Analgesic prescribing trends in a national sample of older veterans with osteoarthritis: 2012-2017

Few investigations examine patterns of opioid and nonopioid analgesic prescribing and concurrent pain intensity ratings before and after institution of safer prescribing programs such as the October 2013 Veterans Health Administration system-wide Opioid Safety Initiative (OSI) implementation. We conducted a quasi-experimental pre–post observational study of all older U.S. veterans (≥50 years old) with osteoarthritis of the knee or hip. All associated outpatient analgesic prescriptions and outpatient pain intensity ratings from January 1, 2012 to December 31, 2016, were analyzed with segmented regression of interrupted time series. Standardized monthly rates for each analgesic class (total, opioid, nonsteroidal anti-inflammatory drug, acetaminophen, and other study analgesics) were analyzed with segmented negative binomial regression models with overall slope, step, and slope change. Similarly, segmented linear regression was used to analyze pain intensity ratings and percentage of those reporting pain. All models were additionally adjusted for age, sex, and race. Before OSI implementation, total analgesic prescriptions showed a steady rise, abruptly decreasing to a flat trajectory after OSI implementation. This trend was primarily due to a decrease in opioid prescribing after OSI. Total prescribing after OSI implementation was partially compensated by continuing increased prescribing of other study analgesics as well as a significant rise in acetaminophen prescriptions (post-OSI). No changes in nonsteroidal anti-inflammatory drug prescribing were seen. A small rise in the percentage of those reporting pain but not mean pain intensity ratings continued over the study period with no changes associated with OSI. Changes in analgesic prescribing trends were not paralleled by changes in reported pain intensity for older veterans with osteoarthritis

Measuring patient-perceived quality of care in US hospitals using Twitter

ABSTRACT Background Patients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches. Objective To assess the use of Twitter as a supplemental data stream for measuring patientperceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures. Design 404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients. Key results Roughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients ( p<0.001) and above the national median for nurse/patient ratio ( p=0.006), and to be a nonprofit hospital ( p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates ( p=0.003). Conclusions Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represen

Military sexual trauma-related posttraumatic stress disorder service-connection: Characteristics of claimants and award denial across gender, race, and compared to combat trauma.

The current study characterizes a cohort of veteran claims filed with the Veterans Benefits Administration for posttraumatic stress disorder secondary to experiencing military sexual trauma, compares posttraumatic stress disorder service-connection award denial for military sexual trauma-related claims versus combat-related claims, and examines military sexual trauma -related award denial across gender and race. We conducted analyses on a retrospective national cohort of veteran claims submitted and rated between October 2017-May 2022, including 102,409 combat-related claims and 31,803 military sexual trauma-related claims. Descriptive statistics were calculated, logistic regressions assessed denial of service-connection across stressor type and demographics, and odds ratios were calculated as effect sizes. Military sexual trauma-related claims were submitted primarily by White women Army veterans, and had higher odds of being denied than combat claims (27.6% vs 18.2%). When controlling for age, race, and gender, men veterans had a 1.78 times higher odds of having military sexual trauma-related claims denied compared to women veterans (36.6% vs. 25.4%), and Black veterans had a 1.39 times higher odds of having military sexual trauma-related claims denied compared to White veterans (32.4% vs. 25.3%). Three-fourths of military sexual trauma-related claims were awarded in this cohort. However, there were disparities in awarding of claims for men and Black veterans, which suggest the possibility of systemic barriers for veterans from underserved backgrounds and/or veterans who may underreport military sexual trauma

Predictors of PTSD service-connection claim denial.

Predictors of PTSD service-connection claim denial.</p

Characteristics of combat-related PTSD claims (<i>N</i> = 102,409).

Characteristics of combat-related PTSD claims (N = 102,409).</p

Predictors of MST service-connection claim denial.

Predictors of MST service-connection claim denial.</p

Reasons for MST and combat-related PTSD claims denial.

Reasons for MST and combat-related PTSD claims denial.</p

Characteristics of MST-related PTSD claims (<i>N</i> = 31,803).

Characteristics of MST-related PTSD claims (N = 31,803).</p

Number of PTSD claims filed per veteran (<i>N</i> = 134,207).

Number of PTSD claims filed per veteran (N = 134,207).</p

Measuring patient-perceived quality of care in US hospitals using Twitter

Background: Patients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches. Objective: To assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures. Design: 404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients. Key results Roughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients (p<0.001) and above the national median for nurse/patient ratio (p=0.006), and to be a non-profit hospital (p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates (p=0.003). Conclusions: Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators