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1.
JAMA Netw Open ; 7(5): e2411006, 2024 May 01.
Article in English | MEDLINE | ID: mdl-38739388

ABSTRACT

Importance: Understanding the association of telehealth use with health care outcomes is fundamental to determining whether telehealth waivers implemented during the COVID-19 public health emergency should be made permanent. The current literature has yielded inconclusive findings owing to its focus on select states, practices, or health care systems. Objective: To estimate the association of telehealth use with outcomes for all Medicare fee-for-service (FFS) beneficiaries by comparing hospital service areas (HSAs) with different levels of telehealth use. Design, Setting, and Participants: This US population-based, retrospective cohort study was conducted from July 2022 to April 2023. Participants included Medicare claims of beneficiaries attributed to HSAs with FFS enrollment in Parts A and B. Exposures: Low, medium, or high tercile of telehealth use created by ranking HSAs according to the number of telehealth visits per 1000 beneficiaries. Main Outcomes and Measures: The primary outcomes were quality (ambulatory care-sensitive [ACS] hospitalizations and emergency department [ED] visits per 1000 FFS beneficiaries), access to care (clinician encounters per FFS beneficiary), and cost (total cost of care for Part A and/or B services per FFS Medicare beneficiary) determined with a difference-in-difference analysis. Results: In this cohort study of claims from approximately 30 million Medicare beneficiaries (mean [SD] age in 2019, 71.04 [1.67] years; mean [SD] percentage female in 2019, 53.83% [2.14%]) within 3436 HSAs, between the second half of 2019 and the second half of 2021, mean ACS hospitalizations and ED visits declined sharply, mean clinician encounters per beneficiary declined slightly, and mean total cost of care per beneficiary per semester increased slightly. Compared with the low group, the high group had more ACS hospitalizations (1.63 additional hospitalizations per 1000 beneficiaries; 95% CI, 1.03-2.22 hospitalizations), more clinician encounters (0.30 additional encounters per beneficiary per semester; 95% CI, 0.23-0.38 encounters), and higher total cost of care ($164.99 higher cost per beneficiary per semester; 95% CI, $101.03-$228.96). There was no statistically significant difference in ACS ED visits between the low and high groups. Conclusions and Relevance: In this cohort study of Medicare beneficiaries across all 3436 HSAs, high levels of telehealth use were associated with more clinician encounters, more ACS hospitalizations, and higher total health care costs. COVID-19 cases were still high during the period of study, which suggests that these findings partially reflect a higher capacity for providing health services in HSAs with higher telehealth intensity than other HSAs.


Subject(s)
COVID-19 , Health Services Accessibility , Medicare , Quality of Health Care , Telemedicine , Humans , United States , Telemedicine/statistics & numerical data , Telemedicine/economics , Retrospective Studies , Medicare/statistics & numerical data , COVID-19/epidemiology , Female , Male , Aged , Quality of Health Care/statistics & numerical data , Health Services Accessibility/statistics & numerical data , SARS-CoV-2 , Fee-for-Service Plans/statistics & numerical data , Aged, 80 and over , Hospitalization/statistics & numerical data , Emergency Service, Hospital/statistics & numerical data
2.
JAMA Netw Open ; 3(3): e200274, 2020 03 02.
Article in English | MEDLINE | ID: mdl-32119095

ABSTRACT

Importance: Although there are many pharmacologic alternatives to opioids, it is unclear whether the structure of Medicare Part D formularies discourages use of the alternatives. Objectives: To quantify the coverage of opioid alternatives and prevalence of prior authorization, step therapy, quantity limits, and tier placement for these drugs, and test whether these formulary exclusions and restrictions are associated with increased opioid prescribing to older adults at the county level. Design, Setting, and Participants: County fixed-effect models were estimated using a panel of counties across the 50 US states and the District of Columbia over calendar years 2015 and 2016. Data analysis was conducted from July 1 to September 23, 2019. The sample included 2721 counties in 2015 and 2671 counties in 2016 with sufficient data on Medicare Part D formulary design and opioid prescribing. Main Outcomes and Measures: County-level opioid prescribing rate (number of opioid claims divided by the number of overall claims) and counts of excluded opioid alternatives and opioid alternatives with prior authorization, step therapy, quantity limits, and high-tier placements. Results: A total of 30 nonopioid analgesics were examined across 28 997 Medicare plans in 2015 and 30 390 plans in 2016. Medicare plans did not cover a mean of 7% of these drugs (interquartile range, 10%; lower to upper limit, 0%-23%). Among covered nonopioids, prior authorization and step therapy were uncommon, with fewer than 5% affected by prior authorization and 0% by step therapy. However, 13% of covered nonopioids had quantity limits (interquartile range, 10%; lower to upper limit, 0%-31%) and 22% were in high-cost tiers (interquartile range, 38%; lower to upper limit, 0%-50%). Increases in the number of nonopioids excluded on Medicare plans in a county were associated with increased opioid prescribing (effect size relative to mean, 2.2%-3.7%; P = .004). Conversely, increases in the number of opioids not covered on Medicare plans in a county was found to be associated with decreased opioid prescribing (effect size relative to mean, 0.8%-1.5%; P = .02). None of the utilization management strategies (prior authorization, step therapy, and quantity limits) examined or high-cost tier placements of nonopioids were associated with increased opioid prescribing. Conclusions and Relevance: Lack of Medicare coverage for pharmacologic alternatives to opioids may be associated with increased opioid prescribing.


Subject(s)
Analgesics, Non-Narcotic/therapeutic use , Analgesics, Opioid/therapeutic use , Insurance Coverage/statistics & numerical data , Medicare Part D/statistics & numerical data , Pain/drug therapy , Practice Patterns, Physicians'/statistics & numerical data , Aged , Anti-Inflammatory Agents, Non-Steroidal/therapeutic use , Anticonvulsants/therapeutic use , Antidepressive Agents, Tricyclic/therapeutic use , Formularies as Topic , Humans , Muscle Relaxants, Central/therapeutic use , Prior Authorization , Serotonin and Noradrenaline Reuptake Inhibitors/therapeutic use , United States
3.
PLoS One ; 13(8): e0201115, 2018.
Article in English | MEDLINE | ID: mdl-30110376

ABSTRACT

In this paper, we establish a statistically important relationship between household agricultural income and women's BMI using a five-year panel dataset of rural households drawn from 18 villages across five Indian states. Using within household variation over time, we estimate both the extent to which short-term changes in agricultural income are associated with short-term changes in BMI, and the effect of agricultural income growth on BMI growth over a longer term. Over the longer term, and for the group of households that regularly farm, we find a 10 percentage point agricultural income growth to be associated with a 0.10 percentage point growth in BMI. Consistent with the literature, this effect is economically modest, but important considering that we do not find a corresponding effect for growth in non-agricultural income. We show that both the own-production and market purchase of food are associated with nutritional improvements. While women's BMI is associated with an increase in the consumption of own-produced cereals, the market plays an important role in facilitating access to more nutritious foods like pulses. Lastly, we also find that effects of agricultural income are driven by younger women, in the age-group 15-25 years, who face a particularly strong nutritional disadvantage in India.


Subject(s)
Agriculture/economics , Body Mass Index , Income , Adolescent , Adult , Cross-Sectional Studies , Diet , Female , Humans , India , Nutritional Status , Rural Population , Time Factors , Young Adult
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