Healthcare Costs of Irritable Bowel Syndrome and Irritable Bowel Syndrome Subtypes in the United States.

INTRODUCTION: Contemporaneous data on healthcare costs of irritable bowel syndrome (IBS) in the United States are lacking. We aimed to estimate all-cause and IBS-specific costs in patients with and without IBS and to compare costs across IBS subtypes. METHODS: Using Optum's deidentified Clinformatics Data Mart Database, we performed a retrospective cohort analysis of patients with and without IBS using data spanning 2016-2021. Patients with IBS were identified by ICD-10 codes. Controls were randomly selected from Clinformatics Data Mart Database participants. Primary outcomes were total all-cause and IBS-specific healthcare costs. Secondary outcomes were costs of individual services associated with any claim. Costs were compared between IBS and control groups and across IBS subtypes using propensity score weighting. Comorbidities were measured with the Elixhauser Comorbidity Index. RESULTS: Comparison of 102,887 patients with IBS (77.9% female; mean ± SD age 60.3 ± 18.4 years; 75.8% white) and 102,887 controls demonstrated higher median (interquartile range) total costs per year ( P < 0.001) for patients with IBS ($13,288 [5,307-37,071]) than controls ($5,999 [1,800-19,426]). IBS was associated with increased healthcare utilization and higher median annual costs per patient for all services. Median (interquartile range) annual IBS-specific spending was $1,127 (370-5,544) per patient. Propensity score-weighted analysis across IBS subtypes revealed differences in total all-cause and IBS-specific costs and in costs of individual services. Highest spending was observed in IBS with constipation (all-cause $16,005 [6,384-43,972]; IBS-specific $2,222 [511-7,887]). DISCUSSION: Individuals with IBS exhibit higher healthcare utilization and incur substantially higher all-cause costs than those without. Care costs differ by IBS subtype.

Dentists' Information Needs and Opinions on Accessing Patient Information via Health Information Exchange: Survey Study.

BACKGROUND: The integration of medical and dental records is gaining significance over the past 2 decades. However, few studies have evaluated the opinions of practicing dentists on patient medical histories. Questions remain on dentists' information needs; their perception of the reliability of patient-reported medical history; satisfaction with the available information and the methods to gather this information; and their attitudes to other options, such as a health information exchange (HIE) network, to collect patient medical history. OBJECTIVE: This study aims to determine Indiana dentists' information needs regarding patients' medical information and their opinions about accessing it via an HIE. METHODS: We administered a web-based survey to Indiana Dental Association members to assess their current medical information-retrieval approaches, the information critical for dental care, and their willingness to access or share information via an HIE. We used descriptive statistics to summarize survey results and multivariable regression to examine the associations between survey respondents' characteristics and responses. RESULTS: Of the 161 respondents (161/2148, 7.5% response rate), 99.5% (n=160) respondents considered patients' medical histories essential to confirm no contraindications, including allergies or the need for antibiotic prophylaxis during dental care and other adverse drug events. The critical information required were medical conditions or diagnosis, current medications, and allergies, which were gathered from patient reports. Furthermore, 88.2% (n=142) of respondents considered patient-reported histories reliable; however, they experienced challenges obtaining information from patients and physicians. Additionally, 70.2% (n=113) of respondents, especially those who currently access an HIE or electronic health record, were willing to use an HIE to access or share their patient's information, and 91.3% (n=147) shared varying interests in such a service. However, usability, data accuracy, data safety, and cost are the driving factors in adopting an HIE. CONCLUSIONS: Patients' medical histories are essential for dentists to optimize dental care, especially for those with chronic conditions. In addition, most dentists are interested in using an HIE to access patient medical histories. The findings from this study can provide an alternative option for improving communications between dental and medical professionals and help the health information technology system or tool developers identify critical requirements for more user-friendly designs.

Medical and economic burden of delirium on hospitalization outcomes of acute respiratory failure: A retrospective national cohort.

Although delirium in patients with acute respiratory failure (ARF) may evolve in any hospital setting, previous studies on the impact of delirium on ARF were restricted to those in the intensive care unit (ICU). The data about the impact of delirium on ARF hospitalizations outside of the ICU is limited. Therefore, we conducted the first national study to examine the effect-magnitude of delirium on ARF in all hospital settings, that is, in the ICU as well as on the general medical floor. We searched the 2016 and 2017 National Inpatient Sample databases for ARF hospitalizations and created "Delirium" and "No delirium" groups. The outcomes of interest were mortality, endotracheal intubation, length of stay (LOS), and hospitalization costs. We also aimed to explore any potential demographic, racial, or healthcare disparities that may be associated with the diagnosis of delirium among ARF patients. Multivariable logistic regression was used to control for demographics and comorbidities. Delirium was present in 12.7% of the sample. Racial disparities among African Americans were also significant. Delirious patients had more comorbidities, higher mortality, and intubation rates (17.5% and 9.2% vs 10.6% and 6.1% in the "No delirium" group [P < .001], respectively). Delirious patients had a longer LOS and higher hospitalization costs (5.9 days and $15,395 USD vs 3.7 days and $9393 USD in "No delirium" [P < .001], respectively). Delirium was associated with worse mortality (adjusted odds ratio 1.49, confidence interval [CI] = 1.41, 1.57), higher intubation rates (adjusted odds ratio 1.46, CI = 1.36, 1.56), prolonged LOS (adjusted mean ratio 1.40, CI = 1.37, 1.42), and increased hospitalization costs (adjusted mean ratio 1.49, CI = 1.46, 1.52). A racial disparity in the diagnosis of delirium among African Americans hospitalized with ARF was noted in our sample. Patients in small, non-teaching hospitals were diagnosed with delirium less frequently compared to large, urban, teaching centers. Delirium predicts worse mortality and morbidity for ARF patients, regardless of bed placement and severity of the respiratory failure.

The Data-Adaptive Fellegi-Sunter Model for Probabilistic Record Linkage: Algorithm Development and Validation for Incorporating Missing Data and Field Selection.

BACKGROUND: Quality patient care requires comprehensive health care data from a broad set of sources. However, missing data in medical records and matching field selection are 2 real-world challenges in patient-record linkage. OBJECTIVE: In this study, we aimed to evaluate the extent to which incorporating the missing at random (MAR)-assumption in the Fellegi-Sunter model and using data-driven selected fields improve patient-matching accuracy using real-world use cases. METHODS: We adapted the Fellegi-Sunter model to accommodate missing data using the MAR assumption and compared the adaptation to the common strategy of treating missing values as disagreement with matching fields specified by experts or selected by data-driven methods. We used 4 use cases, each containing a random sample of record pairs with match statuses ascertained by manual reviews. Use cases included health information exchange (HIE) record deduplication, linkage of public health registry records to HIE, linkage of Social Security Death Master File records to HIE, and deduplication of newborn screening records, which represent real-world clinical and public health scenarios. Matching performance was evaluated using the sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. RESULTS: Incorporating the MAR assumption in the Fellegi-Sunter model maintained or improved F1-scores, regardless of whether matching fields were expert-specified or selected by data-driven methods. Combining the MAR assumption and data-driven fields optimized the F1-scores in the 4 use cases. CONCLUSIONS: MAR is a reasonable assumption in real-world record linkage applications: it maintains or improves F1-scores regardless of whether matching fields are expert-specified or data-driven. Data-driven selection of fields coupled with MAR achieves the best overall performance, which can be especially useful in privacy-preserving record linkage.

Structure with thin SiOx/SiNx bilayer and Al electrodes for high-frequency, large-coupling, and low-cost surface acoustic wave devices.

With the development of fifth-generation wireless systems, the Internet of Things, and health services, surface acoustic wave (SAW)-based filters and sensors have attracted considerable interest. This study presents a new structure for high-frequency, large-coupling, and low-cost SAW devices that helps implement high-frequency and wideband filters and enhances the sensitivity of sensors. The structure is based on 15°Y-X LiNbO3, thin SiOx/SiNx bilayer overlay, and Al electrodes. Furthermore, a low-cost fabrication process for SAW devices based on this structure was designed. Simulation and experimental results show that the bilayer substantially weakens the leaky nature of shear-horizontal-type SAWs with a phase velocity higher than that of a slow-shear bulk wave in LiNbO3. Thus, the limitation related to the velocity of 4029 m/s was overcome, and the phase velocity reached approximately 4500 m/s, which means an increase of 50% compared with that of conventional Cu/15°Y-X LiNbO3 devices. Consequently, the frequency dramatically increases, and the quality of the SAW response is ensured. Simultaneously, a large electromechanical coupling factor close to 20% can be achieved, which is still suitable for wideband filters and sensors with high energy transduction coefficients. This new structure is expected to become a major candidate for SAW devices in the future.

Replication of an emergency department-based recovery coaching intervention and pilot testing of pragmatic trial protocols within the context of Indiana's Opioid State Targeted Response plan.

Solving the opioid crisis requires immediate, innovative, and sustainable solutions. A number of promising strategies are being carried out by U.S. states and territories as part of their Opioid State Targeted Response (STR) plans funded through the 21st Century Cures Act, and they provide an opportunity for researchers to assess effectiveness of these interventions using pragmatic approaches. This paper describes a pilot study of Project Planned Outreach, Intervention, Naloxone, and Treatment (POINT), the intervention that served as the basis for Indiana's STR-funded, emergency department (ED)-based peer specialist expansion that was conducted in preparation for a larger, multisite pragmatic trial. Through the pilot, we identified, documented, and corrected for challenges encountered while implementing planned study protocols. Per the project's funding mechanism, the ability to move to the larger trial was determined by the achievement of 3 milestones: (1) successful replication of the intervention; (2) demonstrated ability to obtain the necessary sample size; and (3) observe a higher level of engagement in medication for addiction treatment in the POINT group compared to standard care. Overall implementation of the study protocols was successful, with only minor refinements to proposed procedures being required in light of challenges with (1) data access, (2) recruitment, and (3) identification of the expansion hospitals. All three milestones were reached. Challenges in implementing protocols and reaching milestones resulted in refinements that improved the study design overall. The subsequent trial will add to the limited but growing evidence on ED-based peer supports. Capitalizing on STR efforts to study an already scaling and promising intervention is likely to lead to faster and more sustainable results with greater generalizability than traditional, efficacy-focused clinical research.

MIND food and speed of processing training in older adults with low education, the MINDSpeed Alzheimer's disease prevention pilot trial.

BACKGROUND: Multiple national organizations and leaders have called for increased attention to dementia prevention in those most vulnerable, for example persons with limited formal education. Prevention recommendations have included calls for multicomponent interventions that have the potential to improve both underlying neurobiological health and the ability to function despite neurobiological pathology, or what has been termed cognitive reserve. OBJECTIVES: Test feasibility, treatment modifier, mechanism, and cognitive function effects of a multicomponent intervention consisting of foods high in polyphenols (i.e., MIND foods) to target neurobiological health, and speed of processing training to enhance cognitive reserve. We refer to this multicomponent intervention as MINDSpeed. DESIGN: MINDSpeed is being evaluated in a 2 × 2 randomized factorial design with 180 participants residing independently in a large Midwestern city. Qualifying participants are 60 years of age or older with no evidence of dementia, and who have completed 12 years or less of education. All participants receive a study-issued iPad to access the custom study application that enables participants, depending on randomization, to select either control or MIND food, and to play online cognitive games, either speed of processing or control games. METHODS: All participants complete informed consent and baseline assessment, including urine and blood samples. Additionally, up to 90 participants will complete neuroimaging. Assessments are repeated immediately following 12 weeks of active intervention, and at 24 weeks post-randomization. The primary outcome is an executive cognitive composite score. Secondary outcomes include oxidative stress, pro-inflammatory cytokines, and neuroimaging-captured structural and functional metrics of the hippocampus and cortical brain regions. SUMMARY: MINDSpeed is the first study to evaluate the multicomponent intervention of high polyphenol intake and speed of processing training. It is also one of the first dementia prevention trials to target older adults with low education. The results of the study will guide future dementia prevention efforts and trials in high risk populations.

Evaluating the effect of data standardization and validation on patient matching accuracy.

OBJECTIVE: This study evaluated the degree to which recommendations for demographic data standardization improve patient matching accuracy using real-world datasets. MATERIALS AND METHODS: We used 4 manually reviewed datasets, containing a random selection of matches and nonmatches. Matching datasets included health information exchange (HIE) records, public health registry records, Social Security Death Master File records, and newborn screening records. Standardized fields including last name, telephone number, social security number, date of birth, and address. Matching performance was evaluated using 4 metrics: sensitivity, specificity, positive predictive value, and accuracy. RESULTS: Standardizing address was independently associated with improved matching sensitivities for both the public health and HIE datasets of approximately 0.6% and 4.5%. Overall accuracy was unchanged for both datasets due to reduced match specificity. We observed no similar impact for address standardization in the death master file dataset. Standardizing last name yielded improved matching sensitivity of 0.6% for the HIE dataset, while overall accuracy remained the same due to a decrease in match specificity. We noted no similar impact for other datasets. Standardizing other individual fields (telephone, date of birth, or social security number) showed no matching improvements. As standardizing address and last name improved matching sensitivity, we examined the combined effect of address and last name standardization, which showed that standardization improved sensitivity from 81.3% to 91.6% for the HIE dataset. CONCLUSIONS: Data standardization can improve match rates, thus ensuring that patients and clinicians have better data on which to make decisions to enhance care quality and safety.

Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data.

OBJECTIVES: Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and "off the shelf" tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. MATERIALS AND METHODS: We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. RESULTS: Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. CONCLUSION: Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing "off the shelf" approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches.

Unmet Need for Help With Activities of Daily Living Disabilities and Emergency Department Admissions Among Older Medicare Recipients.

Purpose of the Study: This study determined whether self-reports of unmet need for help with activities of daily living (ADL) disabilities are prognostic of emergency department (ED) utilization. Design and Methods: This prospective cohort study of 2,194 community-living, ADL-disabled subjects combined 2004 National Long-Term Care Survey responses with linked Medicare data through 2005. A negative binomial count model was computed to assess the association between unmet ADL need and number of subsequent ED admissions while statistically adjusting for predisposing, enabling, and need characteristics associated with ED admissions among older adults. Results: The adjusted annual incidence rate (IR) for ED admissions was 19% higher for unmet versus met need (IR = 1.19; 95% confidence interval [CI] = 1.00-1.40; p = .047). The IR for ED admissions for falls and injuries was higher for those with unmet ADL versus met ADL need (IR = 1.43; 95% CI = 1.10-1.86), and trended toward significance for ED admissions for skin breakdown (IR = 2.02; 95% CI = 0.97-2.88), but was not significant for ED admissions for dehydration (IR = 1.13; 95% CI= 0.79-1.63). Implications: Unmet ADL need is prognostic of ED admissions, especially for falls and injuries. Future research is needed to determine whether resolution of unmet ADL need reduces ED utilization.

Identifying Contextual and Emotional Factors to Explore Weight Disparities between Obese Black and White Women.

BACKGROUND: Obese black women enrolled in weight loss interventions experience 50% less weight reduction than obese white women. This suggests that current weight loss strategies may increase health disparities. OBJECTIVE: We evaluated the feasibility of identifying daily contextual factors that may influence obesity. METHODS: In-home interviews with 16 obese (body mass index ≥ 30) black and white urban poor women were performed. For 14 days, ecological momentary assessment (EMA) was used to capture emotion and social interactions every other day, and day reconstruction method surveys were used the following day to reconstruct the context of the prior day's EMA. RESULTS: Factors included percentage of participants without weight scales (43.8%) or fitness equipment (68.8%) in the home and exposed to food at work (55.6%). The most frequently reported location, activity, and emotion were home (19.4 ± 8.53), working (7.1 ± 8.80), and happy (6.9 ± 10.03), respectively. CONCLUSION: Identifying individual contexts may lead to valuable insights about obesogenic behaviors and new interventions to improve weight management.

The modeling of medical expenditure data from a longitudinal survey using the generalized method of moments (GMM) approach.

Medical expenditure data analysis has recently become an important problem in biostatistics. These data typically have a number of features making their analysis rather difficult. Commonly, they are heavily right-skewed, contain a large percentage of zeros, and often exhibit large numbers of missing observations because of death and/or the lack of follow-up. They are also commonly obtained from records that are linked to large longitudinal data surveys. In this manuscript, we suggest a novel approach to modeling these data through the use of generalized method of moments estimation procedure combined with appropriate weights that account for both dropout due to death and the probability of being sampled from among the National Long Term Care Survey (NLTCS) subjects. This approach seems particularly appropriate because of the large number of subjects relative to the length of observation period (in months). We also use a simulation study to compare our proposed approach with and without the use of weights. The proposed model is applied to medical expenditure data obtained from the 2004-2005 NLTCS-linked Medicare database. The results suggest that the amount of medical expenditures incurred is strongly associated with higher number of activities of daily living (ADL) disabilities and self-reports of unmet need for help with ADL disabilities. Copyright © 2016 John Wiley & Sons, Ltd.

Does targeted cognitive training reduce educational disparities in cognitive function among cognitively normal older adults?

OBJECTIVE: The aim of this study was to investigate educational differences in treatment responses to memory, reasoning, and speed of processing cognitive training relative to no-contact control. METHODS: Secondary analyses of the Advanced Cognitive Training for Independent and Vital Elderly trial were conducted. Two thousand eight hundred older adults were randomized to memory, reasoning, or speed of processing training or no-contact control. A repeated-measures mixed-effects model was used to investigate immediate post-training and 1-year outcomes with sensitivity analyses out to 10 years. Outcomes were as follows: (1) memory composite of Hopkins Verbal Learning Test, Rey Auditory Verbal Learning Test, and Rivermead Behavioral Memory Test; (2) reasoning composite of letter series, letter sets, and word series; and (3) speed of processing measured using three trials of useful field of view and the digit symbol substitution test. RESULTS: The effects of reasoning and memory training did not differ by educational attainment. The effect of speed of processing training did. Those with fewer than 12 years of education experienced a 50% greater effect on the useful field of view test compared with those with 16 or more years of education. The training advantage for those with fewer than 12 years of education was maintained to 3 years post-training. CONCLUSION: Older adults with less than a secondary education are at elevated risk of dementia, including Alzheimer's disease. The analyses here indicate that speed of processing training is effective in older adults with low educational attainment.

Lower provider volume is associated with higher failure rates for endoscopic retrograde cholangiopancreatography.

BACKGROUND: Among physicians who perform endoscopic retrograde cholangiopancreatography (ERCP), the relationship between procedure volume and outcome is unknown. OBJECTIVE: Quantify the ERCP volume-outcome relationship by measuring provider-specific failure rates, hospitalization rates, and other quality measures. RESEARCH DESIGN: Retrospective cohort. SUBJECTS: A total of 16,968 ERCPs performed by 130 physicians between 2001 and 2011, identified in the Indiana Network for Patient Care. MEASURES: Physicians were classified by their average annual Indiana Network for Patient Care volume and stratified into low (<25/y) and high (≥25/y). Outcomes included failed procedures, defined as repeat ERCP, percutaneous transhepatic cholangiography or surgical exploration of the bile duct≤7 days after the index procedure, hospitalization rates, and 30-day mortality. RESULTS: Among 15,514 index ERCPs, there were 1163 (7.5%) failures; the failure rate was higher among low (9.5%) compared with high volume (5.7%) providers (P<0.001). A second ERCP within 7 days (a subgroup of failure rate) occurred more frequently when the original ERCP was performed by a low-volume (4.1%) versus a high-volume physician (2.3%, P=0.013). Patients were more frequently hospitalized within 24 hours when the ERCP was performed by a low-volume (28.3%) versus high-volume physician (14.8%, P=0.002). Mortality within 30 days was similar (low=1.9%, high=1.9%). Among low-volume physicians and after adjusting, the odds of having a failed procedure decreased 3.3% (95% confidence interval, 1.6%-5.0%, P<0.001) with each additional ERCP performed per year. CONCLUSIONS: Lower provider volume is associated with higher failure rate for ERCP, and greater need for postprocedure hospitalization.

Hospital readmission among older adults who return home with unmet need for ADL disability.

PURPOSE: This study determined whether returning to the community from a recent hospitalization with unmet activities of daily living (ADL) need was associated with probability of readmission. METHODS: A total of 584 respondents to the 1994, 1999, and/or 2004 National Long-Term Care Surveys (NLTCS) who were hospitalized within 90 days prior to the interview and reported ADL disability at the time of the interview were considered for analysis. Medicare claims linked to the NLTCS provided information about hospital episodes, so those enrolled in Health Maintenance Organizations or Veterans Affairs Medical Centers were not included (n = 62), resulting in a total sample size of 522. ADL disability was defined as needing human help or equipment to complete the task. Unmet ADL need was defined as receiving inadequate or no help for one or more ADL disabilities. Disability that began within 90 days of the interview was considered new disability. RESULTS: After adjusting for demographic, health, and functioning characteristics, unmet ADL need was associated with increased risk for hospital readmission (HR: 1.37, 95% CI: 1.03-1.82). Risk of readmission was greater for those with unmet need for new disabilities than those with unmet need for disabilities that were present before the index hospitalization (HR: 1.66, 95% CI: 1.01-2.73). IMPLICATIONS: Many older patients are discharged from the hospital with ADL disability. Those who report unmet need for new ADL disabilities after they return home from the hospital are particularly vulnerable to readmission. Patients' functional needs after discharge should be carefully evaluated and addressed.

Joint modeling of medical cost and survival in complex sample surveys.

Medical cost data are typically highly skewed to the right with a large proportion of zero costs. It is also common for these data to be censored because of incomplete follow-up and death. In the case of censoring due to death, it is important to consider the potential dependence between cost and survival. This association can occur because patients who incur a greater amount of medical cost tend to be frailer and hence are more likely to die. To handle this informative censoring issue, joint modeling of cost and survival with shared random effects has been proposed. In this paper, we extend this joint modeling approach to handle a final feature of many medical cost data sets, i.e., Specifically, the fact that data were obtained via a complex survey design. Specifically, we extend the joint model by incorporating the sample weights when estimating the parameters and using the Taylor series linearization approach when calculating the standard errors. We use a simulation study to compare the joint modeling approach with and without these adjustments. The simulation study shows that parameter estimates can be seriously biased when information about the complex survey design is ignored. It also shows that standard errors based on the Taylor series linearization approach provide satisfactory confidence interval coverage. The proposed joint model is applied to monthly hospital costs obtained from the 2004 National Long Term Care Survey.

Evaluating accuracy of diagnostic tests with intermediate results in the absence of a gold standard.

Intermediate test results often occur with diagnostic tests. When assessing diagnostic accuracy, it is important to properly report and account for these results. In the literature, these results are commonly discarded prior to analysis or treated as either a positive or a negative result. Although such adjustments allow sensitivity and specificity to be computed in the standard way, these forced decisions limit the interpretability and usefulness of the results. Estimation of diagnostic accuracy is further complicated when tests are evaluated without a gold standard. Although traditional latent class modeling can be readily applied to analyze these data and account for intermediate results, these models assume that tests are independent conditional on the true disease status, which is rarely valid in practice. We extend both the log-linear latent class model and the probit latent class model to accommodate the conditional dependence among tests while taking the intermediate results into consideration. We illustrate our methods using a simulation study and a published medical study on the detection of epileptiform activity in the brain.

Volume of home- and community-based services and time to nursing-home placement.

OBJECTIVE: The purpose of this study was to determine whether the volume of Home- and Community-Based Services (HCBS) that target Activities of Daily Living disabilities, such as attendant care, homemaking services, and home-delivered meals, increases recipients' risk of transitioning from long-term care provided through HCBS to long-term care provided in a nursing home. DATA SOURCES: Data are from the Indiana Medicaid enrollment, claims, and Insite databases. Insite is the software system that was developed for collecting and reporting data for In-Home Service Programs. STUDY DESIGN: Enrollees in Indiana Medicaid's Aged and Disabled Waiver program were followed forward from time of enrollment to assess the association between the volume of attendant care, homemaking services, home-delivered meals, and related covariates, and the risk for nursing-home placement. An extension of the Cox proportional hazard model was computed to determine the cumulative hazard of nursing-home placement in the presence of death as a competing risk. PRINCIPAL FINDINGS: Of the 1354 Medicaid HCBS recipients followed in this study, 17% did not receive any attendant care, homemaking services, or home-delivered meals. Among recipients who survived through 24 months after enrollment, one in five transitioned from HCBS to a nursing-home. Risk for nursing-home placement was significantly lower for each five-hour increment in personal care (HR=0.95, 95% CI=0.92-0.98) and homemaking services (HR=0.87, 95% CI=0.77-0.99). CONCLUSIONS: Future policies and practices that are focused on optimizing long-term care outcomes should consider that a greater volume of HCBS for an individual is associated with reduced risk of nursing-home placement.

Volume of home- and community-based Medicaid waiver services and risk of hospital admissions.

OBJECTIVES: To evaluate whether type and volume of Medicaid Home- and Community-Based Services (HCBS) waiver program are associated with risk of hospitalization and whether this association changes over time. DESIGN: Prospective. SETTING: Indiana Medicaid claims data from June 2001 to December 2004. PARTICIPANTS: Medicaid recipients (N=1,354) who enrolled in the Aged and Disabled waiver program between January 2002 and June 2004. MEASUREMENTS: Time to hospital admission since enrollment in the HCBS waiver program, adjusted for demographics, comorbidities, prior use of health services, and volume of HCBS received, including attendant care, homemaking, and home-delivered meals. RESULTS: A greater volume of attendant care, homemaking services, and home-delivered meals was associated with a lower risk of hospitalization. This effect diminished over time for attendant care and homemaking. The risk of hospitalization for subjects receiving 5 hours of attendant care per month was 54% (hazard ratio (HR)=0.46, 95% confidence interval (CI)=0.38-0.57) lower during the first month of enrollment and 20% lower by Month 10 (HR=0.80, 95% CI=0.73-0.88) than for those receiving no attendant care. CONCLUSION: Greater volume of HCBS services was associated with lower risk of hospitalization. The findings highlight the potential importance of assessing and monitoring the volume of HCBS patients receive.

A probit latent class model with general correlation structures for evaluating accuracy of diagnostic tests.

Traditional latent class modeling has been widely applied to assess the accuracy of dichotomous diagnostic tests. These models, however, assume that the tests are independent conditional on the true disease status, which is rarely valid in practice. Alternative models using probit analysis have been proposed to incorporate dependence among tests, but these models consider restricted correlation structures. In this article, we propose a probit latent class model that allows a general correlation structure. When combined with some helpful diagnostics, this model provides a more flexible framework from which to evaluate the correlation structure and model fit. Our model encompasses several other PLC models but uses a parameter-expanded Monte Carlo EM algorithm to obtain the maximum-likelihood estimates. The parameter-expanded EM algorithm was designed to accelerate the convergence rate of the EM algorithm by expanding the complete-data model to include a larger set of parameters and it ensures a simple solution in fitting the PLC model. We demonstrate our estimation and model selection methods using a simulation study and two published medical studies.