence(41.5% vs 34.7%). CONCLUSIONS: We identified novel markers of medication use risk that can be determined using insurance claims and can be useful to identify patients for CMM programs and prioritize patients who would benefit from clinical pharmacist intervention. These markers were associated with higher costs, acute care utilization, and gaps in medication use compared with the overall population and within certain subgroups. Providing CMM to these patients may improve health system performance in relevant quality measures. Evaluation of CMM services delivered by a pharmacist using these markers requires further investigation. DISCLOSURES: No outside funding supported this study. All authors are Johns Hopkins employees. The Johns Hopkins University receives royalties for nonacademic use of software based on the Johns Hopkins Adjusted Clinical Group (ACG) methodology. Chang, Kitchen, Weiner, and Kharrazi receive a portion of their salary support from this revenue. The authors have no conflicts of interests relevant to this study.
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Conduta do Tratamento Medicamentoso , Seleção de Pacientes , Atenção Primária à Saúde , Humanos , Adesão à Medicação , Estudos Retrospectivos , Estados UnidosRESUMO
Introduction: The spread of Coronavirus Disease 2019 (COVID-19) across the United States has highlighted the long-standing nationwide health inequalities with socioeconomically challenged communities experiencing a higher burden of the disease. We assessed the impact of neighborhood socioeconomic characteristics on the COVID-19 prevalence across seven selected states (i.e., Arizona, Florida, Illinois, Maryland, North Carolina, South Carolina, and Virginia). Methods: We obtained cumulative COVID-19 cases reported at the neighborhood aggregation level by Departments of Health in selected states on two dates (May 3rd, 2020, and May 30th, 2020) and assessed the correlation between the COVID-19 prevalence and neighborhood characteristics. We developed Area Deprivation Index (ADI), a composite measure to rank neighborhoods by their socioeconomic characteristics, using the 2018 US Census American Community Survey. The higher ADI rank represented more disadvantaged neighborhoods. Results: After controlling for age, gender, and the square mileage of each community we identified Zip-codes with higher ADI (more disadvantaged neighborhoods) in Illinois and Maryland had higher COVID-19 prevalence comparing to zip-codes across the country and in the same state with lower ADI (less disadvantaged neighborhoods) using data on May 3rd. We detected the same pattern across all states except for Florida and Virginia using data on May 30th, 2020. Conclusion: Our study provides evidence that not all Americans are at equal risk for COVID-19. Socioeconomic characteristics of communities appear to be associated with their COVID-19 susceptibility, at least among those study states with high rates of disease.