Predictors of Utilization Frequency of and Expenditure Amount for Chiropractic Care in U.S. Adults: A Retrospective Cohort Study

This study aimed to evaluate the prospective associations of baseline personal characteristics with utilization frequency and expenditure of chiropractic care in US adults (≥18 years). Data are from the 1358 respondents to the 2014 to 2016 Medical Expenditure Panel Survey who utilized chiropractic care. Individual, familial, health-related, and behavioral factors were included as covariates in the multivariate analytic model. Poisson and multinominal logistic regressions were modeled to examine the associations between the predictors and chiropractic utilization and expenditure. The mean annual number of visits was 8.2 visits (95% confidence interval [CI]: 7.9, 8.5), with annual expenditure of $677.43 U.S. dollars (95% CI: $595.47-$759.39) and $84.84 (95% CI: $77.89-$91.78) per visit. Female sex was associated with a 19% higher number of visits (rate ratio [RR] = 1.19, 95% CI: 1.01, 1.41) than males. Publicly insured and uninsured were associated with a 44% higher (RR = 1.44, 95% CI: 1.14, 1.82) and 36% lower (RR = 0.64, 95% CI: 0.48, 0.86) visit frequency, respectively, than those privately insured. Rheumatoid arthritis was associated with 7.38 times the risk of high expenditure (95% CI: 2.61, 24.67) than medium expenditure, compared to no arthritis. Relative to physically active individuals, physical inactivity was associated with a 27% higher visit frequency (RR = 1.27, 95% CI: 1.09, 1.49) and an 82% higher risk (relative risk ratio = 1.82, 95% CI: 1.05, 3.14) of high expenditure than low expenditure. This analysis found distinct usage and expenditure patterns that vary according to specific baseline predictors. Female sex, being publicly insured, having rheumatoid arthritis, and physically inactive were associated with variance in expenditure. Results from this study may help identify chiropractic patients with tendencies for higher utilization or spending and may indirectly assist in predicting patients with slower response to care.