Evaluating rates of chiropractic use and utilization by patient sex within the United States Veterans Health Administration: a serial cross-sectional analysis

BACKGROUND

Within the United States Veterans Health Administration (VHA), the number of patients using healthcare services has increased over the past several decades. Females make up a small proportion of overall patients within the VHA; however, this proportion is growing rapidly. Previous studies have described rates of VHA chiropractic use; however, no prior study assessed differences in use or utilization rates between male and female veterans. The purpose of this study was to assess rates of use and utilization of chiropractic care by sex among VHA patients receiving care at VHA facilities with on-station chiropractic clinics.

METHODS

A serial cross-sectional analysis of VHA national electronic health record data was conducted in Fall 2021 for fiscal year (FY) 2005-2021. The cohort population was defined as VHA facilities with on-station chiropractic clinics, and facilities were admitted to the cohort after the first FY with a minimum of 500 on-station chiropractic visits. Variables extracted included counts of unique users of any VHA on-station facility outpatient services, unique users of VHA on-station facility chiropractic services, number of chiropractic visits, and sex. To calculate use, we determined the proportion of patients of each sex who received chiropractic services to the total patients of the same sex receiving any outpatient care within each facility. To calculate utilization, we determined the number of chiropractic care visits per patient per fiscal year. A linear mixed effects model was applied to examine the difference in chiropractic care utilization by sex.

RESULTS

The percentage of female VHA on-station chiropractic patients increased from 11.7 to 17.7% from FY2005-FY2021. Among VHA facilities with on-station chiropractic care, the percentage of female VHA healthcare users who used chiropractic care (mean = 2.3%) was greater than the percentage of male VHA healthcare users who used chiropractic care (mean = 1.1%). Rates of chiropractic utilization by sex among VHA facilities with on-station chiropractic clinics were slightly higher for females (median = 4.3 visits per year, mean = 4.9) compared to males (median = 4.1 visits per year, mean = 4.6).

CONCLUSION

We report higher use and utilization of VHA chiropractic care by females compared with males, yet for both sexes rates were lower than in the private US healthcare system. This highlights the need for further assessment of the determinants and outcomes of VHA chiropractic care.

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.