The impact of the COVID-19 pandemic and policy response on health care utilization: Evidence from county-level medical claims and cellphone data.
Ontology highlight
ABSTRACT:
SUBMITTER: Cantor J
PROVIDER: S-EPMC8755425 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:To quantify changes in private insurance payments for and utilization of abdominal/pelvic computed tomography scans (CTs) after 2011 changes in CPT coding and Medicare reimbursement rates, which were designed to reduce costs stemming from misvalued procedures.TruvenHealth Analytics MarketScan Commercial Claims and Encounters database.We used difference-in-differences models to compare combined CTs of the abdomen/pelvis to CTs of the abdomen or pelvis only. Our main outcomes were inflation-adjusted log payments per procedure, daily utilization rates, and total annual payments.Claims data were extracted for all abdominal/pelvic CTs performed in 2009-2011 within noncapitated, employer-sponsored private plans.Adjusted payments per combined CTs of the abdomen/pelvis dropped by 23.8 percent (p < .0001), and their adjusted daily utilization rate accelerated by 0.36 percent (p = .034) per month after January 2011. Utilization rate of abdominal-only or pelvic-only CTs dropped by 5.0 percent (p < .0001). Total annual payments for combined CTs of the abdomen/pelvis decreased in 2011 despite the increased utilization.Private insurance payments for combined CTs of the abdomen/pelvis declined and utilization accelerated significantly after 2011 policy changes. While growth in total annual payments was contained in 2011, it may not be sustained if 2011 utilization trends persist.
| S-EPMC4693852 | biostudies-literature
Project description:BackgroundExpansion of virtual health care-real-time video consultation with a physician via the Internet-will continue as use of mobile devices and patient demand for immediate, convenient access to care grow.ObjectiveThe objective of the study is to analyze the care provided and the cost of virtual visits over a 3-week episode compared with in-person visits to retail health clinics (RHC), urgent care centers (UCC), emergency departments (ED), or primary care physicians (PCP) for acute, nonurgent conditions.MethodsA cross-sectional, retrospective analysis of claims from a large commercial health insurer was performed to compare care and cost of patients receiving care via virtual visits for a condition of interest (sinusitis, upper respiratory infection, urinary tract infection, conjunctivitis, bronchitis, pharyngitis, influenza, cough, dermatitis, digestive symptom, or ear pain) matched to those receiving care for similar conditions in other settings. An episode was defined as the index visit plus 3 weeks following. Patients were children and adults younger than 65 years of age without serious chronic conditions. Visits were classified according to the setting where the visit occurred. Care provided was assessed by follow-up outpatient visits, ED visits, or hospitalizations; laboratory tests or imaging performed; and antibiotic use after the initial visit. Episode costs included the cost of the initial visit, subsequent medical care, and pharmacy.ResultsA total of 59,945 visits were included in the analysis (4635 virtual visits and 55,310 nonvirtual visits). Virtual visit episodes had similar follow-up outpatient visit rates (28.09%) as PCP (28.10%, P=.99) and RHC visits (28.59%, P=.51). During the episode, lab rates for virtual visits (12.56%) were lower than in-person locations (RHC: 36.79%, P<.001; UCC: 39.01%, P<.001; ED: 53.15%, P<.001; PCP: 37.40%, P<.001), and imaging rates for virtual visits (6.62%) were typically lower than in-person locations (RHC: 5.97%, P=.11; UCC: 8.77%, P<.001; ED: 43.06%, P<.001; PCP: 11.26%, P<.001). RHC, UCC, ED, and PCP were estimated to be $36, $153, $1735, and $162 more expensive than virtual visit episodes, respectively, including medical and pharmacy costs.ConclusionsVirtual care appears to be a low-cost alternative to care administered in other settings with lower testing rates. The similar follow-up rate suggests adequate clinical resolution and that patients are not using virtual visits as a first step before seeking in-person care.
| S-EPMC5336603 | biostudies-literature
Project description:Preventive medical care may reduce downstream medical costs and reduce population burden of disease. However, although social, demographic, and geographic determinants of preventive care have been studied, there is little information about how the workplace affects preventive care utilization. This study examines how four types of organizational policies and practices (OPPs) are associated with individual workers' preventive care utilization. We used data collected in 2012 from 838 hospital patient care workers, grouped in 84 patient care units at two hospitals in Boston. Via survey, we assessed individuals' perceptions of four types of OPPs on their work units. We linked the survey data to a database containing detailed information on medical expenditures. Using multilevel models, we tested whether individual-level perceptions, workgroup-average perceptions, and their combination were associated with individual workers' preventive care utilization (measured by number of preventive care encounters over a two-year period). Adjusting for worker characteristics, higher individual-level perceptions of workplace flexibility were associated with greater preventive care utilization. Higher average unit-level perceptions of people-oriented culture, ergonomic practices, and flexibility were associated with greater preventive care utilization. Overall, we find that workplace policies and practices supporting flexibility, ergonomics, and people-oriented culture are associated with positive preventive care-seeking behavior among workers, with some policies and practices operating at the individual level and some at the group level. Improving the work environment could impact employers' health-related expenditures and improve workers' health-related quality of life.
| S-EPMC6028933 | biostudies-literature
Project description:BackgroundBecause pregnancy complications, including gestational diabetes mellitus (GDM) and hypertensive disorders in pregnancy, are risk factors for diabetes and cardiovascular disease, post-delivery follow-up is recommended.ObjectiveTo determine predictors of post-delivery primary and obstetric care utilization in women with and without medical complications.Research designFive-year retrospective cohort study using commercial and Medicaid insurance claims in Maryland.Subjects7,741 women with a complicated pregnancy (GDM, hypertensive disorders and pregestational diabetes mellitus [DM]) and 23,599 women with a comparison pregnancy.MeasuresWe compared primary and postpartum obstetric care utilization rates in the 12 months after delivery between the complicated and comparison pregnancy groups. We conducted multivariate logistic regression to assess the association between pregnancy complications, sociodemographic predictor variables and utilization of care, stratified by insurance type.ResultsWomen with a complicated pregnancy were older at delivery (p < 0.001), with higher rates of cesarean delivery (p < 0.0001) and preterm labor or delivery (p < 0.0001). Among women with Medicaid, 56.6% in the complicated group and 51.7% in the comparison group attended a primary care visit. Statistically significant predictors of receiving a primary care visit included non-Black race, older age, preeclampsia or DM, and depression. Among women with commercial health insurance, 60.0% in the complicated group and 49.5% in the comparison group attended a primary care visit. Pregnancy complication did not predict a primary care visit among women with commercial insurance.ConclusionsWomen with pregnancy complications were more likely to attend primary care visits post-delivery compared to the comparison group, but overall visit rates were low. Although Medicaid expansion has potential to increase coverage, innovative models for preventive health services after delivery are needed to target women at higher risk for chronic disease development.
| S-EPMC3965743 | biostudies-literature
Project description: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.
| S-EPMC6167939 | biostudies-literature
Project description:BackgroundThe reimbursement of new technologies in inpatient care is not always linked to a requirement for evidence-based evaluation of patient benefit. In Germany, every new technology approved for market was until recently eligible for reimbursement in inpatient care unless explicitly excluded. The aim of this work was (1) to investigate the type of evidence that was available at the time of introduction of 25 innovative technologies and how this evidence evolved over time, and (2) to explore the relationship between clinical evidence and utilization for these technologies in German inpatient care.MethodsThis study combined different methods. A systematic search for evidence published between 2003 and 2017 was conducted in four bibliographic databases, clinical trial registries, resources for clinical guidelines, and health technology assessment-databases. Information was also collected on funding mechanisms and safety notices. Utilization was measured by hospital procedures captured in claims data. The body of evidence, funding and safety notices per technology were analyzed descriptively. The relationship between utilization and evidence was explored empirically using a multilevel regression analysis.ResultsThe number of included publications per technology ranges from two to 498. For all technologies, non-comparative studies form the bulk of the evidence. The number of randomized controlled clinical trials per technology ranges from zero to 19. Some technologies were utilized for several years without an adequate evidence base. A relationship between evidence and utilization could be shown for several but not all technologies.ConclusionsThis study reveals a mixed picture regarding the evidence available for new technologies, and the relationship between the development of evidence and the use of technologies over time. Although the influence of funding and safety notices requires further investigation, these results re-emphasize the need for strengthening market approval standards and HTA pathways as well as approaches such as coverage with evidence development.
| S-EPMC10546629 | biostudies-literature
Project description:BACKGROUND:Elderly in need of long-term care tend to have worse health and have higher need of medical care than elderly without need for long-term care. Yet, characteristics associated with long-term care need can impede health care access: Higher levels of long-term care need come with physical and cognitive decline such as frailty and memory loss. Yet, it has not been investigated whether level of long-term care need is related to medical care utilization. METHODS:We investigated the association between the level of long-term care and medical specialist utilization among nursing home residents and home care recipients. We applied zero-inflated Poisson regression with robust standard errors based on a sample of statutory health insurance members. The sample consisted of 100.000 elderly over age 60. We controlled for age, gender, morbidity and mortality, residential density, and general practitioner utilization. RESULTS:We found a strong gradient effect of the level of long-term care for 9 out of 12 medical specialties: A higher level of long-term care need was associated with a lower probability of having a medical specialist visit. Yet, we did not find clear effects of the level of long-term care need on the intensity of medical specialist care. These findings were similar for both the nursing home and home care setting. CONCLUSION:The findings indicate that inequalities in medical specialist utilization exist between elderly with differing levels of long-term care need because differences in morbidity were controlled for. Elderly with higher need of long-term care might face more access barriers to specialist medical care.
| S-EPMC7006141 | biostudies-literature
Project description:Periodontal disease has been linked to poor glycemic control among individuals with type 2 diabetes. Using integrated dental, medical, and pharmacy commercial claims from Truven MarketScan® Research Databases, we implement inverse probability weighting and doubly robust methods to estimate a relationship between a periodontal intervention and healthcare costs and utilization. Among individuals newly diagnosed with type 2 diabetes, we find that a periodontal intervention is associated with lower total healthcare costs (-$1799), lower total medical costs excluding pharmacy costs (-$1577), and lower total type 2 diabetes-related healthcare costs (-$408). © 2016 The Authors. Health Economics Published by John Wiley & Sons Ltd.
| S-EPMC5347922 | biostudies-literature
Project description:BackgroundInnovative medical technologies are commonly associated with positive expectations. At the time of their introduction into care, there is often little evidence available regarding their benefits and harms. Accordingly, some innovative medical technologies with a lack of evidence are used widely until or even though findings of adverse events emerge, while others with study results supporting their safety and effectiveness remain underused. This study aims at examining the diffusion patterns of innovative medical technologies in German inpatient care between 2005 and 2017 while simultaneously considering evidence development.MethodsBased on a qualitatively derived typology and a quantitative clustering of the adoption curves, a representative sample of 21 technologies was selected for further evaluation. Published scientific evidence on efficacy/effectiveness and safety of the technologies was identified and extracted in a systematic approach. Derived from a two-dimensional classification according to the degree of utilization and availability of supportive evidence, the diffusion patterns were then assigned to the categories "Success" (widespread/positive), "Hazard" (widespread/negative), "Overadoption" (widespread/limited or none), "Underadoption" (cautious/positive), "Vigilance" (cautious/negative), and "Prudence" (cautious/limited or none).ResultsOverall, we found limited evidence on the examined technologies regarding both the quantity and quality of published randomized controlled trials. Thus, the categories "Prudence" and "Overadoption" together account for nearly three-quarters of the years evaluated, followed by "Success" with 17%. Even when evidence is available, the transfer of knowledge into practice appears to be inhibited.ConclusionsThe successful implementation of safe and effective innovative medical technologies into practice requires substantial further efforts by policymakers to strengthen systematic knowledge generation and translation. Creating an environment that encourages the conduct of rigorous studies, promotes knowledge translation, and rewards innovative medical technologies according to their added value is a prerequisite for the diffusion of valuable health care.
| S-EPMC8556925 | biostudies-literature
Project description:BackgroundSocial distancing and public policy have been crucial for minimizing the spread of SARS-CoV-2 in the United States. Publicly available, county-level time series data on mobility are derived from individual devices with global positioning systems, providing a variety of indices of social distancing behavior per day. Such indices allow a fine-grained approach to modeling public behavior during the pandemic. Previous studies of social distancing and policy have not accounted for the occurrence of pre-policy social distancing and other dynamics reflected in the long-term trajectories of public mobility data.ObjectiveWe propose a differential equation state-space model of county-level social distancing that accounts for distancing behavior leading up to the first official policies, equilibrium dynamics reflected in the long-term trajectories of mobility, and the specific impacts of four kinds of policy. The model is fit to each US county individually, producing a nationwide data set of novel estimated mobility indices.MethodsA differential equation model was fit to three indicators of mobility for each of 3054 counties, with T=100 occasions per county of the following: distance traveled, visitations to key sites, and the log number of interpersonal encounters. The indicators were highly correlated and assumed to share common underlying latent trajectory, dynamics, and responses to policy. Maximum likelihood estimation with the Kalman-Bucy filter was used to estimate the model parameters. Bivariate distributional plots and descriptive statistics were used to examine the resulting county-level parameter estimates. The association of chronology with policy impact was also considered.ResultsMobility dynamics show moderate correlations with two census covariates: population density (Spearman r ranging from 0.11 to 0.31) and median household income (Spearman r ranging from -0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (r=-0.37 and r=-0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (r=0.51, r=0.39). Chronological ordering of policies was a moderate to strong determinant of their effect per county (Spearman r ranging from -0.12 to -0.56), with earlier policies accounting for most of the change in mobility, and later policies having little or no additional effect.ConclusionsChronological ordering, population density, and median household income were all associated with policy impact. The stay-at-home order and the ban on gatherings had the largest impacts on mobility on average. The model is implemented in a graphical online app for exploring county-level statistics and running counterfactual simulations. Future studies can incorporate the model-derived indices of social distancing and policy impacts as important social determinants of COVID-19 health outcomes.
| S-EPMC7759510 | biostudies-literature