Are physical therapy (PT) interventions before and after total knee replacement (TKR) associated with lower risk of postoperative long-term use of opioids?In this cohort study that included 67 322 individuals who underwent TKR, receipt of any pre- and post-TKR PT care, receipt of more than 5 post-TKR PT sessions, and initiation of PT care within 30 days after TKR were associated with lower risk of long-term opioid use.This study suggests that receipt of PT interventions may be associated with lower risk of long-term opioid use after TKR; these data may help inform effective dose and timing of PT interventions after TKR. Many individuals who undergo total knee replacement (TKR) become long-term opioid users after TKR. Associations of physical therapy (PT) interventions before or after TKR with long-term use of opioids are not known.To evaluate associations of PT interventions before and after TKR with long-term opioid use after TKR.This cohort study used data from the OptumLabs Data Warehouse on 67 322 individuals aged 40 years or older who underwent TKR from January 1, 2001, to December 31, 2016, stratified by history of opioid use. The analyses for the study included data from January 1, 1999, to December 31, 2018.Any PT interventions within 90 days before or after TKR, post-TKR PT dose as number of sessions , post-TKR PT timing as number of days to initiation of care , and post-TKR PT type .The association of pre- and post-TKR PT with risk of long-term opioid use occurring more than 90 days after TKR was assessed using logistic regression while adjusting for confounders, including age, sex, race and ethnicity , obesity, type of insurance, geographical location, and physical and mental health comorbidities.A total of 38 408 opioid-naive individuals  and 28 914 opioid-experienced individuals  were included. Receipt of any PT before TKR was associated with lower odds of long-term opioid use in the opioid-naive  and opioid-experienced  cohorts. Receipt of any post-TKR PT was associated with lower odds of long-term use of opioids in the opioid-experienced cohort . Compared with 1 to 5 sessions of PT after TKR, 6 to 12 sessions  and 13 or more sessions  were associated with lower odds in the opioid-experienced cohort. Compared with initiation of PT within 30 days after TKR, initiation 31 to 60 days or 61 to 90 days after TKR were associated with greater odds in the opioid-naive  and opioid-experienced  cohorts. Compared with passive PT, active PT was not associated with long-term opioid use in the opioid-naive  or opioid-experienced  cohorts.This cohort study suggests that receipt of PT intervention before and after TKR, receipt of 6 or more sessions of PT care after TKR, and initiation of PT care within 30 days after TKR were associated with lower odds of long-term opioid use. These findings suggest that PT may help reduce the risk of long-term opioid use after TKR. This cohort study evaluates associations of physical therapy interventions before and after total knee replacement with long-term use of opioids after total knee replacement among US adults. Physical therapy (PT) interventions after TKR are effective in reducing pain and improving functional outcomes and, given these effects, they may be effective in reducing opioid use after TKR.12 This is, to our knowledge, unstudied. Furthermore, while there is agreement on the need for post-TKR rehabilitation supervised by physical therapists,13 there is not yet agreement on the timing and duration of rehabilitation after TKR, contributing to the variability in post-TKR PT interventions.15 Although active interventions  are generally more effective than passive interventions  in reducing pain in people with knee osteoarthritis,17 information regarding the association of type of PT interventions with long-term opioid use, a surrogate for pain management, is lacking for patients undergoing TKR.18 “Prerehabilitation” prior to TKR is associated with benefits in postoperative pain, function, and length of stay,21 but the association with post-TKR long-term opioid use is not known.Opioids are the primary pain reliever typically used to manage postoperative TKR pain acutely.We evaluated the association of PT interventions before and after TKR, relative to not receiving PT care, with long-term opioid use after TKR, including specific characteristics of post-TKR PT interventions . We hypothesized that any PT before and after TKR would be associated with a lower risk of long-term opioid use. Furthermore, we hypothesized that postoperatively, a higher dose of PT, earlier initiation of PT, and receipt of active PT would be associated with a lower risk of long-term opioid use.22 Race was categorized as Asian, Black, Hispanic, White, or unknown and derived using vendor-developed algorithms that rely on the individual’s name and geographical location.22 Because this study involved analysis of preexisting, deidentified data, the Boston Medical Center and Boston University Medical Campus institutional review board granted exemption from approval. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.We used data from the OptumLabs Data Warehouse, which includes deidentified medical and pharmacy claims, laboratory test results, and enrollment records for commercial insurance and Medicare Advantage enrollees. The database contains longitudinal health information on enrollees and patients, representing a diverse mixture of ages, races, ethnicities, and geographical regions across the US. Members in the database had full insurance coverage for physician, hospital, and prescription drug services.Current Procedural Terminology (CPT) procedure codes and International Classification of Diseases, Ninth Revision (ICD-9) and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis codes  and those without prior opioid use (opioid naive).CPT codes (eTable 2 in the CPT code within 90 days prior to TKR were considered as having received a pre-TKR PT intervention (referred to as pre-TKR PT). Patients with 1 or more PT CPT codes at an outpatient facility within 90 days after TKR were considered as having received a post-TKR PT intervention (referred to as post-TKR PT).Our primary exposure variables were occurrence of any visit with a physical therapist before and after TKR, with PT episode of care (EOC) identified using 26To define the attributes of PT interventions after TKR , we identified the first outpatient PT EOC that started within 90 days after surgery. A post-TKR PT EOC was considered to have ended when the EOC was followed by an absence of any PT claims for 12 weeks afterward. The 12-week period was not considered to be part of the EOC; rather, it was included in the period eligible for evaluation of the outcome. Definitions of these exposures are summarized in eTable 3 in the 27 We identified oral and enteral formulations of opioid prescriptions. A rheumatologist investigator (M.D.) selected the opioids , type of insurance , geographical location, and physical and mental health comorbidities. Geographical locations were Midwest, Northeast, South, and West. Comorbidities were identified throughout the 24 months prior to the index date using the Elixhauser Comorbidity Index.32 Physical comorbidities were included as the count of physical comorbidities .32 The following were excluded for calculating the total count of physical comorbidities: obesity, alcohol abuse, drug abuse, psychoses, and depression.33 Obesity was entered into the model as a separate covariate. Psychological comorbidities were identified using the Chronic Condition Data Warehouse algorithm created by the Centers for Medicare & Medicaid Services.34 Each psychological comorbidity was entered into models as an individual covariate,33 with exceptions of bipolar disorder, posttraumatic stress disorder, and schizophrenia disorder, which were combined because of the potential collinearity between these covariates. Furthermore, calendar year was added as a covariate for all models to account for secular trends, while pre-TKR PT was added as a covariate for post-TKR PT models.All analyses were adjusted for potential confounders including age, sex, race and ethnicity ,ICD-9 and ICD-10 codes to identify fibromyalgia, low back pain, neck pain, and shoulder pain as the other long-term musculoskeletal conditions.We identified use of nonsteroidal anti-inflammatory drugs and medications for other common chronic musculoskeletal conditions in these individuals within 24 months prior to TKR. Use of nonsteroidal anti-inflammatory drugs was defined as prescription of 1 or more of the following medications: celecoxib, diclofenac, diflunisal, fenoprofen, flurbiprofen, ibuprofen, indomethacin, ketoprofen, ketorolac, meloxicam, naproxen, nabumetone, oxaprozin, phenylbutazone, rofecoxib, salsalate, sulindac, tolmetin, and valdecoxib. We used Analyses for the study included data from January 1, 1999, to December 31, 2018. Descriptive data were summarized for key variables in the opioid-experienced and opioid-naive cohorts. We evaluated the associations of pre-TKR PT and post-TKR PT  with long-term opioid use in the 12-month period after the first 90 days after TKR (for any pre- and post-TKR PT interventions) or after the PT EOC ended  in the opioid-experienced and opioid-naive cohorts in separate logistic regression models. For sensitivity analyses, we repeated the analyses in the cohort of those who had a single opioid prescription 12 months prior to the index date. All analyses were conducted using SAS, version 9.4 (SAS Institute Inc). The statistical significance level was set at a 2-sided α level of .05 for all analyses.We identified 257 793 patients who underwent TKR from 2001 to 2016, of whom 67 322 patients met our inclusion criteria : 38 408 During the study period, 9035 of 63 249 participants (14.3%) received pre-TKR PT interventions . OverallA total of 47 738 of 63 249 participants (75.5%) received outpatient PT interventions within 90 days after surgery . Median Overall, 560 of 25 841 participants in the opioid-naive cohort (2.2%) and 5928 of 18 239 participants in the opioid-experienced cohort (32.5%) had long-term opioid use . A totalCompared with care initiation within 30 days after TKR, care initiation 31 to 60 days or 61 to 90 days after TKR was associated with greater odds of long-term opioid use for the opioid-naive cohort  and for the opioid-experienced cohort  . CompareSensitivity analyses were conducted among those with history of a single opioid prescription during the 12-month period prior to TKR. Results were either similar to those of the opioid-experienced cohort or in between the opioid-naive and opioid-experienced cohorts  was not associated with the outcome. These results suggest that PT interventions may be associated with lower risk of long-term opioid use after TKR and provide insights for developing guidelines on effective dose and timing of PT interventions after TKR.35 We also observed that most individuals (75.5%) received post-TKR outpatient PT care within 90 days of TKR, consistent with other studies.36 The lower odds of long-term opioid use after TKR with pre-TKR PT interventions complements findings of prior studies that reported benefits associated with pre-TKR rehabilitation for postoperative pain, function, and length of hospital stay.38 However, a recent consensus statement from the Enhanced Recovery After Surgery Society recommended against preoperative PT interventions for patients with TKR.18 The recommendations were made based on the moderate-quality evidence from meta-analyses that show small and short-term associations of pre-TKR PT interventions with postoperative outcomes; these outcomes did not include long-term opioid use.40 We also noted lower odds of long-term opioid use with any post-TKR PT intervention in the opioid-experienced group. We focused on outpatient PT care after TKR, given that all patients generally receive acute PT during their post-TKR hospitalization.36We noted that 14.3% of individuals received any pre-TKR PT intervention, similar to 10.5% reported in a prior study.41 Greater number of PT sessions has been associated with better pain and function after TKR.41 Our findings in a large real-world cohort provide data regarding an additional benefit associated with post-TKR PT, with higher PT dose being associated with lower odds of long-term opioid use in the opioid-experienced cohort; the opioid-naive cohort exhibited a similar, although nonsignificant, trend.A total of 47.0% of participants received 13 or more PT sessions after TKR; prior studies have reported a range from 10.1 to 14.5 sessions.42 The American Physical Therapy Association identified the need for research to investigate the optimal timing of post-TKR rehabilitation.43 Our findings suggest that initiating outpatient PT care within 30 days after TKR may be associated with a lower risk of long-term opioid use.Initiation of outpatient PT care within 1 month after TKR was associated with lower odds of long-term opioid use after TKR than later initiation in both the opioid-experienced and opioid-naive cohorts. In prior studies, earlier initiation of PT was associated with better function and lower pain; however, opioid use was not studied.17 However, associations of the type of PT intervention with outcomes in people with TKR are relatively understudied. A meta-analysis concluded that the level of evidence regarding the association of passive vs active PT interventions with opioid use was low.44 The recent American Physical Therapy Association guidelines recommend both passive and active PT interventions after TKR, but are mostly related to the immediate postoperative phase.43 Hence, further work is needed to understand the relative association of active and passive PT interventions with outcomes in outpatient settings after TKR.We did not observe an association between active vs passive PT interventions after TKR and long-term opioid use. In people with knee osteoarthritis, active PT interventions are more effective than passive interventions.48 Although the incidence of long-term opioid use among opioid-naive individuals is lower than among those with prior opioid use, these results are concerning given the large number of TKR surgical procedures performed annually in the US and projected increases in this surgery in the future.5 Our results suggest that pre-TKR and post-TKR PT interventions are associated with lower risk of long-term opioid use regardless of prior opioid use.Similar to prior studies, 32.5% to 35.0% of opioid-experienced individuals and 2.2% to 2.4% of opioid-naive individuals in our study had long-term opioid use after TKR.50 Second, the outcome assessment periods for the pre-TKR and post-TKR PT exposures were slightly different because the post-TKR PT EOC could extend beyond 90 days after TKR, and we aimed to examine the risk of long-term opioid use after completion of post-TKR PT. Third, while patients with inadequate improvement in pain and function are usually referred to PT, we cannot rule out the possibility that some patients who received pre-TKR and post-TKR PT were simply those who were less likely to be treated with opioids (and vice versa—that those taking opioids may be less inclined to undergo PT); thus our findings could reflect confounding by indication. A randomized clinical trial would be required to disentangle these issues. Fourth, we did not adjust for access to PT care across different states. Because regulations regarding access to PT can change over time, it was not possible to adjust for this variation in a study such as ours comprising data from a 15-year period, although we accounted for calendar year to attempt to address secular trends. We were also unable to account for physician-owned vs nonphysician-owned PT practices, which can have an association with the number and type of PT sessions that patients receive.36 Fifth, we did not have information on functional status during acute care after TKR that may be associated with the use of PT. Sixth, we adjusted for obesity instead of body mass index because not all individuals within the claims database have body mass index data; thus, there may be residual confounding, although the magnitude of residual confounding that would remain after accounting for obesity status is unclear. Seventh, we are unable to comment on more granular insights regarding whether PT interventions are associated with use of lower dosages of opioids, as we did not evaluate morphine milligram equivalent doses.This study has some limitations. First, we did not adjust for opioid use within 90 days after surgery because opioids are commonly prescribed for acute postoperative pain at discharge.This cohort study found that both pre-TKR and post-TKR PT interventions, and certain characteristics of post-TKR PT, such as greater number of sessions and earlier initiation, were associated with lower long-term use of opioids during the year after TKR, when pain and function have generally stabilized. Assessment of long-term opioid use after TKR may be a pertinent end point to consider in recommendations regarding PT interventions before and after TKR.


This cohort study compares orally administered anticancer medication uptake, out-of-pocket spending, and health plan spending in states with parity with and without spending caps. How does orally administered anticancer medication (OAM) use and spending differ among states that adopted parity with vs without out-of-pocket spending caps?In this cohort study of 23 states and 207 579 OAM prescription fills, out-of-pocket spending caps were associated with higher OAM use and lower out-of-pocket spending by $831 per OAM prescription fill among the highest spenders. Savings were larger for enrollees in states with caps that applied predeductible and postdeductible vs those that applied postdeductible only.Caps may offer improved financial protection for the highest spenders without increasing mean health plan spending on OAMs. By 2020, nearly all states had adopted oncology parity laws in the US, ensuring that patients in fully insured private health plans pay no more for orally administered anticancer medications (OAMs) than infused therapies. Between 2013 and mid-2017, 11 states implemented parity with out-of-pocket spending caps, which may further reduce patient out-of-pocket spending.To compare OAM uptake and out-of-pocket and health plan spending on OAMs in states with parity with and without spending caps, as well as to assess out-of-pocket spending for caps that apply predeductible vs postdeductible.This cohort study analyzed OAM users enrolled in commercial health plans offered by Aetna, Humana, and United Healthcare in the US from 2011 to 2017, aggregated by the Health Care Cost Institute, using difference-in-difference-in-differences (DDD) analysis. Data analysis was conducted between June and August 2020.Time (before vs after parity), whether the state parity law included an out-of-pocket spending cap, and whether the plan was fully insured (subject to parity) or self-funded (not subject to parity). Among states with caps, out-of-pocket spending was also compared by whether the cap was applied predeductible and postdeductible vs only postdeductible.Monthly OAM prescription fills per 100 000 enrollees, per-OAM prescription-fill out-of-pocket spending, and annual per-user health plan spending on OAMs.In this study of 23 states (11 with caps and 12 without) and 207 579 OAM prescription fills, caps were associated with a modest increase in OAM use . There was no difference in mean out-of-pocket spending comparing fully insured and self-funded enrollees in states with vs without caps , but caps were associated with lower spending among OAM users in the 95th percentile of out-of-pocket spending by $831  per OAM prescription fill. Caps applied predeductible were associated with greater out-of-pocket savings relative to caps applied only postdeductible. This included per-OAM prescription-fill savings at the 75th, 90th, and 95th percentiles. Postparity, mean annual spending on OAMs among users was $113 589 in states without caps and $102 252 in states with caps, with no differences between groups .In this cohort study, among states adopting oncology parity laws between 2013 and 2017, mean out-of-pocket spending per OAM prescription fill and mean health plan spending among OAM users was similar in states with and without caps. However, enrollees in states with parity plus out-of-pocket caps had greater reductions in out-of-pocket spending among the highest spenders. Caps may offer improved financial protection for the highest spenders without increasing mean health plan spending on OAMs. Specifically, higher out-of-pocket costs can result in patients abandoning medications5 or taking less than prescribed,6 even for highly effective anticancer drugs.The number of orally administered anticancer medications (OAMs) has increased rapidly in recent years, from 13 in 2010 to more than 50 by 2019.7 These concerns have resulted in widespread adoption of oral anticancer drug parity laws since 2008. By early 2019, all but 7 states had adopted oral oncology parity laws, and federal legislation to extend parity8 has been introduced in both the US House and Senate.Because OAMs are generally covered under a plan’s pharmacy benefit, advocates have voiced concern that patients may face higher out-of-pocket costs for these drugs than they would for drugs offered under a plan’s medical benefit .9 who saw increases in OAM spending. Since 2013, 11 states adopting parity have also included out-of-pocket spending caps, ranging from $50 to $300 for approximately 1 month of OAM supply. In addition, 6 of these states required that caps apply both before and after a deductible is met  with those in self-funded plans (not subject to parity) by the presence or absence of an out-of-pocket spending cap. We hypothesized that fully insured enrollees in states that include out-of-pocket spending caps in addition to parity would have greater use and lower out-of-pocket spending on OAMs than those in states with parity alone. We also assessed differences in out-of-pocket spending among states with spending caps applied both predeductible and postdeductible vs only postdeductible. Finally, we assessed the association of caps with mean annual per-user OAM spending by health plans to evaluate whether caps increase health plan spending on these drugs.STROBE) reporting guidelines for cohort studies. Data analysis was conducted between June and August 2020.We used 2011-2017 national health plan claims for privately insured members of Aetna, Humana, and UnitedHealthcare, aggregated by the Health Care Cost Institute. We included OAM prescription fills . We examined changes in mean out-of-pocket spending and the distribution of out-of-pocket spending among OAM prescription fills. We also measured mean annual OAM spending by health plans among OAM users.We included indicators for time (before vs after parity), whether the state parity law included an out-of-pocket spending cap, and whether the plan was fully insured (subject to parity) or self-funded (not subject to parity). Main effect sizes and 2-way and 3-way interactions were modeled. The 3-way interaction (the difference-in-difference-in-differences [DDD] estimator) indicates whether differences in utilization or spending postparity are similar among fully insured plans in states with and without caps, adjusting for trends among self-funded plans over the same period.We also examined whether out-of-pocket spending varied by the cap applied predeductible and postdeductible vs only postdeductible. For these models, we compared changes by group preparity and postparity among fully insured enrollees and self-funded enrollees in states with parity and caps .11 for estimating OAM prescription fills per 100 000 enrollees via a generalized estimating equation with an identity link and normal distribution. We tested for differential trends in monthly OAM prescription use during the 24 months preparity and did not find evidence for differential trends.We used a DDD design13 to examine changes in the distribution of out-of-pocket spending. In addition to accounting for time-invariant characteristics through our modeling approach, we estimated propensity score–weighted (adjusted) models, including age group, sex, and quarter when the prescription was filled. We used a multinomial logistic regression model to generate the propensity score for each group , providing an average of health plan spending on OAMs across all OAM users each year. We also used quantile regressionP < .05 considered statistically significant. All spending was inflation adjusted to 2017 dollars using the medical component of the consumer price index. Unadjusted results were similar to the propensity score–weighted estimates. Finally, among states with caps, we replicated our out-of-pocket spending analyses to test whether out-of-pocket spending differed among states with caps that applied predeductible and postdeductible vs postdeductible only. All analyses were conducted using SAS, version 9.4 (SAS Institute).We estimated 2 sets of models for each out-of-pocket spending measure: (1) DD models stratified by the presence or absence of an out-of-pocket spending cap (estimating changes preparity and postparity in fully insured members vs self-funded members in the same state), and (2) DDD models that compared changes in states with vs without caps. The former allows us to determine how spending has changed over time within states with parity alone and, separately, for states implementing parity with caps. The latter allows us to compare across parity implementation strategies (caps vs no caps). We estimated separate propensity score models for each contrast and produced unadjusted and propensity score–weighted estimates and 95% CIs, with 2-sided We tested alternate model specifications for the link functions and distributions for continuous outcomes; these changes did not meaningfully alter our estimates. Second, while most states that implemented parity during the study period did so between 2013 and 2015, 5 states implemented parity in later years (2016 or 2017) and had fewer than 2 years of follow-up. We restricted analysis to states passing parity between 2013 and 2015 and found no differences in the results. Third, to ensure findings were not driven by a few large states, we excluded California, Florida, and Georgia because they contributed more than half of the observations within fully insured plans with caps. Excluding these states did not change the results. Fourth, 2 generic OAMs were approved during the study period , which may have lowered out-of-pocket costs for these treatments independent of parity laws owing to lower prices through generic competition or health plan policies that use lower co-payments for generic drugs. Sensitivity analyses excluding the branded and generic versions of these products were nearly identical to the primary analysis. Finally, in the primary model of plan spending we focused on OAM spending alone. In sensitivity analyses, we included total outpatient prescription drug spending (OAMs and other prescriptions) with similar results , and 21% were from states with parity alone (n = 12). Approximately 65% of OAM prescription fills were among fully insured plan members subject to parity, and 35% were among self-funded plan members exempt from parity (within-state controls) . Among sWe observed increased OAM use over time among all fully insured enrollees, with greater increases among those in states with parity and caps (from 33.2 to 50.1 OAM prescription fills per 100 000 enrollees) vs those with parity alone (from 33.2 to 47.5 OAM prescription fills per 100 000 enrollees) . ControlMean per-OAM prescription fill out-of-pocket spending decreased by $87  among states with parity and caps, and by $69  among states with parity without caps for fully insured enrollees as compared with self-funded enrollees in those states over the same period . There wWe observed statistically significant savings for fully insured plan members after parity for both states with caps and states without caps as compared with self-funded members within the state . At the P < .001) per OAM prescription fill and savings of $106 , $124 , and $888  at the 75th, 90th, and 95th percentiles, respectively . Despite modestly increased use of OAM prescriptions per 100 000 enrollees in states whose parity laws implemented caps, we found no evidence that parity with caps increased mean annual per-user health plan spending on OAMs relative to parity without caps. We also found that, among states with caps, those that required that caps apply predeductible resulted in savings for individuals at the mean and at the 75th, 90th, and 95th percentiles of out-of-pocket spending relative to states that required that caps apply only postdeductible.9 In fact, savings appeared to be achieved primarily through plans lowering co-payments from approximately $30 to $0 per OAM prescription fill. Our previous findings differ from the current study, where we observed that parity (with or without spending caps) was associated with reduced out-of-pocket spending, even for the highest spenders. This difference could be related to the more recent time period, for which more OAMs were available and prices were higher, differences in plan generosity for states that were early vs late adopters of parity, or changes in insurance design over time . Indeed, the level of preparity out-of-pocket spending has increased substantially among fully insured plan members over time. For states passing parity between 2008 and 2012, the 90th and 95th percentiles of preparity out-of-pocket spending among fully insured adults were $105 and $184 per OAM prescription fill, respectively,9 vs $535 and $862 among states without caps and $559 and $1635 among states with caps in the current study (for years 2013-2017).Our prior work evaluating state parity laws and out-of-pocket spending between 2008 and 2012 found modest savings for most enrollees but increases in out-of-pocket spending for individuals with high spending.16 as would be expected under a high-deductible health plan. Given the increased number of enrollees with deductibles, the growth in the amount required to meet the deductible, and the long-term use of many OAMs today, these additional measures may be increasingly important for ensuring adequate cost protections and medication adherence for patients.While out-of-pocket savings were achieved for all enrollees subject to parity in the current study, the results suggest that caps combined with traditional parity could help to ensure that patients with the highest out-of-pocket spending are more directly targeted than under traditional parity laws. Additionally, states that required caps to apply both predeductible and postdeductible further shielded patients from very high out-of-pocket spending on OAMs relative to states that required that caps apply only postdeductible. Prior work has shown high rates of cost-related nonadherence of OAMs in the face of high out-of-pocket expenditures,17It is important to recognize the growing costs of OAMs for health plans. While we did not observe a statistically significant increase in mean annual medication spending per OAM prescription user for those subject to parity and caps relative to those subject to parity alone, the magnitude of per-user spending on OAMs was high and growing among all enrollees. In the postparity period, mean annual OAM spending per anticancer medication user ranged from $106 073 to $152 065. While cancer—and OAM use—is relatively rare among commercially insured adults, these increases in prescription-drug spending are considerable over a relatively short time frame. Higher spending on OAMs over time may be due to use of OAMs instead of infused therapies, greater adherence to OAMs, or price increases. Efforts to improve patient access to anticancer medication should be paired with efforts aimed at managing total drug and health care spending. This is particularly important because even anticancer drugs with low or no clinical benefit typically have very high prices for payers and patients, which may increase spending without improving outcomes.3 though we were not able to test that association in this study. Finally, we focused only on individuals in states that implemented parity between 2013 and 2017, and did not include a nonparity control group because only 7 states have not yet adopted parity laws. Instead, self-funded plan members (exempt from parity) were used to determine changes expected in absence of parity.Our study has limitations. First, we cannot determine if greater access to care or overuse was associated with the increased uptake of OAM prescriptions observed. We focused only on OAM users and did not evaluate use of infused therapies, other treatment modalities, or health outcomes among the cohort. Future studies with richer clinical data should evaluate whether high out-of-pocket spending for OAM prescriptions results in therapeutic substitution  or results in poor outcomes for patients. Second, we could not observe plan-level utilization management policies  and use of manufacturer coupons or other forms of patient co-payment assistance, nor could we account for patients who never filled a prescription owing to cost (their prescriptions are unobserved). Although coupons and co-payment assistance may lower actual out-of-pocket spending, the difference in out-of-pocket spending (our primary interest) would still be valid assuming similar program use among self-funded and fully insured patients in the same state and time period. Third, we studied patients in 3 national health plans; results may not be generalizable to other commercial insurers. Although the sample size was large, there were many more patients observed from states that implemented caps relative to those with traditional parity during the study period. This was partly because the traditional parity group was composed of less densely populated states; these states may have also had less payer representation. Fourth, most states requiring out-of-pocket spending caps required co-payments of $100 or $200 per OAM prescription fill, but caps varied from $50 in Florida to $300 in Utah, and these latter states both applied caps predeductible and postdeductible, complicating comparisons of how the cap level is associated with outcomes. Prior work suggests that paying more than $100 is an important indicator of prescription drug abandonment,In this cohort study of oral oncology parity laws, out-of-pocket spending caps were associated with modestly increased OAM use, as well as similar reductions in mean out-of-pocket spending per OAM prescription fill and mean health plan spending on OAMs compared with traditional parity laws alone. However, out-of-pocket caps reduced out-of-pocket spending among those with the highest spending preparity, with the greatest savings observed among states that applied caps both predeductible and postdeductible . US Congress has proposed federal reforms related to oncology parity in recent years, along with many other measures aiming to improve access to high-priced drugs. Federal parity legislation would represent an important advance from state efforts because it would extend parity to individuals in states that do not currently have parity and to self-funded plan members across all states. The present results suggest that federal parity efforts should include both traditional parity and an out-of-pocket spending cap— ideally applied predeductible and postdeductible—to ensure that those most in need of financial protections benefit from parity legislation.


To understand these anti-vax attitudes and their associated psychological characteristics, we examined several predictors of vaccine hesitancy for COVID-19 and anti-vax attitudes generally. We surveyed 1004 adults  in September-October 2020 across the United States , prior to widespread availability of the COVID-19 vaccines. Attitudes toward vaccinations were influenced by a variety of factors, especially political attitudes. We should therefore anticipate and attempt to mitigate these challenges to achieving widespread vaccination to reduce the spread of COVID-19 and other communicable diseases.The novel coronavirus (COVID-19) is a highly contagious disease responsible for millions of deaths worldwide. Effective vaccines against COVID-19 are now available, however, an extreme form of vaccine hesitancy known as  The Centers for Disease Control (CDC) reports that vaccination is the single most effective tool in preventing infectious disease . The socIn March 2020, the novel coronavirus (COVID-19) was declared a pandemic and has since infected over 393 million people worldwide and claimed more than 5.7 million lives, with the U.S. accounting for almost 15% of these deaths , 5.anti-vax attitudes. The World Health Organization (WHO) has identified anti-vax attitudes as a major threat to global health [As of February 2022, 64% of Americans were fully vaccinated against COVID-19 . Howeverl health . Anti-val health . People Vaccine hesitancy has existed since the introduction of vaccines. In 1796, Edward Jenner developed the first vaccine against a communicable illness  effectively preventing infection and the spread of the disease . This diWhile the anti-vax movement gained ground on the political left in 1998, a sizable far-right political group more recently joined the anti-vax movement, citing mistrust of the government and “Big Pharma”  as their main reasons for vaccine avoidance . Prior tOur goal was to conduct an examination of the correlates of anti-vax attitudes in general and COVID-19 vaccination hesitancy specifically. Besides socio-political attitudes, it is important to understand how demographic characteristics are associated with vaccination hesitancy. Recent studies of COVID-19 vaccine hesitancy in the UK and the U.S. found that Black and other non-White members of the population were less willing to receive a COVID-19 vaccine . This trLow levels of educational attainment have been generally associated with greater vaccine hesitancy . ResearcIn research exploring COVID-19 vaccine uptake, women reported greater vaccine hesitancy than men . Women oThe role of broad psychological characteristics such as personality, mental health, and substance use have been understudied as a correlates of anti-vax attitudes. However, they play an important role in health behaviors and may therefore contribute to anti-vax attitudes and hesitancy. For example, personality traits such as low conscientiousness and neuroticism as well as depression, smoking, and heavy alcohol use are all associated with poorer physical health and health-related behaviors in general, and so may contribute to anti-vax attitudes or slow vaccine uptake –31.Liberal and conservative social and political attitudes are likely to have strong associations with anti-vax attitudes. Given that Donald Trump was the standing president during when the COVID-19 pandemic began, we examined Trump job approval as it related to COVID-19 vaccine hesitancy and anti-vax attitudes generally. Additionally, the COVID-19 pandemic has been characterized by substantial misinformation or “fake news” regarding origin of the virus, contents of the COVID-19 vaccine, etc., which has been propagated through social media, now the main source of news among a large proportion of Americans , 33. AntFinally, it is likely that people’s individual experiences during the COVID-19 pandemic will relate to their attitudes about vaccination over and above more general characteristics like demographics, personality, mental health, and political attitudes. Therefore, we included several measures related to COVID-19 experiences, emotions, and behaviors including COVID-19 related worry and stress and negative impacts associated with the pandemic such as illness to self or relatives and friends, financial stress, restriction of activities, etc. We also included people’s adherence to recommendations for safety behaviors and mitigation strategies  and their attitudes about government mandates and restrictions.We examined the role of political attitudes, personality, mental health, and substance use on anti-vax attitudes and vaccine hesitancy. In consideration of the current political landscape, we measured alignment with former President Trump’s ideology regarding COVID-19 vaccine hesitancy and broad anti-vax attitudes. Also, as social media has been an important way through which false information regarding COVID-19 has been propagated, we examined the impact of social media use on both COVID-19 vaccine hesitancy and broad anti-vax attitudes. Finally, we explored the impact of several proximal social and environmental factors related to the COVID-19 pandemic itself; negative impacts associated with the pandemic, COVID-19 related worry and stress, adherence to recommendations for safety behaviors and mitigation strategies, and attitudes about government mandates and restrictions.We hypothesized that more conservative and less liberal sociopolitical attitudes, greater approval of Trump, less conscientiousness and more neuroticism, greater depressive symptoms, greater substance use, and social media use and problematic use would be associated with greater anti-vax attitudes and COVID-19 vaccine hesitancy. We also predicted that people who had experienced more COVID-19 related stress/worry, more negative impacts, and were more adherent to mitigation strategies and had higher approval of government restrictions would endorse less anti-vax attitudes in general and less COVID-19 vaccine hesitancy.Data were collected from September 24 through October 25, 2020, using an actively managed, double-opt-in research panel using Qualtrics XM survey software. Recruitment was designed to ascertain a sample consistent with major demographic characteristics of the United States general population for age , gender , and race/ethnicity . Quotas were created for each variable and monitored while the survey was in the field. Respondents were recruited using a dashboard-style web page on the Qualtrics website and cellphone app where participants see a list of surveys that they have the option to participate in. Recruitment was also conducted through emails sent to established panel members within the Qualtrics database. In all recruitment methods, potential participants received information on the estimated length of the survey and compensation for completing it. Specific details about the survey content were not available until the participants opted-in to avoid self-selection bias. Upon opting into the study, participants read and provided an electronic signature on a consent form containing an overview of the survey contents. Participation was voluntary and anonymous as no individually identifying information was collected. Upon completion, participants were awarded credits by Qualtrics which they could cash out or use to purchase gift cards. Contact information for the research team was provided in case participants had questions about the survey. The University of Michigan Medical School Institutional Review Board (IRB) reviewed all study protocols.The survey was completed by 1024 respondents. Data were manually checked, and 20 respondents were excluded due to inconsistent and illogical responses, resulting in a final sample size of 1004 respondents . Examples of excluded responses include those who failed item-level quality control checks and those who consistently (>75%) chose only one response option, e.g., all “2”s. Single measures were excluded on a case-by-case basis if all other responses from that participant were within a plausible range of values. The final sample included responses from participants aged 18 to 98 years old . The median response time for completing the survey was 28.1 minutes.The demographic characteristics of the sample are reported in Anti-vax scale. Participants responded to six questions assessing general support for vaccinations  [a = 2.52 (range .94 to 6.29) and the mean standardized factor loading was λ = .72 (range .48 to .97). The marginal reliability for the scale was .86 and internal consistency (Cronbach’s α) was .79. Graded response models were estimated using flexMIRT version 3.6 with full information maximum likelihood estimation [= 17.11) . Item cotimation .COVID-19 vaccine hesitancy. We assessed openness to receiving a vaccination for the COVID-19 virus with the single item “How likely are you to get a COVID-19 vaccine that has gone through the normal protocol for development ?” . A COVID-19 vaccination had not yet been released in any country at the time of assessment.Demographics. Demographic characteristics used in the prediction models included age, sex assigned at birth , race , annual household income, and educational attainment.Personality. The Big Five Inventory-2 short form (30-items) was used to assess extraversion , agreeableness , conscientiousness , negative emotionality , and open-mindedness  [α = .68) .Mental health. We used the general Depression scale  from the Inventory of Depression and Anxiety Symptoms to provide a measure of overall depressive symptoms and dysphoria [ysphoria .Binge drinking. We assessed alcohol use by asking participants to report how often they had engaged in binge drinking  in the past 30 days .Nicotine use. Participants reported frequency of smoking cigarettes, using smokeless tobacco, or e-cigarettes . Response options “regularly in the past” and “regularly now” were coded the same for the analyses. The highest frequency reported among the three nicotine questions was used for the nicotine use variable.Social media use. Participants indicated their use of a list of social media platforms. If they indicated the use of any social media platform, they were coded as a “1” on a binary social media use variable. If they indicated a response of “None”, they were coded as a “0”.Problematic social media use. A 9-item scale (α = .88) was used to assess problematic social media use. Items were modeled on facets of behavioral addiction including overuse, functional impairment, using social media to reduce negative emotions, and attempts to cut down on use without success .Liberal and conservative social attitudes. We used the Right-Wing Authoritarianism  scale to provide a measure of liberal and conservative political and social attitudes [r = -.54), but still had sufficient unique variance to provide incremental predictive power relative to a single scale.ttitudes . Ten iteApproval of President Trump. We asked respondents to report their approval of President Trump’s job performance in general .COVID-19 related safety behaviors and attitudes about government mandates and restrictions. For safety behaviors, participants were asked how often they followed the “social distancing” or “shelter-in-place” restrictions put in place in your community in the past 3 months, and how often they wear a mask when in public . Participants were also asked three questions to assess attitudes about government responses to COVID-19 including the necessity of COVID-19 related restrictions , approval of their state government’s social distancing restrictions , and attitudes about the pace at which their community was lifting social distancing restrictions . A composite variable of COVID-19 related safety behaviors and attitudes about government restrictions was calculated by taking the mean of the five items (α = .81).COVID-19 stress/worry. Seven items from the COVID-19 Adolescent Symptom and Psychological Experience Questionnaire were used to assess COVID-19 related negative emotions during the past 30 days [ 30 days . ParticiNegative impacts of the COVID-19 pandemic. We assessed the number of negative impacts incurred due to the COVID-19 pandemic by providing participants with a multiple-response list of negative events including: Self/loved one has the virus; having to stay home; not seeing friends in person; many people are dying because of the virus; not going to school/work; self/child participating in virtual education; spending more time with family; financial difficulties; concern about the health of self/loved ones; concern about the government; missing important events; and a free-response option. A composite variable of negative impacts was calculated by taking the sum of all responses .Separate multiple regression models were fit to examine the extent to which predictor variables were associated with the anti-vax beliefs and hesitancy to receive a COVID-19 vaccine. All models were fit in Mplus version 8.5 using full information maximum likelihood estimation . Confidehttps://osf.io/cqa6m/?view_only=659f436af1ce4049b2e1d01d8b540343 for full data set. See r = .48, p < .001, indicating people with more anti-vax beliefs were also likely to report greater COVID-19 vaccine hesitancy.For hesitancy to receive a COVID-19 vaccine, the endorsement rates for the response options to the question, “How likely are you to get a COVID-19 vaccine?”, were: 38.3% Very likely, 30.8% Somewhat likely, 17.0% Somewhat unlikely, and 13.8% Very unlikely. Scores on the anti-vax scale and responses to the COVID-19 vaccine hesitancy item were correlated at ds for the mean differences. Non-White participants  and people with lower incomes and less education were more likely to endorse anti-vax attitudes, while older participants were less likely to have anti-vax attitudes. Women, non-White participants , and people with lower incomes and less education reported greater COVID-19 vaccine hesitancy. Participants that were older and reported higher incomes expressed greater willingness to receive a COVID-19 vaccine.Descriptive information for the vaccine outcomes across different demographic groups is presented in R2 values from the multiple regression models predicting COVID-19 vaccine hesitancy are presented in Zero-order correlations, standardized regression coefficients, and β = .38) and less adherence to COVID-19 safety behaviors and lower approval of government restrictions (β = -.22). We also detected small effects for less social media problem use (β = -.11), less liberal social attitudes (β = -.10), and demographic variables including younger age (β = -.09), female sex (β = -.12), non-White race (β = .10), and lower income (β = -.08). The negative association between vaccine hesitancy and social media problem use seems to be a suppressor effect that emerged after adjusting for the common variance between social media problem use and age  and anti-vax attitudes , and so is not interpreted further.The final model that included all the predictor variables accounted for 36% of the variance in COVID-19 vaccine hesitancy. The largest effects were observed for anti-vax attitudes , conservative social attitudes (β = .18), and less adherence to COVID-19 safety behaviors and less approval of government restrictions (β = -.22). We also detected small effects for less liberal social attitudes (β = -.12), lower income (β = -.11), greater social media problem use (β = .12), and greater nicotine use (β = .07).The final regression model that included all the predictors accounted for 32% of the variance in anti-vax attitudes. The largest unique effects were observed for non-White race  and lower educational attainment consistently predicted anti-vax attitudes and COVID-19 vaccine hesitancy. Lower educational attainment may discourage vaccine reception via susceptibility to misinformation and reduced capability to consider the scientific nuances of vaccines [Younger age was associated with greater vaccine hesitancy, though it was not a significant predictor of anti-vax attitudes in the multiple regression model. Older adults are at greater risk of serious illness and death from COVID-19, which likely accounts for the greater willingness of older participants to receive a COVID-19 vaccine. In contrast, younger people may be more likely to believe that receiving a COVID-19 vaccine is relatively unimportant or would not incur significant benefits . These avaccines .Next, the negative attitudes towards vaccines in non-White participants may be traced to historical and present-day racial injustices and disparities in the American medical establishment. Systematic racial disparities in medical care are widespread as evidenced, for example, in the high maternal and infant mortality rates, bias in pain management, and the disparities in illness and death from COVID-19 in Black Americans –46. ConsSocial and political attitudes had strong associations with anti-vax attitudes and significant but weaker associations with COVID-19 vaccine hesitancy. We found that people who disagreed with relatively liberal social attitudes  tended not to support vaccination—both in general and specifically for the COVID-19 vaccine. Conservative and authoritarian attitudes were also strongly associated with anti-vax attitudes, though they had a weaker association with COVID-19 vaccine hesitancy. Both liberal and conservative social attitudes were assessed using items from the Right-Wing Authoritarian scale, which conceptualizes the authoritarian personality as having unwavering obedience to a respected leader and the desire for strict societal rules . This deFinally, anti-vax attitudes and COVID-19 vaccine hesitancy were associated with a lack of adherence to COVID-19 safety behaviors and support for government restrictions to mitigate the spread of COVID-19. In other words, if an individual does not believe that vaccines against COVID-19 and other infectious diseases are necessary and important, then they will not be motivated to participate in related health and safety behaviors. This aligns with recent COVID-19 related literature showing that greater belief in general vaccine effectiveness and/or importance is negatively associated with willingness to receive a COVID-19 vaccine .Anti-vax attitudes were also associated with low conscientiousness, depression, binge drinking, nicotine use, problematic social media use, higher approval of President Trump, and experiencing slightly fewer negative impacts due to the COVID-19 pandemic. We hypothesized that several facets of psychological adjustment might relate to anti-vax attitudes, though it may be more constructive to conceptualize these specific facets as converging onto a broader psychological structure associated with poor health behavior in general. These psychological and behavioral characteristics include the small associations with low conscientiousness, smoking, heavy drinking, and depression. These traits and behaviors may be indicators of a general lack of concern about one’s health and tendency to disregard health recommendations, or they may be psychological characteristics that impair one’s ability to learn about best health practices or the ability to implement changes necessary to experience the benefit of those practices. These associations, however, were small and many were no longer significant after adjusting for other more salient variables .r = .57), binge drinking (r = .44), smoking (r = .39), and conscientiousness (r = -.43). Problematic social media use, however, continued to have an incremental association with anti-vax attitudes after controlling for these psychological variables, suggesting problematic social media use also has a direct effect on the acquisition of anti-vax attitudes, which is reasonable given the effort anti-vax groups have invested in propagating their beliefs through social media platforms [The association between anti-vax attitudes and problematic social media use might also be attributed to poor psychological adjustment in general, as problematic social media use exhibited strong correlations with depression , this significantly reduces the effectiveness of vaccination as a public health intervention to achieve herd immunity and eradicate a disease like COVID-19. The consequence of this is that the disease continues to circulate within the population, infecting the unvaccinated and allowing time for mutations of the virus to occur that could result in variants that are resistant to medical intervention and vaccines. Similarly, having allowed anti-vax attitudes to persist in American society has created conditions that are now contributing to a vulnerability in effectively fighting this and future pandemics.An understanding of the correlates of anti-vax attitudes beyond COVID-19 could help public health officials develop targeted approaches for certain vaccine-hesitant populations. Several positive public health outcomes can be achieved if anti-vax attitudes are appropriately addressed and curbed, including the eradication of diseases and fewer medical and public health costs. On the other hand, failure to appropriately curb vaccine refusal may incur negative public health consequences. For example, caregivers have been increasingly refusing vaccinations for their kindergarteners, citing personal or moral reasons, which has led to a direct increase in measles cases, which was thought to be eradicated in the United States just 20 years ago . It is pTaken together, these results help explicate factors related to the maintenance of ongoing public health concerns. As COVID-19 cases proliferate while a large percentage of the population remains unvaccinated, this demonstration of the relative predictive power of demographic, trait, and state factors can be utilized to develop educational campaigns and public health interventions.While this study provides novel insights into anti-vax attitudes, there were some limitations. Importantly, we were unable to examine effects in non-Black minority groups due to a small sample size. All racial and ethnic disparities related to public health should be a focus of future study. Next, we did not examine the reasons why or why not respondents are willing to receive a COVID-19 vaccine. Thus, we were unable to consider potential barriers to receiving a vaccine, that is, lack of health insurance, transportation issues, and even fear of needles . Some inr = .48) between COVID-19 vaccine hesitancy and our Anti-Vax attitudes scale.Next, we faced inevitable challenges related to reliability and validity for our COVID-19 vaccine hesitancy variable. First, internal consistency cannot be computed with a single-item response. The current study was also cross-sectional thus retest reliability could not be computed. Further, it is difficult to establish convergent and discriminant validity when, to our knowledge, no validated scales of willingness to receive a COVID-19 exist. Therefore, we rely on the face validity of the question and associations with other variables in the data set. For example, the large correlation  to a liberal political party. These events may have led to substantial changes in attitudes regarding vaccination. Future work will follow up with respondents over the course of the pandemic and post-pandemic to determine the trajectory of anti-vax attitudes and the long-term health impacts of vaccine hesitancy.


Minority ethnic and migrant groups face an elevated risk of compulsory admission for mental illness. There are overlapping cultural, socio-demographic, and structural explanations for this risk that require further investigation.By linking Swedish national register data, we established a cohort of persons first diagnosed with a psychotic disorder between 2001 and 2016. We used multilevel mixed-effects logistic modelling to investigate variation in compulsory admission at first diagnosis of psychosis across migrant and Swedish-born groups with individual and neighbourhood-level covariates.s.d.) increase in the exposure: 1.12, 95% CI 1.06–1.18], an effect that appeared to be driven by own-region migrant density .Our cohort included 12 000 individuals, with 1298 (10.8%) admitted compulsorily. In an unadjusted model, being a migrant  or child of a migrant  increased risk of compulsory admission. However after multivariable modelling, region-of-origin provided a better fit to the data than migrant status; excess risk of compulsory admission was elevated for individuals from sub-Saharan African , Middle Eastern and North African , non-Nordic European , and mixed Swedish-Nordic backgrounds . Risk of compulsory admission was greater in more densely populated neighbourhoods [OR per standard deviation (Inequalities in the risk of compulsory admission by migrant status, region-of-origin, urban living and own-region migrant density highlight discernible factors which raise barriers to equitable care and provide potential targets for intervention. There is strong evidence that risk of compulsory psychiatric care is greater for certain ethnic minority groups than majority white populations  is associated with greater risk of compulsory admission for psychosis. One study of any psychiatric disorder (not limited to psychosis) in Italy found no differences in compulsory admission for migrants  migrants and children of migrants were at greater risk of compulsory admission at first diagnosis of psychotic disorder than the Swedish-born population, and (2) if this was attributable to cultural or structural factors, including region-of-origin and own-region migrant density, or family income, neighbourhood population density, and deprivation, respectively. As in other countries, Sweden has a legal mechanism for compulsory psychiatric care via three health and social care acts, of which the Compulsory Psychiatric Care Act is most commonly enacted Reitan, . This alth Revision  in 2001, and is known to be reliable and valid for research purposes . All individuals were official residents of Sweden at diagnosis. Individuals without permanent residency in Sweden  were not included in this study.We extracted nationwide data from Psychiatry Sweden, an anonymized database of linked national registers. From the Register of the Total Population (RTP), we identified all individuals born between 1985 and 1997 who were diagnosed with an International Classification of Diseases, 10Our primary outcome was compulsory admission status at the time of the first diagnosis of psychotic disorder. This is recorded in the NPR for each contact with health services (in- or out-patient) under the Compulsory Psychiatric Care Act  and the multigenerational register. We categorised individuals into three migrant groups: Swedish-Nordic heritage , Swedish-migrant heritage  or other mixed migrant heritage .As a second exposure variable, we defined a ‘region-of-origin’ variable to further categorise individuals into seven broad regions (online Supplementary Table S1): Sweden, other Nordic countries, other European countries, Northern Africa and the Middle East, sub-Saharan Africa, Asia and Oceania, and North and South America  and sex were recorded from the RTP and NPR at time of first psychotic disorder diagnosis. Disposable family income as a potential confounder was obtained from the Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA), which records total disposable family income from all sources each year , weighted for family size, with younger children receiving less weight. In each year, we divided family income for all Swedish residents into quintiles, implicitly accounting for inflation, and assigned individuals to their family disposable income quintile in the year of their 15We obtained the Small Area Market Statistics (SAMS) neighbourhood that individuals were living in their year of diagnosis from the RTP. SAMS areas contain between 1000 and 2000 individuals and are devised by Statistics Sweden based on election districts in more rural areas, and by dividing cities into neighbourhoods of socioeconomically homogenous housing , as the proportion of people from each region-of-origin as a function of the total population of each SAMS in a given year. Migrants and children of migrants were assigned their own-region migrant density, with children of migrants from mixed backgrounds being assigned either as of Swedish-migrant or mixed-migrant origin, except those of Swedish-Nordic heritage who were assigned Nordic migrant density values in these analyses. All neighbourhood-level covariates  were We compared the distribution of compulsory admission, demographic and neighbourhood-level characteristics by migrant status using percentages and Pearson's chi-square tests. Next, we fitted unadjusted and multivariable multilevel logistic regression models to examine the effect of migrant status, region-of-origin and own-region migrant density on risk of compulsory admission. Multilevel models were fitted with random intercepts at the neighbourhood (SAMS) level to take into account the nested structure of the data  and to quantify variation in compulsory admission attributable to the neighbourhood-level, in null (i.e. outcome-only) and fully adjusted models, expressed as intraclass correlation coefficients (ICC).a priori confounders to examine the effect of migrant status on compulsory admission. Other confounders  were added to the model in order of the strength of their univariable associations (estimated via Akaike's Information Criterion), which we retained if they improved model fit, assessed via a likelihood ratio test (LRT). Next, we added region-of-origin to our model to examine whether this provided a better fit of the data than migrant status, assessed via LRT and reported effect sizes by region-of-origin if appropriate. Third, to examine whether own-region migrant density was associated with the risk of compulsory admission we repeated the analyses above on a sample restricted to migrants and children of migrants (i.e. excluding the Swedish-born sample). Finally, we examined whether region-of-origin, own-region migrant density, population density and deprivation had differential effects for migrants v. children of migrants by inspecting stratified models and reporting LRT p values for effect modification between migrant status and these variables on the risk of compulsory admission. Analyses were based on complete case analysis, with minimal missing data . OR were presented with 95% confidence intervals (95%CI) and statistical significance set at p < 0.05. OR for compulsory admission associated with all neighbourhood variables were expressed as the OR for a one standard deviation change (increase) in the exposure. All modelling was conducted in Stata 14.Modelling proceeded as follows. First, we reported unadjusted odds ratios (OR) to characterise their univariable association with compulsory admission at first diagnosis of psychotic disorder. Second, we fitted a multivariable model with age and sex as th birthday. One individual was missing data on the migrant status and another on region-of-origin (0.02%). There were also 165 (1.4%) individuals missing data on neighbourhood population density and deprivation, 92 (0.8%) on own-region migrant density and 201 (1.7%) on family income , and, in particular, were more likely to have been involuntarily admitted  and be migrants .Our cohort included 12 000 individuals born between 1985 and 1997 and diagnosed with a psychotic disorder in Sweden after their 16y income . The com2p < 0.001). Median age at first diagnosis was 22.2 years old , though this differed between children of migrants , Swedish-born  and migrant  individuals . Most of the sample were male (59.6%) and Swedish-born (60.0%). Individuals were over-represented in more densely populated and deprived neighbourhood quintiles, although this was more pronounced amongst migrants and children of migrants (2p < 0.001). Migrants were also more likely to be in lower-income quintiles than children of migrants and the Swedish-born population (χ2p < 0.001). Migrants were most likely to come from non-Nordic European countries (27.0%), the Middle East and North Africa (25.9%) or sub-Saharan Africa (20.8%), while children of migrants were predominantly of dual Swedish-migrant (32.8%), Swedish-Nordic origin (23.6%) or Middle Eastern and North African origin (12.6%).In total, 1298 (10.8%) individuals were admitted compulsorily at their first admission for psychotic disorder, a figure that was higher in migrants (13.5%) than their children (12.0%) or the Swedish-born population . These remained statistically significant in the final adjusted model (p = 0.02).A null multilevel logistic model indicated that about 5.5% (95% CI 2.9–10.3) of variance in compulsory admission could be attributed to neighbourhood level effects  or child of a migrant  was associated with increased risk of compulsory admission compared with the Swedish-born population . Being mMale sex and older age at first diagnosis were associated with increased risk of compulsory admission in our adjusted model , as wellN = 4533). In univariable models, greater population density, deprivation and migrant density were all associated with increased risk of compulsory admission in this sample , population density (LRT p = 0.55), or own-region migrant density (LRT p = 0.47) on compulsory admission differed between migrants and children of migrants, although the effects of region-of-origin and own-region migrant density appeared more robust to statistical chance in children of migrants (online Supplementary Table S4).In observing data during primary analyses, a potential differential effect of region of origin on compulsory admission between migrants and children of migrants emerged, and we conducted post-hoc analyses to establish significance. However, there was no evidence that the effect of region-of-origin , which was recorded in the NPR based on clinical discharge diagnoses made downstream of our outcome (admission status); this may therefore not have been independent of outcome status, potentially introducing bias. We were also limited in being able to understand potential legal barriers to care and their implications for the risk of compulsory admission, as the Swedish registers do not include data on those living in the country without permanent residency. An additional limitation of using register-based was our reliance on available variables; we were unable to include data on potentially relevant factors such as symptom severity at presentation, grounds for a compulsory admission decision, or access to healthcare services prior to the first admission. Nonetheless, such challenges are common to all register-based research, and the variation in compulsory admission we observed could not be attributed to demographic differences, family income or major neighbourhood-level socioenvironmental factors, which should inform current theories for why many migrant and ethnic minority groups face an elevated risk of compulsory admission for psychiatric care.Our study used longitudinal data from Sweden's national registers, allowing us to access information on all adults diagnosed with a psychotic disorder for the first time in Sweden, with minimal missing data. While those individuals missing data differed significantly from our analytical sample on a number of variables, the small amount of missing data in our sample (3%) decreased the likelihood that these differential patterns of missingness had substantial import on our results. The Swedish NPR began recording involuntary admission status in 2001, giving us up to 15 years of follow-up data on this sample with almost complete coverage of psychiatric care over this period  was lower than observed elsewhere. For example, compulsory admission in first-episode psychosis samples has been reported to be between 19% and 29% in the Netherlands , this did not account for the excess risk of compulsory admission for people of mixed Swedish-Nordic heritage or other European backgrounds (online Supplementary Table S3). This suggests further research on post-migratory pathways to care for psychosis in different migrant groups appears warranted to contextualise these patterns.In England, Weich et al.  identifiFor migrants and children of migrants, we also found that higher own-region migrant density was associated with a higher risk of compulsory admission, independent of population density. This finding suggests that culture-bound understanding of mental health (Sharpley et al., We have shown that migrants and children of migrants living in Sweden, and coming from several regions-of-origin, are at increased risk of compulsory admission for their first diagnosis of psychotic disorder, drawing unenviable parallels with findings from the UK, the Netherlands and Canada. Our results provide further contextualization of these issues and highlight the likely interactions between cultural, socio-demographic and structural barriers which may shape variation in risk of compulsory admission for care for psychotic disorders. This phenomenon requires an international research response to elucidate the qualitative mechanisms that maintain this inequality so we can provide more equitable access, pathways and treatment for first-epsiode psychosis, regardless of race, ethnicity or migrant status.


Neonatal sepsis is a significant global health issue associated with marked regional disparities in mortality. Antimicrobial resistance (AMR) is a growing concern in Gram-negative organisms, which increasingly predominate in neonatal sepsis, and existing WHO empirical antibiotic recommendations may no longer be appropriate. Previous systematic reviews have been limited to specific low- and middle-income countries. We therefore completed a systematic review and meta-analysis of available data from all low- and lower-middle-income countries (LLMICs) since 2010, with a focus on regional differences in Gram-negative infections and AMR.Escherichia coli, Klebsiella, Enterobacter, Pseudomonas, Acinetobacter and AMR. We included 88 studies  comprising 10,458 Gram-negative isolates from 19 LLMICs. No studies were identified outside of Africa and Asia. The estimated pooled proportion of neonatal sepsis caused by Gram-negative organisms was 60% (95% CI 55% to 65%). Klebsiella spp. was the most common, with a pooled proportion of 38% of Gram-negative sepsis (95% CI 33% to 43%). Regional differences were observed, with higher proportions of Acinetobacter spp. in Asia and Klebsiella spp. in Africa. Resistance to aminoglycosides and third-generation cephalosporins ranged from 42% to 69% and from 59% to 84%, respectively. Study limitations include significant heterogeneity among included studies, exclusion of upper-middle-income countries, and potential sampling bias, with the majority of studies from tertiary hospital settings, which may overestimate the burden caused by Gram-negative bacteria.All studies published from 1 January 2010 to 21 April 2021 about microbiologically confirmed bloodstream infections or meningitis in neonates and AMR in LLMICs were assessed for eligibility. Small case series, studies with a small number of Gram-negative isolates (<10), and studies with a majority of isolates prior to 2010 were excluded. Main outcomes were pooled proportions of Gram-negative bacteria are an important cause of neonatal sepsis in LLMICs and are associated with significant rates of resistance to WHO-recommended first- and second-line empirical antibiotics. AMR surveillance should underpin region-specific empirical treatment recommendations. Meanwhile, a significant global commitment to accessible and effective antimicrobials for neonates is required. Sophie Wen and co-workers report a meta-analysis on bacterial isolates and antimicrobial resistance in neonatal sepsis in low- and lower-middle-income countries. Neonatal sepsis is a significant cause of childhood mortality with marked disparities across world regions and countries.Gram-negative bacteria are becoming increasingly predominant in neonatal sepsis, particularly in low- and middle-income countries, along with growing concerns of multidrug resistance.It is likely that the World Health Organization (WHO) empirical neonatal sepsis antibiotic recommendations are no longer adequate. We analysed data on neonatal sepsis from low- and lower-middle-income countries (LLMICs) to assess the burden of Gram-negative infections and associated antimicrobial resistance (AMR).E. coli, Klebsiella, Enterobacter, Pseudomonas, and Acinetobacter. We also examined the rates of AMR against key empirical antibiotics used in neonatal sepsis.We pooled data from 88 published studies from 19 LLMICs on neonatal sepsis  and summarised the proportions of Gram-negative sepsis caused by Gram-negative bacteria accounted for 60% of neonatal sepsis in LLMICs. Regional variations in types of Gram-negative bacteria were observed, along with high rates of resistance against the WHO-recommended empirical antibiotics for neonatal sepsis.The WHO empirical antibiotic recommendations for neonatal sepsis are likely inadequate in many LLMICs.Robust AMR surveillance and reporting in LLMICs is necessary to develop region-specific empirical antibiotic recommendations for neonatal sepsis. Neonatal sepsis is a major cause of mortality and morbidity, accounting for approximately 22% of global annual neonatal deaths . ImproveNeonatal sepsis is historically categorised as either early-onset sepsis (EOS) or late-onset sepsis (LOS), with EOS variably defined as sepsis within 72 hours or up to 7 days of birth. EOS is traditionally thought to be caused by organisms such as group B streptococcus and enteric Gram-negative bacteria, acquired peripartum from the maternal genital tract. LOS, on the other hand, is considered to arise due to the acquisition of pathogens during hospitalisation, with very low birth weight and early gestational age being strong risk factors . IncreasThe World Health Organization (WHO) recommends the use of gentamicin with either ampicillin or benzylpenicillin as first-line treatment for neonatal and paediatric sepsis in resource-limited settings, with ceftriaxone as recommended second-line therapy . Recent with.organd.gov domains to include relevant materials from local shelves. The full search strategy and details of the quality assessment performed on each article can be found in This systematic literature search and review was performed using a predesigned study protocol  and adheres to the PRISMA guideline for reporting systematic reviews and meta-analyses see . We searStudies were excluded if they presented aggregated data from which country-specific data could not be clearly identified, or from which neonates could not clearly be distinguished from older children or adults. Studies were also excluded if they presented insufficient information on the type of Gram-negative organisms or antimicrobial susceptibility, or if they reported infections of non-sterile sites. To ensure data were relevant to the current epidemiology of neonatal sepsis in LLMICs, a decision was made to exclude studies prior to 2010 and studies reporting a majority of isolates prior to 2010. Case reports or series  were excluded due to study design, as they were likely to have significant biased selection of participants. Abstracts and titles were compiled in Endnote, and duplicates were removed. Two investigators (SCHW and YE) individually reviewed the identified articles to determine eligibility. All eligible articles were retrieved in full text. For references where we were unable to retrieve the full text and those with results that required clarification to assess eligibility, direct email contacts were sent to the corresponding authors. Non-English articles were included if data were able to be reliably extracted using Google translate. Disagreement over inclusion was resolved by consensus.E. coli, Klebsiella species, Enterobacter species, Pseudomonas species, and Acinetobacter species), results of antimicrobial susceptibility testing , and burden of extended-spectrum beta-lactamases. Setting variables included country, city, setting , study design, publication year, and study years. Two investigators (SCHW and LR) independently extracted the above variables into an Excel spreadsheet.A data extraction checklist was developed based on the PICOS domains. Population variables included demographics of the neonatal population  and definition of neonatal sepsis . Outcome variables included timing of neonatal sepsis , total number of neonatal sepsis cases, mortality rate, number of blood or cerebrospinal fluid (CSF) cultures performed and number of positive cultures, microbiological methods, number of Gram-negative organisms identified  using either the Newcastle–Ottawa quality assessment tool  or Cochrane risk of bias tool randomised controlled trials. Consensus was reached by panel discussion between 3 investigators . The results of quality assessment are summarised in Klebsiella, Enterobacter, Pseudomonas, and Acinetobacter spp. as these bacteria are intrinsically resistant. Stratification was done by continent of study (Asia versus Africa). Pooled prevalence was calculated as effect size with 95% confidence intervals (CIs) using logistic-normal random-effect models. Given the variability of the patient characteristics within the studies, the random-effect model was applied irrespective of the I2 statistics.Meta-analysis was conducted to calculate pooled prevalence of positive blood or CSF culture, and of the 5 major Gram-negative bacterial species using the ‘metaprop’ command of the ‘metan’ package in Stata 16 . Pooled I2 measure of inconsistency.Sensitivity and subgroup analyses as well as meta-regression models were used to investigate sources of heterogeneity and the factors that affect the magnitudes of estimates, where data were available. The sensitivity analyses were conducted by excluding 1 study each time and recalculating the pooled prevalence. Funnel plots and Egger’s meta-regression test were used to assess small study effects. Study heterogeneity was reported using the https://www.naturalearthdata.com), through the ‘rnaturalearth’ package (version 0.1.0) [All statistical analyses were done using Stata 16 . The results are illustrated on a world map, using data from the public domain map dataset Natural Earth (n 0.1.0) .n = 81) used a cross-sectional design. Study settings were reported in 84 studies, with a majority of the studies undertaken in a hospital setting. Forty-nine studies reported data from neonatal intensive care units (NICUs), 10 studies from special care baby units, 22 studies from a paediatric ward or unspecified hospital setting, and only 3 studies from the community. The definition of EOS was documented in 58 studies (66%): sepsis occurring within 48 hours of birth (1 study), within 72 hours of birth (43 studies), and within 1 week of birth (14 studies). There was also variation in the definition of the neonatal period (reported in 45 studies): 0–4 days (1 study), 0–28 days (38 studies), 0–30 days (2 study), and 0–60 days (4 studies). Bacteriological identification and antimicrobial susceptibility testing methods were reported in 70 studies (80%) and 76 studies (86%), respectively. Disk diffusion was the most commonly reported antimicrobial susceptibility testing method (70/72 studies). Most studies included data on all major Gram-negative species. Five studies reported on a subgroup of Gram-negative bacteria . Significant heterogeneity was observed across all meta-analyses and subgroup analyses. We could not identify explicit sources of heterogeneity due to the limitations of the available data. The characteristics of all included studies are summarised in Our search yielded 766 results, of which 446 studies were eligible for full-text screening; 358 studies were excluded after full-text screening, and 88 studies were included in the full synthesis . Two stuI2 97%), and was 58%  and 61%  for Africa and Asia, respectively  of Gram-negative neonatal sepsis, followed by 15% E. coli , 7% Pseudomonas spp. , 6% Acinetobacter , and 3% Enterobacter spp. . We observed a higher proportion of neonatal sepsis caused by Klebsiella spp. in Africa than Asia , while Acinetobacter was more commonly reported in Asia than Africa . Significant heterogeneity was noted with these findings . In 43 studies, a median of 60% (range 26% to 95%) of positive blood/CSF cultures were reported to have been taken in the study’s defined period of EOS. In 21 studies, a median of 62% (range 13% to 82%) of positive blood/CSF cultures were from premature neonates. The estimated pooled proportion of neonatal sepsis caused by Gram-negative organisms was 60% . We observed a higher prevalence of 3GC resistance in Africa compared to Asia, particularly with Klebsiella and Pseudomonas spp.; however, there was significant heterogeneity for these findings. Pooled prevalence of 3GC resistance in Africa for Klebsiella spp. was 88%  versus 77% in Asia . For Pseudomonas spp., 3GC resistance was 59%  in Africa versus 46% in Asia . The prevalence of ciprofloxacin resistance was higher in Asia across all 5 key groups of Gram-negative bacteria compared to Africa , 10% for Klebsiella spp. , 15% for Pseudomonas spp. , and 42% for Acinetobacter spp. . There was a predominance of studies from South Asia in the Asian continent, and care must be taken not to extrapolate these results to other countries in the region where there were no data available. Significant regional gaps were also noted from the Americas and the Western Pacific region, with no studies available for inclusion from the Pacific Island countries and territories. It has been estimated that a newborn infant dies every 2 minutes in the Western Pacific region, with infection being an important cause of death . Yet theKlebsiella spp. was the most common causative Gram-negative bacteria, accounting for 38% of Gram-negative neonatal sepsis. This predominance appears more pronounced in studies from LMICs in Africa, which is also consistent with previous reports [Acinetobacter spp. in Asia is both interesting and of substantial concern. This observation may reflect the NICU setting of these studies (over 60% of studies in Asia were from NICUs), with increased likelihood of early invasive interventions, particularly in premature infants. Similarly, there is a high rate of cesarean delivery reported in South Asia [Acinetobacter during hospitalisation. Of note, the Aetiology of Neonatal Infections in South Asia (ANISA) study [Our findings echo similar reports from systematic reviews focussing on sub-Saharan Africa and South Asia ,12,115. uth Asia , which mA) study , one of Inappropriate empirical antibiotic therapy has recently been shown to be associated with increased mortality in young children and neonates, highlighting the importance of appropriate empirical antibiotic recommendations . WHO-recOur findings provide important insight into the role of Gram-negative pathogens in neonatal sepsis in LLMICs, the burden of AMR in this context, and the appropriateness of existing recommendations for antimicrobial therapy. With limited access to third-line therapies (such as carbapenems) and the development of resistance even to these, robust surveillance of infection and AMR, along with infection prevention and antimicrobial stewardship strategies, is critical to address this global health threat . An incrNeonatal sepsis is increasingly caused by Gram-negative bacteria with alarming rates of multidrug resistance. Mortality is increased in neonatal sepsis caused by MDR organisms. The development of robust AMR surveillance and reporting in LLMICs should be prioritised to underpin region-specific empirical antimicrobial recommendations. There is an urgent need for high-quality antimicrobial trials in neonates and ensuring equitable access to new and effective antimicrobials.S1 Data(DOCX)Click here for additional data file.S2 Data(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.