Technological advancements to study young adult smoking, relapse, and to deliver interventions remotely offer conceptual appeal, but the incorporation of technological enhancement must demonstrate benefit over traditional methods without adversely affecting outcomes. Further, integrating remote biochemical verification of smoking and abstinence may yield value in the confirmation of self-reported smoking, in addition to ecologically valid, real-time assessments.The goal of this study was to evaluate the impact of remote biochemical verification on 24-hour self-reported smoking and biochemical verification agreement, retention, compliance with remote sessions, and abstinence during a brief, 5-week cessation attempt and relapse monitoring phase.Participants  who smoked cigarettes daily engaged in a 5-week cessation and monitoring study . Smoking (cigarettes per day) was self-reported through ecological momentary assessment (EMA) procedures, and participants were randomized to either (1) the inclusion of remote biochemical verification (EMA + remote carbon monoxide [rCO]) 2× per day or (2) in-person, weekly CO (wCO). Groups were compared on the following outcomes: (1) agreement in self-reported smoking and breath carbon monoxide (CO) at common study time points, (2) EMA session compliance, (3) retention in study procedures, and (4) abstinence from smoking during the 2-day quit attempt and at the end of the 5-week study.No significant differences were demonstrated between the rCO group and the wCO (weekly in-person study visit) group on agreement between 24-hour self-reported smoking and breath CO (moderate to poor), compliance with remote sessions, or retention, though these outcomes numerically favored the wCO group. Abstinence was numerically higher in the wCO group after the 2-day quit attempt and significantly different at the end of treatment (day 35), favoring the wCO group.Though study results should be interpreted with caution given the small sample size, findings suggest that the inclusion of rCO breath added to EMA compared to EMA with weekly, in-person CO collection in young adults did not yield benefit and may have even adversely affected outcomes. Our results suggest that technological advancements may improve data accuracy through objective measurement but may also introduce barriers and burdens and could result in higher rates of missing data. The inclusion of technology to inform smoking cessation research and intervention delivery among young adults should consider (1) the research question and necessity of biochemical verification and then (2) how to seamlessly incorporate monitoring into personalized and dynamic systems to avoid the added burden and detrimental effects to compliance and honesty in self-report. Cigarette smoking remains the leading cause of preventable death and disease in the United States -3. CigarEvaluation of novel treatment strategies among young adults is limited by traditional study designs and methods of data collection. The accuracy and veracity of self-reported smoking as a primary end point have been problematic, and inaccuracies have been attributed to factors, such as forgetting, dishonesty, underreporting, response fatigue, or disengagement in procedures -26. FurtAmbulatory assessment of smoking may contribute to more efficacious interventions and improved outcomes. Ecological momentary assessment (EMA)  samples While technological advancements to study young adult smoking offer conceptual appeal, incorporation of these methods must demonstrate benefits over traditional methods without adversely impacting outcomes . In a single-arm, proof-of-concept, feasibility study by our group, we implemented remote biochemical verification, in addition to self-report during a brief quit attempt and monitoring period (11 days) among young adults. We found that cigarette entries decreased across the 11 days (a mix of low rates of smoking and days of no cigarette entries), and missed sessions also increased. Given the inclusion of frequent, remote biochemical verification, we suspect that smoking was likely being underreported . That stYoung adults who smoked cigarettes daily were recruited from the Charleston, South Carolina, community from April 2017 through May 2019 through social media advertisements, fliers, word of mouth, friend referrals, etc. Participants had to meet the following inclusion criteria: (1) being 18-25 years old, (2) smoking at least 5 cigarettes daily for at least 3 months, (3) willing to limit or abstain from other tobacco products, and if applicable, limit or abstain from using cannabis during the study, (4) willing to engage in a 48-hour quit attempt, and (5) having interest in quitting smoking . While young adults who vaped nicotine (e-cigarettes) primarily were allowed into the study in the final months of enrollment, these participants (n=7) were removed from this analysis given that CO cannot be used to verify nicotine vaping. Exclusion criteria included (1) any serious or unstable psychiatric or medical disorder or any other concern that may impact safety, compliance, or could confound results, (2) current use of smoking cessation medication, or (3) pregnancy or plans to become pregnant in the next month. There were no exclusions for smartphone access, and participants were loaned a study smartphone, if they did not have one. Results from this study focused on emotion differentiation, and using EMA data affect and craving have been published elsewhere .A total of 62 participants were screened , 39 of wThe study design is shown in The quit attempt began on day 7 and lasted through day 9 (48 hours), though it may have lasted longer per participant discretion. All participants were provided with a 2-week starter pack of nicotine replacement therapy  at day 0, brief cessation counseling from study research staff leading up to their quit attempt (screening and day 0: ~5-10 minutes per study visit), and financial incentives for abstinence only during the quit attempt (days 7-8). Financial incentives for abstinence ceased after day 9. Treatment delivery was identical across groups, with the exception of incentive delivery. During the quit attempt (days 7-8), incentives were provided to promote abstinence for both groups US $40 possible). Abstinence was verified for the rCO group by remote breath CO submissions through the app (2 submissions each day on days 7 and 8 to confirm abstinence). For the wCO group, abstinence was verified by a single in-person CO sample collected at the day 9 visit. Abstinence was defined as ≤6 parts per million (ppm) or a CO value with a 75% reduction from baseline smoking CO averages. The CO cutoff of 6 ppm was based on recommendations in the literature [ possibleParticipants in both groups were asked to complete EMA sessions (up to 4 prompts per day plus an evening report) as well as log cigarettes smoked in the app in real time. Days consisted of participant-selected 12-hour blocks within which sessions were prompted. Two event-based EMA sessions were prompted each day following cigarette entries. Random EMA session prompts were delivered throughout the day for at least 30 minutes following the last cigarette entry. EMA items were repeated, and CO samples were required for the rCO group . Two CO samples per day were deemed necessary to adequately characterize smoking, abstinence, and relapse given the approximate 12-hour half-life of breath CO . FrequenAll compensation was provided in US dollars. Participants in both groups could earn the same amount of compensation for the completion of study procedures (US $404 possible). Participants in the wCO group could earn US $140 for completing in-person study visits, US $210 for EMA sessions, US $40 for quit attempt abstinence (confirmed at day 9), and US $14 for data use on their own mobile device or incentive for study device return (if applicable). Participants in the rCO group could earn US $80 for in-person study visits, US $244 for EMA session completion, US $40 for quit attempt abstinence (paid 2× per day on days 7-8), and US $40 for data use on their own mobile device or incentive for study device return. Compensation was provided to participants in cash. Participants in the wCO were compensated at each weekly study visit. Participants in the rCO group were compensated at the end of the study but were informed of their total amount earned throughout to enhance motivation.All study procedures were reviewed and approved by the institutional review board (PRO 00060290) at the Medical University of South Carolina. Written informed consent was obtained from all participants by trained research staff prior to any study procedures being completed. Consent could be withdrawn at any time during the study. The study team followed all procedures to protect participant privacy and confidentiality. All data were deidentified with a participant ID, and all protected health information were kept separate from research records in locked cabinets or password-protected databases and were only accessible to approved research staff. Details of study compensation are provided above.Demographic and smoking history was collected along with past 30-day cigarette use, other nicotine use, alcohol, and other drugs via timeline follow-back procedures ,45. NicoWe used previously established EMA items here ,26,33, i+ basic monitors).Breath CO was captured twice per day (morning and evening) for the rCO group starting at day 1 and for the duration of the study or at weekly in-person study visits for the wCO group. Breath CO was captured using Bedfont Scientific Ltd breath CO monitors (piCO Smokerlyzer and the MicroAgreement between self-reported (EMA-based) past 24-hour smoking and CO-verified smoking was calculated (yes or no) at common time points  for both groups.Retention was defined as completing study visit procedures (in-person or phone visit) at the postquit attempt visit (day 9) and at the end of the study visit (day 35).EMA session compliance for both groups was defined as the mean proportion of event-based and random prompt EMA sessions  and evening reports completed versus expected. Event-based EMA sessions relied on cigarette entries being logged and therefore, no expected number was known, and calculations were based on events reported.Abstinence was defined based on negative breath CO samples and based on CO-confirmed self-reported abstinence at 2 time points. Abstinence was defined as a negative breath CO and self-reported abstinence from cigarettes during the quit attempt (days 7-8). For the rCO participants, abstinence was defined as having at least 3 out of 4 negative CO measures (≤6 ppm or a 75% decrease from mean prequit CO). For the wCO group, since there was only 1 time point available to determine abstinence, it was defined as a CO ≤6 ppm or a 75% decrease from prequit CO at the day 9 visit. End of study abstinence was defined as CO-confirmed self-reported 7-day point prevalence abstinence (PPA) at day 35. Abstinence is presented for CO confirmed only and CO-confirmed self-reported abstinence.Standard descriptive statistics were used to summarize demographic and smoking characteristics for this study cohort as well as stratified by group (rCO vs wCO) and were compared across randomized groups using nonparametric tests. Within each study group, agreement between self-reported (EMA) and CO-verified smoking was estimated for each day using Cohen κ statistic . GeneralP values.To examine group differences in retention, logistic regression with a sandwich variance estimate  was usedThis study was powered to detect a difference in the percent agreement of 24-hour self-reported and CO-verified abstinence between those randomized to the rCO versus the wCO group. Participants in both groups self-reported cigarettes smoked per day, but the rCO group had 58 possible CO samples during this study, while those in the wCO group had only 4 CO samples collected at in-person visits . As suchOverall, the sample was majority male , White , and the average age was 21.6 (SD 2.1) years. Participants began smoking on average at 15 (SD 3.2) years old and reported 9.2 cigarettes per day (SD 5.1) at screening .Agreement analyses were conducted using available self-reported smoking and CO . When exP=.98). At the final study visit (day 35), wCO group retention was 90% (17/19) compared to 75% (15/20) in the rCO group , but this was not statistically different.Retention, compliance, and abstinence rates are shown in P=.24. Similarly, evening report compliance was consistent between groups: rCO=68% (SD 27.6) versus wCO=74% (SD 29.2); P=.36. Random prompt EMA sessions had lower rates of compliance, which were significantly lower in the rCO group as compared to the wCO group: rCO=38% (SD 21.1) versus wCO=57% .Sessions with predictable delivery times, including event-based EMA sessions (sessions immediately following a cigarette entry) and evening reports, showed high rates of completion: 96% (SD 16.8) for event-based EMA sessions and 71% (SD 28.1) for evening reports. Mean event-based EMA sessions were similarly high between the rCO and wCO groups: rCO=98% sessions completed (SD 6.7) versus wCO=93% sessions completed (SD 23.1); P=.10). When assessing abstinence based on CO-confirmed 2-day PPA (self-reported abstinence verified by CO), results followed a similar pattern; not statistically different but numerically favored the wCO group . End of study (day 35) abstinence determined by breath CO only showed that abstinence was significantly lower in the rCO group as compared to the wCO group . Abstinence determined by CO-confirmed 7-day PPA (self-report verified by CO) showed a similar trend and was numerically lower in the rCO group as compared to the wCO group but not statistically significant, and only 2 wCO group participants were abstinent . Between-group comparisons were conducted based on available CO and self-reported data, and results were similar to intent-to-treat results (not presented here).Quit attempt (day 9) abstinence based on breath CO only found numerically lower rates of abstinence in the rCO group as compared to wCO group  may function more effectively.Agreement in past 24-hour self-reported smoking and breath CO was poor to moderate across both groups. It was hypothesized that rCO capture would lead to a higher agreement, which was not supported. Indeed, agreement was numerically higher  in the wCO group. A related finding was that CO-confirmed abstinence and CO-verified 7-day PPA for all participants revealed numerically lower abstinence in the rCO group compared to the wCO group following the quit attempt and statistically lower abstinence rates at the end of treatment . Lower rates of abstinence in the rCO group cannot be explained by treatment differences or readiness to quit between groups, so it is likely that the accurate collection of abstinence through biochemical verification was the driver of group differences. These results suggest that more frequent CO assessment may provide a more accurate picture of abstinence, though it is also possible that the inclusion of rCO led to measurement reactivity, whereby the process of monitoring may have influenced smoking during the quit attempt and throughout monitoring. Research has shown evidence of measurement reactivity during EMA monitoring of smoking cessation , and it Study retention following the quit attempt was high , was not statistically different between groups, and was comparable to other studies using an EMA monitoring protocol leading up to a quit attempt ,54. AlthFinally, EMA compliance was consistent between groups for sessions with predictable delivery times (event-based sessions and evening reports) but not for random prompt sessions, which included a CO sample for the rCO group. Random prompt session compliance was significantly lower in the rCO group, which is likely a function of the increased burden of session completion . Sessions may have been prompted when participants did not have the CO device readily accessible, which may have resulted in skipping the session completely; therefore, missing the self-report data as well as CO collection. Random prompt sessions that require equipment to be available may adversely affect compliance. Particularly for CO, which can detect smoking for 12-24 hours, participant-initiated CO samples may be preferable and yield better compliance rates. Despite the difference in compliance between groups, overall compliance across session types  was comparable to pooled compliance rates  found in a recent meta-analysis of EMA studies .This study had several limitations. First, missing data were prevalent, particularly in the rCO group, which affected the ability to detect group differences. This may have been the result of burden associated with logging each cigarette smoked per day for 5 weeks and completing random sessions throughout that time and insufficient compensation to promote continued engagement. Further, because this group was required to provide a CO sample twice per day during the study, the data may not be missing at random, but rather be due to the additional burden to frequent sampling. Second, while all participants were interested in quitting smoking  and received treatment , treatment was minimal and may not have been sufficient to promote continued abstinence and sustained motivation throughout the trial. Treatment-seeking participants may be more likely to engage in monitoring activities, particularly if cessation content is provided through the same monitoring app. Third, we were underpowered to detect differences in agreement between self-reported smoking and CO measurement. Due to a slow rate of enrollment and the increased prevalence of nicotine vaping during study recruitment, our proposed sample size was not successfully met. Numeric trends favoring the wCO group on many outcomes were found, which indicates that even with the proposed sample size, our hypotheses may not have been supported. Fourth, while study procedures across experimental conditions were kept similar, they varied between remote versus in-person depending on the condition, which may have contributed to group differences. This difference may have even played a larger role in group differences beyond rCO collection. Finally, those participants with serious or unstable psychiatric disorders were excluded from participating, which limits the generalizability of the study findings. Participants with psychiatric disorders who were deemed stable  were assessed and included, though this highlights the need to focus young adult smoking research on those with co-occurring disorders in order accurately represent the population in research studies.The goal of this study was to evaluate the benefits of technological advancement for young adult smoking cessation research methods. The rCO group did not show benefit over the in-person group (wCO), and in some cases, outcomes were worse for the rCO group. Remote methods did not translate into improved compliance or retention, though they may provide a more accurate and fine-grained characterization of the quit, lapse, and relapse processes. EMA is an important tool in this regard, as assessment in one’s natural environment maximizes ecological validity , yet com


Anticitrullinated protein antibodies (ACPAs) are serological biomarkers associated with early, rapidly progressing rheumatoid arthritis (RA), including more severe disease and joint damage. ACPA testing has become a routine tool for RA diagnosis and prognosis. Furthermore, treatment efficacy has been shown to vary by ACPA-positive status. However, it is not clear if the economic burden of patients with RA varies by ACPA status.To determine if the economic burden of RA varies by patient ACPA status.IMS PharMetrics Plus health insurance claims and electronic medical record (EMR) data from 2010-2015 were used to identify patients with incident RA. Patients were aged ≥ 18 years, had ≥ 1 inpatient or ≥ 2 outpatient claims reporting an RA diagnosis code (ICD-9-CM code 714.0), and had an anticyclic citrullinated peptide  antibody test within 6 months of diagnosis. Incident patients were defined as those who had no claims with an RA diagnosis code in the 6 months before the first observed RA diagnosis. The primary outcome of interest was RA-related medical expenditures, defined as the sum of payer- and patient-paid amounts for all claims with an RA diagnosis code. Secondary outcomes included health care utilization metrics such as treatment with a disease-modifying antirheumatic drug (DMARD) and physician visits. Generalized linear regression models were used for each outcome, controlling for ACPA-positive status (defined as anti-CCP ≥ 20 AU/mL), age, sex, and Charlson Comorbidity Index score as explanatory variables.P < 0.001) or biologic  DMARDs during the first year after diagnosis and had more physician visits . Annual RA-associated total expenditures were $7,941 for ACPA-positive and $5,243 for ACPA-negative patients . RA-associated medical expenditures were $4,380 for ACPA-positive and $3,427 for ACPA-negative patients , whereas DMARD expenditures were $3,560 and $1,817, respectively .Of 647,171 patients diagnosed with RA, 89,296 were incident cases, and 47%  had an anti-CCP test. After restricting this sample to patients with a linked EMR and reported anti-CCP test result, 859 remained, with 24.7% (n = 212) being ACPA-positive. Compared with ACPA-negative patients, adjusted results showed that ACPA-positive patients were more likely to use either conventional (71.2% vs. 49.6%; RA-related economic burden is higher for patients who are ACPA-positive compared with those who are ACPA-negative. Providers may wish to inform patients diagnosed with ACPA-positive RA about the likely future disease and economic burden in hopes that both stakeholders can be more proactive in addressing them. What is already known about this subjectAnticitrullinated protein antibodies (ACPAs) are established biomarkers for identifying early, rapidly progressing rheumatoid arthritis (RA).The efficacy of RA treatments varies depending on whether patients are ACPA-positive.What this study addsThis study is the first to measure the economic burden of patients with RA by ACPA status in the United States.RA-associated total expenditures were 51% higher among patients with the ACPA biomarker compared with those without the biomarker.3 While there is a large number of treatments available for RA, patients often try multiple therapies before finding an optimal one, and the effectiveness of some therapies declines over time. Biomarkers such as anticitrullinated protein antibodies (ACPAs) and rheumatoid factor (RF) can be used to better understand disease progression.5In the United States, 1.3 million people have rheumatoid arthritis (RA). RA patients incur $19.3 billion in direct and indirect costs in the United States annually.7 As medicine has moved toward a more personalized treatment approach in recent years, published research has found that being ACPA-positive is an independent predictor of response for some biologic disease-modifying antirheumatic drugs (bDMARDs) more than others.9 DMARDs act on the immune system and block inflammation to slow the progression of RA; targeted DMARDs block pathways inside immune cells whereas bDMARDs are produced by living cells and work on immune proteins called cytokines.10 There is a wide range of published estimates for the percentage of patients with RA who are ACPA-positive, ranging from a low of 5% to as high as 91%.13 ACPA status has crucial prognostic value for RA diagnosis and observation of RA progression, as it can be observed in serum samples earlier than RF. 14 The RF biomarker, on the other hand, has not shown the same predictive power.15 However, both ACPA and RF are associated with more severe disease and joint damage.16 This is because ACPA and RF formation occurs prior to clinical manifestation of RA and irreversible joint damage.18In particular, the ACPA serological biomarker of RA is being increasingly used to inform patient prognosis.19 little is known about the differences in the economic burden of disease for ACPA-positive and ACPA-negative patients. While there has been substantial research into the health care resource utilization and costs of RA in general, the lack of specific data on ACPA status may result in a misunderstanding of the uniqueness of these subpopulations in terms of burden and unmet need. Further, ACPA status may be particularly relevant for patients who have an early, rapidly advancing form of RA.15 Recent studies from phase 3b clinical trials have shown benefit of some biologic DMARDs in this specific population.22Despite the relatively recent understanding of the importance of ACPA status on treatment response to specific medications,23 little is known about the differences in burden of disease for ACPA-positive and ACPA-negative patients. This study measured the difference in economic cost burden between patients with RA who were ACPA-positive compared with those who were ACPA-negative using a health insurance claims database linked to an electronic medical record (EMR).As understanding of the importance of ACPA status on treatment response to specific medications, particularly as a component of defining the early, rapidly progressing RA patient, is fairly recent,24This study is an observational, retrospective cohort study that measured differences in the economic burden of RA between patients with newly diagnosed  cases of RA who are ACPA-positive compared with those who are not. We used health insurance claims data linked to EMR data containing laboratory results. After conducting sensitivity analysis differentiating ACPA as strongly or weakly positive, we defined patients as ACPA-positive if their anticyclic citrullinated peptide (anti-CCP) test result was ≥ 20 AU/mL.26We used 5 years (2010-2015) of IMS PharMetrics Plus data for this study. The aggregated IMS PharMetrics Plus database is composed of adjudicated claims for more than 150 million unique enrollees across the United States. Enrollees with both medical and pharmacy coverage in 2013 represent 40 million active lives. The PharMetrics Plus data have diverse representation of geography, employers, payers, providers, and therapy areas. Patients in the majority of 3-digit ZIP codes and in every metropolitan statistical area of the United States are represented, with coverage of data from 90% of U.S. hospitals and 80% of all U.S. doctors and representation from 85% of the Fortune 100 companies. IMS PharMetrics Plus data have been used in previous RA studies.To obtain ACPA status, the PharMetrics Plus dataset was linked to available ambulatory EMR records. The EMR data contained laboratory results that were used to measure ACPA and RF from a patient’s anti-CCP and RF tests, respectively.International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 714.0). We matched the RA cohort selected based on the PharMetrics Plus health plan claims database using the deidentified patient number with the IMS ambulatory EMR database. EMR-linked patients with valid ACPA test results were selected to be included in the analytical sample.Our dataset contained 647,171 distinct patients with an RA diagnosis  and were captured over a 12-month period following diagnosis. Costs measured included pharmacy and medical costs . Both all-cause costs and RA-related costs were analyzed, with the latter defined from all medical claims carrying an ICD-9-CM diagnosis code for RA in any diagnosis position. All costs were expressed in 2015 constant dollars, adjusted using the medical care component of the Consumer Price Index.The secondary outcome of interest was RA-related health care resource utilization. This included hospitalizations, ED visits, physician visits, and number of prescriptions. For the small proportion of patients who had a follow-up period less than 12 months, we annualized their health care costs and utilization to reflect an annual rate.30 Additional independent variables included patient demographics , region of residence, and patient health status (Charlson Comorbidity Index [CCI] score).The key independent variable of interest was whether patients were ACPA-positive. ACPA-positive status was defined by anti-CCP test ≥ 20 AU/mL per American College of Rheumatology (ACR) guidelines.For each outcome variable, descriptive statistics were tabulated through univariate analysis by ACPA status. The distributions of test results were also presented as means, medians, standard deviations, and ranges. Patient characteristics including age, gender, region, and other demographic and clinical variables  were summarized. In addition, Wilcoxon rank-sum tests were conducted to determine whether any differences in resource use or cost were statistically significant across these cohorts.31Health care costs were measured using the financial fields on administrative claims in the IMS PharMetrics Plus databases and include the gross covered payments for all health care services or products . All costs were expressed in 2015 constant dollars, adjusted using the medical care component of the Consumer Price Index.Health care spending was compared using a regression analysis to control for observed differences between ACPA-positive and ACPA-negative patients. In our baseline approach, we used a generalized linear model (GLM) to adjust for confounders/covariates. This approach was used both for the cost and utilization numbers; since the utilization numbers are annualized, they may not be integer values and thus the same GLM was used.19 We also compared differences in cost between patients who had no serological biomarkers against those with a single biomarker (ACPA-positive or RF-positive) compared against those who were both ACPA-positive and RF-positive. In our second sensitivity analysis, we no longer limited the analysis to patients with incident RA but also included patients with prevalent forms of RA. For the prevalent RA patient analysis, we no longer required a 6-month clean period prior to first diagnosis.We conducted 2 sensitivity analyses. First, rather than measure patients who are likely rapidly progressing or have a poor prognosis using a single ACPA-positive indicator, we used the ACR definition of rapidly progressing or poor prognosis patients, which requires the presence of at least 1 of the following clinical dimensions: anti-CCP test ≥ 60 AU/mL or RF test ≥ 15 IU/mL or the presence of RA vasculitis (ICD-9-CM diagnosis code 714.2) or Felty’s syndrome (ICD-9-CM diagnosis code 714.1).Of 647,171 patients diagnosed with RA, 89,296 were incident cases meeting inclusion criteria and 47%  had an anti-CCP test. After restricting the sample to 9,747 patients with a linked EMR, 859 reported an ACPA test result . Of thes32 Age and gender distributions were similar comparing ACPA-positive and ACPA-negative patients , outpatient service costs (P < 0.05), and total costs (P < 0.05) compared with ACPA-negative patients. However, the differences were less significant and even reversed for all-cause costs. ACPA-positive patients had significantly lower all-cause expenditures for doctor visits and other outpatient services .Health care costs directly associated with the treatment of patients with RA were higher among ACPA patients, and all-cause costs were similar across ACPA status. Descriptive analysis showed no statistically significant difference in all-cause health care cost by ACPA status . ResultsP = 0.236) lower among ACPA-positive individuals, with higher prescription drug cost  and lower medical cost  among ACPA-positive compared with ACPA-negative individuals. None of these results, however, were statistically different from zero.Similar differences were observed in multivariable analyses controlling for differences in patient characteristics. After controlling for age, gender, and CCI score in the multivariate regression analysis , all-cauP = 0.002; P = 0.168).On the other hand, annual RA-associated total expenditures were $7,941 for ACPA-positive and $5,243 for ACPA-negative patients  or biologic  DMARDs during the first year after diagnosis and had more physician visits .Descriptive analysis of health care resource utilization also showed higher RA-associated health care utilization among ACPA-positive patients compared with ACPA-negative patients (P = 0.477), but patients who were ACPA-positive and RF-positive had significantly higher RA-related total cost . In addition, the overall pattern of disease burden by ACPA status in the prevalent cohort  was similar to the findings from the primary analysis in the incident cohort. However, differences in cost by ACPA status are less apparent, potentially due to the more heterogeneous nature of the prevalent cohort, which contains a mix of patients who have varying disease prognosis and severity or unobserved differences in disease severity across ACPA-positive and ACPA-negative patients.The results were similar among the rapidly progressing subgroup; ACPA-positive patients had higher costs than ACPA-negative patients (P = 0.002). Setting aside the cost for DMARDs, annual medical cost was $954 higher for ACPA-positive compared with ACPA-negative patients (P = 0.168). The difference in medical cost by ACPA status was mainly driven by outpatient hospital costs  and doctor visit costs . Similar patterns were observed in sensitivity analyses conducted using rapidly progressing status and a prevalent cohort. On the other hand, we found no statistically significant difference in overall cost between patients with RA who were ACPA-positive compared with ACPA-negative.ACPA-positive patients generally had higher RA-related costs. Annual RA-associated expenditures were $2,698 higher for ACPA-positive compared with ACPA-negative patients (16 At the same time, the failure to find any difference in overall cost between ACPA-positive and ACPA-negative patients could be interpreted to mean that there is truly no difference in spending across these groups or that overall disease burden outside of RA or RA disease progression itself may vary across patients by ACPA status. For instance, whereas 74.1% of ACPA-positive patients had a CCI score of 0, for ACPA-negative patients, this figure was 68.3%. More broadly, this study’s estimates of the economic burden of being ACPA-positive align with the findings from a study of the economic burden of patients with RA in Italy.33Overall, the results suggest that ACPA-positive patients may have more aggressive treatment of RA and higher RA-related disease burden but also may be in better overall health due to a decreased prevalence of comorbid conditions. Although speculative, the observed difference in RA-related cost aligns with results from the literature that ACPA positivity is associated with more severe disease progression and joint damage.19 Although many of the biologic agents are expensive relative to conventional DMARDs, recent research suggests, however, that certain bDMARDs may be more efficacious among ACPA-positive patients compared with ACPA-negative patients compared with the more widely used tumor necrosis factor inhibitor class.23 Given the various options for treatment, the results for this study imply that evidence on ACPA status can be important in the choice of treatment.Research from other studies has provided some suggestions of clinical approaches to improve patient health outcomes among ACPA-positive patients, although the effect of these treatments on economic burden is uncertain. The ACR suggests the use of DMARDs and biologic agents or biologics based on indications of early RA followed by established RA.This study has a number of limitations. First, not all patients or even all patients with RA receive regular anti-CCP or RF tests, and it is likely that these lab tests were not applied randomly. Although our baseline approach is limited to patients receiving an anti-CCP test, our sensitivity analysis also examines the economic burden among RA patients based on whether they are ACPA-positive or RF-positive.34Second, although our health insurance claims data captured any patient out-of-pocket payments from services covered in part by insurers, we could not observe any patient health care spending made without knowledge of the payer. Furthermore, we allowed for ICD-9-CM codes to count as valid diagnoses if they were listed in any diagnosis position on the claim, which may be a less specific indicator of RA status than would be the case of requiring a primary diagnosis code.36 and researchers should be cautious in extrapolating the results of this study beyond the commercially insured population. Fifth, the EMR data for this study only included the lab results; thus, we were unable to include disease severity measures to assess baseline disease states for the ACPA subpopulations. In other words, the demographics and comorbidities captured in this study do not fully capture disease severity. However, DMARD use is much higher among ACPA-positive patients in the study sample, suggesting higher disease severity.Third, the sample size of our analysis is fairly small, less than 1,000 people. To address this issue, we conducted a sensitivity analysis to expand our comparison to patients with prevalent RA and we found qualitatively similar results. Fourth, the sample comes from a largely commercially insured patient population, and individuals aged 65 years and above are underrepresented. This challenge has been faced by other studies using administrative datasets,Finally, the results of this study should be interpreted as demonstrating the correlation between ACPA status and economic burden as there exists significant uncertainty regarding the direction of causation of ACPA status on economic burden. On one hand, it could be the case that physicians believe that these patients who are ACPA-positive will have higher RA-related disease burden and thus they would be more likely to prescribe more aggressive medications and request more frequent follow-up visits. On the other hand, physicians may treat ACPA-positive patients similarly to ACPA-negative patients, but ACPA-positive patients may be more likely to have RA-related complications due to more rapidly progressing disease.This study found that patients who were ACPA-positive had a similar overall cost burden but had higher RA-related costs compared with patients who were ACPA-negative. Future research should examine the relationship between ACPA status, disease severity, and economic outcomes and should also examine whether targeting RA-related treatment based on clinical factors such as ACPA-positive status leads to differential clinical and economic outcomes.


However, robust prospective data and low-and middle-income country perspective are lacking. We aimed to study the patients with first STEMI and assess the influence of SMuRFs on clinical outcomes by comparing the patients with and without SMuRFs.Patients with ST elevation myocardial infarction (STEMI) without thextended SMuRF (eSMuRF). Primary outcome was in-hospital mortality. Secondary outcomes included in-hospital complications and one-year all-cause mortality.We included all consecutive STEMI patients without prior coronary artery disease enrolled in the Madras Medical College STEMI Registry from September 2018 to October 2019. We collected baseline clinical characteristics, revascularisation strategies and clinical outcome. We analysed suboptimal self-reported sleep duration as a 5th eSMuRF yielded similar results.Among 2,379 patients, 605 patients (25.4%) were SMuRF-less. More women were SMuRF-less than men . SMuRF-less patients were older , more often former tobacco users , with more anterior wall MI . The primary outcome [in-hospital mortality ] and secondary outcomes [in-hospital complications  and one-year all-cause mortality ] were similar in both groups. Addition of suboptimal self-reported sleep duration as a 525% of first STEMI patients were SMuRF-less. Clinical outcomes of patients without SMuRFs were similar to those with SMuRFs. Suboptimal sleep duration did not account for the risk associated with the SMuRF-less status. Coronary artery disease is a major cause of death worldwide. Cardiovascular disease is the most common cause of mortality in India, accounting for a third of the certified deaths . Precurs235However, it is well known that myocardial infarction also occurs among persons without these traditional risk factors . Contemp91091112Madras Medical College STEMI (M-STEMI) Registry is a prospective registry enrolling acute STEMI patients above 18 years of age seeking care in a public hospital in a metropolitan city in India. All consecutive patients with first diagnosis of STEMI enrolled from September 2018 to October 2019 were included in this analysis after getting their informed consent. Diagnosis of STEMI was based on classic chest pain and diagnostic ST elevation as per standard guidelines . PatientThe data collection instrument was developed by the authors in hard copy format convertible into corresponding Microsoft excel tables. The data collection instrument and strategy were put to use in 1,500 consecutive patients admitted with STEMI from October 2017 to August 2018. The data collection instrument and strategy underwent multiple revisions and improvements during this period of trial enrolment of patients to ensure no missing variables. Final data collection was done using a hardcopy of the data collection instrument (Supplementary file-1), which was subsequently updated into an online Microsoft excel spreadsheet by one of the authors.Baseline demographic factors, cardiovascular risk factors, comorbid conditions and present symptoms and their chronology were collected prospectively for all patients. All patients had detailed clinical evaluation, 12-lead electrocardiogram, echocardiogram and risk stratification on admission. Standard echocardiographic techniques were used to measure left ventricular ejection fraction . Tricuspstandard modifiable cardiovascular risk factors (SMuRFs): hypertension, hyperlipidaemia, diabetes mellitus and current tobacco use, were considered as the SMuRF-less group. Patients with one or more of these four risk factors were considered as the SMuRF-plus group. Current tobacco use was defined as regular use of smoking or non-smoking tobacco for at least the previous one year. Patients who had stopped tobacco use at least 12 months before were labelled as former tobacco users. Considering the high prevalence of smokeless tobacco use in India, we sought for smokeless tobacco use and included it under tobacco use [Patients who have none of the four acco use . HyperteIn addition to the four main SMuRFs, suboptimal sleep duration, defined as self-reported sleep duration ≤ 6 hours and > 9 hours, was evaluated as a potential fifth modifiable risk factor. The group with any of these five modifiable risk factors was termed as the extended SMuRF (eSMurRF) group, and the group with none of these as the extended SMuRF-less (eSMurRF-less) group.All patients were discharged with aspirin 150 mg, clopidogrel 75 mg and atorvastatin 80 mg as per protocol, unless contraindicated. All patients were prescribed betablockers and angiotensin converting enzyme inhibitors as permitted by the discharge hemodynamics and biochemistry. Follow-up details were collected at 1, 3, 6 and 12 months. Patients who did not turn up to the outpatient department in time were reminded by telephonic calls by our dedicated and trained follow-up team. Postal letters in vernacular language were sent to the patients who were not reachable by telephone. Our follow-up team performed house visit for the final defaulters.In-hospital mortality is the primary outcome. The secondary outcomes include a composite of in-hospital complications and one-year all-cause mortality. We are continuing active follow-up of patients.Differences between the SMuRF-less group and SMuRF-plus groups in baseline demographics, clinical parameters, reperfusion therapy offered, in-hospital course and complications and follow-up outcome were analysed. An additional similar analysis was performed to find the differences between the eSMuRF and eSMuRF-less groups. Categorical variables are presented as frequencies and percentages and compared using Pearson’s Chi-squared test or Fisher’s Exact Test. Continuous variables are presented as mean, standard deviation (SD) and median (interquartile range), and are compared using student’s t-test  or Mann-Whitney test . Covariates with p < 0.10 on univariable analysis were planned to be included in the final multivariable model.We had a prespecified plan to include any covariates with p < 0.10 on univariate testing in a final multivariate model. Multivariable analysis was done with logistic regression. The results of regression analyses are expressed as an odds ratio (OR) with respective confidence interval (CI) and p-values. Significance was assumed at a two-sided value of p < 0.05. Analyses were performed using SPSS version 28.0 .Between September 2018 to October 2019, 2,499 adults with acute STEMI were enrolled in the M-STEMI registry. Of those, 120 patients with a history of prior CAD were excluded. Of the remaining 2,379 patients with first STEMI analysed, 605 patients with no documented SMuRFs constituted the SMuRF-less group. The rest formed the SMuRFs group. Ninety-nine percent of the study population belonged to the lower socioeconomic category. The Baseline differences in the distribution of demographic variables between the groups are given in The SMuRF-less group had a higher mean age  with a larger proportion of patients above 60 years of age (46.6% vs 39.7%) compared to the SMuRF-plus group. The study participants were predominantly men (76.6%). However, a larger proportion of women were SMuRF-less compared to men . The age and sex differences were not significant in the multivariable analysis. The time from symptom onset to presentation in the hospital and presentation Killip class was similar in both the groups. Though a higher proportion of patients in the SMuRF-less group presented with anterior wall MI, the mean left ventricular ejection fraction was similar in both groups .The proportion of patients with inferior infarction and right ventricular dysfunction was lower in the SMuRF-less group. Fibrinolysis (51%) was the predominant mode of reperfusion, used with only 10% receiving primary PCI. However, the proportion of patients receiving the various modes of reperfusion  was similar in both groups.Angiographic details were available for 1,089 of the 2,379 patients. LAD involvement was non-significantly higher in the SMuRF-less group. There was no significant difference in the culprit lesion profile and proportion of patients with multivessel disease between both the groups .Of the 2,379 patients included in the analysis, 265 patients (11.1%) died in the hospital. There was no difference in the in-hospital course or complications between the groups with and without SMuRFs . SMuRF-lFollow-up information at 12 months was available for 85.5% of the patients included in the study analysis. Of the total, 344 patients were lost to follow-up. The SMuRF status and eSMuRF status of patients who were lost to follow-up and were available for follow-up were similar. (Supplementary Table-1). Of the 2,114 patients discharged alive, there were 194 additional deaths reported by 12 months. Thus, one-year all-cause mortality was 22.6%. Post discharge mortality  and all-cause mortality at 12 months  were similar in both the groups .With self-reported suboptimal sleep duration added as a fifth modifiable risk factor, there were 1,851 patients (87.8%) with at least one of the five eSMuRFs and 528 patients (22.2%) without any of these. The eSMURF analysis showed similar results to the main SMuRF analysis, with no significant differences in the in-hospital or 12-month outcome between the groups with and without eSMuRFs (Supplementary Table-2).Our study has four main findings . First, The proportion of SmuRF-less STEMI in our study (25.4%) was higher than observed in earlier studies (14.9% & 19%) 12. SinceAs our study data was collected over 22 months only, we could not comment on the variably reported increasing trend of prevalence of SMuRF-less STEMI over years 1112. Sim1111911Though the proportion of patients with hypertension, diabetes and current tobacco use in our study was similar to the earlier studies, the proportion of patients with dyslipidaemia was very low. This could be reflective of the unmet needs in diagnosis and management of dyslipidaemia in LMICs  compound209The window period of presentation was similar in both groups, suggesting that being SMuRF-less did not induce delay in seeking medical help. Similar to earlier observations, we observed a higher proportion of patients with anterior wall myocardial infarction and a non-significant higher involvement of LAD as culprit vessel in the SMURF-less group 12. The rOnly 10% of our patients underwent primary PCI in our study. Though this number is low, it is not different from earlier reported data from low- and middle-income countries 2425. How24Our study showed that in-hospital mortality was equal in both the groups with and without SMuRFs. Mortality in acute coronary syndromes has been reported to be worse in SMuRF-less patients compared to those with SMuRFs 121326. V1213Our study, with a similar proportion of cardiac arrhythmia in both the groups, did not support this postulation. Though we found that SMuRF-less status was more common in women, mortality was similar in women with and without SMuRFs, unlike earlier observations . The infAssociation between reduced self-reported sleep duration and coronary artery disease and adverse outcome 2829 has 28Our study has the strength of being a large prospective study, evaluating the role of SMuRF-less status in STEMI. This study also evaluated the role of suboptimal sleep duration as an additional fifth modifiable cardiovascular risk factor in STEMI. Our results are not generalisable to populations with improved and widespread preventive health care availability. Our study has several limitations. Data on potential confounders, such as baseline, in-hospital and discharge pharmacotherapy, history of malignancy and peripheral occlusive vascular disease, were not routinely collected and hence could not be analysed as covariates. Data on risk factors like family history of premature coronary artery disease, body weight, body mass index, waist circumference, HBA1C, lipoprotein (a), high-sensitivity C-reactive protein, socio-cultural factors, or psychosocial risk factors were not available. Information on possible differences in access to or use of preventive healthcare was not available. Though the relationship between the risk factors and MI is loglinear, with no identified threshold above which the likelihood of MI increases, we chose to stick with the conventional definitions of the SMuRFs with thresholds and specific cut offs. This helped us to categorise them to two different groups for comparison purpose. However, we acknowledge the global cardiovascular risk assessment should ideally consider the linear relationship of different risk factors with morbidity and mortality outcomes. Coronary angiogram was not done for all patients, bringing in a possibility of our population having patients with spontaneous coronary artery dissection (SCAD) and myocardial infarction with non-obstructive coronary arteries (MINOCA) as a potential limitation. Twelve-month follow-up data was available for only 85.5% of the study participants. Though we could capture the follow-up event data, the date of event was not available for all patients due to the COVID-19 pandemic. Hence, the follow-up outcome could not be presented in a Cox regression  model.We observed that one-fourth of patients with STEMI were SMuRF-less. More women were SMuRF-less than men. The clinical outcomes of patients with STEMI without SMuRFs was similar to those with SMuRFs, highlighting that being SMuRF-less does not necessarily confer a lower risk in STEMI. This underscores the need for evidence based on timely revascularisation therapy and pharmaco-therapy for both patients with and without SMuRFs, and the need for studies to evaluate the role of non-conventional and yet-to-be-identified risk factors in STEMI.The deidentified data underlying this article can be shared on reasonable request to the corresponding author. However, data shall be shared after approval from the Institutional Ethics Committee of Madras Medical College.The additional files for this article can be found as follows:10.5334/gh.1189.s1Supplementary File 1.Madras Medical College Stemi (M-Stemi) Registry – Proforma.10.5334/gh.1189.s2Supplementary Table-1.Baseline characteristics of patients lost to follow up.10.5334/gh.1189.s3Supplementary Table-2.Extended SMuRF analysis.


This cohort study investigates monthly rates of receipt of COVID-19 pharmacotherapies among nonhospitalized patients in the VHA from January 2022 to January 2023. How have anti–SARS-CoV-2 pharmacotherapies been used among nonhospitalized US Veterans in the Veterans Affairs health care system?In this cohort study of 285 710 outpatient US veterans who tested positive for SARS-CoV-2 from January 2022 through January 2023, the proportion receiving any pharmacotherapy increased from 3.2% in January 2022 to 23.9% in August 2022 and declined to 20.8% by January 2023. Black, Hispanic, and older veterans with a higher number of underlying conditions were more likely to receive treatment.These results suggest the need for continued support of infrastructure and education to facilitate treatment for individuals at highest risk of progression to severe COVID-19. Several pharmacotherapies have been authorized to treat nonhospitalized persons with symptomatic COVID-19. Longitudinal information on the use of these therapies is needed.To analyze trends and factors associated with prescription of outpatient COVID-19 pharmacotherapies within the Veterans Health Administration (VHA).This cohort study evaluated nonhospitalized veterans in VHA care who tested positive for SARS-CoV-2 from January 2022 through January 2023 using VHA and linked Community Care and Medicare databases.Demographic characteristics, underlying medical conditions, COVID-19 vaccination, and regional and local systems of care, including Veterans Integrated Services Networks (VISNs).Monthly receipt of any COVID-19 pharmacotherapy  was described. Multivariable logistic regression was used to identify factors independently associated with receipt of any vs no COVID-19 pharmacotherapy.Among 285 710 veterans  who tested positive for SARS-CoV-2 between January 2022 and January 2023, the proportion receiving any pharmacotherapy increased from 3285 of 102 343 veterans (3.2%) in January 2022 to 5180 of 21 688 veterans (23.9%) in August 2022. The proportion declined to 2194 of 10 551 veterans (20.8%) by January 2023. Across VISNs, the range in proportion of patients who tested positive who received nirmatrelvir-ritonavir or molnupiravir during January 2023 was 41 of 692 veterans (5.9%) to 106 of 494 veterans (21.4%) and 2.1% to 120 of 1074 veterans (11.1%), respectively. Veterans receiving any treatment were more likely to be older  and have a higher Charlson Comorbidity Index score . Compared with White veterans, Black veterans  were more likely to receive treatment, and compared with non-Hispanic veterans, Hispanic veterans  were more likely to receive treatment.This study found that prescription of outpatient COVID-19 pharmacotherapies in the VHA peaked in August 2022 and declined thereafter. There were large regional differences in patterns of nirmatrelvir-ritonavir and molnupiravir use. This system provides an opportunity to examine how these therapies have been allocated to patients infected with SARS-CoV-2 over time.9 We sought to describe trends and factors associated with prescription of COVID-19 pharmacotherapies from January 2022 through January 2023, focusing on nirmatrelvir-ritonavir, molnupiravir, and historically available neutralizing monoclonal antibodies prescribed during this period.The Veterans Health Administration (VHA), operated by the US Department of Veterans Affairs (VA), is the largest integrated health care system in the US, serving more than 9 million enrolled veterans each year at 171 medical centers and 1113 outpatient sites of care.STROBE) reporting guideline.This cohort study was approved by the VA Central Institutional Review Board, which determined that patient consent was not required. We followed the Strengthening the Reporting of Observational Studies in Epidemiology , which is confirmed by manual EHR review.We used the VA COVID-19 Shared Data Resource, provisioned by the VA Informatics and Computing Infrastructure, which integrates multiple data sources to provide patient-level COVID-19–related information on VA enrollees.COVID-19 pharmacotherapies were ascertained using 3 sources: (1) the VA Corporate Data Warehouse (CDW), a linked database of VHA EHRs that contains prescription records from inpatient and outpatient care; (2) Medicare claims data from the Centers for Medicare & Medicaid Services (CMS) provided by the VA Information Resource Center, which includes claims for veterans who used Medicare; and (3) COVID-19 monoclonal antibody claims data from the VA Community Care program, which coordinates and reimburses local care provided outside the VHA. For this analysis, CMS-Medicare and VA Community Care data were available through September 30, 2022. We also used the CDW to obtain patient-level demographic, clinical, and administrative data. Hospitalization data were obtained from CDW and CMS-Medicare.9We identified veterans aged 18 years or older with a first positive SARS-CoV-2 test between January 1, 2022, and January 31, 2023. The study population was limited to VA enrollees who had at least 1 VHA primary care outpatient encounter during the 18 months before the positive SARS-CoV-2 test and were not hospitalized on or within 7 days prior to the positive test.16 associated with the SARS-CoV-2 test and rurality of residence based on the Rural-Urban Commuting Areas system.17 We also determined smoking status, alcohol or substance use disorder, Charlson Comorbidity Index (CCI) score, COVID-19 vaccination status, and underlying medical conditions, including immunocompromised status, as previously described.9Using the date of the positive SARS-CoV-2 test as the index date, we ascertained baseline demographic characteristics, including race and ethnicity (associated with COVID-19 care) as reported in the VA EHR and enrollment records. Race and ethnicity were self-reported. Race options include American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White, and unknown. Ethnicity options include Hispanic or Latino, not Hispanic or Latino, and unknown. American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander were aggregated into other race due to low population numbers; other race included self-identification as other or more than 1 race. Race and ethnicity were assessed because they are associated with levels of COVID-19 care. Additional demographic information included VHA facility and Veterans Integrated Service Network 2 By January 2022, use of bamlanivimab and etesevimab, as well as casirivimab and imdevimab, was already very limited due to reduced activity against Omicron variants; we therefore did not include these in the monoclonal antibody group.18 Although remdesivir was authorized by the FDA for the treatment of COVID-19 in nonhospitalized patients in January 2022,19 the number of individuals who received outpatient remdesivir was small and not readily distinguished from inpatient remdesivir; thus, we did not include this as a separate treatment group. Eligible veterans were assigned to a treatment group based on the first treatment received within 7 days before or after their first positive SARS-CoV-2 test. Individuals who did not receive any outpatient COVID-19 pharmacotherapy within 7 days before or after their test were assigned to the no treatment group.We identified receipt of nirmatrelvir-ritonavir, molnupiravir, and 2 anti–SARS-CoV-2 monoclonal antibodies in use during the study period , as captured by VA CDW prescriptions, CMS-Medicare, and VA Community Care claims. Due to circulation of Omicron variants with reduced sensitivity to monoclonal antibodies, FDA authorization for sotrovimab was removed in April 2022 and authorization for bebtelovimab was removed in November 2022.Among patients who tested positive, we calculated the proportion prescribed each COVID-19 pharmacotherapy between January 2022 and January 2023 by month and according to VISN, VA facility, and demographic and clinical characteristics. To investigate factors associated with receipt of any treatment vs no treatment, we estimated unadjusted and adjusted odds ratios (aORs) with 95% CIs with binomial logistic regression models using data from April 2022 to January 2023, when the relative proportion of veterans who tested positive receiving outpatient COVID-19 pharmacotherapies had stabilized. Models were adjusted for age, sex, race, ethnicity, VISN, and CCI. To avoid overadjustment, we did not include CCI when evaluating individual underlying conditions. Final models used complete data for all included covariates. Analyses were conducted using R statistical software version 4.1.2 .18 During January 2022, more than 102 343 veterans tested positive for SARS-CoV-2; this decreased to 20 450 the following month and remained relatively stable thereafter  tested positive for SARS-CoV-2 and fulfilled study inclusion criteria ; Table 1ereafter . Of all ereafter . CMS-MedCharacteristics by treatment group are shown in The proportion of veterans who completed primary or booster vaccination was highest in the molnupiravir group , followed by nirmatrelvir-ritonavir , monoclonal antibody , and no treatment  groups. The proportion of veterans with a CCI score of 4 or greater was highest in the monoclonal antibody group , followed by molnupiravir , nirmatrelvir-ritonavir , and no treatment  groups. Monoclonal antibody and molnupiravir groups had the highest prevalence of cardiovascular disease , chronic kidney disease , and immunocompromised status .The proportion of patients who tested positive for SARS-CoV-2 and received any treatment increased from 3285 of 102 343 patients (3.2%) in January 2022 to a peak of 5180 of 21 688 patients (23.9%) in August 2022 and was 2194 of 10 551 patients (20.8%) by January 2023 . The proThere was substantial variation by VISN in the proportion of patients who tested positive who received nirmatrelvir-ritonavir and molnupiravir, which increased notably in April 2022 and persisted through the end of the study . By Janu18Between April 2022 and January 2023, factors independently associated with higher likelihood of COVID-19 treatment were older age , Black vs White race , Hispanic ethnicity , higher CCI score , and receipt of vaccination vs no vaccination . Factors independently associated with lower likelihood of treatment included rural vs urban residence , current smoking , alcohol use disorder , and substance use disorder . Compared with veterans in VISN 1, those enrolled in all other VISNs except VISN 9 were less likely to receive treatment .18This cohort study found that among 285 710 nonhospitalized US veterans who tested positive for SARS-CoV-2 between January 2022 and January 2023, the proportion receiving any outpatient pharmacotherapy increased substantially from 3.2% to 23.9% between January and August 2022 and decreased to 20.8% by January 2023. Although nirmatrelvir-ritonavir remained the most prescribed treatment, the ratio of nirmatrelvir-ritonavir to molnupiravir prescribing decreased from May 2022 to January 2023. There were notable regional differences by VISN in the relative use of different pharmacotherapies. Older veterans with a higher burden of underlying conditions and Black and Hispanic individuals were more likely to receive treatment, whereas unvaccinated veterans and those living in rural areas were less likely to receive treatment.20 Furthermore, overall reductions in COVID-19–related hospitalizations and deaths, as well as relaxation of restrictions implemented during the pandemic may have been associated with changes in risk perception and care-seeking behavior.21 Changes in infrastructure supporting COVID-19 care may also have been associated with prescribing. Although trends in early uptake of COVID-19 pharmacotherapies within the VHA mirrored other settings, a more recent decline has not been widely reported in nonveteran populations.22Several factors may have been associated with the overall decline in the proportion of veterans receiving treatment after August 2022. The absence of available CMS-Medicare or VA Community Care data after September 2022 may explain a part of the decline given that these data contributed an additional 0.8% to the monthly treatment rate between January and September 2022. A relative increase in asymptomatic infections or milder disease could have occurred in the setting of improved COVID-19 vaccination or previous infection.4 In contrast, dispensing of molnupiravir increased during the study period, most notably after FDA authorization for monoclonal antibodies was withdrawn in the setting of reduced susceptibility of circulating Omicron variants. Patients receiving molnupiravir, similar to patients who had received monoclonal antibodies, were older and had a higher burden of underlying conditions compared with other groups and therefore may not have been eligible to receive nirmatrelvir-ritonavir due to kidney or drug-drug contraindications.23The overall small decline in treatment was largely associated with reduced nirmatrelvir-ritonavir dispensing, which remained the most prescribed pharmacotherapy. Possible reasons for this observation include clinician and patient concerns about COVID-19 rebound after completion of nirmatrelvir-ritonavir, as well as challenges in identifying and managing drug-drug interactions.24 Altogether, these differences may be associated with regional and local variation in policy, infrastructure, education, and clinician preferences. For example, higher likelihood of treatment in the VA New England Healthcare System (VISN 1) compared with nearly all other VISNs may have been associated with the regional Test To Treat pilot program.Use of COVID-19 pharmacotherapies varied greatly across VISNs and VA facilities and was not limited by distribution of drug supplies to VHA pharmacies. Although nirmatrelvir-ritonavir was preferentially recommended over molnupiravir and had more robust evidence supporting its clinical effectiveness, a number of facilities prescribed more molnupiravir than nirmatrelvir-ritonavir.9 However, over an expanded period of time, Black and Hispanic veterans were more likely to receive treatment. This suggests important progress in improving outreach to these minority racial and ethnic groups. Moreover, compared with nonveteran populations, VHA populations often have less pronounced racial and ethnic disparities in COVID-19–related care.11Our previous work showed that Black and Hispanic veterans who tested positive for SARS-CoV-2 during January and February 2022 were less likely to receive outpatient COVID-19 treatment.26 A treatment pattern consistent with higher risk was not observed among veterans who were unvaccinated, who were less likely to receive any pharmacotherapy. This may reflect differences in patient behaviors, with veterans who seek vaccination also more likely to pursue treatment for COVID-19. In contrast to our earlier findings, rural veterans were less likely to receive any pharmacotherapy, which may reflect challenges in meeting a higher volume of oral antiviral treatment demand over time.9 It is also possible that rural veterans were more likely to seek care outside of the VHA, and although we included data from VA Community Care and CMS-Medicare claims, we may still have underascertained prescribing in this group. Persons with alcohol and substance use disorders were also less likely to receive treatment, suggesting a need for targeted outreach to these groups at increased risk.The association of older age and underlying conditions with receipt of COVID-19 treatment is consistent with evidence that these groups are at higher risk for severe COVID-19 outcomes.9 Trends in prescribing may have been impacted by changes in the relative proportion of asymptomatic infections over time. Second, although we integrated multiple databases, including the VHA CDW, CMS-Medicare, and VA Community Care claims to determine receipt of COVID-19 pharmacotherapies, we may not have accounted for all treatments provided outside of the VHA, particularly among veterans younger than age 65 years not enrolled in Medicare. In addition, data from CMS-Medicare and VA Community Care were available only through September 2022. However, the relative contributions to ascertainment of COVID-19 treatments between January and September 2022 were small and fairly stable. Third, we did not capture positive SARS-CoV-2 laboratory tests performed outside the VHA or results of self-testing not documented in VHA clinical records. Given our study eligibility criteria, this may have impacted measurement of additional infections and COVID-19 treatments. Fourth, we were not able to enumerate the number of veterans who were offered but declined treatment. Fifth, given differences in veteran demographic and clinical characteristics compared with the general US population and differences in care delivery between VHA and non-VHA systems, findings from this study may not be generalizable to other groups.This study has several limitations. First, as previously described, we could not fully ascertain whether veterans were symptomatic at the time of testing positive and truly eligible for treatment under EUA criteria.In this nationwide cohort study of US veterans in VHA care who tested positive for SARS-CoV-2 between January 2022 and January 2023, prescription of outpatient COVID-19 pharmacotherapies steadily increased until August 2022 and declined slightly thereafter. This occurred in the setting of a relative increase in molnupiravir use, a reduction in nirmatrelvir-ritonavir use, and cessation of monoclonal antibody administration after removal of FDA authorization. Demographic, clinical, and geographic differences in systems of care were associated with the likelihood of receiving treatment. These results highlight important progress in reaching certain groups, including Black and Hispanic veterans. They also suggest the need for continued support of education and infrastructure, including staffing, to facilitate treatment for individuals at highest risk of progression to severe COVID-19. Future research should focus on evaluating the relative benefits and risks of available outpatient COVID-19 pharmacotherapies across different patient populations.


The coronavirus disease 2019 (COVID-19) pandemic has significantly affected the global population, with People Living with HIV (PLWH) being particularly vulnerable due to their compromised immune systems. Although vaccination is a crucial preventative measure against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, little is understood about the willingness of PLWH to receive a second COVID-19 booster dose and the factors that may influence this decision. This study investigates the willingness of PLWH in China to receive a second COVID-19 booster dose and its influencing factors, comparing these with a group of healthy individuals.via participant self-administered questionnaires included demographic information, willingness to receive a second COVID-19 booster dose, and knowledge about HIV and COVID-19 vaccination. Factors influencing vaccination willingness were identified using multivariable logistic regression analyzes.A multicenter cross-sectional study was conducted across five Chinese cities, namely, Beijing, Tianjin, Zhengzhou, Hohhot, and Harbin. Participants were recruited through five community-based organizations. Data were collected p = 0.009). Lower willingness was associated with HIV positive status , perceived barriers , and perceived severity .A total of 156 PLWH and 151 healthy individuals were included in the study. After adjusting for potential confounders, it was found that PLWH demonstrated a lower willingness to receive a second COVID-19 booster dose compared to healthy individuals (77.6% vs. 88.7%, PLWH in China demonstrated a lower willingness to receive a second COVID-19 booster dose compared to healthy individuals. The findings suggest that perceptions and understanding of the COVID-19 vaccination and its necessity for protection against SARS-CoV-2 could influence this willingness. Efforts should be made to strengthen and disseminate knowledge about HIV and COVID-19 vaccinations among this population. In addition, developing interventions and policies that target specific subgroups and address misconceptions about vaccination could be instrumental in improving vaccination rates among PLWH. VaccGiven the potential decrease in immunogenicity and effectiveness over time, PLWH should receive a second COVID-19 booster dose at an appropriate time. Further research indicates enhanced immunogenicity and safety with the second COVID-19 booster dose in PLWH . In respIn the past, significant hesitancy was observed among PLWH in China regarding full-dose COVID-19 vaccination. The vaccination coverage among this group was significantly lower than the international average for the PLWH population (6.2% vs. 63.5%) . With thAccording to the World Health Organization (WHO), it is still necessary for individuals who have previously been infected with SARS-CoV-2 to receive a second COVID-19 booster dose . HoweverPrevious studies have highlighted the concern about vaccine side effects as a significant factor influencing the hesitation of PLWH to receive the COVID-19 vaccine. Whether side effects after the first COVID-19 booster dose influence the willingness to receive the second booster dose remains unclear. Addressing these knowledge gaps would offer valuable insights to guide the administration of the second COVID-19 booster dose among PLWH.The HBM is one of the most extensively utilized theories for understanding health and illness behaviors. The model is predicated on the understanding that a person’s belief in a personal threat of an illness or disease and belief in the effectiveness of the recommended health behavior or action will predict the likelihood that the person will adopt the behavior. The HBM has been previously employed to analyze COVID-19 complete vaccination willingness and behavior among cancer patients and PLWH, as well as in health education activities related to vaccine promotion , 24. AltIn this study, we developed a questionnaire based on the HBM to conduct an anonymous survey among the PLWH population in mainland China. This study aims to provide a theoretical basis for guiding the effective adjustment and implementation of vaccination strategies in our country and other nations in response to the continuously evolving disease situation.2.2.1.This cross-sectional survey is derived from a registered prospective cohort study . The prospective cohort study aimed to assess changes in immunogenicity and adverse reactions within 6 months following the first COVID-19 booster dose in China among PLWH. The prospective cohort study initially recruited both PLWH and healthy individuals in five Chinese cities , with participant recruitment and selection criteria described in our previous work . Based o2.2.In this study, the inclusion criteria for participants included: (1) aged between 18 and 65 years, (2) no history of SARS-CoV-2 infection, (3) having received full immunization (two doses of COVID-19 inactivated vaccine) and the first COVID-19 inactivated booster dose, (4) the second COVID-19 booster dose has not been vaccinated yet, and (5) willingness to participate in the study activities and having signed written informed consent. The HIV infection status was preliminarily self-reported by participants before attending this site study. We re-identified the HIV serostatus for PLWH using the Abbott ARCHITECT HIV Ag/Ab Combo assay, which has high sensitivity and specificity  at the study site. The exclusion criteria were: (1) interviewees with severe hearing loss, visual impairment, or intellectual disability and (2) major mental illness (schizophrenia or bipolar disorder) or neurocognitive impairment as assessed by the clinician.2.3.PLWH was recruited from five community-based organizations that collaborated with HIV clinical service providers and offered services to PLWH, one in each city. Recruitment advertisements were disseminated through WeChat public accounts, a widely used social media platform in China. Then, interested PLWH contacted project staff via social media and were briefly informed of the study’s purpose and procedure. Potential PLWH participants and the healthy control population received a detailed informed consent form. Upon signing, they were screened using inclusion criteria and a free HIV test through the HIV rapid test kit. Eligible HIV-negative individuals were also invited to participate in the study. Investigators issued an anonymous questionnaire through the online survey platform (Golden Data) at the prevaccination (before 2–4 weeks of receiving the first COVID-19 booster dose) and the fourth-week follow-up of the prospective cohort study to understand their feelings and willingness after the first COVID-19 booster dose. Questionnaires that did not meet the length (less than 100 s) to fill in the questionnaire and had logical errors (For instance: the time of COVID-19 vaccination was before the occurrence of COVID-19) were excluded.2.4.The questionnaire used in this survey consisted of five sections: Socio-demographic characteristics and health status; Adverse reactions after vaccination; Willingness to receive the second COVID-19 booster dose; HBM project; HIV-related information and immunization status.To ensure effectiveness, all questions were constructed and evaluated by an expert team .α = 0.835).In the HBM section, we set up 16 items across six dimensions, including perceived susceptibility (3 items), perceived severity (3 items), perceived harm (1 item), perceived benefits (2 items), behavioral cues (1 item), and self-efficacy (1 item). The score for each item ranged from 1 to 5, allocated to “strongly disagree, ““disagree, ““neutral, ““agree, “and “strongly agree.” The scale’s reliability was verified by Cronbach’s α coefficient .This study aimed to evaluate Chinese PLWH’s willingness to vaccinate with the second COVID-19 booster dose relative to healthy individuals. Based on the results of published peer-reviewed studies in Greece, Italy, and China, it was estimated that the acceptance rate of the second COVID-19 booster dose among PLWH is 70%. The acceptance rate among the healthy control group is 85% . The con2.6.p < 0.05. After that, the variables with p < 0.05 were added to the multivariable logistic regression analysis to correct for the bias introduced by background information. Associations between the independent variables of interest  and the dependent variables were assessed by adjusted odds ratios (AORs) and 95% confidence intervals. Each AOR was obtained by fitting a logistic regression model involving an independent variable of interest and all significant background characteristics. All statistical analyzes were performed using SPSS software . A two-tailed p value of less than 0.05 was considered statistically significant.For continuous variables, normality was assessed by the Shapiro–Wilk test. Variables conforming to normal distribution were analyzed using the t-test method, and for non-conforming variables, the Mann–Whitney test method was used. For categorical variables, the chi-square/Fisher method was used. A logistic regression analysis was performed to investigate the factors influencing vaccination willingness. First, a binary logistic regression analysis was performed for demographic characteristics information to obtain variables with 3.3.1.A total of 339 participants aged between 18 and 65 years old were approached for participation. Thirty-two participants were excluded from the study for four reasons: non-provision of informed consent, failure to complete the online questionnaire, presence of logical errors in the questionnaire responses, and inappropriate completion time. Of the remaining 307 participants, 50.81% were PLWH (156/307), and 49.19% were healthy individuals (151/307) .p = 0.003) and individuals aged 30–45  than the healthy individual group. Conversely, the healthy individual group had a significantly lower proportion of single/divorced/widowed  and engaged in full-time work  than the PLWH group. Regarding HIV-related information and immunization status, 84.6% of PLWH had been infected with HIV for over 2 years, with the majority (92.9%) receiving ART. 51.9% of PLWHs reported that their last HIV viral load test was undetectable, and 78.8% of PLWHs reported that their last CD4+ T cell count was over 200 cells/mm3. There were no significant differences (p > 0.05) between the PLWH and healthy individual group regarding education level, monthly income, or prevalence of chronic underlying diseases. More details of the background characteristics can be found in All participants had been vaccinated with the preliminary schedule of two doses of inactivated COVID-19 before recruitment for this study. On average, they received the first COVID-19 booster dose 203 days after receiving the initial two doses. The PLWH group had a significantly higher proportion of males .A significant difference was observed in the willingness to receive the second COVID-19 booster dose between the PLWH group and the healthy individual group . Regarding adverse reactions within 1 month of receiving the first COVID-19 booster dose, 5.1% of the PLWH group and 2.0% of the healthy individual group reported adverse reactions. The primary adverse reactions were local, with 4.5% of the PLWH group and 2.0% of the healthy individual group experiencing them. In the PLWH group, the main complaint was pain at the inoculation site (4.6%), while in the healthy individual group, the main complaint was redness at the inoculation site (2.0%). No significant difference was found between the two groups in the incidence of adverse reactions, local adverse reactions, and systemic adverse reactions (3.3.p < 0.001). Conversely, in the dimension of perceived barriers, the PLWH group scored significantly higher than the healthy individual group . These results suggest that the PLWH group may face more obstacles and be less motivated to receive the COVID-19 booster than the healthy individuals group.3.4.After adjusting for statistically significant sociodemographic characteristics, the outcome of lower willingness to receive the second booster dose was independently associated with HIV positivity , perceived barriers (indicating the expectation of more adverse effects from the COVID-19 vaccine booster) , and perceived severity (referring to negative attitudes toward the COVID-19 vaccine booster dose) .Conversely, a higher inclination towards receiving the second booster dose was associated with perceived benefits (indicating the expectation of more benefits from the COVID-19 vaccine booster)  and  . Furthermore, consistent with perceived benefits, a stronger inclination towards receiving the second booster dose showed positive correlations  with perceived susceptibility, cues to action, self-efficacy, and detectable HIV viral load. Detailed information  can be referenced in 4.To our knowledge, this is the first multicenter cross-sectional study to explore the willingness of PLWH to receive a second COVID-19 booster dose and its influencing factors in China. Our findings suggest that PLWH were more hesitant to receive a second COVID-19 booster dose than the healthy population. The reasons for this hesitation appear to be multifactorial, with HIV infection status, more significant than expected adverse effects after the first COVID-19 booster dose, and negative attitudes toward the COVID-19 vaccine booster dose being the main factors contributing to vaccine hesitancy. Our findings provide important insights into the willingness of PLWH in China to receive a second COVID-19 booster dose and the associated factors influencing this decision. Moreover, our results could inform both the theoretical framework and practical measures for institutions aiming to understand and address the vaccination intentions of PLWH and the factors influencing them. This, in turn, may assist in designing more effective public health interventions and educational campaigns for PLWH, aiming to boost vaccination coverage and minimize the risk of co-infection and severe clinical outcomes.Our study observed a lower willingness among PLWH to receive a second COVID-19 booster dose compared to full immunization and the first COVID-19 booster dose reported in the United States, Italy, and Latin America and the Caribbean (78–86.2%) . One posTo date, abundant studies investigating COVID-19 vaccine acceptance among PLWH has generated valuable insights into the underlying influencing factors. For instance, a cross-sectional study demonstrated that negative attitudes towards prime vaccines was associated with the diminished likelihood of vaccine acceptance in China . ConversOur study is the first to investigate the relationship between the willingness of PLWH in China to receive the second COVID-19 booster dose and the six main dimensions of the HBM. These dimensions include perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy. Our findings indicate that perceived barriers negatively correlate with vaccine willingness, suggesting fears and misconceptions may dissuade PLWH from receiving the booster dose . On the In light of these findings, health departments in China should amplify their efforts to communicate the benefits of the second COVID-19 booster dose. This includes providing clear and reassuring information about the vaccine’s safety, encouraging social support networks to promote vaccination, and fostering a sense of self-efficacy among PLWH. Addressing these factors can reduce vaccine hesitancy and increase the second COVID-19 booster dose uptake among PLWH.Our multivariable logistic regression analysis revealed that PLWH with a detectable HIV viral load (≥50 copies/mL) demonstrated a higher willingness to receive the second COVID-19 booster dose than those with an undetectable viral load (<50 copies/mL). This result diverges from a US study, which reported a higher willingness to vaccinate among PLWH with an undetectable HIV viral load . The disThis study has important practical implications, as it found that the willingness of PLWH to receive the second COVID-19 booster dose in China is notably lower than that of the general adult population. It identifies inhibiting factors such as perception barriers and negative attitudes, suggesting a need for targeted educational campaigns to enhance booster vaccine coverage among PLWH.However, several limitations in our study should be acknowledged: First, as with all cross-sectional studies, establishing causal relationships between independent variables and different outcomes of interest is impossible. Longitudinal studies or randomized controlled trials would be needed to examine causal relationships. Second, subjectively self-administered questionnaires may introduce recall bias, which is difficult to avoid considering the need for anonymity in our study. Future research could consider using alternative methods, such as structured interviews or electronic data collection, to minimize this bias. Third, while most of the items and scales used in this study were self-constructed based on those used in the general population, the external validation of these measures was limited. Further research should seek to validate these measures against established scales or through other external validation methods. Finally, there were variations in the distribution of sociodemographic characteristics between the two groups. Although we adjusted for these characteristics in the multivariable logistic regression model, their potential impact on the study results should be considered. Future studies could explore the potential influence of these characteristics on vaccine willingness more comprehensively and consider other statistical techniques, such as propensity score matching, to address these imbalances.5.In conclusion, our study highlights the lower willingness of Chinese PLWH to receive a second COVID-19 booster dose compared to healthy individuals. Concerns about adverse effects and negative attitudes toward the booster dose primarily drive this reluctance. Strengthening and promoting knowledge about HIV and COVID-19 vaccination, including the importance of vaccine protection against SARS-CoV-2, is crucial. Based on the findings of this study, targeted interventions should be implemented to increase the willingness of PLWH to receive the second COVID-19 booster dose. This may include tailored education and communication strategies, providing comprehensive information and support, and engaging community resources to address the specific concerns and needs of PLWH.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.JX was responsible for the conceptualization of the study and funding acquisition. XL was responsible for data curation and analysis. JX, MS, SL, MY, BS, YQ, and LW were responsible for project administration and securing resources. JX and YW were responsible for supervision and reviewing and editing the article. XL and YW were responsible for writing the original draft of the paper. All authors contributed to the article and approved the submitted version.This work was funded by the Shenzhen Science and Technology Innovation Committee Projects (No. JCYJ20220818102817038) and the Scientific Research Foundation of Peking University Shenzhen Hospital (No. KYQD2022216).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The reviewer XZ declared a past co-authorship with the authors YQ, MY, and JX to the handling editor.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.