County-Level Mandates Were Generally Effective At Slowing COVID-19 Transmission.

Throughout the COVID-19 pandemic in the US, counties adopted numerous nonpharmaceutical interventions, such as mask mandates and stay-at-home orders, to slow COVID-19 transmission and prevent hospitals from reaching full capacity. Early evidence has been mixed about whether these interventions are effective. However, most studies only covered the early waves of COVID-19 and did not account for county-level variation in the adoption and repeal of such policies. Using daily county-level data from the Centers for Disease Control and Prevention, we evaluated the joint impact of bans on large gatherings, stay-at-home orders, mask mandates, and bar and restaurant closures on slowing COVID-19 transmission during waves 1-4 of the pandemic in the US (March 1, 2020-June 30, 2021). Our survival analysis showed that these interventions were generally effective at slowing COVID-19 transmission during this period. The mitigating effect was particularly strong during waves 2 and 3 and less substantial during waves 1 and 4. We also found strong evidence of the overall protective effect of mask mandates and, to a lesser degree, anticongregation policies. These study findings provide crucial evidence for public health officials to reference for support when using nonpharmaceutical interventions to flatten the curve of future waves of COVID-19 or other infectious disease outbreaks.

Identifying Medicare beneficiaries with Alzheimer's disease and related dementia using home health OASIS assessments.

BACKGROUND: Home health services are an important site of care following hospitalization among Medicare beneficiaries, providing health assessments that can be leveraged to detect diagnoses that are not available in other data sources. In this work, we aimed to develop a parsimonious and accurate algorithm using home health outcome and assessment information set (OASIS) measures to identify Medicare beneficiaries with a diagnosis of Alzheimer's disease and related dementia (ADRD). METHODS: We conducted a retrospective cohort study of Medicare beneficiaries with a complete OASIS start of care assessment in 2014, 2016, 2018, or 2019 to determine how well the items from various versions could identify those with an ADRD diagnosis by the assessment date. The prediction model was developed iteratively, comparing the performance of different models in terms of sensitivity, specificity, and accuracy of prediction, from a multivariable logistic regression model using clinically relevant variables, to regression models with all available variables and predictive modeling techniques, to estimate the best performing parsimonious model. RESULTS: The most important predictors of having a diagnosis of ADRD by the start of care OASIS assessment were a prior discharge diagnosis of ADRD among those admitted from an inpatient setting, and frequently exhibiting symptoms of confusion. Results from the parsimonious model were consistent across the four annual cohorts and OASIS versions with high specificity (above 96%), but poor sensitivity (below 58%). The positive predictive value was high, over 87% across study years. CONCLUSIONS: The proposed algorithm has high accuracy, requires a single OASIS assessment, is easy to implement without sophisticated statistical models, and can be used across four OASIS versions and in situations where claims are not available to identify individuals with a diagnosis of ADRD, including the growing population of Medicare Advantage beneficiaries.

Biting midges (Diptera: Ceratopogonidae) as putative vectors of zoonotic Onchocerca lupi (Nematoda: Onchocercidae) in northern Arizona and New Mexico, southwestern United States.

Onchocerca lupi (Rodonaja, 1967) is an understudied, vector-borne, filarioid nematode that causes ocular onchocercosis in dogs, cats, coyotes, wolves, and is also capable of infecting humans. Onchocercosis in dogs has been reported with increasing incidence worldwide. However, despite the growing number of reports describing canine O. lupi cases as well as zoonotic infections globally, the disease prevalence in endemic areas and vector species of this parasite remains largely unknown. Here, our study aimed to identify the occurrence of O. lupi infected dogs in northern Arizona, New Mexico, and Utah, United States and identify the vector of this nematode. A total of 532 skin samples from randomly selected companion animals with known geographic locations within the Navajo Reservation were collected and molecularly surveyed by PCR for the presence of O. lupi DNA (September 2019-June 2022) using previously published nematode primers (COI) and DNA sequencing. O. lupi DNA was detected in 50 (9.4%) sampled animals throughout the reservation. Using positive animal samples to target geographic locations, pointed hematophagous insect trapping was performed to identify potential O. lupi vectors. Out of 1,922 insects screened, 38 individual insects and 19 insect pools tested positive for the presence of O. lupi, all of which belong to the Diptera family. This increased surveillance of definitive host and biological vector/intermediate host is the first large scale prevalence study of O. lupi in companion animals in an endemic area of the United States, and identified an overall prevalence of 9.4% in companion animals as well as multiple likely biological vector and putative vector species in the southwestern United States. Furthermore, the identification of these putative vectors in close proximity to human populations coupled with multiple, local zoonotic cases highlight the One Health importance of O. lupi.

Increased Medicare Advantage Penetration Is Associated With Lower Postacute Care Use For Traditional Medicare Patients.

Medicare Advantage (MA) plans, which accounted for 45 percent of total Medicare enrollment in 2022, are incentivized to minimize spending on low-value services. Prior research indicates that MA plan enrollment is associated with reduced postacute care use without adverse impacts on patient outcomes. However, it is unclear whether a rising MA enrollment level is associated with a change in postacute care use in traditional Medicare, especially given growing participation in traditional Medicare Alternative Payment Models that have been found to be associated with lower postacute care spending. We hypothesize that market-level MA expansion is associated with reduced postacute care use among traditional Medicare beneficiaries-a "spillover" effect of providers modifying their practice patterns in response to MA plans' incentives. We found increased MA market penetration associated with reduced postacute care use among traditional Medicare beneficiaries, without a corresponding increase in hospital readmissions. This association was generally stronger in markets with a greater share of traditional Medicare beneficiaries attributed to accountable care organizations, suggesting that policy makers should account for MA penetration when evaluating potential savings in Alternative Payment Models within traditional Medicare.

The Effects of Increasing State Minimum Wage on Family and Paid Caregiving.

Older adults may receive either or a combination of unpaid family/friend and paid caregiving. The consumption of family/friend and paid caregiving may be sensitive to minimum wage policies. We used data (n = 11,698 unique respondents) from the Health and Retirement Study and a difference-in-differences design to evaluate associations between increases in state minimum wage between 2010 and 2014 and family/friend and paid caregiving consumed by adults age 65+ years. We also examined responses to increases in minimum wage for respondents with dementia or Medicaid beneficiaries. People living in states that increased their minimum wage did not consume substantially different hours of family/friend, paid, or any family/friend or paid caregiving. We did not observe differential responses between increases in minimum wage and hours of family/friend or paid caregiving among people with dementia or Medicaid beneficiaries. Increases in state minimum wage were not associated with changes in caregiving consumed by adults age 65+.

Trends in post-acute care and outcomes for Medicare beneficiaries hospitalized for heart failure between 2008 and 2015.

BACKGROUND: Heart failure (HF) is the leading cause of hospitalization among older adults in the United States and results in high rates of post-acute care (PAC) utilization. Federal policies have focused on shifting PAC to less intensive settings and reducing length of stay to lower spending. This study evaluates the impact of policy changes on PAC use among Medicare beneficiaries hospitalized with HF between 2008 and 2015 by (1) characterizing trends in PAC use and cost and (2) evaluating changes in readmission, mortality, and days in the community, overall and by frailty. METHODS: Annual cross-section prospective cohorts of all HF admissions between 1/1/2008 and 9/30/2015 among a 20% random sample of all Medicare Fee-for-Service beneficiaries (n = 718,737). The Claims-based Frailty Index (CFI) was used to classify frailty status. Multivariable regression models were used to evaluate trends in first discharge location, readmissions, mortality, days alive in the community, and costs; overall and by frailty status. RESULTS: Frailty was prevalent among HF patients: 54.1% were prefrail, 37.0% mildly frail, and 6.9% moderate to severely frail. Between 2008 and 2015, almost 4% more HF beneficiaries received PAC, with most of the increase concentrated in skilled nursing facilities (SNF) (+2.3%) and home health agencies (HHA) (+1.1%), and PAC cost increased by $123 (3.5%). Over the 180-days follow-up after hospitalization, hospital readmissions decreased significantly (-3.4% at 30-day; -6.3% at 180-day), days alive in the community increased (+1.5), and 180-day Medicare costs declined $2948 (-18.7%) without negative impact in mortality (except a minor increase in the pre-frail group). Gains were greatest among the frailest patients. CONCLUSIONS: Medicare beneficiaries hospitalized with HF spent more time in the community and experienced lower rehospitalization rates at lower cost without significant increases in mortality. However, important opportunities remain to optimize care for frail older adults hospitalized with HF.

The Impact of Telehealth on Primary Care Physician Panel Sizes: A Modeling Study.

INTRODUCTION: Most research on the use of telehealth in lieu of in-office visits has focused on its growth, its impact on access, and the experience of physicians and patients. One important issue that has not gotten much attention is the potential for telehealth to significantly increase physician capacity by reducing nonvalue adding activities and patient no-shows. We explore this in this article. METHODS: We use data from the electronic health records of 2 health care systems and information gathered from family medicine physician focus groups to develop estimates of visit durations and no-show rates for tele-visits. We use these in a simulation model to determine how patient panel sizes could be increased while maintaining high levels of access by substituting tele-visits for in-person visits. RESULTS: We found that tele-visits reduce the nonvalue-added time physicians spend with patients as well as patient no-shows. At current levels of tele-visit utilization, the use of tele-visits may translate into more than a 10% increase in patient panel sizes assuming a modest reduction in visit durations and no-shows, and as much as a 30% increase assuming that half of all visits could be effectively conducted virtually and result in a greater reduction in visit durations and no-shows. DISCUSSION: Our study provides evidence that a major benefit of using telehealth for many routine encounters is a reduction in wasted physician time and a substantial increase in the number of patients that a primary care physician can care for without jeopardizing access to care.

Microsimulation Model Calibration with Approximate Bayesian Computation in R: A Tutorial.

Mathematical health policy models, including microsimulation models (MSMs), are widely used to simulate complex processes and predict outcomes consistent with available data. Calibration is a method to estimate parameter values such that model predictions are similar to observed outcomes of interest. Bayesian calibration methods are popular among the available calibration techniques, given their strong theoretical basis and flexibility to incorporate prior beliefs and draw values from the posterior distribution of model parameters and hence the ability to characterize and evaluate parameter uncertainty in the model outcomes. Approximate Bayesian computation (ABC) is an approach to calibrate complex models in which the likelihood is intractable, focusing on measuring the difference between the simulated model predictions and outcomes of interest in observed data. Although ABC methods are increasingly being used, there is limited practical guidance in the medical decision-making literature on approaches to implement ABC to calibrate MSMs. In this tutorial, we describe the Bayesian calibration framework, introduce the ABC approach, and provide step-by-step guidance for implementing an ABC algorithm to calibrate MSMs, using 2 case examples based on a microsimulation model for dementia. We also provide the R code for applying these methods.

Mass spectrometry of individual particles between 50 and 750 nm in diameter at the Baltimore Supersite.

The performance of the real-time single-particle mass spectrometer RSMS III is evaluated for ambient fine and ultrafine particle number concentration measurements. The RSMS III couples aerodynamic size selection with laser ablation time-of-flight mass spectrometry for single-particle analysis. It was deployed at the Baltimore particulate matter Supersite for semi-continuous operation over an 8-month period. The sampling protocol adopted for this study permitted the analysis of on average 2000 particles per day. The number of particles analyzed is a tradeoff between generating a statistically significant data set and maintaining instrument operation over a long period of time. The optimum particle size range of analysis was found to be ca. 50-770 nm in diameter, although particles as small as 45 nm and as large as 1250 nm were also analyzed. While nitrate, sulfate, and carbon (elemental and organic) were found to dominate the ambient aerosol, over 10% of the detected particles contained transition and/or heavy metals. The (size-dependent) detection efficiency, defined as the fraction of particles entering the inlet that are analyzed, was determined by comparison with scanning mobility particle sizing data. Using the experimentally determined detection efficiencies, particle number concentrations of specific chemical components were estimated. While the sampling protocol allowed the particle concentrations of major chemical components to be followed as a function of both time and particle size, minor components required averaging over time and/or size to achieve adequate precision.