Complementary and Integrative Health Approaches and Pain Care Quality in the Veterans Health Administration Primary Care Setting: A Quasi-Experimental Analysis.

Background: Complementary and integrative health (CIH) approaches have been recommended in national and international clinical guidelines for chronic pain management. We set out to determine whether exposure to CIH approaches is associated with pain care quality (PCQ) in the Veterans Health Administration (VHA) primary care setting. Methods: We followed a cohort of 62,721 Veterans with newly diagnosed musculoskeletal disorders between October 2016 and September 2017 over 1-year. PCQ scores were derived from primary care progress notes using natural language processing. CIH exposure was defined as documentation of acupuncture, chiropractic or massage therapies by providers. Propensity scores (PSs) were used to match one control for each Veteran with CIH exposure. Generalized estimating equations were used to examine associations between CIH exposure and PCQ scores, accounting for potential selection and confounding bias. Results: CIH was documented for 14,114 (22.5%) Veterans over 16,015 primary care clinic visits during the follow-up period. The CIH exposure group and the 1:1 PS-matched control group achieved superior balance on all measured baseline covariates, with standardized differences ranging from 0.000 to 0.045. CIH exposure was associated with an adjusted rate ratio (aRR) of 1.147 (95% confidence interval [CI]: 1.142, 1.151) on PCQ total score (mean: 8.36). Sensitivity analyses using an alternative PCQ scoring algorithm (aRR: 1.155; 95% CI: 1.150-1.160) and redefining CIH exposure by chiropractic alone (aRR: 1.118; 95% CI: 1.110-1.126) derived consistent results. Discussion: Our data suggest that incorporating CIH approaches may reflect higher overall quality of care for patients with musculoskeletal pain seen in primary care settings, supporting VHA initiatives and the Declaration of Astana to build comprehensive, sustainable primary care capacity for pain management. Future investigation is warranted to better understand whether and to what degree the observed association may reflect the therapeutic benefits patients actually received or other factors such as empowering provider-patient education and communication about these approaches.

Characteristics of Chiropractic Patients in the Veterans Health Administration During the COVID-19 Pandemic: A Cross-Sectional Analysis.

OBJECTIVE: The purpose of this study was to determine whether patient characteristics were associated with face-to-face (F2F) and telehealth visits for those receiving chiropractic care for musculoskeletal conditions in the US Veterans Health Administration (VHA) during the COVID-19 pandemic. METHODS: A retrospective cross-sectional analysis of all patients (veterans, dependents, and spouses) who received chiropractic care nationwide at the VHA from March 1, 2020, to February 28, 2021, was performed. Patients were allocated into 1 of the following 3 groups: only telehealth visits, only F2F visits, and combined F2F and telehealth visits. Patient characteristics included age, sex, race, ethnicity, marital status, and Charlson Comorbidity Index. Multinomial logistic regression estimated associations of these variables with visit type. RESULTS: The total number of unique patients seen by chiropractors between March 2020 and February 2021 was 62 658. Key findings were that patients of non-White race and Hispanic or Latino ethnicity were more likely to attend telehealth-only visits (Black [odds ratio 1.20, 95% confidence interval {1.10-1.31}], other races [1.36 {1.16-1.59}], and Hispanic or Latino [1.35 {1.20-1.52}]) and combination telehealth and F2F care (Black [1.32 {1.25-1.40}], other races [1.37 {1.23-1.52}], and Hispanic or Latino [1.63 {1.51-1.76}]). Patients younger than 40 years of age were more likely to choose telehealth visits ([1.13 {1.02-1.26}], 66-75 years [1.17 {1.01-1.35}], and >75 years [1.26 {1.06-1.51}] vs those 40-55 years of age). Sex, visit frequency, and Charlson Comorbidity Index showed significant relationships as well, while marital status did not. CONCLUSION: During the COVID-19 pandemic, VHA patients with musculoskeletal complaints using chiropractic telehealth were more ethnically and racially diverse than those using F2F care alone.

Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization.

BACKGROUND: Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservative interventions for spinal pain conditions, including chiropractic care, have been associated with lower healthcare costs and improvements in pain status in different clinical populations, including veterans. Little is currently known about predicting healthcare service utilization in the domain of conservative interventions for spinal pain conditions, including the frequency of use of chiropractic services. The purpose of this retrospective cohort study was to explore the use of supervised machine learning approaches to predicting one-year chiropractic service utilization by veterans receiving VA chiropractic care. METHODS: We included 19,946 veterans who entered the Musculoskeletal Diagnosis Cohort between October 1, 2003 and September 30, 2013 and utilized VA chiropractic services within one year of cohort entry. The primary outcome was one-year chiropractic service utilization following index chiropractic visit, split into quartiles represented by the following classes: 1 visit, 2 to 3 visits, 4 to 6 visits, and 7 or greater visits. We compared the performance of four multiclass classification algorithms (gradient boosted classifier, stochastic gradient descent classifier, support vector classifier, and artificial neural network) in predicting visit quartile using 158 sociodemographic and clinical features. RESULTS: The selected algorithms demonstrated poor prediction capabilities. Subset accuracy was 42.1% for the gradient boosted classifier, 38.6% for the stochastic gradient descent classifier, 41.4% for the support vector classifier, and 40.3% for the artificial neural network. The micro-averaged area under the precision-recall curve for each one-versus-rest classifier was 0.43 for the gradient boosted classifier, 0.38 for the stochastic gradient descent classifier, 0.43 for the support vector classifier, and 0.42 for the artificial neural network. Performance of each model yielded only a small positive shift in prediction probability (approximately 15%) compared to naïve classification. CONCLUSIONS: Using supervised machine learning to predict chiropractic service utilization remains challenging, with only a small shift in predictive probability over naïve classification and limited clinical utility. Future work should examine mechanisms to improve model performance.

Evaluation of Complementary and Integrative Health Approaches Among US Veterans with Musculoskeletal Pain Using Propensity Score Methods.

Objectives: To examine the treatment effectiveness of complementary and integrative health approaches (CIH) on chronic pain using Propensity Score (PS) methods. Design, Settings, and Participants: A retrospective cohort of 309,277 veterans with chronic musculoskeletal pain assessed over three years after initial diagnosis. Methods: CIH exposure was defined as one or more clinical visits for massage, acupuncture, or chiropractic care. The treatment effect of CIH on self-rated pain intensity was examined using a longitudinal model. PS-matching and inverse probability of treatment weighting (IPTW) were used to account for potential selection and confounding biases. Results: At baseline, veterans with (7,621) and without (301,656) CIH exposure differed significantly in 21 out of 35 covariates. During the follow-up period, on average CIH recipients had 0.83 (95% confidence interval [CI] = 0.77 to 0.89) points higher pain intensity ratings (range = 0-10) than nonrecipients. This apparent unfavorable effect size was reduced to 0.37 (95% CI = 0.28 to 0.45) after PS matching, 0.36 (95% CI = 0.29 to 0.44) with IPTW on the treated (IPTW-T) weighting, and diminished to null when integrating IPTW-T with PS matching (0.004, 95% CI = -0.09 to 0.10). An alternative IPTW model and conventional covariate adjustment appeared least powerful in terms of potential bias reduction. Sensitivity analyses restricting the follow-up period to one year after CIH initiation derived consistent results. Conclusions: PS-based causal methods successfully eliminated baseline difference between exposure groups in all measured covariates, yet they did not detect a significant difference in the self-rated pain intensity outcome between veterans who received CIHs and those who did not during the follow-up period.