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Access to Chiropractic Care and the Cost of Spine Conditions Among Older Adults
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Access to Chiropractic Care and the Cost of Spine Conditions Among Older Adults

Matthew A. Davis, PhD, DC, MPH; Olga Yakusheva, PhD; Haiyin Liu, MA; Joshua Tootoo, MS; Marita G. Titler, PhD, RN; and Julie P.W. Bynum, MD, MPH
Among older adults who have a spine condition, access to chiropractic care may reduce medical spending on diagnostic services.
Chiropractic Care Accessibility

For each year, we gathered data on provider location from historic versions of the National Plan and Provider Enumeration System (NPPES). We used provider specialty code 35 in NPPES data to identify the practice locations of chiropractors.18 We removed chiropractors who were clinically inactive in Medicare by linking NPPES provider data to the 20% Carrier file and removing any providers who did not submit a claim in the relevant year. Medicare beneficiaries were aggregated to Zip Code Tabulation Areas (ZCTAs) according to their zip code of residence for each year. ZCTAs are generalized areal representations of US Postal Service zip code service areas. Once assigned a ZCTA, beneficiaries in our study cohort were assigned measures of chiropractic care accessibility. For each ZCTA, we measured chiropractic care accessibility using an enhanced version of the variable-distance enhanced 2-step floating catchment area method first developed by Luo and Wang15 and modified by others.16

Our measure was constructed based on the 2010 US Census block–level population aggregated up to ZCTA level in order to assign each patient estimates of chiropractic care accessibility. First, we calculated a drive time–based service area for each practice location. US Census block centroids within each service area provided population estimates. These estimates were summed to generate provider to population ratios for each practice location. Next, we estimated chiropractic care accessibility based on the weighted sum of all practice locations within a given drive time from each Census block centroid. For locations within a threshold distance (eg, 5, 10, or 15 miles), scores were summed and scaled according to drive time from the block centroid. These estimates of chiropractic care accessibility were then aggregated to the ZCTA in order to assign a measure of provider accessibility around each ZCTA population-weighted centroid.

Medicare Spine Spending

Our primary dependent variable was annual spine-related spending. We used the combination of ResDAC Carrier, Medical Provider Analysis and Review (referred to as MedPAR), and Outpatient files to calculate total spending on back and neck pain diagnoses by summing across all types of inpatient and outpatient care for each calendar year. We calculated spine-related spending on inpatient and ambulatory care (office-based and outpatient claims combined) separately. To examine more subtle differences in spending, we used the Berenson-Eggers Type of Service categories for “evaluation and management” and “procedures”; furthermore, we combined the categories “imaging” and “testing.” Healthcare spending was adjusted for inflation to 2014 dollars using the Consumer Price Index for medical services.19

Covariates

To adjust for baseline differences, we extracted several socio­demographic characteristics, including age, sex, and race/ethnicity, from the Master Beneficiary Summary File. To account for differences in health status (and changes in health status over time) for each calendar year, we used all administrative data to calculate a comorbidity score using the Charlson-Deyo Comorbidity Index.20 We adjusted for differences in accessibility of primary care physicians.21 Accessibility of primary care physicians (internal medicine, family practice, and general practitioners) was calculated using an approach identical to that described for chiropractic care.

Statistical Analyses

We displayed our national estimate of chiropractic care accessibility for each ZCTA and calculated the coefficient of variation. In order to illustrate high versus low area accessibility, we converted data to standard normal deviations for 2014 ZCTAs (the most recent year of data). We also plotted the unadjusted chiropractor to population ratios collapsed by quintile (eAppendix Figure 3).

We used data from the year before and the year after relocation (referred to herein as baseline year and postrelocation year). The estimated effect of a change in chiropractic care accessibility was evaluated in 2 ways. First, we simply identified whether the beneficiary relocated to a higher or a lower chiropractic care accessibility quintile relative to their baseline location. Second, we examined the magnitude of the increase or decrease in chiropractic care accessibility by reporting results according to the number of quintiles up or down a beneficiary moved by relocating, regardless of starting point, to identify the equivalent of a dose response. The comparison group in these analyses was all beneficiaries who moved but did not experience a change in accessibility (ie, remained in the same quintile). The 2 approaches highlight different dimensions of relocation change: The first focuses on the initial level of accessibility but aggregates change into broad categories of any increase or decrease, and the second captures how much change in accessibility occurred, with less focus on the starting level.


 
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