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Atsa'am DD, Agjei RO, Akingbade TJ, Balogun OS, Adusei-Mensah F. A novel scale for assessing the risk of low birthweight: Birthweight questionnaire. J Eval Clin Pract 2024. [PMID: 38923095 DOI: 10.1111/jep.14038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 05/07/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND The birthweight of a newborn is critical to their health, development, and well-being. Previous studies that used maternal characteristics to predict birthweight did not employ a harmonised scale to assess the risk of low birthweight (LBW). OBJECTIVE The goal of this study was to develop a new instrument that uses items on a uniform scale to assess the risk of an LBW in a pregnant woman. METHODS Item response theory was employed to evaluate a similar existing scale, and some weaknesses were identified. RESULTS Based on the observed weaknesses of the existing scale, a new uniform scale was developed, which is a 3-point Likert scale consisting of seven items. CONCLUSION The scale, termed birthweight questionnaire, is a valuable tool for collecting data that could assist in assessing the risk of an LBW at every stage of pregnancy.
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Affiliation(s)
- Donald D Atsa'am
- Department of Computer Science, College of Physical Sciences, Joseph Sarwuan Tarka University, Makurdi, Nigeria
| | - Richard O Agjei
- Department of Health Administration and Education, University of Education, Winneba, Ghana
| | | | - Oluwafemi S Balogun
- School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- Centre for Multidisciplinary Research and Innovation, Abuja, Nigeria
| | - Frank Adusei-Mensah
- Centre for Multidisciplinary Research and Innovation, Abuja, Nigeria
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
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Álvarez-Gálvez J, Ortega-Martín E, Carretero-Bravo J, Pérez-Muñoz C, Suárez-Lledó V, Ramos-Fiol B. Social determinants of multimorbidity patterns: A systematic review. Front Public Health 2023; 11:1081518. [PMID: 37050950 PMCID: PMC10084932 DOI: 10.3389/fpubh.2023.1081518] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/02/2023] [Indexed: 03/28/2023] Open
Abstract
Social determinants of multimorbidity are poorly understood in clinical practice. This review aims to characterize the different multimorbidity patterns described in the literature while identifying the social and behavioral determinants that may affect their emergence and subsequent evolution. We searched PubMed, Embase, Scopus, Web of Science, Ovid MEDLINE, CINAHL Complete, PsycINFO and Google Scholar. In total, 97 studies were chosen from the 48,044 identified. Cardiometabolic, musculoskeletal, mental, and respiratory patterns were the most prevalent. Cardiometabolic multimorbidity profiles were common among men with low socioeconomic status, while musculoskeletal, mental and complex patterns were found to be more prevalent among women. Alcohol consumption and smoking increased the risk of multimorbidity, especially in men. While the association of multimorbidity with lower socioeconomic status is evident, patterns of mild multimorbidity, mental and respiratory related to middle and high socioeconomic status are also observed. The findings of the present review point to the need for further studies addressing the impact of multimorbidity and its social determinants in population groups where this problem remains invisible (e.g., women, children, adolescents and young adults, ethnic groups, disabled population, older people living alone and/or with few social relations), as well as further work with more heterogeneous samples (i.e., not only focusing on older people) and using more robust methodologies for better classification and subsequent understanding of multimorbidity patterns. Besides, more studies focusing on the social determinants of multimorbidity and its inequalities are urgently needed in low- and middle-income countries, where this problem is currently understudied.
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Affiliation(s)
- Javier Álvarez-Gálvez
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cádiz, Spain
- The University Research Institute for Sustainable Social Development (Instituto Universitario de Investigación para el Desarrollo Social Sostenible), University of Cadiz, Jerez de la Frontera, Spain
| | - Esther Ortega-Martín
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cádiz, Spain
- *Correspondence: Esther Ortega-Martín
| | - Jesús Carretero-Bravo
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cádiz, Spain
| | - Celia Pérez-Muñoz
- Department of Nursing and Physiotherapy, University of Cadiz, Cádiz, Spain
| | - Víctor Suárez-Lledó
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cádiz, Spain
| | - Begoña Ramos-Fiol
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cádiz, Spain
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Comotti A, Fattori A, Greselin F, Bordini L, Brambilla P, Bonzini M. Psychometric Evaluation of GHQ-12 as a Screening Tool for Psychological Impairment of Healthcare Workers Facing COVID-19 Pandemic. LA MEDICINA DEL LAVORO 2023; 114:e2023009. [PMID: 36790406 PMCID: PMC9987474 DOI: 10.23749/mdl.v114i1.13918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/23/2023] [Indexed: 02/16/2023]
Abstract
BACKGROUND The General Health Questionnaire (GHQ) is a widely used tool, both in clinical and research settings, due to its brevity and easy administration. Researchers often adopt a dichotomous measurement method, considering a total score above or below a certain threshold. This leads to an extreme simplification of the gathered data and therefore to the loss of clinical details. In a multi-step evaluation study aimed at assessing health care workers' mental health during the Covid-19 pandemic, GHQ-12 proved to be the most effective tool to detect psychological distress compared to other scales adopted. These results led to deepen the understanding of GHQ-12 properties through a statistical study by focusing on items' properties and characteristics. METHODS GHQ-12 responses were analyzed using Item Response Theory (IRT), a suitable method for scale assessment. Instead of considering the single overall score, in which each item accounts equally, it focuses on individual items' characteristics. Moreover, IRT models were applied combined with the latent class (LC) analysis, aiming to the determination of subgroups of individuals according to their level of psychological distress. RESULTS GHQ-12 was administered to 990 health-care workers and responses were scored using the binary method (0-0-1-1). We applied the two-parameter logistic (2-PL) model, finding that the items showed different ways of responses and features. The latent class analysis classified subjects into three sub-groups according to their responses to GHQ-12 only: 47% of individuals with general well-being, 38% expressing signs of discomfort without severity and 15% of subjects with a high level of impairment. This result almost reproduces subjects' classification obtained after administering the six questionnaires of the study protocol. CONCLUSIONS Accurate statistical techniques and a deep understanding of the latent factors underlying the GHQ-12 resulted in a more effective usage of such psychometric questionnaire - i.e. a more refined gathering of data and a significant time and resource efficiency. We underlined the need to maximize the extraction of data from questionnaires and the necessity of them being less lengthy and repetitive.
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Affiliation(s)
- Anna Comotti
- Occupational Health Unit, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Alice Fattori
- Department of Clinical Science and Community Health, University of Milan, Milan, Italy.
| | - Francesca Greselin
- Department of Statistics and Quantitative Methods, University of Milan Bicocca, Milan, Italy.
| | - Lorenzo Bordini
- Occupational Health Unit, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy and Department of Neurosciences and Mental Health, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Matteo Bonzini
- Occupational Health Unit, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy and Department of Clinical Science and Community Health, University of Milan, Milan, Italy.
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Yang S, Varghese P, Stephenson E, Tu K, Gronsbell J. Machine learning approaches for electronic health records phenotyping: a methodical review. J Am Med Inform Assoc 2023; 30:367-381. [PMID: 36413056 PMCID: PMC9846699 DOI: 10.1093/jamia/ocac216] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/27/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Accurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings. This study evaluates ML-based phenotyping with respect to (1) the data sources used, (2) the phenotypes considered, (3) the methods applied, and (4) the reporting and evaluation methods used. MATERIALS AND METHODS We searched PubMed and Web of Science for articles published between 2018 and 2022. After screening 850 articles, we recorded 37 variables on 100 studies. RESULTS Most studies utilized data from a single institution and included information in clinical notes. Although chronic conditions were most commonly considered, ML also enabled the characterization of nuanced phenotypes such as social determinants of health. Supervised deep learning was the most popular ML paradigm, while semi-supervised and weakly supervised learning were applied to expedite algorithm development and unsupervised learning to facilitate phenotype discovery. ML approaches did not uniformly outperform rule-based algorithms, but deep learning offered a marginal improvement over traditional ML for many conditions. DISCUSSION Despite the progress in ML-based phenotyping, most articles focused on binary phenotypes and few articles evaluated external validity or used multi-institution data. Study settings were infrequently reported and analytic code was rarely released. CONCLUSION Continued research in ML-based phenotyping is warranted, with emphasis on characterizing nuanced phenotypes, establishing reporting and evaluation standards, and developing methods to accommodate misclassified phenotypes due to algorithm errors in downstream applications.
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Affiliation(s)
- Siyue Yang
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | | | - Ellen Stephenson
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Karen Tu
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Jessica Gronsbell
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
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Carretero-Bravo J, Ramos-Fiol B, Ortega-Martín E, Suárez-Lledó V, Salazar A, O’Ferrall-González C, Dueñas M, Peralta-Sáez JL, González-Caballero JL, Cordoba-Doña JA, Lagares-Franco C, Martínez-Nieto JM, Almenara-Barrios J, Álvarez-Gálvez J. Multimorbidity Patterns and Their Association with Social Determinants, Mental and Physical Health during the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16839. [PMID: 36554719 PMCID: PMC9778742 DOI: 10.3390/ijerph192416839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND The challenge posed by multimorbidity makes it necessary to look at new forms of prevention, a fact that has become heightened in the context of the pandemic. We designed a questionnaire to detect multimorbidity patterns in people over 50 and to associate these patterns with mental and physical health, COVID-19, and possible social inequalities. METHODS This was an observational study conducted through a telephone interview. The sample size was 1592 individuals with multimorbidity. We use Latent Class Analysis to detect patterns and SF-12 scale to measure mental and physical quality-of-life health. We introduced the two dimensions of health and other social determinants in a multinomial regression model. RESULTS We obtained a model with five patterns (entropy = 0.727): 'Relative Healthy', 'Cardiometabolic', 'Musculoskeletal', 'Musculoskeletal and Mental', and 'Complex Multimorbidity'. We found some differences in mental and physical health among patterns and COVID-19 diagnoses, and some social determinants were significant in the multinomial regression. CONCLUSIONS We identified that prevention requires the location of certain inequalities associated with the multimorbidity patterns and how physical and mental health have been affected not only by the patterns but also by COVID-19. These findings may be critical in future interventions by health services and governments.
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Affiliation(s)
- Jesús Carretero-Bravo
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
| | - Begoña Ramos-Fiol
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
| | - Esther Ortega-Martín
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
| | - Víctor Suárez-Lledó
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
| | - Alejandro Salazar
- Department of Statistics and Operational Research, University of Cadiz, Polígono Río San Pedro, 11510 Puerto Real, Spain
| | | | - María Dueñas
- Department of Statistics and Operational Research, University of Cadiz, Polígono Río San Pedro, 11510 Puerto Real, Spain
| | - Juan Luis Peralta-Sáez
- Department of Statistics and Operational Research, University of Cadiz, Polígono Río San Pedro, 11510 Puerto Real, Spain
| | - Juan Luis González-Caballero
- Department of Statistics and Operational Research, University of Cadiz, Polígono Río San Pedro, 11510 Puerto Real, Spain
| | - Juan Antonio Cordoba-Doña
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
- Preventive Medicine Area, Hospital of Jerez, Ctra. Trebujena, s/n, 11407 Jerez de la Frontera, Spain
| | - Carolina Lagares-Franco
- Department of Statistics and Operational Research, University of Cadiz, Polígono Río San Pedro, 11510 Puerto Real, Spain
| | | | - José Almenara-Barrios
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
| | - Javier Álvarez-Gálvez
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
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Berkman ND, Chang E, Seibert J, Ali R. Characteristics of High-Need, High-Cost Patients : A "Best-Fit" Framework Synthesis. Ann Intern Med 2022; 175:1728-1741. [PMID: 36343343 DOI: 10.7326/m21-4562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Accurately identifying high-need, high-cost (HNHC) patients to reduce their preventable or modifiable health care use for their chronic conditions is a priority and a challenge for U.S. policymakers, health care delivery systems, and payers. PURPOSE To identify characteristics and criteria to distinguish HNHC patients. DATA SOURCES Searches of multiple databases and gray literature from 1 January 2000 to 22 January 2022. STUDY SELECTION English-language studies of characteristics and criteria to identify HNHC adult patients, defined as those with high use (emergency department, inpatient, or total services) or high cost. DATA EXTRACTION Independent, dual-review extraction and quality assessment. DATA SYNTHESIS The review included 64 studies comprising multivariate exposure studies (n = 47), cluster analyses (n = 11), and qualitative studies (n = 6). A National Academy of Medicine (NAM) taxonomy was an initial "best-fit" framework for organizing the synthesis of the findings. Patient characteristics associated with being HNHC included number and severity of comorbid conditions and having chronic clinical conditions, particularly heart disease, chronic kidney disease, chronic lung disease, diabetes, cancer, and hypertension. Patients' risk for being HNHC was often amplified by behavioral health conditions and social risk factors. The reviewers revised the NAM taxonomy to create a final framework, adding chronic pain and prior patterns of high health care use as characteristics associated with an increased risk for being HNHC. LIMITATION Little evidence distinguished potentially preventable or modifiable health care use from overall use. CONCLUSION A combination of characteristics can be useful for identifying HNHC patients. Because of the complexity of their conditions and circumstances, improving their quality of care will likely also require an individualized assessment of care needs and availability of support services. PRIMARY FUNDING SOURCE Agency for Healthcare Research and Quality. (PROSPERO: CRD42020161179).
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Affiliation(s)
- Nancy D Berkman
- RTI-University of North Carolina Evidence-based Practice Center and RTI International, Research Triangle Park, North Carolina (N.D.B., R.A.)
| | - Eva Chang
- RTI-University of North Carolina Evidence-based Practice Center and RTI International, Research Triangle Park, North Carolina, and Advocate Aurora Health, Advocate Aurora Research Institute, Downers Grove, Illinois (E.C.)
| | - Julie Seibert
- RTI-University of North Carolina Evidence-based Practice Center and RTI International, Research Triangle Park, and North Carolina Department of Health and Human Services, Division of Mental Health, Developmental Disability and Substance Abuse Services, Raleigh, North Carolina (J.S.)
| | - Rania Ali
- RTI-University of North Carolina Evidence-based Practice Center and RTI International, Research Triangle Park, North Carolina (N.D.B., R.A.)
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Hutchins F, Thorpe J, Zhao X, Zhang H, Rosland AM. Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report. BMC Health Serv Res 2022; 22:1341. [PMID: 36371216 PMCID: PMC9652993 DOI: 10.1186/s12913-022-08757-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 10/31/2022] [Indexed: 11/13/2022] Open
Abstract
Background Segmentation models such as latent class analysis are an increasingly popular approach to inform group-tailored interventions for high-risk complex patients. Multiple studies have identified clinically meaningful high-risk segments, but few have evaluated change in groupings over time. Objectives To describe population-level and individual change over time in latent comorbidity groups among Veterans at high-risk of hospitalization in the Veterans Health Administration (VA). Research design Using a repeated cross-sectional design, we conducted a latent class analysis of chronic condition diagnoses. We compared latent class composition, patient high-risk status, and patient class assignment in 2018 to 2020. Subjects Two cohorts of eligible patients were selected: those active in VA primary care and in the top decile of predicted one-year hospitalization risk in 2018 (n = 951,771) or 2020 (n = 978,771). Measures Medical record data were observed from January 2016–December 2020. Latent classes were modeled using indicators for 26 chronic health conditions measured with a 2-year lookback period from study entry. Results Five groups were identified in both years, labeled based on high prevalence conditions: Cardiometabolic (23% in 2018), Mental Health (18%), Substance Use Disorders (16%), Low Diagnosis (25%), and High Complexity (10%). The remaining 8% of 2018 patients were not assigned to a group due to low predicted probability. Condition prevalence overall and within groups was stable between years. However, among the 563,725 patients identified as high risk in both years, 40.8% (n = 230,185) had a different group assignment in 2018 versus 2020. Conclusions In a repeated latent class analysis of nearly 1 million Veterans at high-risk for hospitalization, population-level groups were stable over two years, but individuals often moved between groups. Interventions tailored to latent groups need to account for change in patient status and group assignment over time. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-08757-x.
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Hutchins F, Thorpe J, Maciejewski ML, Zhao X, Daniels K, Zhang H, Zulman DM, Fihn S, Vijan S, Rosland AM. Clinical Outcome and Utilization Profiles Among Latent Groups of High-Risk Patients: Moving from Segmentation Towards Intervention. J Gen Intern Med 2022; 37:2429-2437. [PMID: 34731436 PMCID: PMC9360385 DOI: 10.1007/s11606-021-07166-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 09/24/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND The ability of latent class models to identify clinically distinct groups among high-risk patients has been demonstrated, but it is unclear how healthcare data can inform group-specific intervention design. OBJECTIVE Examine how utilization patterns across latent groups of high-risk patients provide actionable information to guide group-specific intervention design. DESIGN Cohort study using data from 2012 to 2015. PATIENTS Participants were 934,787 patients receiving primary care in the Veterans Health Administration, with predicted probability of 12-month hospitalization in the top 10th percentile during 2014. MAIN MEASURES Patients were assigned to latent groups via mixture-item response theory models based on 28 chronic conditions. We modeled odds of all-cause mortality, hospitalizations, and 30-day re-hospitalizations by group membership. Detailed outpatient and inpatient utilization patterns were compared between groups. KEY RESULTS A total of 764,257 (81.8%) of patients were matched with a comorbidity group. Groups were characterized by substance use disorders (14.0% of patients assigned), cardiometabolic conditions (25.7%), mental health conditions (17.6%), pain/arthritis (19.1%), cancer (15.3%), and liver disease (8.3%). One-year mortality ranged from 2.7% in the Mental Health group to 14.9% in the Cancer group, compared to 8.5% overall. In adjusted models, group assignment predicted significantly different odds of each outcome. Groups differed in their utilization of multiple types of care. For example, patients in the Pain group had the highest utilization of in-person primary care, with a mean (SD) of 5.3 (5.0) visits in the year of follow-up, while the Substance Use Disorder group had the lowest, with 3.9 (4.1) visits. The Substance Use Disorder group also had the highest rates of using services for housing instability (25.1%), followed by the Liver group (10.1%). CONCLUSIONS Latent groups of high-risk patients had distinct hospitalization and utilization profiles, despite having comparable levels of predicted baseline risk. Utilization profiles pointed towards system-specific care needs that could inform tailored interventions.
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Affiliation(s)
- Franya Hutchins
- Center for Health Equity Research and Promotion, VA Pittsburgh Health Care System, University Drive (151C), Pittsburgh, PA, 15240, USA.
- Caring for Complex Chronic Conditions Research Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
| | - Joshua Thorpe
- Center for Health Equity Research and Promotion, VA Pittsburgh Health Care System, University Drive (151C), Pittsburgh, PA, 15240, USA
- Division of Pharmaceutical Outcomes & Policy, Eshelman School of Pharmacy, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA
| | - Matthew L Maciejewski
- Department of Population Health Sciences, Duke University Medical Center, Durham, NC, USA
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham VA Medical Center, Durham, NC, USA
| | - Xinhua Zhao
- Center for Health Equity Research and Promotion, VA Pittsburgh Health Care System, University Drive (151C), Pittsburgh, PA, 15240, USA
| | - Karin Daniels
- Center for Health Equity Research and Promotion, VA Pittsburgh Health Care System, University Drive (151C), Pittsburgh, PA, 15240, USA
- Caring for Complex Chronic Conditions Research Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Hongwei Zhang
- Center for Health Equity Research and Promotion, VA Pittsburgh Health Care System, University Drive (151C), Pittsburgh, PA, 15240, USA
| | - Donna M Zulman
- Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA, USA
| | - Stephan Fihn
- Division of General Internal Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Sandeep Vijan
- VA Center for Clinical Management Research, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Ann-Marie Rosland
- Center for Health Equity Research and Promotion, VA Pittsburgh Health Care System, University Drive (151C), Pittsburgh, PA, 15240, USA
- Caring for Complex Chronic Conditions Research Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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Arnold J, Thorpe J, Mains-Mason J, Rosland AM. Empiric segmentation of high-risk patients: a structured literature review. THE AMERICAN JOURNAL OF MANAGED CARE 2022; 28:e69-e77. [PMID: 35139299 PMCID: PMC9623575 DOI: 10.37765/ajmc.2022.88752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OBJECTIVES Empiric segmentation is a rapidly growing, learning health system approach that uses large health care system data sets to identify groups of high-risk patients who may benefit from similar interventions. We aimed to review studies that used data-driven approaches to segment high-risk patient populations and describe how their designs and findings can inform health care leaders who are interested in applying similar techniques to their patient populations. STUDY DESIGN Structured literature review. METHODS We searched for original research articles published since 2000 that identified high-risk adult patient populations and applied data-driven analyses to segment the population. Two reviewers independently extracted study population source and criteria for high-risk designation, segmentation method, data types included, model selection criteria, and model results from the identified studies. RESULTS Our search identified 224 articles, 12 of which met criteria for full review. Of these, 8 segmented high-risk patients and 4 segmented diagnoses without assigning patients to unique groups. Studies segmenting patients more often had clinically interpretable results. Common groups were defined by high prevalence of diabetes, cardiovascular disease, psychiatric conditions including substance use disorders, and neurologic disease (eg, stroke). Few studies incorporated patients' functional or social factors. Resulting patient and diagnosis clusters varied in ways closely linked to the model inputs, patient population inclusion criteria, and health care system context. CONCLUSIONS Empiric segmentation can yield clinically relevant groups of patients with complex medical needs. Segmentation results are context dependent, suggesting the need for careful design and interpretation of segmentation models to ensure that results can inform clinical care and program design in the target setting.
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Affiliation(s)
- Jonathan Arnold
- Division of General Internal Medicine, University of Pittsburgh, 200 Lothrop St, Pittsburgh, PA 15213.
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Chang ET, Yoon J, Esmaeili A, Zulman DM, Ong MK, Stockdale SE, Jimenez EE, Chu K, Atkins D, Denietolis A, Asch SM. Outcomes of a randomized quality improvement trial for high-risk Veterans in year two. Health Serv Res 2021; 56 Suppl 1:1045-1056. [PMID: 34145564 PMCID: PMC8515223 DOI: 10.1111/1475-6773.13674] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/14/2021] [Accepted: 04/17/2021] [Indexed: 11/28/2022] Open
Abstract
OBJECTIVE The Veterans Health Administration (VHA) conducted a randomized quality improvement evaluation to determine whether augmenting patient-centered medical homes with Primary care Intensive Management (PIM) decreased utilization of acute care and health care costs among patients at high risk for hospitalization. PIM was cost-neutral in the first year; we analyzed changes in utilization and costs in the second year. DATA SOURCES VHA administrative data for five demonstration sites from August 2013 to March 2019. DATA SOURCES Administrative data extracted from VHA's Corporate Data Warehouse. STUDY DESIGN Veterans with a risk of 90-day hospitalization in the top 10th percentile and recent hospitalization or emergency department (ED) visit were randomly assigned to usual primary care vs primary care augmented by PIM. PIM included interdisciplinary teams, comprehensive patient assessment, intensive case management, and care coordination services. We compared the change in mean VHA inpatient and outpatient utilization and costs (including PIM expenses) per patient for the 12-month period before randomization and 13-24 months after randomization for PIM vs usual care using difference-in-differences. PRINCIPAL FINDINGS Both PIM patients (n = 1902) and usual care patients (n = 1882) had a mean of 5.6 chronic conditions. PIM patients had a greater number of primary care visits compared to those in usual care (mean 4.6 visits/patient/year vs 3.7 visits/patient/year, p < 0.05), but ED visits (p = 0.45) and hospitalizations (p = 0.95) were not significantly different. We found a small relative increase in outpatient costs among PIM patients compared to those in usual care (mean difference + $928/patient/year, p = 0.053), but no significant differences in mean inpatient costs (+$245/patient/year, p = 0.97). Total mean health care costs were similar between the two groups during the second year (mean difference + $1479/patient/year, p = 0.73). CONCLUSIONS Approaches that target patients solely based on the high risk of hospitalization are unlikely to reduce acute care use or total costs in VHA, which already offers patient-centered medical homes.
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Affiliation(s)
- Evelyn T. Chang
- VA Center for the Study of Healthcare InnovationImplementation and Policy (CSHIIP)Los AngelesCaliforniaUSA
- Department of MedicineVA Greater Los Angeles Healthcare SystemLos AngelesCaliforniaUSA
- Department of MedicineDavid Geffen School of Medicine, University of California at Los AngelesLos AngelesCaliforniaUSA
| | - Jean Yoon
- VA Health Economics Resource Center (HERC)Menlo ParkCaliforniaUSA
- Department of General Internal MedicineUCSF School of MedicineSan FranciscoCaliforniaUSA
| | - Aryan Esmaeili
- VA Health Economics Resource Center (HERC)Menlo ParkCaliforniaUSA
| | - Donna M. Zulman
- VA HSR&D Center for Innovation to ImplementationMenlo ParkCaliforniaUSA
- Division of Primary Care and Population HealthStanford University School of MedicineMenlo ParkCaliforniaUSA
| | - Michael K. Ong
- Department of MedicineVA Greater Los Angeles Healthcare SystemLos AngelesCaliforniaUSA
- Department of MedicineDavid Geffen School of Medicine, University of California at Los AngelesLos AngelesCaliforniaUSA
- Department of Health Policy and ManagementFielding School of Public Health, University of California at Los AngelesLos AngelesCaliforniaUSA
| | - Susan E. Stockdale
- VA Center for the Study of Healthcare InnovationImplementation and Policy (CSHIIP)Los AngelesCaliforniaUSA
- Department of Psychiatry and Biobehavioral SciencesUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Elvira E. Jimenez
- VA Center for the Study of Healthcare InnovationImplementation and Policy (CSHIIP)Los AngelesCaliforniaUSA
- Behavioral NeurologyGeffen School of Medicine, University of California at Los AngelesLos AngelesCaliforniaUSA
| | - Karen Chu
- VA Center for the Study of Healthcare InnovationImplementation and Policy (CSHIIP)Los AngelesCaliforniaUSA
| | - David Atkins
- VA Health Services Research and DevelopmentWashingtonDistrict of ColumbiaUSA
| | | | - Steven M. Asch
- VA HSR&D Center for Innovation to ImplementationMenlo ParkCaliforniaUSA
- Division of Primary Care and Population HealthStanford University School of MedicineMenlo ParkCaliforniaUSA
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Blalock DV, Maciejewski ML, Zulman DM, Smith VA, Grubber J, Rosland AM, Weidenbacher HJ, Greene L, Zullig LL, Whitson HE, Hastings SN, Hung A. Subgroups of High-Risk Veterans Affairs Patients Based on Social Determinants of Health Predict Risk of Future Hospitalization. Med Care 2021; 59:410-417. [PMID: 33821830 PMCID: PMC8034377 DOI: 10.1097/mlr.0000000000001526] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Population segmentation has been recognized as a foundational step to help tailor interventions. Prior studies have predominantly identified subgroups based on diagnoses. In this study, we identify clinically coherent subgroups using social determinants of health (SDH) measures collected from Veterans at high risk of hospitalization or death. STUDY DESIGN AND SETTING SDH measures were obtained for 4684 Veterans at high risk of hospitalization through mail survey. Eleven self-report measures known to impact hospitalization and amenable to intervention were chosen a priori by the study team to identify subgroups through latent class analysis. Associations between subgroups and demographic and comorbidity characteristics were calculated through multinomial logistic regression. Odds of 180-day hospitalization were compared across subgroups through logistic regression. RESULTS Five subgroups of high-risk patients emerged-those with: minimal SDH vulnerabilities (8% hospitalized), poor/fair health with few SDH vulnerabilities (12% hospitalized), social isolation (10% hospitalized), multiple SDH vulnerabilities (12% hospitalized), and multiple SDH vulnerabilities without food or medication insecurity (10% hospitalized). In logistic regression, the "multiple SDH vulnerabilities" subgroup had greater odds of 180-day hospitalization than did the "minimal SDH vulnerabilities" reference subgroup (odds ratio: 1.53, 95% confidence interval: 1.09-2.14). CONCLUSION Self-reported SDH measures can identify meaningful subgroups that may be used to offer tailored interventions to reduce their risk of hospitalization and other adverse events.
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Affiliation(s)
- Dan V. Blalock
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC
| | - Matthew L. Maciejewski
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC
- Department of Population Health Sciences, Duke University, Durham NC
- Division of General Internal Medicine, Department of Medicine, Duke University, Durham, NC
| | - Donna M. Zulman
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA
- Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford CA
| | - Valerie A. Smith
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC
- Department of Population Health Sciences, Duke University, Durham NC
- Division of General Internal Medicine, Department of Medicine, Duke University, Durham, NC
| | - Janet Grubber
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC
| | - Ann-Marie Rosland
- VA Pittsburgh Center for Health Equity Research and Promotion, Pittsburgh PA
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh PA
| | - Hollis J. Weidenbacher
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC
| | - Liberty Greene
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA
- Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford CA
| | - Leah L. Zullig
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC
- Department of Population Health Sciences, Duke University, Durham NC
| | - Heather E. Whitson
- Geriatric Research, Education, and Clinical Center, Durham Veterans Affairs Health Care System, Durham, NC
- Center for the Study of Human Aging and Development, Duke University, Durham, NC
- Department of Medicine, Duke University School of Medicine, Durham, NC
| | - Susan N. Hastings
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC
- Department of Population Health Sciences, Duke University, Durham NC
- Geriatric Research, Education, and Clinical Center, Durham Veterans Affairs Health Care System, Durham, NC
- Center for the Study of Human Aging and Development, Duke University, Durham, NC
- Department of Medicine, Duke University School of Medicine, Durham, NC
| | - Anna Hung
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, Durham, NC
- Department of Population Health Sciences, Duke University, Durham NC
- Duke Clinical Research Institute, Duke University, Durham, NC
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12
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Optimal Examination Sites for Periodontal Disease Evaluation: Applying the Item Response Theory Graded Response Model. J Clin Med 2020; 9:jcm9113754. [PMID: 33233427 PMCID: PMC7700480 DOI: 10.3390/jcm9113754] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 11/16/2020] [Accepted: 11/19/2020] [Indexed: 01/09/2023] Open
Abstract
Periodontal examination data have a complex structure. For epidemiological studies, mass screenings, and public health use, a simple index that represents the periodontal condition is necessary. Periodontal indices for partial examination of selected teeth have been developed. However, the selected teeth vary between indices, and a justification for the selection of examination teeth has not been presented. We applied a graded response model based on the item response theory to select optimal examination teeth and sites that represent periodontal conditions. Data were obtained from 254 patients who participated in a multicenter follow-up study. Baseline data were obtained from initial follow-up. Optimal examination sites were selected using item information calculated by graded response modeling. Twelve sites—maxillary 2nd premolar (palatal-medial), 1st premolar (palatal-distal), canine (palatal-medial), lateral incisor (palatal-central), central incisor (palatal-distal) and mandibular 1st premolar (lingual, medial)—were selected. Mean values for clinical attachment level, probing pocket depth, and bleeding on probing by full mouth examinations were used for objective variables. Measuring the clinical parameters of these sites can predict the results of full mouth examination. For calculating the periodontal index by partial oral examination, a justification for the selection of examination sites is essential. This study presents an evidence-based partial examination methodology and its modeling.
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Batten AJ, Thorpe J, Piegari RI, Rosland AM. A Resampling Based Grid Search Method to Improve Reliability and Robustness of Mixture-Item Response Theory Models of Multimorbid High-Risk Patients. IEEE J Biomed Health Inform 2019; 24:1780-1787. [PMID: 31689220 DOI: 10.1109/jbhi.2019.2948734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
There are many statistics available to the applied statistician for assessing model fit and even more methods for assessing internal and external validity. We detail a useful approach using a grid search technique that balances the internal model consistency with generalizability and can be used with models that naturally lend themselves to multiple assessment techniques. Our method relies on resampling and a simple grid search method over 3 commonly used statistics that are simple to calculate. We apply this method in a latent traits framework using a mixture Item Response Theory (MIXIRT) model of common chronic health conditions. Model fit is assessed using Akaike's Information Criteria (AIC), latent class similarity is measured with the Variance of Information (VI), and the consistency of condition complexity and prevalence across latent classes is compared using Kendall's τ rank order statistic. From two patient cohorts at high risk for hospitalization in 2014 and 2018, we generated 19 MIXIRT models (allowing 2-20 latent classes) on 21 common comorbid conditions identified via healthcare encounter diagnosis codes. We ran these models on 100 bootstrap samples of size 10% for each cohort. Among the resulting models, combined AIC and VI statistics identified 5-7 latent classes, but the rank order correlation of condition complexity revealed that only the 5 class solutions had consistent condition complexity. The 5 class solutions were combined to produce a single parsimonious MIXIRT solution that balanced clinical significance with model fit, cluster similarity, and consistency of condition complexity.
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