A longitudinal study of chiropractic use among older adults in the United States

Our analyses were performed using two linked data sources: (1) the baseline (1993-1994) interview responses of self-respondents in the Survey on Assets and Health Dynamics Among the Oldest Old (AHEAD); and, (2) the Medicare carrier claims for those respondents from 1993 to 2007. The design and sampling approach in the AHEAD have been well described elsewhere [16‐19]. All analyses are weighted to adjust for the over-sampling of African-Americans, Hispanics, and residents of Florida.

There were 7,447 older adults who completed baseline AHEAD interviews in 1993-1994. A total of 1,937 people were excluded from the analytic sample due to (a) the inability to link their Medicare claims (N = 802), (b) being in a managed care Medicare plan at baseline (N = 605), or (c) not being a self-respondent at baseline (N = 530). The final analytic sample consisted of 5,510 individuals, some of whom were censored post-baseline due to death (N = 3,369) or subsequent enrollment in managed care (N = 988). In previous work propensity score re-weighting was used to address the potential sample selection bias introduced by these exclusion criteria; however, such adjustments did not meaningfully alter the results and thus were not used here [20].

Chiropractic visits were identified by using the Health Care and Financing Administration's (HCFA) specialty provider code for chiropractors in the Medicare carrier claims file. Claims were aggregated to the individual level in each calendar year of service, as well as across the entire period for which the user was in the sample. Users were partitioned into two groups: those exceeding the 12 chiropractic visit "soft-cap" in any calendar year (high volume users) and those with 12 or fewer annual chiropractic visits in any calendar year (lower volume users).

The AHEAD survey data contains a litany of information on individuals, the totality of which has not been available in previous studies of chiropractic use. In order to bring structure to our inclusion of covariates, we selected them based on Andersen's Behavioral Model of Health Services Use [21]. This model highlights predisposing characteristics that play a role in predicting and explaining health services use in general, and for our analysis, chiropractic use. Variability in demographic factors, such as age, gender, and race could be expected to play a role in explaining chiropractic utilization variation. Social structure variables like education, marital status, and income might also influence why services are sought. Personal enabling resources like having a job, having supplemental insurance, and being able to drive a car would predictably improve access to chiropractic. Need for services can be differentiated by "evaluated" need, such as that identified by a health provider (i.e. arthiritis), or "perceived" need, such as how people view their own health status, functional limitations, psycho-social state, or experience symptoms of pain and illness. Health behavior and lifestyle choices, such as smoking, alcohol consumption, and weight also arguably reveal individual preferences for health that may affect demand for chiropractic. Prior health services utilization measures indicate individual propensity to use health services and prior access to these services. Measures of physician supply, rural-urban characteristics, and distance to a chiropractic college are also included in our model to provide indicators of access and possible familiarity with chiropractic as a health profession. Geographic location measures may reflect differences in regional preferences for chiropractic. All covariates were obtained from baseline interview responses to the AHEAD survey, with the exception of the distance to chiropractic college measure, which was calculated as the distance between a subject's baseline census tract and that of the nearest chiropractic college.

Demographic covariates are age at baseline, sex, race, and marital status. Socioeconomic measures included educational attainment, income distribution (quintiles), the number of supplemental health insurance policies (zero vs. one or more), whether the respondent was working for pay at baseline, and whether the subject was able to drive a car or not. We included a set of indicators measuring geographic location based on the Health Resources and Services Administration's (HRSA) ten region definition [22], with the Midwest region as the reference group. A measure of rurality was also included, defined by whether a person lived in a non-metropolitan (rural) or metropolitan (non-rural) area. The final measure of geographic interest was distance to nearest chiropractic college. This was included because relative nearness to a chiropractic college might influence local demand for chiropractic care, and chiropractic college graduates may be more likely to locate closer to these institutions thereby increasing supply of the service. Distance was re-coded into two categorical levels: near (under 150 miles to the nearest chiropractic college) and far (greater than 150 miles to the nearest chiropractic college).

Disease history and comorbidity were measured by participants' responses to survey questions about whether they were ever told by a medical doctor they had a specific health condition. The health conditions included were arthritis, cancer, any heart condition, diabetes, lung disease, hip fracture, or hypertension. In order to reflect the extent of a respondent's comorbidity, we re-coded the count of comorbid conditions into four categories: zero, one (reference category), two, or three or more comorbid conditions. Self-rated health measures at baseline assessed each respondent's view of their own health in terms of "excellent", "very good", "good", "fair", or "poor".

Functional health status was measured in multiple ways. The first was how the respondent answered the question "Are you often bothered by pain?"(yes/no). In addition to the standard activities of daily living (ADLs) and instrumental activities of daily living (IADLs), the AHEAD respondents were asked about five additional measures of upper and lower body limitations to further assess physical impairment. These measures were 'difficulty picking up a dime', 'difficulty lifting ten pounds', 'difficulty pushing or pulling large objects', 'difficulty climbing a flight of stairs', and 'difficulty walking several blocks'. We included pain, ADLs, IADLs, and the additional measures of physical function in our analysis because these are conditions likely to be associated with seeking chiropractic care.

Because pain and lack of physical function are also associated with depression and cognition in older adults, we also included measures of depressive symptoms based on a respondent's score on the Centers for Epidemiologic Studies Depression (CES-D) [23] and measures of cognition based on their scores from the Telephone Interview for Cognitive Status (TICS-7) [24]. In assessing depressive symptoms we classified individuals into three categories: zero depressive symptoms (reference group), one or two depressive symptoms, or three or more depressive symptoms. Similarly we classified cognitive function into three discrete groups: zero-to-ten on the TICS-7 (low cognitive functioning), eleven-to-thirteen on the TICS-7 (normal functioning, and the reference category), and fourteen-to-fifteen on the TICS-7 (high cognitive functioning).

Health lifestyle related factors included cigarette smoking, alcohol consumption, and body mass. Cigarette smoking and alcohol consumption could be related to a person's way of coping with physical pain [25]. Respondents were asked to describe themselves as a current smoker, former smoker, or someone who has never smoked. We re-coded these responses into a single indicator of 'never smoked' versus 'current and/or former smoker' in order to discern between those who might have other underlying health conditions from smoking from those who never smoked. Regarding alcohol consumption, respondents were asked if they ever drank beer, wine or liquor, and if they did how many drinks they averaged per week. In this analysis we used the 'ever drink' variable indicating that a respondent has at least one alcoholic drink during a week vs. never drinking alcohol. BMI measures (kg/m²) were included to reflect a potential association between carrying excess weight and back pain. In our study we have four BMI categories: obese ≥ 30 BMI, overweight 25 ≤ BMI < 30, normal weight 18.5 ≤ BMI < 25, and underweight < 18.5 BMI. The normal weight group was the reference category.

Two measures of health services use in the twelve months prior to baseline interview were included: hospital stays (raw count) and the number of physician visits. With regard to the measure of physician visits, the AHEAD survey asks how many times the respondent talked to a medical doctor about their health in the last 12 months, so this measure does not capture visits to non-MD health providers. These are included as baseline indicators of access to health services as well as health status markers. To capture non-linearities in outpatient services use this variable was re-coded into four levels based on the number of physician visits: one or fewer physician visits, two to three physician visits, four to six physician visits, and seven or more physician visits. The reference category was two to three physician visits. A third variable was included to represent the supply of physicians at the local level, as measured by the number of active, nonfederal MDs per 1,000 people in the respondent's county of residence.

Two separate binomial logistic regression was used to identify factors associated with (1) chiropractic use, and (2) conditional upon any use, to identify factors associated with high levels of annual chiropractic use (i.e., those exceeding a "soft cap" of 12 visits in a calendar year versus lower levels of annual use). The logistic regression models used forced entry that included all covariates described above to determine the odds of using chiropractic versus not using. The odds ratios for each of the covariates in the second regression are of being in the high user group versus not being in the high user group. No interaction terms were hypothesized or included in these analyses. In each logistic regression we followed standard procedures for model development and evaluation [26‐28].

Among the 5,510 subjects there were 15,716 primary and secondary chiropractic visits in the 15-year period. There were 806 users resulting in a period prevalence of 14.6%. The mean annual prevalence of chiropractic use was 4.8% (range 4.1% - 5.4%) and the average number of visits per chiropractic user was 19.5. To test statistical significance we used t-tests for continuous variables and χ2 tests for indicator variables.

Group means for all variables across nonusers and users appear in Table 1. Based on a comparison of the means of the sub-samples, chiropractic users were on average younger than nonusers, a higher percentage were white, had more education and higher income, had more insurance policies, were able to drive, and were married at baseline. Geographically, a lower percentage of chiropractic users lived in the Northeast, Mid-Atlantic, Southwest, and Mountain regions while a higher percentage lived in the North-Central, Midwest, and Pacific-Northwest regions of the United States. A higher percentage of chiropractic users lived in rural areas.

Of the disease conditions, on average fewer chiropractic users had been told they had a heart condition, diabetes, lung disease, hip fracture, or hypertension. A higher percentage of chiropractic users had zero comorbid conditions while a lower percentage had three or more comorbid conditions. Among functional status measures, chiropractic users had a lower mean value for functional limitations such as difficulty picking up a dime, difficulty lifting ten pounds, difficulty pushing or pulling large objects, difficulty climbing one flight of stairs, and difficulty walking several blocks. Chiropractic users had lower ADL and IADL means, indicating fewer difficulties with activities of daily living and instrumental activities of daily living. In the depressive symptoms categories, chiropractic users on average had fewer depressive symptoms. A higher proportion of chiropractic users relative to nonusers also had higher TICS-7 scores, reflecting higher cognitive functioning. Among the health lifestyle factors a higher percentage of chiropractic users relative to nonusers drank alcohol and were overweight. On average, more chiropractic users rated their health as excellent or very good compared to nonusers, and fewer had any hospitalizations in the prior twelve months. A greater proportion of chiropractic users relative to nonusers also lived in counties with fewer physicians per 1,000 capita

Table 2 shows the adjusted odds ratios (AORs) and 95% confidence intervals for all covariates in the model. Statistically significant effects are shown in bold face.

The odds of using chiropractic were lower for men, African-Americans, and other non-Hispanic races. For people who never married or were widowed, the odds of chiropractic use were lower relative to those who were married. Respondents with three or more comorbid conditions were 1.83 times more likely than those with one comorbid condition to be a chiropractic user. People with lung disease and hypertension were at lower odds of using chiropractic, as well as those with higher mean IADLs. People who experienced pain often had 1.5 times the odds of using chiropractic relative to those not experiencing pain often. People who were able to drive at baseline had almost two times the odds of those not able to drive of using chiropractic. Among the weight categories, people that were overweight (relative to normal weight) were 1.26 times more likely to be a chiropractic user, while those who were underweight were at lower odds of using chiropractic. People with "very good" and "excellent" self-rated health had 1.6 times the odds of using chiropractic relative to those with "poor" self-rated health. High physician service utilizers, as measured by those with greater than seven visits to a physician in the past year, were 1.4 times more likely than those with two to three visits in the prior year to use chiropractic. Geographically, people living in the Northeast, Midatlantic, Southeast, Southwest, Mountain, and West regions had lower odds of using chiropractic than those living in the Midwest. Respondents living in counties with a higher number of physicians per capita had slightly lower odds (0.916) of using chiropractic. The C-statistic for the model was 0.715, indicating a good fit of the model to the data. The Hosmer and Lemeshow goodness-of-fit test provided additional support based on a p-value of 0.1651, indicating no evidence of a lack of fit to the data.

Forty-two percent of the 806 chiropractic users made their visits over a single calendar year. Nearly 20% of chiropractic users had visits over a two-year period, while 38% had visits spanning three or more calendar years, indicating substantial heterogeneity in the consistency of chiropractic use over time.

Of the 806 respondents who used chiropractic, there were 130 users (16%) who exceeded Medicare's "soft cap" of 12 chiropractic visits in any of the calendar years in which they used chiropractic. Although small, this high volume user group accounted for nearly 58% of the total chiropractic claims volume (9,080 out of 15,716). On a calendar year basis the portion of all chiropractic users that exceeded the "soft cap" of 12 chiropractic visits (high volume user group) grew from 4.6% in 1993 to almost 15% by 2007, although this percentage in part reflected a declining number of people in the total chiropractic user group due to death and censoring while the number per year of high volume users was relatively flat over the period. For the same reasons, high volume chiropractic users' claim volume in each calendar year grew from 18% in 1993 to over 45% in 2007, averaging about 45% of the total chiropractic claim volume in the 1998-2007 period. Approximately 15% of the claims from this group would be disallowed if a "hard cap" threshold of 12 visits per calendar year was enforced. Figure 1 shows the distribution of chiropractic users by year and the percentage of high volume users over the 15-year period. Figure 2 shows the annual claim volume attributable to high volume users and the percentage of claims that exceed a 12 visit threshold.

Descriptively, the high volume chiropractic users were on average younger than the lower volume users, had a higher mean proportion of one or more health insurance policies, a lower mean for IADLs, and more often lived in the North-Central region of the United States compared to lower volume users. Logistic regression revealed that high volume users had 0.516 the odds of lower volume users of having a high school education relative to "some high school". High volume users were almost 2.5 times more likely than lower volume users to have arthritis, and about 2.4 times more likely to have difficulty picking up a dime. The C-statistic for the model was 0.707. The Hosmer and Lemeshow test p-value was 0.2550, an indication of adequate fit between model and data. The AORs are reported in Table 3.

High volume users saw chiropractors more than any other specialty provider covered by Medicare. Among all provider claims for this group, chiropractic ranked first with 21.5% of the total volume, more than internal medicine and family practice combined. Comparatively, chiropractic claims volume was only 4.1% of the total claims volume for the lower volume chiropractic user group, similar to the distribution pattern of the nonuser group. Table 4 shows the distribution of the top most frequently seen provider specialties by user and nonuser groups.