Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 119
Filtrar
1.
J Electromyogr Kinesiol ; 61: 102599, 2021 Sep 17.
Artigo em Inglês | MEDLINE | ID: mdl-34624604

RESUMO

The purpose of this narrative review is to provide a critical reflection of how analytical machine learning approaches could provide the platform to harness variability of patient presentation to enhance clinical prediction. The review includes a summary of current knowledge on the physiological adaptations present in people with spinal pain. We discuss how contemporary evidence highlights the importance of not relying on single features when characterizing patients given the variability of physiological adaptations present in people with spinal pain. The advantages and disadvantages of current analytical strategies in contemporary basic science and epidemiological research are reviewed and we consider how analytical machine learning approaches could provide the platform to harness the variability of patient presentations to enhance clinical prediction of pain persistence or recurrence. We propose that machine learning techniques can be leveraged to translate a potentially heterogeneous set of variables into clinically useful information with the potential to enhance patient management.

2.
PLoS One ; 16(6): e0252657, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34153069

RESUMO

Neuromuscular impairments are frequently observed in patients with chronic neck pain (CNP). This study uniquely investigates whether changes in neck muscle synergies detected during gait are sensitive enough to differentiate between people with and without CNP. Surface electromyography (EMG) was recorded from the sternocleidomastoid, splenius capitis, and upper trapezius muscles bilaterally from 20 asymptomatic individuals and 20 people with CNP as they performed rectilinear and curvilinear gait. Intermuscular coherence was computed to generate the functional inter-muscle connectivity network, the topology of which is quantified based on a set of graph measures. Besides the functional network, spectrotemporal analysis of each EMG was used to form the feature set. With the use of Neighbourhood Component Analysis (NCA), we identified the most significant features and muscles for the classification/differentiation task conducted using K-Nearest Neighbourhood (K-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms. The NCA algorithm selected features from muscle network topology as one of the most relevant feature sets, which further emphasize the presence of major differences in muscle network topology between people with and without CNP. Curvilinear gait achieved the best classification performance through NCA-SVM based on only 16 features (accuracy: 85.00%, specificity: 81.81%, and sensitivity: 88.88%). Intermuscular muscle networks can be considered as a new sensitive tool for the classification of people with CNP. These findings further our understanding of how fundamental muscle networks are altered in people with CNP.


Assuntos
Dor Crônica/fisiopatologia , Eletromiografia/métodos , Músculos do Pescoço/fisiopatologia , Cervicalgia/fisiopatologia , Máquina de Vetores de Suporte , Caminhada/fisiologia , Adulto , Algoritmos , Dor Crônica/classificação , Dor Crônica/diagnóstico , Feminino , Marcha/fisiologia , Humanos , Masculino , Modelos Teóricos , Sistema Musculoesquelético/fisiopatologia , Cervicalgia/classificação , Cervicalgia/diagnóstico , Músculos Paraespinais/fisiopatologia , Músculos Superficiais do Dorso/fisiopatologia , Adulto Jovem
3.
J Med Internet Res ; 23(6): e29395, 2021 06 09.
Artigo em Inglês | MEDLINE | ID: mdl-34106074

RESUMO

BACKGROUND: In 2020, the number of internet users surpassed 4.6 billion. Individuals who create and share digital data can leave a trail of information about their habits and preferences that collectively generate a digital footprint. Studies have shown that digital footprints can reveal important information regarding an individual's health status, ranging from diet and exercise to depression. Uses of digital applications have accelerated during the COVID-19 pandemic where public health organizations have utilized technology to reduce the burden of transmission, ultimately leading to policy discussions about digital health privacy. Though US consumers report feeling concerned about the way their personal data is used, they continue to use digital technologies. OBJECTIVE: This study aimed to understand the extent to which consumers recognize possible health applications of their digital data and identify their most salient concerns around digital health privacy. METHODS: We conducted semistructured interviews with a diverse national sample of US adults from November 2018 to January 2019. Participants were recruited from the Ipsos KnowledgePanel, a nationally representative panel. Participants were asked to reflect on their own use of digital technology, rate various sources of digital information, and consider several hypothetical scenarios with varying sources and health-related applications of personal digital information. RESULTS: The final cohort included a diverse national sample of 45 US consumers. Participants were generally unaware what consumer digital data might reveal about their health. They also revealed limited knowledge of current data collection and aggregation practices. When responding to specific scenarios with health-related applications of data, they had difficulty weighing the benefits and harms but expressed a desire for privacy protection. They saw benefits in using digital data to improve health, but wanted limits to health programs' use of consumer digital data. CONCLUSIONS: Current privacy restrictions on health-related data are premised on the notion that these data are derived only from medical encounters. Given that an increasing amount of health-related data is derived from digital footprints in consumer settings, our findings suggest the need for greater transparency of data collection and uses, and broader health privacy protections.


Assuntos
Comportamento do Consumidor/estatística & dados numéricos , Informação de Saúde ao Consumidor/estatística & dados numéricos , Coleta de Dados/ética , Conjuntos de Dados como Assunto/provisão & distribuição , Entrevistas como Assunto , Privacidade/psicologia , Pesquisa Qualitativa , Adolescente , Adulto , Estudos de Coortes , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estados Unidos , Adulto Jovem
5.
J Med Internet Res ; 23(5): e26616, 2021 05 03.
Artigo em Inglês | MEDLINE | ID: mdl-33938807

RESUMO

BACKGROUND: The wide adoption of social media in daily life renders it a rich and effective resource for conducting near real-time assessments of consumers' perceptions of health services. However, its use in these assessments can be challenging because of the vast amount of data and the diversity of content in social media chatter. OBJECTIVE: This study aims to develop and evaluate an automatic system involving natural language processing and machine learning to automatically characterize user-posted Twitter data about health services using Medicaid, the single largest source of health coverage in the United States, as an example. METHODS: We collected data from Twitter in two ways: via the public streaming application programming interface using Medicaid-related keywords (Corpus 1) and by using the website's search option for tweets mentioning agency-specific handles (Corpus 2). We manually labeled a sample of tweets in 5 predetermined categories or other and artificially increased the number of training posts from specific low-frequency categories. Using the manually labeled data, we trained and evaluated several supervised learning algorithms, including support vector machine, random forest (RF), naïve Bayes, shallow neural network (NN), k-nearest neighbor, bidirectional long short-term memory, and bidirectional encoder representations from transformers (BERT). We then applied the best-performing classifier to the collected tweets for postclassification analyses to assess the utility of our methods. RESULTS: We manually annotated 11,379 tweets (Corpus 1: 9179; Corpus 2: 2200) and used 7930 (69.7%) for training, 1449 (12.7%) for validation, and 2000 (17.6%) for testing. A classifier based on BERT obtained the highest accuracies (81.7%, Corpus 1; 80.7%, Corpus 2) and F1 scores on consumer feedback (0.58, Corpus 1; 0.90, Corpus 2), outperforming the second best classifiers in terms of accuracy (74.6%, RF on Corpus 1; 69.4%, RF on Corpus 2) and F1 score on consumer feedback (0.44, NN on Corpus 1; 0.82, RF on Corpus 2). Postclassification analyses revealed differing intercorpora distributions of tweet categories, with political (400778/628411, 63.78%) and consumer feedback (15073/27337, 55.14%) tweets being the most frequent for Corpus 1 and Corpus 2, respectively. CONCLUSIONS: The broad and variable content of Medicaid-related tweets necessitates automatic categorization to identify topic-relevant posts. Our proposed system presents a feasible solution for automatic categorization and can be deployed and generalized for health service programs other than Medicaid. Annotated data and methods are available for future studies.


Assuntos
Mídias Sociais , Teorema de Bayes , Serviços de Saúde , Humanos , Medicaid , Processamento de Linguagem Natural , Estados Unidos
6.
JAMA Netw Open ; 4(5): e2110918, 2021 05 03.
Artigo em Inglês | MEDLINE | ID: mdl-34009347

RESUMO

Importance: Curbing COVID-19 transmission is currently the greatest global public health challenge. Consumer digital tools used to collect data, such as the Apple-Google digital contact tracing program, offer opportunities to reduce COVID-19 transmission but introduce privacy concerns. Objective: To assess uses of consumer digital information for COVID-19 control that US adults find acceptable and the factors associated with higher or lower approval of use of this information. Design, Setting, and Participants: This cross-sectional survey study obtained data from a nationally representative sample of 6284 US adults recruited by email from the web-based Ipsos KnowledgePanel in July 2020. Respondents evaluated scenarios reflecting uses of digital data for COVID-19 control (case identification, digital contact tracing, policy setting, and enforcement of quarantines). Main Outcomes and Measures: Levels of support for use of personal digital data in 9 scenarios to mitigate the spread of COVID-19 infection, rated on a Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Multivariable linear regression models were fitted for each scenario and included factors hypothesized to be associated with views about digital data use for COVID-19 mitigation measures. Black and Hispanic survey respondents were oversampled; thus, poststratification weights were used so that results are representative of the general US population. Results: Of 6284 individuals invited to participate in the study, 3547 responded, for a completion rate of 56%. A total of 1762 participants (52%) were female, 715 (21%) identified as Black, 790 (23%) identified as Hispanic, and 1224 (36%) were 60 years or older; mean (SD) age was 51.7 (16.6) years. Approval of scenarios was low, ranging from 28% to 43% (52%-67% when neutral responses were included). Differences were found based on digital data source (smartphone vs social media: coefficient, 0.29 [95% CI, 0.23-0.35]; P < .001; smart thermometer vs social media: coefficient, 0.09 [95% CI, 0.03-0.16]; P = .004). County COVID-19 rates (coefficient, -0.02; 95% CI, -0.16 to 0.13 for quartile 4 compared with quartile 1) and prior family diagnosis of COVID-19 (coefficient, 0.00; 95% CI, -0.25 to 0.25) were not associated with support. Compared with self-described liberal individuals, conservative (coefficient, -0.81; 95% CI, -0.96 to -0.66; P < .001) and moderate (coefficient, -0.52; 95% CI, -0.67 to -0.38; P < .001) individuals were less likely to support the scenarios. Similarly, large political differences were observed in support of the Apple-Google digital contact tracing program, with less support from conservative (coefficient, -0.99; 95% CI, -1.11 to -0.87; P < .001) and moderate (coefficient, -0.59; 95% CI, -0.69 to -0.48; P < .001) individuals compared with liberal individuals. Respondents from racial/ethnic minority groups were more supportive of the scenarios than were White, non-Hispanic respondents. For example, compared with White respondents, Black respondents were more supportive of the Apple-Google contact tracing program (coefficient, 0.20; 95% CI, 0.07-0.32; P = .002). Conclusions and Relevance: In this survey study of US adults, many were averse to their information being used on digital platforms to mitigate transmission of COVID-19. These findings suggest that in current and future pandemics, public health departments should use multiple strategies to gain public trust and accelerate adoption of tools such as digital contact tracing applications.


Assuntos
Atitude , COVID-19/prevenção & controle , Busca de Comunicante , Tecnologia Digital , Pandemias , Privacidade , Opinião Pública , Adulto , Idoso , Atitude/etnologia , Controle de Doenças Transmissíveis/métodos , Estudos Transversais , Coleta de Dados , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Grupos Minoritários , Política , SARS-CoV-2 , Smartphone , Mídias Sociais , Inquéritos e Questionários , Telemedicina , Estados Unidos
7.
J Biomech ; 118: 110190, 2021 03 30.
Artigo em Inglês | MEDLINE | ID: mdl-33581443

RESUMO

People with chronic neck pain (CNP) often present with altered gait kinematics. This paper investigates, combines, and compares the kinematic features from linear and nonlinear walking trajectories to design supervised machine learning models which differentiate asymptomatic individuals from those with CNP. For this, 126 features were extracted from seven body segments of 20 asymptomatic subjects and 20 individuals with non-specific CNP. Neighbourhood Component Analysis (NCA) was used to identify body segments and the corresponding significant features which have the maximum discriminative power for conducting classification. We assessed the efficacy of NCA combined with K- Nearest Neighbour (K-NN), Support Vector Machine and Linear Discriminant Analysis. By applying NCA, all classifiers increased their performance for both linear and nonlinear walking trajectories. Notably, features selected by NCA which magnify the classification power of the computational model were solely from the head, trunk and pelvis kinematics. Our results revealed that the nonlinear trajectory provides the best classification performance through the NCA-K-NN algorithms with an accuracy of 90%, specificity of 100% and sensitivity of 83.3%. The selected features by NCA are introduced as key biomarkers of gait kinematics for classifying non-specific CNP. This paper provides insight into changes in gait kinematics which are present in people with non-specific CNP which can be exploited for classification purposes. The result highlights the importance of curvilinear gait kinematic features which potentially could be utilized in future research to predict recurrent episodes of neck pain.


Assuntos
Marcha , Cervicalgia , Algoritmos , Biomarcadores , Fenômenos Biomecânicos , Humanos , Cervicalgia/diagnóstico , Caminhada
8.
Milbank Q ; 99(1): 99-125, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33320389

RESUMO

Policy Points As Medicaid programs grow in scale and complexity, greater consumer input may guide successful program design, but little is known about the extent to which state agencies are engaging consumers in the design and implementation of programs and policies. Through 50 semistructured interviews with Medicaid leaders in 14 states, we found significant variation in consumer engagement approaches, with many common facilitators, including leadership commitment, flexible strategies for recruiting and supporting consumer participation, and robust community partnerships. We provide early evidence on how state Medicaid agencies are integrating consumers' experiences and perspectives into their program design and governance. CONTEXT: Consumer engagement early in the process of health care policymaking may improve the effectiveness of program planning and implementation, promote patient-centric care, enhance beneficiary protections, and offer opportunities to improve service delivery. As Medicaid programs grow in scale and complexity, greater consumer input may guide successful program design, but little is known about the extent to which state agencies are currently engaging consumers in the design and implementation of programs and policies, and how this is being done. METHODS: We conducted semistructured interviews with 50 Medicaid program leaders across 14 states, employing a stratified purposive sampling method to select state Medicaid programs based on US census region, rurality, Medicaid enrollment size, total population, ACA expansion status, and Medicaid managed care penetration. Interview data were audio-recorded, professionally transcribed, and underwent iterative coding with content and thematic analyses. FINDINGS: First, we found variation in consumer engagement approaches, ranging from limited and largely symbolic interactions to longer-term deliberative bodies, with some states tailoring their federally mandated standing committees to engage consumers. Second, most states were motivated by pragmatic considerations, such as identifying and overcoming implementation challenges for agency programs. Third, states reported several common facilitators of successful consumer engagement efforts, including leadership commitment, flexible strategies for recruiting and supporting consumers' participation, and robust community partnerships. All states faced barriers to authentic and sustained engagement. CONCLUSIONS: Sharing best practices across states could help strengthen programs' engagement efforts, identify opportunities for program improvement reflecting community needs, and increase participation among a population that has traditionally lacked a political voice.


Assuntos
Participação da Comunidade , Planejamento em Saúde/métodos , Medicaid/organização & administração , Órgãos Estatais de Desenvolvimento e Planejamento em Saúde , Planos Governamentais de Saúde/organização & administração , Centers for Medicare and Medicaid Services, U.S. , Planejamento em Saúde/organização & administração , Política de Saúde , Humanos , Entrevistas como Assunto , Medicaid/legislação & jurisprudência , Patient Protection and Affordable Care Act , Governo Estadual , Estados Unidos
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5162-5166, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019148

RESUMO

Chronic Neck Pain (CNP) can be associated with biomechanical changes. This paper investigates the changes in patterns of walking kinematics along a curvilinear trajectory and uses a specially designed feature space, coupled with a machine learning framework to conduct a data-driven differential diagnosis, between asymptomatic individuals and those with CNP. For this, 126 kinematic features were collected from seven body segments of 40 participants (20 asymptomatic, 20 individuals with CNP). The features space was processed through a Neighbourhood Component Analysis (NCA) algorithm to systematically select the most significant features which have the maximum discriminative power for conducting the differential diagnosis. The selected features were then processed by a K-Nearest Neighbors (K-NN) classifier to conduct the task. Our results show that, through a systematic selection of feature space, we can significantly increase the classification accuracy. In this regard, a 35% increase is reported after applying the NCA. Thus, we have shown that using only 13 features (of which 61% belong to kinematic features and 39% to statistical features) from five body segments (Head, Trunk, Pelvic, Hip and Knee) we can achieve an accuracy, sensitivity and specificity of 82.50%, 80.95% and 84.21% respectively. This promising result highlights the importance of curvilinear kinematic features through the proposed information processing pipeline for conducting differential diagnosis and could be tested in future studies to predict the likelihood of people developing recurrent neck pain.


Assuntos
Cervicalgia , Caminhada , Biomarcadores , Fenômenos Biomecânicos , Diagnóstico Diferencial , Humanos , Cervicalgia/diagnóstico
11.
J Med Internet Res ; 22(8): e18401, 2020 08 17.
Artigo em Inglês | MEDLINE | ID: mdl-32804085

RESUMO

BACKGROUND: Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans. OBJECTIVE: We aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are using Twitter to communicate with the public. METHODS: Using Twitter's public application programming interface, we collected 158,714 public posts ("tweets") from active Twitter profiles of state Medicaid agencies and MCOs, spanning March 2014 through June 2019. Manual content analyses identified 5 broad categories of content, and these coded tweets were used to train supervised machine learning algorithms to classify all collected posts. RESULTS: We identified 15 state Medicaid agencies and 81 Medicaid MCOs on Twitter. The mean number of followers was 1784, the mean number of those followed was 542, and the mean number of posts was 2476. Approximately 39% of tweets came from just 10 accounts. Of all posts, 39.8% (63,168/158,714) were classified as general public health education and outreach; 23.5% (n=37,298) were about specific Medicaid policies, programs, services, or events; 18.4% (n=29,203) were organizational promotion of staff and activities; and 11.6% (n=18,411) contained general news and news links. Only 4.5% (n=7142) of posts were responses to specific questions, concerns, or complaints from the public. CONCLUSIONS: Twitter has the potential to enhance community building, beneficiary engagement, and public health outreach, but appears to be underutilized by the Medicaid program.


Assuntos
Aprendizado de Máquina/normas , Medicaid/normas , Mídias Sociais/normas , Humanos , Estados Unidos
12.
J Gen Intern Med ; 35(10): 3040-3042, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32813219

RESUMO

The COVID-19 pandemic is poised to drastically alter the Medicaid program. While state Medicaid programs are currently expanding coverage policies and enrollment to address acute public health needs, states will soon face significant budget shortfalls. These impending changes may renew partisan debates about restrictive policies like work requirements, which generally require beneficiaries to verify their participation in certain activities-such as employment, job search, or training programs-in order to receive or retain coverage. We argue that restrictive Medicaid policies are driven, to a great extent, by political party affiliation, highlighting the outsized role of partisanship in Medicaid policy adoption. To combat these dynamics, additional efforts are needed to improve community-informed decision-making, strengthen evaluation approaches to tie evidence to policymaking, and boost participation in and understanding of the political processes that affect policy change.


Assuntos
Infecções por Coronavirus/economia , Política de Saúde/economia , Medicaid/economia , Pandemias/economia , Pneumonia Viral/economia , Betacoronavirus , COVID-19 , Política de Saúde/legislação & jurisprudência , Humanos , Medicaid/legislação & jurisprudência , Patient Protection and Affordable Care Act , Política , SARS-CoV-2 , Estados Unidos
13.
JAMA Netw Open ; 3(7): e208285, 2020 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-32644138

RESUMO

Importance: Digital technology is part of everyday life. Digital interactions generate large amounts of data that can reveal information about the health of individual consumers (the digital health footprint). Objective: Τo describe health privacy challenges associated with digital technology. Design, Setting, and Participants: For this qualitative study, In-depth, semistructured, qualitative interviews were conducted with 26 key experts from diverse fields in the US between January 1 and July 31, 2018. Open-ended questions and hypothetical scenarios were used to identify sources of digital information that contribute to consumers' health-relevant digital footprints and challenges for health privacy. Participants also completed a survey instrument on which they rated the health relatedness of digital data sources. Main Outcomes and Measures: Health policy challenges associated with digital technology based on qualitative responses to expert interviews. Results: Although experts' ratings of digital data sources suggested a possible distinction between health and nonhealth data, qualitative interviews uniformly indicated that all data can be health data, particularly when aggregated across sources and time. Five key characteristics of the digital health footprint were associated with health privacy policy challenges: invisibility (people are unaware of how their data are tracked), inaccuracy (data in the digital health footprint can be inaccurate), immortality (data have no expiration date and are aggregated over time), marketability (data have immense commercial value and are frequently bought and sold), and identifiability (individuals can be readily reidentified and anonymity is nearly impossible to achieve). There are virtually no regulatory structures in the US to protect health privacy in the context of the digital health footprint. Conclusions and Relevance: The findings suggest that a sector-specific approach to digital technology privacy in the US may be associated with inadequate health privacy protections.


Assuntos
Segurança Computacional , Confidencialidade/normas , Tecnologia Digital , Tecnologia Digital/métodos , Tecnologia Digital/normas , Política de Saúde , Humanos , Gestão da Informação/organização & administração , Gestão da Informação/normas , Determinação de Necessidades de Cuidados de Saúde , Pesquisa Qualitativa , Estados Unidos
14.
Am J Manag Care ; 26(7): 310-316, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32672916

RESUMO

OBJECTIVES: To evaluate the impact of the Community-Based Care Management (CBCM) program on total costs of care and utilization among adult high-need, high-cost patients enrolled in a Medicaid managed care organization (MCO). CBCM was a Medicaid insurer-led care coordination and disease management program staffed by nurse care managers paired with community health workers. STUDY DESIGN: Retrospective cohort analysis. METHODS: We obtained deidentified health plan claims data, enrollment information, and the MCO's monthly registry of the top 10% of costliest patients. The analysis included 896 patients enrolled in CBCM over the course of 2 years (January 2016 to December 2017) and a propensity score-matched cohort of high-cost patients (n = 2152) who received primary care at sites that did not participate in CBCM during the same time period. The primary outcomes were total costs of care and utilization in the 12-month period after enrollment. Secondary outcomes included utilization by care setting: outpatient, inpatient, emergency department, pharmacy, postacute care, and all other remaining sites. We used zero-inflated gamma and Poisson regression models to estimate average differences in postperiod costs and utilization between CBCM enrollees versus non-CBCM enrollees. RESULTS: We did not observe meaningful differences in total costs or visit frequency among CBCM enrollees relative to non-CBCM enrollees. CONCLUSIONS: Although our study found no association between the CBCM program and subsequent cost or utilization outcomes, understanding why these outcomes were not achieved will inform how future Medicaid programs are designed to achieve better patient outcomes and lower costs.


Assuntos
Seguradoras , Programas de Assistência Gerenciada/organização & administração , Medicaid/organização & administração , Aceitação pelo Paciente de Cuidados de Saúde/estatística & dados numéricos , Administração dos Cuidados ao Paciente/organização & administração , Adulto , Fatores Etários , Agentes Comunitários de Saúde/organização & administração , Feminino , Humanos , Masculino , Programas de Assistência Gerenciada/economia , Medicaid/economia , Pessoa de Meia-Idade , Administração dos Cuidados ao Paciente/economia , Equipe de Assistência ao Paciente/organização & administração , Estudos Retrospectivos , Fatores Sexuais , Fatores Socioeconômicos , Estados Unidos
15.
Health Aff (Millwood) ; 39(2): 207-213, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-32011942

RESUMO

Interventions that address socioeconomic determinants of health are receiving considerable attention from policy makers and health care executives. The interest is fueled in part by expected returns on investment. However, many current estimates of returns on investment are likely overestimated, because they are based on pre-post study designs that are susceptible to regression to the mean. We present a return-on-investment analysis that is based on a randomized controlled trial of Individualized Management for Patient-Centered Targets (IMPaCT), a standardized community health worker intervention that addresses unmet social needs for disadvantaged people. We found that every dollar invested in the intervention would return $2.47 to an average Medicaid payer within the fiscal year.


Assuntos
Agentes Comunitários de Saúde , Medicaid , Atenção à Saúde , Humanos , Investimentos em Saúde , Fatores Socioeconômicos , Estados Unidos
16.
Clin Infect Dis ; 71(7): 1664-1670, 2020 10 23.
Artigo em Inglês | MEDLINE | ID: mdl-31630192

RESUMO

BACKGROUND: With the current opioid crisis in the United States, infectious complications related to injection drug use are increasingly reported. Pennsylvania is at the epicenter of the opioid crisis, with the third highest rate of drug overdose deaths in the United States. METHODS: A retrospective cohort study was performed using the Pennsylvania Health Care Cost Containment Council database of all residents hospitalized for infective endocarditis (IE) in an acute care hospital from 1 January 2013 through 31 March 2017. Patients were separated into those with and those without substance use via diagnosis codes. The primary outcome was length of stay. Secondarily, we evaluated demographics, infection history, hospital charges, and insurance status. RESULTS: Of the 17 224 hospitalizations, 1921 (11.1%) were in patients with drug use-associated IE (DU-IE). Total quarterly IE admissions increased 20%, with a 6.5% increase in non-drug use-associated IE (non-DU-IE) admissions and a 238% increase in DU-IE admissions. In adjusted models, DU-IE was not associated with significant changes in length of stay (incidence rate ratio, 1.02; 95% confidence interval, .975-1.072; P = .36). Patients with DU-IE were predominantly insured by Medicaid (68.3% vs 13.4% for non-DU-IE), they had higher hospital charges ($86 622 vs $66 802), and they were more likely to leave against medical advice (15.7% vs 1.1%) (all P < .001). CONCLUSIONS: Our study demonstrates an increase in IE admissions, driven by an increase in admissions for DU-IE. The higher charges, proportion of patients on Medicaid, and rates of leaving against medical advice among the DU-IE group shows the downstream effects of the opioid crisis.


Assuntos
Endocardite , Epidemia de Opioides , Analgésicos Opioides/efeitos adversos , Endocardite/epidemiologia , Hospitalização , Humanos , Pennsylvania/epidemiologia , Estudos Retrospectivos , Estados Unidos/epidemiologia
17.
J Gen Intern Med ; 35(3): 662-671, 2020 03.
Artigo em Inglês | MEDLINE | ID: mdl-31602561

RESUMO

BACKGROUND: Prescribing limits are one policy strategy to reduce short-term opioid prescribing, but there is limited evidence of their impact. OBJECTIVE: Evaluate implementation of a state prescribing limit law and health system electronic medical record (EMR) alert on characteristics of new opioid prescriptions, refill rates, and clinical encounters. DESIGN: Difference-in-differences study comparing new opioid prescriptions from ambulatory practices in New Jersey (NJ) to controls in Pennsylvania (PA) from 1 year prior to the implementation of a NJ state prescribing limit (May 2016-May 2017) to 10 months after (May 2017-March 2018). PARTICIPANTS: Adults with new opioid prescriptions in an academic health system with practices in PA and NJ. INTERVENTIONS: State 5-day opioid prescribing limit plus health system and health system EMR alert. MAIN MEASURES: Changes in morphine milligram equivalents (MME) and tablet quantity per prescription, refills, and encounters, adjusted for patient and prescriber characteristics. KEY RESULTS: There were a total of 678 new prescriptions in NJ and 4638 in PA. Prior to the intervention, median MME/prescription was 225 mg in NJ and 150 mg in PA, and median quantity was 30 tablets in both. After implementation, median MME/prescription was 150 mg in both states, and median quantity was 20 in NJ and 30 in PA. In the adjusted model, there was a greater decrease in mean MME and tablet quantity in NJ relative to PA after implementation of the policy plus alert (- 82.99 MME/prescription, 95% CI - 148.15 to - 17.84 and - 10.41 tabs/prescription, 95% CI - 19.70 to - 1.13). There were no significant differences in rates of refills or encounters at 30 days based on exposure to the interventions. CONCLUSIONS: Implementation of a prescribing limit and EMR alert was associated with an approximately 22% greater decrease in opioid dose per new prescription in NJ compared with controls in PA. The combination of prescribing limits and alerts may be an effective strategy to influence prescriber behavior.


Assuntos
Analgésicos Opioides , Registros Eletrônicos de Saúde , Padrões de Prática Médica , Adulto , Idoso , Feminino , Humanos , Masculino , Medicare , New Jersey , Pennsylvania/epidemiologia , Prescrições , Estados Unidos
18.
J Ambul Care Manage ; 43(1): 30-40, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31770184

RESUMO

Missed primary care appointments occur frequently among urban, low-income patients-some of the costliest and sickest patients. We conducted semi-structured interviews with 43 patients who reside in West Philadelphia (100% insured by Medicaid, 95% were non-Hispanic African Americans, and 47.1 years old on average) to identify why recent primary care appointments were or might have been missed. Existing transportation options, including public transportation, were considered unreliable and alternative options too costly. In addition, we discovered poor health, family obligations, and work requirements prevented appointment attendance. Intervening on the barriers identified may reduce missed appointment rates among disadvantaged populations.


Assuntos
Agendamento de Consultas , Cooperação do Paciente/estatística & dados numéricos , Atenção Primária à Saúde , Adulto , Afro-Americanos/estatística & dados numéricos , Feminino , Pesquisa sobre Serviços de Saúde , Humanos , Entrevistas como Assunto , Masculino , Pessoa de Meia-Idade , Philadelphia , Pobreza , Pesquisa Qualitativa , População Urbana
19.
Am J Manag Care ; 25(3): 135-139, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30875182

RESUMO

OBJECTIVES: Adequate access to primary and dental care is essential for population health, and some state Medicaid programs have expanded insurance coverage for both. However, there are few data on new Medicaid enrollees' ability to access services. We examined the relationship between provider supply and enrollees' identification of usual sources of care. STUDY DESIGN: Between November 2015 and February 2016, we surveyed low-income adults newly insured through Medicaid in Philadelphia, Pennsylvania, to determine if they had a usual source of care. Additionally, we used geospatial methods to calculate adult population per provider ratios by Census tract for primary and dental care providers who accepted Medicaid patients, then identified low-supply clusters. METHODS: We used multivariable logistic regression models to describe the odds of identifying usual sources of care based on being in low- or high-supply clusters, adjusting for patient demographics. RESULTS: Of 1000 contacted individuals, 312 completed the survey. Among respondents, 168 were previously uninsured and newly enrolled in Medicaid; 66.7% of this group identified a usual primary care provider and 42.3% identified a usual dental care provider. In adjusted analyses, individuals living in low- and high-supply areas had similar likelihoods of identifying a usual source of primary or dental care. CONCLUSIONS: Many new Medicaid enrollees did not have usual sources of primary or dental care, regardless of nearby provider supply. Efforts to understand what improves access or engagement in healthcare among Medicaid enrollees are critical after low-income adults gain insurance.


Assuntos
Assistência Odontológica/estatística & dados numéricos , Acesso aos Serviços de Saúde/estatística & dados numéricos , Medicaid/estatística & dados numéricos , Pobreza/estatística & dados numéricos , Atenção Primária à Saúde/estatística & dados numéricos , Adolescente , Adulto , Feminino , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Assistência Centrada no Paciente/estatística & dados numéricos , Estados Unidos , Adulto Jovem
20.
Acad Pediatr ; 19(3): 325-332, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30218840

RESUMO

OBJECTIVE: To examine insurance-based disparities in provider-related barriers to care among children in California in the wake of changes to the insurance market resulting from the Affordable Care Act. METHODS: Our sample included 6514 children (ages 0 to 11 years) from the 2014-2016 California Health Interview Survey. We examined parent reports in the past year of 1) having trouble finding a general provider for the child, 2) the child not being accepted by a provider as a new patient, 3) the child's health insurance not being accepted by a provider, or 4) any of the above. Multivariable models estimated the associations of insurance type-Medi-Cal (Medicaid), employer-sponsored insurance, or privately purchased coverage-and parent reports of these problems. RESULTS: Approximately 8% of parents had encountered at least one of these problems. Compared with parents of children with employer-sponsored insurance, parents of children with Medi-Cal or privately purchased coverage had over twice the odds of experiencing at least one of the barriers. Parents of children with Medi-Cal had over twice the odds of being told a provider would not accept their children's coverage or having trouble finding a general provider and 3times the odds of being told a provider would not accept their children as new patients. Parents of children with privately purchased coverage had over 3times the odds of being told a provider would not accept their children's coverage. CONCLUSIONS: Our study found significant disparities in provider-related barriers by insurance type among children in California.


Assuntos
Acesso aos Serviços de Saúde/estatística & dados numéricos , Disparidades em Assistência à Saúde/estatística & dados numéricos , Seguro Saúde/estatística & dados numéricos , Medicaid , Pediatras , California , Criança , Pré-Escolar , Feminino , Humanos , Lactente , Recém-Nascido , Cobertura do Seguro , Masculino , Patient Protection and Affordable Care Act , Estados Unidos
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...