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Objective: This study examined adherence to clinical practice guidelines (CPGs) and U.S. Food and Drug Administration (FDA) medication labels when prescribing psychotropic medications to Medicaid-insured children with attention-deficit hyperactivity disorder (ADHD). Methods: A retrospective analysis was conducted with 2016–2018 Medicaid claims data across 46 states for children with an ADHD diagnosis and at least one psychotropic medication prescription (N=1,236,068 children). Medications recommended for pediatric use were identified with CPGs from the American Academy of Child and Adolescent Psychiatry and the Florida Medicaid Drug Therapy Management Program for Behavioral Health. Results: Across 46 states, 86.7% of children received guideline-recommended and FDA-approved medications for the treatment of ADHD and comorbid mental disorders. Antidepressants, antihistamines, and second-generation antipsychotics that were not included in CPGs were prescribed for 23.4% of children. Conclusions: High adherence to CPGs and FDA medication labels for medications prescribed for ADHD was observed, suggesting the effectiveness of guidelines in driving evidence-based care. [ABSTRACT FROM AUTHOR]

Nash equilibrium is arguably the most fundamental concept in game theory, which is used to analyze and predict the behavior of the players. In many games, there exist multiple equilibria, with different expected payoffs for the players, which in turn raises the question of equilibrium selection. In this paper, we study the NP -hard problem of identifying a socially optimal Nash equilibrium in two-player normal-form games (called bimatrix games), which may be represented by a mixed integer linear program (MILP). We characterize the properties of the equilibria and develop several classes of valid inequalities accordingly. We use these theoretical results to provide a decomposition-based reformulation of the MILP, which we solve by a branch-and-cut algorithm. Our extensive computational experiments demonstrate superiority of our approach over solving the MILP formulation through feeding it into a commercial solver or through the "traditional" Benders' decomposition. Of note, our proposed approach can find provably optimal solutions for many instances. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms. Funding: This work was supported by the National Institute of Dental and Craniofacial Research [Grant R01DE028283]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding agreements ensured the authors' independence in designing the study, interpreting the data, writing, and publishing the report. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0072) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0072). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Objective: This study evaluates the dentists' availability to deliver preventive dental care to children in schools and the impact of school‐based programs on access. Methods: The study population included Florida elementary‐school children, differentiated by dental insurance (Medicaid, CHIP, private, or none). We considered the implementation of school‐based programs using optimization modeling to (re)allocate the dentists' caseload to schools to meet demand for preventive care under resource constraints. We considered multiple settings for school‐based program implementation: (i) school prioritization; and (ii) dentists' participation in public insurance. Statistical inference was used to identify communities to improve access and reduce disparities. Results: School‐based programs reduced unmet demand (3%–12%), being more efficient if prioritizing schools in communities targeted to improve access. The access improvement varied by insurance status and geography. Uninsured urban children benefited most from school‐based programs, with 15%–75% unmet need reduction. The percentage of urban communities targeted to improve access decreased by 12% against no‐school program. Such percentage remained large for suburban (15%–100%) and rural (50%–100%) communities. Disparity in access for public‐insured vs. private‐insured children persisted under school‐based programs (32%–84% identified communities). Conclusion: School‐based programs improve dental care access; the improvement was however different by insurance status, with uninsured children benefiting the most. Accounting to the dentists' availability in prioritizing schools resulted in effective resource allocation to school‐based programs. Access disparities between public and private‐insured children did not improve; school‐based programs shifted resources from public‐insured to uninsured. School‐based programs are effective in addressing access barriers to those children experiencing them most. [ABSTRACT FROM AUTHOR]

Background: Thousands of school systems have struggled with the decisions about how to deliver education safely and effectively amid the COVID19 pandemic. This study evaluates the public health impact of various school reopening scenarios (when, and how to return to in-person instruction) on the spread of COVID19.Methods: An agent-based simulation model was adapted and used to project the impact of various school reopening strategies on the number of infections, hospitalizations, and deaths in the state of Georgia during the study period, i.e., February 18th-November 24th, 2020. The tested strategies include (i) schools closed, i.e., all students receive online instruction, (ii) alternating school day, i.e., half of the students receive in-person instruction on Mondays and Wednesdays and the other half on Tuesdays and Thursdays, (iii) alternating school day for children, i.e., half of the children (ages 0-9) receive in-person instruction on Mondays and Wednesdays and the other half on Tuesdays and Thursdays, (iv) children only, i.e., only children receive in-person instruction, (v) regular, i.e., all students return to in-person instruction. We also tested the impact of universal masking in schools.Results: Across all scenarios, the number of COVID19-related deaths ranged from approximately 8.8 to 9.9 thousand, the number of cumulative infections ranged from 1.76 to 1.96 million for adults and 625 to 771 thousand for children and youth, and the number of COVID19-related hospitalizations ranged from approximately 71 to 80 thousand during the study period. Compared to schools reopening August 10 with a regular reopening strategy, the percentage of the population infected reduced by 13%, 11%, 9%, and 6% in the schools closed, alternating school day for children, children only, and alternating school day reopening strategies, respectively. Universal masking in schools for all students further reduced outcome measures.Conclusions: Reopening schools following a regular reopening strategy would lead to higher deaths, hospitalizations, and infections. Hybrid in-person and online reopening strategies, especially if offered as an option to families and teachers who prefer to opt-in, provide a good balance in reducing the infection spread compared to the regular reopening strategy, while ensuring access to in-person education. [ABSTRACT FROM AUTHOR]

The use of Automated Teller Machines (ATMs) has become increasingly popular throughout the world due to the widespread adoption of electronic financial transactions and better access to financial services in many countries. As the network of ATMs is becoming denser while the users are accessing them at a greater rate, the current financial institutions are faced with addressing inventory and replenishment optimal policies when managing a large number of ATMs. An excessive ATM replenishment will result in a large holding cost whereas an inadequate cash inventory will increase the frequency of the replenishments and the probability of stock-outs along with customer dissatisfaction. To facilitate informed decisions in ATM cash management, in this paper, we introduce an approach for optimal replenishment amounts to minimize the total costs of money holding and customer dissatisfaction by taking the replenishment costs into account including stock-outs. An important aspect of the replenishment strategy is that the future cash demands are not available at the time of planning. To account for uncertainties in unobserved future cash demands, we use prediction intervals instead of point predictions and solve the cash replenishment-planning problem using robust optimization with linear programming. We illustrate the application of the optimal ATM replenishment policy under future demand uncertainties using data consisting of daily cash withdrawals of 98 ATMs of a bank in Istanbul. We find that the optimization approach introduced in this paper results in significant reductions in costs as compared to common practice strategies. [ABSTRACT FROM AUTHOR]

Objective: To assess the effect of adolescent birth on the health and wellness of these infants within their first year of life. Methods: Our study focused on 2011 Medicaid births nationwide. The study group (infants born to adolescents, aged 10 to 19 at time of birth) was matched with infants born to adults (aged 20 to 44 at time of birth), based on demographics. Statistical tests (proportion test and Poisson test) were used to compare the outcomes of these two groups to determine if differences were significant. Results: The outcomes assessed were: low birth weight (LBW), substance exposure, foster care, health status, infant mortality, emergency department (ED) visits, and wellness visits. Of the 68,562 infant pairs included in the study, we found statistically significant higher rates of LBW (P ≤ 0·005), infant mortality (P = 0·05), and ED visits (P ≤ 0·005) for infants born to adolescents at the 95% confidence interval. The rate of wellness visits for all infants was well below the recommended amount. Additional differences were found at the race/ethnicity and urbanicity levels. Conclusion for Practice: Infants born to adolescents had a higher rate of ED visits within the first year of life, however, the increased rates of LBW and mortality for the Medicaid population are not as significant as previous national studies suggest. Analysis of outcomes across stratification helped identify vulnerable populations (i.e. urban infants). Public health programs are urged to examine ED visits in infants born to adolescents among the Medicaid population. Improved health education or phone-based resources could help reduce unnecessary visits and reduce cost. [ABSTRACT FROM AUTHOR]

Background: Recent research has been conducted by various countries and regions on the impact of non-pharmaceutical interventions (NPIs) on reducing the spread of COVID19. This study evaluates the tradeoffs between potential benefits (e.g., reduction in infection spread and deaths) of NPIs for COVID19 and being homebound (i.e., refraining from interactions outside of the household).Methods: An agent-based simulation model, which captures the natural history of the disease at the individual level, and the infection spread via a contact network assuming heterogeneous population mixing in households, peer groups (workplaces, schools), and communities, is adapted to project the disease spread and estimate the number of homebound people and person-days under multiple scenarios, including combinations of shelter-in-place, voluntary quarantine, and school closure in Georgia from March 1 to September 1, 2020.Results: Compared to no intervention, under voluntary quarantine, voluntary quarantine with school closure, and shelter-in-place with school closure scenarios 4.5, 23.1, and 200+ homebound adult-days were required to prevent one infection, with the maximum number of adults homebound on a given day in the range of 119 K-248 K, 465 K-499 K, 5388 K-5389 K, respectively. Compared to no intervention, school closure only reduced the percentage of the population infected by less than 16% while more than doubling the peak number of adults homebound.Conclusions: Voluntary quarantine combined with school closure significantly reduced the number of infections and deaths with a considerably smaller number of homebound person-days compared to shelter-in-place. [ABSTRACT FROM AUTHOR]

Objective: To introduce a statistical inference framework for policy decision making on access to pediatric dental care. Data Sources: Secondary data were collected for the state of Colorado for year 2019. Study Design: The access model was an optimization model, matching the demand (patients) and supply (providers) of dental care. Sampling distributions of model inputs were specified using hierarchical Bayesian models, with hyperparameters informed by prior information derived from multiple data sources. Simultaneous inference was applied to identify areas for access improvement. The model was applied to make inference on the pediatric dental care in Colorado, accounting for financial access, differentiated into public (Medicaid and CHIP), private (commercial and out-of-pocket), and without financial access. Data Collection/Extraction Methods: Multiple data sources informed the access measurement approach including: 2017 American Community Survey, 2019 Colorado Dental Board, and 2019 National Provider Plan and Enumeration System, 2019 InsureKidsNow.gov among others. Principal Findings: The median access measure (travel distance) was greater than the Colorado access standards in 16.9% and 65.1% of census tracts for children with private financial access and publicly insured, respectively. Accounting for uncertainty (confidence level 99%), these percentages decreased to 14.6% and 25.6%, respectively, with mostly suburban and rural tracts failing to meet the standards. The median disparity for Medicaid and CHIP versus private financial access was greater than 5 miles in 84.5% and 81.6% of census tracts, respectively. Accounting for uncertainty (confidence level 99%), these percentages declined to 19.5% and 10.5%, respectively, with significant disparities around the metropolitan areas. Conclusions: While many communities failed to meet access standards, when accounting for uncertainty, most urban ones did not fail. Disparities in spatial access between publicly and privately insured were most acute in urban communities. Medicaid insured experienced higher disparities than CHIP insured; those differences were not identified when not accounting for uncertainty. [ABSTRACT FROM AUTHOR]

As the spread of COVID19 in the US continues to grow, local and state officials face difficult decisions about when and how to transition to a "new normal." The goal of this study is to project the number of COVID19 infections and resulting severe outcomes, and the need for hospital capacity under social distancing, particularly, shelter-in-place and voluntary quarantine for the State of Georgia. We developed an agent-based simulation model to project the infection spread. The model utilizes COVID19-specific parameters and data from Georgia on population interactions and demographics. The simulation study covered a seven and a half-month period, testing different social distancing scenarios, including baselines (no-intervention or school closure only) and combinations of shelter-in-place and voluntary quarantine with different timelines and compliance levels. The following outcomes are compared at the state and community levels: the number and percentage of cumulative and daily new symptomatic and asymptomatic infections, hospitalizations, and deaths; COVID19-related demand for hospital beds, ICU beds, and ventilators. The results suggest that shelter-in-place followed by voluntary quarantine reduced peak infections from approximately 180K under no intervention and 113K under school closure, respectively, to below 53K, and delayed the peak from April to July or later. Increasing shelter-in-place duration from four to five weeks yielded 2–9% and 3–11% decrease in cumulative infection and deaths, respectively. Regardless of the shelter-in-place duration, increasing voluntary quarantine compliance decreased daily new infections from almost 53K to 25K, and decreased cumulative infections by about 50%. The cumulative number of deaths ranged from 6,660 to 19,430 under different scenarios. Peak infection date varied across scenarios and counties; on average, increasing shelter-in-place duration delayed the peak day by 6 days. Overall, shelter-in-place followed by voluntary quarantine substantially reduced COVID19 infections, healthcare resource needs, and severe outcomes. [ABSTRACT FROM AUTHOR]

Objective: We estimated the caseload of providers, practices, and clinics for psychosocial services (including psychotherapy) to Medicaid-insured children to improve the understanding of the current supply of such services and to inform opportunities to increase their accessibility. Methods: We used 2012-2013 Medicaid claims data and data from the 2013 National Plan and Provider Enumeration System to identify and locate therapists, psychiatrists, and mental health centers along with primary, rehabilitative, and developmental care providers in the United States who provided psychosocial services to Medicaid-insured children. We estimated the per-provider, per-location, and state-level caseloads of providers offering these services to Medicaid-insured children in 34 states with sufficiently complete data to perform this analysis, by using the most recent year of Medicaid claims data available for each state. We measured caseload by calculating the number of psychosocial visits delivered by each provider in the selected year. We compared caseloads across states, urbanicity, provider specialty (eg, psychiatry, psychology, primary care), and practice setting (eg, mental health center, single practitioner). Results: We identified 63 314 providers, practices, or centers in the Medicaid claims data that provided psychosocial services to Medicaid-insured children in either 2012 or 2013. The median provider-level per-year caseload was <25 children and <250 visits across all provider types. Providers with a mental health center–related taxonomy accounted for >40% of visits for >30% of patients. Fewer than 10% of providers and locations accounted for >50% of patients and visits. Conclusions: Psychosocial services are concentrated in a few locations, thereby reducing geographic accessibility of providers. Providers should be incentivized to offer care in more locations and to accept more Medicaid-insured patients. [ABSTRACT FROM AUTHOR]

Background. Intensive multidisciplinary intervention (IMI) represents a well-established treatment for pediatric feeding disorders (PFDs), but program availability represents an access care barrier. We develop an economic analysis of IMI for weaning from gastronomy tube (G-tube) treatment for children diagnosed with PFDs from the Medicaid programs' perspective, where Medicaid programs refer to both fee-for-service and managed care programs. Methods. The 2010–2012 Medicaid Analytic eXtract claims provided health care data for children aged 13 to 72 months. An IMI program provided data on average admission costs. We employed a finite-horizon Markov model to simulate PFD treatment progression assuming 2 treatment arms: G-tube only v. IMI targeting G-tube weaning. We compared the expenditure differential between the 2 arms under varying time horizons and treatment effectiveness. Results. Overall Medicaid expenditure per member per month was $6814, $2846, and $1550 for the study population of children with PFDs and G-tube treatment, the control population with PFDs without G-tube treatment, and the no-PFD control population, respectively. The PFD-diagnosed children with G-tube treatment only had the highest overall expenditures across all health care settings except psychological services. The expenditure at the end of the 8-year time horizon was $405,525 and $208,218 per child for the G-tube treatment only and IMI arms, respectively. Median Medicaid expenditure was between 1.7 and 2.2 times higher for the G-tube treatment arm than for the IMI treatment arm. Limitations. Data quality issues could cause overestimates or underestimates of Medicaid expenditure. Conclusions. This study demonstrated the economic benefits of IMI to treat complex PFDs from the perspective of Medicaid programs, indicating this model of care not only holds benefit in terms of improving overall quality of life but also brings significant expenditure savings in the short and long term. [ABSTRACT FROM AUTHOR]

Objective: To quantify the economic impact of using silver diamine fluoride (SDF) to arrest the progression of dental caries in Medicaid-enrolled children (aged 1-5 years) relative to the standard restorative treatment from the Medicaid programs' perspective.Methods: We used Monte Carlo simulation to estimate averted restorative visits and associated expenditures for varying SDF effectiveness and intervention penetration levels. We compared the current standard of care for treating caries to applying SDF. We estimated expenditures from the 2010-2012 Medicaid Analytic Extract files for seven US states and the incremental cost effectiveness ratio for SDF application on averted restorative visits.Results: Across the seven states, averted restorative visits ranged from 2,049 (Vermont) to 60,542 (North Carolina), assuming an SDF penetration level of 50%. Averted per-restorative visit costs ranged from $100 to $350 per-visit. There were higher averted per-restorative visit costs in nonmetropolitan counties than metropolitan counties.Conclusions: Providing SDF as a caries management strategy can reduce Medicaid program dental care expenditures by averting expensive caries treatment options. It could also prevent stressful restorative procedures. State Medicaid programs should consider reimbursing for SDF to arrest the progression of dental caries in young children. [ABSTRACT FROM AUTHOR]

Objective: To quantify the economic impact of using silver diamine fluoride (SDF) to arrest the progression of dental caries in Medicaid‐enrolled children (aged 1–5 years) relative to the standard restorative treatment from the Medicaid programs' perspective. Methods: We used Monte Carlo simulation to estimate averted restorative visits and associated expenditures for varying SDF effectiveness and intervention penetration levels. We compared the current standard of care for treating caries to applying SDF. We estimated expenditures from the 2010–2012 Medicaid Analytic Extract files for seven US states and the incremental cost effectiveness ratio for SDF application on averted restorative visits. Results: Across the seven states, averted restorative visits ranged from 2,049 (Vermont) to 60,542 (North Carolina), assuming an SDF penetration level of 50%. Averted per‐restorative visit costs ranged from $100 to $350 per‐visit. There were higher averted per‐restorative visit costs in nonmetropolitan counties than metropolitan counties. Conclusions: Providing SDF as a caries management strategy can reduce Medicaid program dental care expenditures by averting expensive caries treatment options. It could also prevent stressful restorative procedures. State Medicaid programs should consider reimbursing for SDF to arrest the progression of dental caries in young children. [ABSTRACT FROM AUTHOR]

Background. The authors' aims were to compare, according to strata, dentists' participation in Medicaid and Medicaid provider-level caseload measured as the number of patients or visits for preventive or restorative care for 2 comparison years. Methods. The data sources were the 2012-2013 Medicaid Analytic eXtract claims and 2013 National Plan and Provider Enumeration System data sets. The authors measured Medicaid participation as the proportion of dentists participating in Medicaid among those in the National Plan and Provider Enumeration System. The authors measured provider-level caseload according to the number of patients or visits. The authors stratified oral health care providers according to state; whether practicing in rural, suburban, or urban communities; and provider type. Results. The differences in participation rates for rural versus suburban and versus urban communities ranged from -4% through 27% and -6% through 37%, respectively. The 2012 state median number of patients per provider for preventive care ranged from 99 through 358. The provider-level caseload increased from rural to urban and from other provider to general dentist to pediatric dentist. The difference in caseload from 2012 to 2013 was not statistically significant except for the pediatric dentist type. Conclusions. This study's results suggest that the realized caseload for children enrolled in Medicaid varies according to provider type and urbanicity. The state median caseload for preventive care is lower than the 500:1 patient to provider ratio used as the minimum caseload in access estimates from other studies. Practical Implications. This study's results can assist states in gauging the level of oral health care provided to children insured by Medicaid compared with that in other states, with implications for the specification of oral health policies. [ABSTRACT FROM AUTHOR]

Solving a large batch of linear programs (LPs) with varying parameters is needed in stochastic programming and sensitivity analysis, among other modeling frameworks. Solving the LPs for all combinations of given parameter values, called the brute-force approach, can be computationally infeasible when the parameter space is high-dimensional and/or the underlying LP is computationally challenging. This paper introduces a computationally efficient approach for solving a large number of LPs that differ only in the right-hand side of the constraints (b of A x = b). The computational approach builds on theoretical properties of the geometry of the space of critical regions, where a critical region is defined as the set of b 's for which a basis is optimal. To formally support our computational approach we provide proofs of geometric properties of neighboring critical regions. We contribute to the existing theory of parametric programming by establishing additional results, providing deeper geometric understanding of critical regions. On the basis of the geometric properties of critical regions, we develop an algorithm that solves the LPs in batches by finding critical regions that contain multiple b 's. Moreover, we suggest a data-driven version of our algorithm that uses the distribution (e.g., shape) of a sample of b 's for which the LPs need to be solved. We empirically compared our approach and three other methods on various instances. The results show the efficiency of our approach in comparison with the other methods but also indicate some limitations of the algorithm. The online supplement is available at https://doi.org/10.1287/ijoc.2018.0838. [ABSTRACT FROM AUTHOR]

Objective: Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder of childhood. Clinical guidelines recommend behavior therapy as the first-line treatment for preschool-age children with ADHD. This study evaluated longitudinal patterns of services received by Medicaid-enrolled children ages 2 to 5 with ADHD in seven southeastern states (Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, and South Carolina).Methods: A discrete sequence clustering analysis was used with 2005-2012 Medicaid Analytic eXtract data to profile patient-level utilization for each state, with a focus on receipt of psychological services and medication. The model output was used to assess utilization behaviors longitudinally relative to recommended care guidelines and to characterize sources of variation in utilization patterns by demographic and ecological factors.Results: Five states had a utilization profile with a high probability of receipt of psychological services before medication among children with ADHD, covering 16% of the total study population. Most young children's ADHD care experience in the seven states (65%) fit utilization profiles characterized by a high probability of receiving any ADHD medication. Black race was significantly associated with higher utilization of psychological services in three states.Conclusions: About 16% of Medicaid-enrolled preschool-age children with ADHD received care during 2005-2012 that appeared to be consistent with 2011 recommended care guidelines. State-level and subpopulation variations in utilization for ADHD-related clinical care were found. The findings indicate that there were major gaps in treatment for ADHD among young children and that the gaps are wider for some states and subpopulations of children. [ABSTRACT FROM AUTHOR]

Objective: To quantify the impact of multiyear utilization of preventive dental services on downstream dental care utilization and expenditures for children.Data Sources/study Setting: We followed 0.93 million Medicaid-enrolled children who were 3-6 years old in 2005 from 2005 to 2011. We used Medicaid claims data of Alabama, Georgia, Mississippi, North Carolina, South Carolina, and Texas.Study Design: We clustered each state's study population into four groups based on utilization of topical fluoride and dental sealants before caries-related treatment using machine learning algorithms. We evaluated utilization rates and expenditures across the four groups and quantified cost savings of preventive care for different levels of penetration.Data Extraction Method: We extracted all dental-related claims using CDT codes.Principal Findings: In all states, Medicaid expenditures were much lower for children who received topical fluoride and dental sealants before caries development than for all other children, with a per-member per-year difference ranging from $88 for Alabama to $156 for Mississippi.Conclusions: The cost savings from topical fluoride and sealants across the six states ranged from $1.1M/year in Mississippi to $12.9M/year in Texas at a 10 percent penetration level. Preventive dental care for children not only improves oral health outcomes but is also cost saving. [ABSTRACT FROM AUTHOR]

Objective: This article discusses the sources of overestimation of spatial access as recently provided by the Health Policy Institute (HPI) of the American Dental Association.Methods: Sources of overestimation of access included estimation of Medicaid participation and capacity and limitations of the access measurement approach.Results: While the HPI analysis used a 30% Medicaid acceptance rate for Florida, 10.2% of dentists accepted ≥100 Medicaid patients in 2015. The nationwide median number of Medicaid-enrolled children per provider ranges from 36 (Nebraska) to 265 (Florida). HPI estimated that 94% of publicly insured children in Georgia lived within 15 minutes of participating dentists. More rigorous modeling for access estimated that 23% of the total child population do not have access within the state access standards in Georgia.Conclusions: The estimates provided by HPI substantially overestimate access for children with public insurance. The overestimation comes from both the data limitations and the shortcomings of the methodology employed. [ABSTRACT FROM AUTHOR]

Many studies in health services research rely on regression models with a large number of covariates or predictors. In this article, we introduce novel methodology to estimate and perform model selection for high-dimensional non-parametric multivariate regression problems, with application to many healthcare studies. We particularly focus on multi-responses or multi-task regression models. Because of the complexity of the dependence between predictors and the multiple responses, we exploit model selection approaches that consider various level of groupings between and within responses. The novelty of the method lies in its ability to account simultaneously for between and within group sparsity in the presence of non-linear effects. We also propose a new set of algorithms that can identify inactive and active predictors that are common to all responses or to a subset of responses. Our modeling approach is applied to uncover factors that impact healthcare expenditure for children insured through the Medicaid benefits program. We provide important findings on the association between healthcare expenditure and a large number of well-cited factors for two neighboring states, Georgia and North Carolina, which have similar demographics but different Medicaid systems. We also validate our methods with a benchmark cancer data set and simulated data examples. [ABSTRACT FROM AUTHOR]

Objective: To quantify disparities in accessibility and availability of pediatric primary care by modeling interventions across multiple states that compare publicly insured versus privately insured children, and urban versus rural communities.Data Sources: Secondary data sources include 2013 National Plan and Provider Enumeration System, 2009 MAX Medicaid claims, 2012 American Community Survey.Study Design: The study models accessibility and availability of care for all children in seven states.Methods: Optimization modeling with access constraints is used to estimate access. Statistical hypothesis testing is used to quantify systematic disparities.Principal Findings: California has the best accessibility for privately insured children and Minnesota for publicly insured children. Mississippi has the lowest availability for both populations. Overall, the disparities in availability for pediatric primary care are not as significant as in accessibility. Both rural and urban communities are in need of improvement in accessibility to primary care for publicly insured children, although at varying levels across states.Conclusions: Disparities in availability are not as significant as disparities in accessibility. Opportunities to improve access to pediatric primary care vary by state. Generating specific recommendations for small areas is critical to enabling health policy decision makers to improvement access. [ABSTRACT FROM AUTHOR]

We introduce a modeling approach for characterizing heterogeneity in healthcare utilization using massive medical claims data. We first translate the medical claims observed for a large study population and across five years into individual-level discrete events of care called utilization sequences. We model the utilization sequences using an exponential proportional hazards mixture model to capture heterogeneous behaviors in patients’ healthcare utilization. The objective is to cluster patients according to their longitudinal utilization behaviors and to determine the main drivers of variation in healthcare utilization while controlling for the demographic, geographic, and health characteristics of the patients. Due to the computational infeasibility of fitting a parametric proportional hazards model for high-dimensional, large-sample size data we use an iterative one-step procedure to estimate the model parameters and impute the cluster membership. The approach is used to draw inferences on utilization behaviors of children in the Medicaid system with persistent asthma across six states. We conclude with policy implications for targeted interventions to improve adherence to recommended care practices for pediatric asthma. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Background. This study introduces a framework for analyzing and visualizing health care utilization for millions of children, with a focus on pediatric asthma, one of the major chronic respiratory conditions. Methods. The data source is the 2005 to 2012 Medicaid Analytic Extract claims for 10 Southeast states. The study population consists of Medicaid-enrolled children with persistent asthma. We translate multiyear, individual-level medical claims into sequences of discrete utilization events, which are modeled using Markov renewal processes and model-based clustering. Network analysis is used to visualize utilization profiles. The method is general, allowing the study of other chronic conditions. Results. The study population consists of 1.5 million children with persistent asthma. All states have profiles with high probability of asthma controller medication, as large as 60.6% to 90.2% of the state study population. The probability of consecutive asthma controller prescriptions ranges between 0.75 and 0.95. All states have utilization profiles with uncontrolled asthma with 4.5% to 22.9% of the state study population. The probability for controller medication is larger than for short-term medication after a physician visit but not after an emergency department (ED) visit or hospitalization. Transitions from ED or hospitalization generally have a lower probability into physician office (between 0.11 and 0.38) than into ED or hospitalization (between 0.20 and 0.59). Conclusions. In most profiles, children who take asthma controller medication do so regularly. Follow-up physician office visits after an ED encounter or hospitalization are observed at a low rate across all states. Finally, all states have a proportion of children who have uncontrolled asthma, meaning they do not take controller medication while they have severe outcomes. [ABSTRACT FROM AUTHOR]

Objective We evaluated the impact of loan repayment programmes, revising Medicaid fee-for-service rates, and changing dental hygienist supervision requirements on access to preventive dental care for children in Georgia. Methods We estimated cost savings from the three interventions of preventive care for young children after netting out the intervention cost. We used a regression model to evaluate the impact of changing the Medicaid reimbursement rates. The impact of supervision was evaluated by comparing general and direct supervision in school-based dental sealant programmes. Results Federal loan repayments to dentists and school-based sealant programmes (SBSPs) had lower intervention costs (with higher potential cost savings) than raising the Medicaid reimbursement rate. General supervision had costs 56% lower than direct supervision of dental hygienists for implementing a SBSP. Raising the Medicaid reimbursement rate by 10 percentage points would improve utilization by <1% and cost over $38 million. Given one parameter set, SBSPs could serve over 27 000 children with an intervention cost between $500 000 and $1.3 million with a potential cost saving of $1.1 million. Loan repayment could serve almost 13 000 children for a cost of $400 000 and a potential cost saving of $176 000. Conclusions The three interventions all improved met need for preventive dental care. Raising the reimbursement rate alone would marginally affect utilization of Medicaid services but would not substantially increase acceptance of Medicaid by providers. Both loan repayment programmes and amending supervision requirements are potentially cost-saving interventions. Loan repayment programmes provide complete care to targeted areas, while amending supervision requirements of dental hygienists could provide preventive care across the state. [ABSTRACT FROM AUTHOR]

Objectives: Demand for dental care is expected to outpace supply through 2025. The objectives of this study were to determine the extent of pediatric dental care shortages in Georgia and to develop a general method for estimation that can be applied to other states. Methods: We estimated supply and demand for pediatric preventive dental care for the 159 counties in Georgia in 2015. We compared pediatric preventive dental care shortage areas (where demand exceeded twice the supply) designated by our methods with dental health professional shortage areas designated by the Health Resources & Services Administration. We estimated caries risk from a multivariate analysis of National Health and Nutrition Examination Survey data and national census data. We estimated county-level demand based on the time needed to perform preventive dental care services and the proportion of time that dentists spend on pediatric preventive dental care services from the Medical Expenditure Panel Survey. Results: Pediatric preventive dental care supply exceeded demand in Georgia in 75 counties: the average annual county-level pediatric preventive dental care demand was 16 866 hours, and the supply was 32 969 hours. We identified 41 counties as pediatric dental care shortage areas, 14 of which had not been designated by the Health Resources & Services Administration. Conclusions: Age- and service-specific information on dental care shortage areas could result in more efficient provider staffing and geographic targeting. [ABSTRACT FROM AUTHOR]

Accessibility and equity across populations are important measures in public health. This paper is specifically concerned with potential spatial accessibility, or the opportunity to receive care as moderated by geographic factors, and with horizontal equity, or fairness across populations regardless of need. Both accessibility and equity were goals of the 2009 vaccination campaign for the novel H1N1a influenza virus, including during the period when demand for vaccine exceeded supply. Distribution system design can influence equity and accessibility at the local level. We develop a general methodology that integrates optimization, game theory, and spatial statistics to measure potential spatial accessibility across a network, where we quantify spatial accessibility by travel distance and scarcity. We estimate and make inference on local (census-tract level) associations between accessibility and geographic, socioeconomic, and health care infrastructure factors to identify potential inequities in vaccine accessibility during the 2009 H1N1 vaccination campaign in the U.S. We find that there were inequities in access to vaccine at the local level and that these were associated with factors including population density and health care infrastructure. Our methodology for measuring and explaining accessibility leads to policy recommendations for federal, state, and local public health officials. The spatial-specific results inform the development of equitable distribution plans for future public health efforts. [ABSTRACT FROM AUTHOR]

Objectives. To evaluate how met need for accessibility and availability of primary care among nonelderly individuals in Georgia will be affected by the Patient Protection and Affordable Care Act (ACA) over the next 10 years. Methods. We used a stock-and-flow model to predict the number of available visits from 2013 to 2025, regression models to project needed visits, and an optimization model to estimatemet need. The outputs of thesemodelswere used to estimate unmet need and the availability and accessibility of primary care. Results. Our findings showed that the number of primary care providers will increase by 9.2% to 11.7% by 2025 and that the number of needed visits will increase by 20%. Under Medicaid expansion, the percentage of met need will increase from 67% to 80%. Accessibility will improve by 20% under expansion, and availability will decrease by 13% to 19% under expansion. Conclusions. The ACAs' provisions will reduce unmet need and positively affect accessibility while reducing availability in some communities. Increased need because of a larger Medicaid population under Medicaid expansion will not be a significant burden on the privately insured population. [ABSTRACT FROM AUTHOR]

Local or small-area estimates to capture emerging trends across large geographic regions are critical in identifying and addressing community-level health interventions. However, they are often unavailable due to lack of analytic capabilities in compiling and integrating extensive datasets and complementing them with the knowledge about variations in state-level health policies. This study introduces a modeling approach for small-area estimation of spatial access to pediatric primary care that is data "rich" and mathematically rigorous, integrating data and health policy in a systematic way. We illustrate the sensitivity of the model to policy decision making across large geographic regions by performing a systematic comparison of the estimates at the census tract and county levels for Georgia and California. Our results show the proposed approach is able to overcome limitations of other existing models by capturing patient and provider preferences and by incorporating possible changes in health policies. The primary finding is systematic underestimation of spatial access, and inaccurate estimates of disparities across population and across geography at the county level with respect to those at the census tract level with implications on where to focus and which type of interventions to consider. [ABSTRACT FROM AUTHOR]

Background: Measurement of healthcare spatial access over a network involves accounting for demand, supply, and network structure. Popular approaches are based on floating catchment areas; however the methods can overestimate demand over the network and fail to capture cascading effects across the system. Methods: Optimization is presented as a framework to measure spatial access. Questions related to when and why optimization should be used are addressed. The accuracy of the optimization models compared to the two-step floating catchment area method and its variations is analytically demonstrated, and a case study of specialty care for Cystic Fibrosis over the continental United States is used to compare these approaches. Results: The optimization models capture a patient's experience rather than their opportunities and avoid overestimating patient demand. They can also capture system effects due to change based on congestion. Furthermore, the optimization models provide more elements of access than traditional catchment methods. Conclusions: Optimization models can incorporate user choice and other variations, and they can be useful towards targeting interventions to improve access. They can be easily adapted to measure access for different types of patients, over different provider types, or with capacity constraints in the network. Moreover, optimization models allow differences in access in rural and urban areas. [ABSTRACT FROM AUTHOR]

Background: Measurement of healthcare spatial access over a network involves accounting for demand, supply, and network structure. Popular approaches are based on floating catchment areas; however the methods can overestimate demand over the network and fail to capture cascading effects across the system. Methods: Optimization is presented as a framework to measure spatial access. Questions related to when and why optimization should be used are addressed. The accuracy of the optimization models compared to the two-step floating catchment area method and its variations is analytically demonstrated, and a case study of specialty care for Cystic Fibrosis over the continental United States is used to compare these approaches. Results: The optimization models capture a patient’s experience rather than their opportunities and avoid overestimating patient demand. They can also capture system effects due to change based on congestion. Furthermore, the optimization models provide more elements of access than traditional catchment methods. Conclusions: Optimization models can incorporate user choice and other variations, and they can be useful towards targeting interventions to improve access. They can be easily adapted to measure access for different types of patients, over different provider types, or with capacity constraints in the network. Moreover, optimization models allow differences in access in rural and urban areas. [ABSTRACT FROM AUTHOR]

One of the most commonly used methods for modeling multivariate time series is the vector autoregressive model (VAR). VAR is generally used to identify lead, lag, and contemporaneous relationships describing Granger causality within and between time series. In this article, we investigate the VAR methodology for analyzing data consisting of multilayer time series that are spatially interdependent. When modeling VAR relationships for such data, the dependence between time series is both a curse and a blessing. The former because it requires modeling the between time-series correlation or the contemporaneous relationships which may be challenging when using likelihood-based methods. The latter because the spatial correlation structure can be used to specify the lead–lag relationships within and between time series, within and between layers. To address these challenges, we propose anL1\L2regularized likelihood estimation method. The lead, lag, and contemporaneous relationships are estimated using an efficient algorithm that exploits sparsity in the VAR structure, accounts for the spatial dependence, and models the error dependence. We consider a case study to illustrate the applicability of our method. In the supplementary materials available online, we assess the performance of the proposed VAR model and compare it with existing methods within a simulation study. [ABSTRACT FROM PUBLISHER]

Health care delivery processes consist of complex activity sequences spanning organizational, spatial, and temporal boundaries. Care is human-directed so these processes can have wide variations in cost, quality, and outcome making systemic care process analysis, conformance testing, and improvement challenging. We designed and developed an interactive visual analytic process exploration and discovery tool and used it to explore clinical data from 5784 pediatric asthma emergency department patients. [ABSTRACT FROM AUTHOR]

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Degradation signalsare sensor-based signals that are correlated with degradation processes of engineering components. In this article, we present a flexible modeling framework for characterizing degradation signals that can only be observed up to a prespecified failure threshold. The underlying assumption of this framework is that the engineering components degrade according to a similar trend, referred to as thecommon shape function, but at different degradation rates. Under this assumption, the degradation signals of different components are synchronized using a random time warping process that transforms the common trend function into degradation processes that progress at varying rates. Our primary objective is to obtain real-time predictions for the residual lifetime of components deployed in the field. In the initial step, the historical degradation signals are used to recover the distribution of the degradation processes under the assumptions of the proposed time warping model. Next, the distribution of the degradation process is updated using the signal(s) of partially degraded component(s). The updated model is then used to predict the residual lifetime distributions of these components. We test the performance of our methodology using vibration-based degradation signals from a rotating machinery experiment and simulated degradation signals. Additional information and codes are available as supplementary material online. [ABSTRACT FROM AUTHOR]

We provide insights into new methodology for the analysis of multilevel binary data observed longitudinally, when the repeated longitudinal measurements are correlated. The proposed model is logistic functional regression conditioned on three latent processes describing the within- and between-variability, and describing the cross-dependence of the repeated longitudinal measurements. We estimate the model components without employing mixed-effects modeling but assuming an approximation to the logistic link function. The primary objectives of this article are to highlight the challenges in the estimation of the model components, to compare two approximations to the logistic regression function, linear and exponential, and to discuss their advantages and limitations. The linear approximation is computationally efficient whereas the exponential approximation applies for rare events functional data. Our methods are inspired by and applied to a scientific experiment on spectral backscatter from long range infrared light detection and ranging (LIDAR) data. The models are general and relevant to many new binary functional data sets, with or without dependence between repeated functional measurements. [ABSTRACT FROM AUTHOR]

In this article, the authors addressed shortcomings in existing research on pediatric oral health care access using rigorous data and methods for identifying statistically significant disparities in oral health care access for children. The study population included children, differentiated by insurance status (Medicaid, Children's Health Insurance Program, private, none). The authors measured provider-level supply as the number of oral health care visits, stratified by provider type and urbanicity-rurality. The authors defined demand as the number of dental visits for children and derived demand and supply mainly from 2019 and 2020 data. Using statistical modeling, the authors evaluated where disparities in travel distance across communities or by insurance status were statistically significant. Although Dental Health Professional Shortage Areas are primarily rural, this study found that the proportions of rural, suburban, and urban communities identified for access interventions ranged from 24% through 66% and from 8% through 86%, respectively. For some states (Florida, Louisiana, Texas), rural and suburban communities showed a need for interventions for all children, whereas in the remaining states, the lack of Medicaid and Children's Health Insurance Program access mainly contributed to these disparities. Variations in access disparities with respect to insurance status across states or by urbanicity-rurality were extensive, with the rate of communities identified for reducing disparities ranging from 1% through 100%. All states showed a need for access interventions and for reducing disparities due to geographic location or insurance status. The sources of disparities were different across states, suggesting need for different policies and interventions across the 10 states. The study findings support the need for policies toward reducing disparities in oral health care access. [ABSTRACT FROM AUTHOR]

Advancements in condition monitoring techniques have facilitated the utilization of sensor technology for predicting failures of engineering systems. Within this context, failure is defined as the point where a sensor-based degradation signal reaches a pre-specified failure threshold. Parametric degradation models rely on complete signals to estimate the parametric functional form and do not perform well with sparse historical data. On the other hand, non-parametric models that address the challenges of data sparsity usually assume that signal observations can be made beyond the failure threshold. Unfortunately, in most applications, degradation signals can only be observed up to the failure threshold resulting in what this article refers to as truncated degradation signals. This article combines a non-parametric degradation modeling framework with a signal transformation procedure, allowing different types of truncated degradation signals to be characterized. This article considers (i) complete signals that result from constant monitoring of a system up to its failure; (ii) sparse signals resulting from sparse observations; and (iii) fragmented signals that result from dense observations over disjoint time intervals. The goal is to estimate and update the residual life distributions of partially degraded systems using in situ signal observations. It is showed that the proposed model outperforms existing models for all three signal types. [ABSTRACT FROM PUBLISHER]

In this article, we investigate clustering methods for multilevel functional data, which consist of repeated random functions observed for a large number of units (e.g., genes) at multiple subunits (e.g., bacteria types). To describe the within- and between variability induced by the hierarchical structure in the data, we take a multilevel functional principal component analysis (MFPCA) approach. We develop and compare a hard clustering method applied to the scores derived from the MFPCA and a soft clustering method using an MFPCA decomposition. In a simulation study, we assess the estimation accuracy of the clustering membership and the cluster patterns under a series of settings: small versus moderate number of time points; various noise levels; and varying number of subunits per unit. We demonstrate the applicability of the clustering analysis to a real data set consisting of expression profiles from genes activated by immunity system cells. Prevalent response patterns are identified by clustering the expression profiles using our multilevel clustering analysis. [ABSTRACT FROM AUTHOR]

Service accessibility is defined as the access of a community to the nearby site locations in a service network consisting of multiple geographically distributed service sites. Leveraging new statistical methods, this article estimates and classifies service accessibility patterns varying over a large geographic area (Georgia) and over a period of 16 years. The focus of this study is on financial services but it generally applies to any other service operation. To this end, we introduce a model-based method for clustering random time-varying functions that are spatially interdependent. The underlying clustering model is nonparametric with spatially correlated errors. We also assume that the clustering membership is a realization from a Markov random field. Under these model assumptions, we borrow information across functions corresponding to nearby spatial locations resulting in enhanced estimation accuracy of the cluster effects and of the cluster membership as shown in a simulation study. Supplementary materials including the estimation algorithm, additional maps of the data, and the C++ computer programs for analyzing the data in our case study are available online. [ABSTRACT FROM AUTHOR]

The article focuses on the discussions regarding the limitations and challenges of the proposed methodology in clustering spatially interdependent data. It says that the discussions are related to the potential sensitivity in the estimates of cluster membership to the model of statements. It mentions that the use of wavelet basis in the clustering approach might furnish specific results for the simulation example in the discussion.

This paper translates a qualitative theory of enterprise transformation into a quantitative, mathematical theory. This enables computational exploration of the phenomena outlined in the theory, as well as the sensitivity of these phenomena to a range of parameters intended to reflect the elements of the theory. The overall question addressed is, 'What should an enterprise do in response to perceptions of impending substantial change?' A mixture of three types of responses is considered-predict better, learn faster, and act faster. As these responses all require investments, it is not the case that an enterprise should always pursue all of them. Indeed, there are conditions under which an enterprise should not pursue any of them. This paper elaborates the nature of these conditions, explains why they arise, and discusses the implications for enterprises entertaining transformation. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

This paper elaborates the conceptual underpinnings needed to understand and influence change in complex socio-technical systems. The nature of causality is first addressed, followed by consideration of the nature of complexity. It is argued that, at least from a practical perspective, the difficulty in understanding causality increases as complexity increases. The possibility of influencing change is addressed in terms of concepts, principles and models for analysis and design in a range of domains or contexts. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to develop yield and price forecasting models employed in informed crop decision planning—a key aspect of effective farm management. For yearly yield prediction, we introduce a weather-based regression model with time-dependent varying coefficients. In order to allow for within-year climate variations, we predict yearly crop yield using weekly temperature and rainfall summaries resulting in a large number of correlated predictors. To overcome this difficulty, we reduce the space of predictors to a small number of uncorrelated predictors using Functional Principal Component Analysis (FPCA). For detailed price forecasting, we develop a futures-based model for long-range cash price prediction. In this model, the cash price is predicted as a sum of the nearby settlement futures price and the predicted commodity basis. We predict the one-year commodity basis as a mixture of historical basis data using a functional model-based approach. In both forecasting models, we estimate approximate prediction confidence intervals that are further integrated in a decision planning model. We applied our methods to corn yield and price forecasting for Hancock County in Illinois. Our forecasting results are more accurate in comparison to predictions based on existing methods. The methods introduced in this paper generally apply to other locations in the US and other crop types. The supplemental materials for this article are available online. [ABSTRACT FROM AUTHOR]

The technique introduced in this paper is a means for estimating and discovering underlying patterns for a large number of curves observed with heteroscedastic errors. Therefore, both the mean and the variance functions of each curve are assumed unknown and varying over time. The method consists of a series of steps. We transform using an orthonormal basis of functions in L2. In the transform domain, the non-parametric regression is reduced to a means model. To estimate the means in the transform domain, we consider the class of linear or modulation estimators and proceed as in Beran and Dumbgen (R. Beran and L. Dumbgen, Modulation of estimators and confidence sets, Ann. Stat. 26(5) (1998), pp. 1826-1856.) by minimising the Stein's unbiased risk estimate. By minimising the risk over a nested subset selection of modulators, we reduce the dimensionality of the means space. We show that in the transform space, the risk estimate is asymptotically optimal in the Pinsker's minimax sense over Sobolev ellipsoids under heteroscedastic errors. Coefficient estimation and dimensionality reduction via optimal risk estimation is essential for accurate clustering membership estimation. We illustrate our technique by estimating and clustering a large number of curves both within a synthetic example and within a specific application. In this application, we analyse the research and development expenditure of a subset of companies in the Compustat Global database. We show that our method compares favourably to two alternative approaches. [ABSTRACT FROM AUTHOR]