Abstract
OBJECTIVE
To examine the impact of provider continuity on preventive care among adults who have a regular site of care.
DESIGN
Logistic regression analyses were conducted to explore whether continuity, categorized as having no regular care, site continuity, or provider continuity, was associated with receipt of 3 preventive care services (influenza vaccination, receipt of a mammogram, and smoking cessation advice), independent of predisposing, need, and enabling factors.
PARTICIPANTS
This study examined 42,664 persons with private, Medicaid, Medicare, or no health insurance coverage who reported either having no site of care or being seen in a physician's office, HMO, hospital outpatient department, or other health center.
SETTING
The 1996/1997 Community Tracking Study (CTS) household survey, a telephone-based survey providing a cross-sectional sample of 60,446 U.S. adults aged 18 and older representing the U.S. housed, noninstitutionalized population.
MEASUREMENTS AND MAIN RESULTS
After adjustment for differences in predisposing, enabling, and need factors, site continuity was associated with significant increases of 10.4% in influenza vaccinations (P = .006) and 12.6% in mammography (P = .001), and a nonsignificant increase of 5.6% in smoking cessation advice (P = .13) compared to having no regular site of care. After adjustment for these factors, provider continuity was associated with an additional improvement of 6.0% in influenza vaccinations (P = .01) and 6.2% in mammography (P = .04), and a nonsignificant increase of 2.5% in smoking cessation advice (P = .30) compared to site continuity.
CONCLUSIONS
Provider continuity and site continuity are independently associated with receipt of preventive services. Compared to having no regular site of care, having site continuity was associated with increased receipt of influenza vaccination and mammography and, compared to having site continuity, having provider continuity was associated with further increases in the receipt of these two preventive services.
Keywords: patient-physician relationship, continuity of care, satisfaction with care
Having a sustained relationship with a primary care provider is considered to be a key component of primary care.1 Yet trends in health care delivery, including involuntary disenrollment from health insurance coverage, the emergence of managed care and larger physician groups, and the use of physician extenders, diminish opportunities for provider continuity.2,3 Thus, research is needed to examine the impact of provider continuity on outcomes, such as the receipt of evidence-based preventive services.
While studies 4–7 have reported that identifying a regular site of care is associated with receipt of preventive services, particularly for women and children, very few studies have examined the relationship between continuity of care with a regular provider and preventive care.4,8,9 In the one study that used a nationally representative U.S. sample (1987) to evaluate the effect of provider continuity beyond having a regular site of care, Lambrew et al.4 reported that having a regular physician offered no statistically significant advantage over having a “mainstream” site of care (physician's office, clinic, or health maintenance organization) in increasing receipt of preventive care by women (clinical breast examinations, Pap smears, and mammograms) and children (measles, mumps, rubella [MMR] and polio vaccinations).
We sought to revisit this question using a more recent, nationally representative sample. We used data from the Community Tracking Study (CTS) household survey of 1996/1997 to examine the independent associations of self-report of having a regular site of care and having a regular provider on receipt of 3 types of preventive services: influenza vaccinations, mammography, and tobacco cessation advice. Specifically, we hypothesized that compared to having no regular site of care, having a regular site would be associated with increased receipt of these preventive services and, compared to having a regular site of care, having provider continuity would be associated with additional gains in the receipt of these services.
METHODS
Data Source
The Community Tracking Study Household Survey, a telephone survey of 60,446 individuals, was conducted in 1996/1997, representing the U.S. housed, noninstitutionalized population.10 Sixty communities were randomly selected using stratified sampling with probability in proportion to population size in order to ensure representation of the U.S. population. Random-digit dialing was used to select most households; a small subsample of households without phones was included in the sample by providing these respondents with cellular phones for the interviews. The survey included information regarding access to care, health care use, preventive care, satisfaction and health insurance, health status, and sociodemographic attributes. The final response for the CTS was 65%. The study sample for these analyses consisted of 42,664 adults aged 18 and over with private, Medicaid, Medicare, or no health insurance coverage who reported either having no site of care or being seen in a physician's office, HMO, hospital outpatient department, or other health center.
Variables
Primary Independent Variables.
Continuity was categorized into 3 levels, as follows:
Category 1 (no regular care): included all persons who reported having no regular site of care.
Category 2 (site continuity): included all those who reported having a regular site of care (doctor's office, HMO, a hospital outpatient department, or other health center) that they used when sick or in need of medical advice, but who did not report having a regular provider.
Category 3 (provider continuity): included all subjects who reported having site continuity and who additionally identified a regular provider at their site of care (“Do you usually see the same provider each time you go there?”).
Secondary Independent Variables.
Covariates were selected based on the Andersen model of health care access.11 This model describes the numerous factors that influence access to health care and classifies access into predisposing factors, need factors, and enabling factors. This conceptual framework was selected because it includes characteristics of the population at risk, consideration of health policy, and utilization of health services. Predisposing factors were: age (18–29, 30–44, or 45–64 years); gender; race/ethnicity (non-Hispanic white, African American, primarily English-speaking Hispanic, primarily Spanish-speaking Hispanic, or Other); marital status; household size; community size (large metropolitan region of >200,000 population, small metropolitan region of <200,000 population, or nonmetropolitan region); and education (less than high school, high school degree or equivalent, some college, or college graduate). Need factors were: smoking status (current, former, or never; included in analyses of influenza vaccinations and mammography) and perceived health status from the Medical Outcomes Study Short Form 12-item health survey (SF-12). The SF-12 includes 2 summary scores, one for physical health The Physical Component Summary Scale (PCS 12) (range 10–70; mean 49 in this sample) and one for mental health The Mental Health Component Summary Scale (PCS 12) (range 9–72; mean 52 in this sample). It has been shown to be reliable and valid compared to the well-established, longer SF-36.12,13Enabling factors were: type of health insurance (none, Medicaid, Medicare, or private; insurance coverage was further classified based on the respondents’ response to survey items asking whether their insurance plan was an HMO or not) and household income expressed as a percentage of the federal poverty level for 1996 (<100%; 100% to 199%; 200% to 299%; 300% to 399%; ≥400%). To control for confounding of provider continuity with visit frequency, all analyses were adjusted for the number of physician visits over the preceding 12 months. Analyses also adjusted for the type of site of care (physician's office, HMO, hospital outpatient department, or other health center).
Dependent Variables.
Dichotomous measures of preventive health services utilization available in the CTS were:
Influenza vaccination. Respondents were asked whether they had received an influenza vaccination in the past year (adults ≥55 years old; N = 11,249).
Mammography. Respondents were asked whether they had received a mammogram in the past year (women ≥50 years old; N = 8,043).
Smoking cessation advice. Respondents were asked whether their physician had advised them to quit smoking in the past year (adults ≥18 years old who smoked; N = 7,824).
Analyses.
Because of the complex survey design of CTS, analyses were conducted with SUDAAN software (Research Triangle Institute, Research Triangle Park, NC) to yield appropriate standard errors.14 Logistic regression analyses were performed to obtain screening rates adjusted for the independent measures listed above for each preventive care measure. Models that are presented retain all covariates irrespective of their statistical significance because a group of variables may account for confounding only when taken together. However, to address the possibility that fully saturated models might be overfit, more parsimonious models excluding factors that were not significant at the P < .05 also were run. Potential collinearity was addressed by running models with and without 1 of the 2 covariates of the 2 pairs of factors that had correlation coefficients of 0.3 or greater. As this resulted in no significant changes in the magnitude or significance of estimates for other factors, we retained all factors in our models. The 5% of respondents with missing data were omitted from the multivariate analyses. Linear contrasts were used to perform pairwise comparisons of persons in Categories 1 and 2 and also persons in Categories 2 and 3. Because changes in care location disrupt continuity, analyses comparing subjects in Category 2 (site continuity) to those in Category 3 (provider continuity) were performed using the subset of persons who reported having no change in their site, provider, or both during the 12 months preceding the survey. This subset included 90% of Categories 2 and 3 subjects. Interactions between continuity category and the measures of race/ethnicity, income, education, and insurance status also were evaluated. To facilitate ease of interpretation of the size of the site and provider continuity effects, adjusted predicted marginal effects were calculated.15
RESULTS
The characteristics of respondents by continuity category are shown in Table 1 Compared to persons in Category 1 (no regular care) or Category 2 (site continuity), those in Category 3 (provider continuity) were more likely to be older, female, white, married, nonsmokers, in larger families, and more educated. They were more likely to reside in rural areas, have higher incomes, have private insurance or Medicare coverage, have lower scores on the physical component summary of the SF-12, have higher scores on the mental component summary of the SF-12, and have more annual physician visits. Among those reporting a regular site of care, persons in Category 3 (provider continuity) were significantly more likely to be seen in physicians’ offices and less likely to be seen in health centers or hospital outpatient departments than their Category 2 (site continuity) counterparts.
Table 1.
Sample Characteristics by Provider Continuity Category
| Category 1: No Regular Care (N= 7,996; 20.2%) | Category 2: Site Continuity (N= 4,689; 11.0%) | Category 3: Provider Continuity (N= 29,979; 68.8%) | |
|---|---|---|---|
| Characteristic | % or Mean (SE) | % or Mean (SE) | % or Mean (SE) |
| Age, P < .0001 | |||
| 18 to 29 (2,700) | 33.2% (0.9) | 14.0% (0.5) | 52.8% (0.7) |
| 30 to 44 (14,679) | 21.8% (0.8) | 13.3% (0.4) | 64.9% (0.7) |
| 45 to 64 (12,891) | 14.8% (0.5) | 9.8% (0.4) | 75.3% (0.6) |
| 65+ (6,309) | 10.3% (0.6) | 5.4% (0.3) | 84.3% (0.8) |
| Gender, P < .0001 | |||
| Female (22,696) | 16.4% (0.5) | 11.7% (0.3) | 72.9% (0.6) |
| Male (19,968) | 24.1% (0.6) | 11.4% (0.3) | 64.4% (0.7) |
| Race/Ethnicity, P < .0001 | |||
| Black (4,615) | 26.7% (1.1) | 14.0% (0.8) | 59.3% (1.4) |
| Hispanic, English speaking (2,135) | 27.0% (1.4) | 13.4% (0.9) | 59.6% (1.4) |
| Hispanic, Spanish speaking (1,554) | 41.3% (2.8) | 13.4% (1.8) | 45.3% (4.0) |
| Other (2,564) | 23.8% (1.4) | 12.1% (0.9) | 64.0% (1.5) |
| White (31,786) | 16.8% (0.3) | 10.1% (0.2) | 73.1% (0.4) |
| Marital Status, P < .0001 | |||
| Married (25,780) | 15.6% (0.6) | 9.9% (0.3) | 74.5% (0.7) |
| Not married (16,884) | 26.6% (0.5) | 12.6% (0.4) | 60.8% (0.6) |
| Mean Family Size, P < .0001 | 2.2 (0.02) | 2.5 (0.04) | 2.4 (0.02) |
| Residence, P < .0001 | |||
| Large metropolitan area (≥200,000 residents) (36,085) | 21.5% (0.6) | 11.4% (0.2) | 67.2% (0.6) |
| Small metropolitan area (<200,00 residents) (1,538) | 20.9% (0.7) | 12.5% (0.6) | 66.7% (0.7) |
| Nonmetropolitan area (5,041) | 15.7% (1.0) | 9.5% (0.6) | 74.7% (1.3) |
| Education, P < .0001 | |||
| <High school (5,410) | 25.4% (0.9) | 11.0% (0.9) | 63.5% (1.2) |
| High school/GED (15,588) | 21.0% (0.7) | 10.6% (0.8) | 68.4% (0.7) |
| Some college (10.033) | 18.2% (0.7) | 11.9% (0.4) | 69.8% (0.8) |
| College degree (11,633) | 16.6% (0.5) | 10.8% (0.4) | 72.6% (0.5) |
| Smoking Status, P < .0001 | |||
| Current (10,464) | 26.9% (0.8) | 12.5% (0.4) | 60.6% (0.9) |
| Former (10,202) | 13.9% (0.5) | 8.9% (0.3) | 77.2% (0.6) |
| Never (21,851) | 19.8% (0.6) | 11.2% (0.3) | 68.9% (0.6) |
| Health Status * | |||
| Mean PCS12 score, P < .0001 | 50.6 (0.2) | 49.7 (0.2) | 48.5 (0.1) |
| Mean MCS12 score, P < .0001 | 51.4 (0.1) | 51.2 (0.2) | 52.7 (0.1) |
| Type of Health Insurance, P < .0001 | |||
| None (5,570) | 48.7% (1.4) | 13.3% (0.7) | 38.0% (1.2) |
| Medicaid-HMO (324) | 25.0% (3.0) | 12.7% (1.9) | 62.3% (3.3) |
| Medicaid-non-HMO (883) | 24.6% (2.2) | 17.0% (1.6) | 58.3% (2.6) |
| Medicare (7,204) | 10.8% (0.6) | 6.0% (0.3) | 83.2% (0.7) |
| Private-other (1,117) | 16.0% (1.1) | 10.5% (0.3) | 73.5% (1.6) |
| Private-HMO (12,567) | 14.5% (0.5) | 12.8% (0.4) | 72.8% (0.6) |
| Private-non-HMO (13,964) | 16.7% (0.5) | 10.4% (0.4) | 72.9% (0.6) |
| Type of Care Location, P < .0001 | |||
| Health center (3,183) | NA | 32.4% (1.2) | 67.6% (1.2) |
| Hospital outpatient department (1,435) | NA | 42.1% (1.5) | 57.9% (1.5) |
| HMO (1,514) | NA | 25.5% (1.3) | 74.5% (1.3) |
| Physician's office (23,847) | NA | 6.1% (0.2) | 93.9% (0.2) |
| Income as a Percent of Poverty Level, P < .0001 | |||
| <100% (9,015) | 29.9% (0.9) | 12.0% (0.5) | 58.1% (1.0) |
| 100% to 199% (7,927) | 23.8% (0.8) | 11.9% (0.5) | 64.4% (0.8) |
| 200% to 399% (13,380) | 16.5% (0.5) | 10.3% (0.3) | 73.2% (0.6) |
| >400% (12,342) | 12.7% (0.5) | 10.3% (0.4) | 77.0% (0.5) |
| Mean Number of Physician | |||
| Visits in past 12 months, P < .0001 | 1.8 (0.06) | 3.0 (0.06) | 3.9 (0.04) |
Higher scores on the PCS12 and the MCS12 indicate better health status.
PCS12, MCS12, SE, standard error.
Table 2 shows the unadjusted annual percentage of preventive care receipt by continuity category. The linear trend test for preventive care receipt by continuity category was highly significant for each test (P < .0001; data not reported in the tables). Pairwise comparisons performed with linear contrasts revealed that compared to those in Category 1 (no regular care), 11.4% (95% confidence interval [CI], 5.8% to 17.1%) more of those in Category 2 (site continuity) reported having received an influenza vaccination and 20.6% (95% CI, 14.9% to 26.2%) more reported having received a mammogram. The gain of 4.9% (95% CI, −0.6% to 10.3%) in those having received smoking cessation advice was not significant. Moreover, compared to those in Category 2 (site continuity), those in Category 3 (provider continuity) reported having increased receipt of preventive services for each test. These included an improvement of 12.3% (95% CI, 7.5% to 17.2%) for influenza vaccination, 5.7% (95% CI, 0.8% to 10.6%) for mammography, and 5.3% (95% CI, 0.7% to 9.9%) for smoking cessation advice.
Table 2.
Unadjusted Annual Percentage of Service Receipt by Care Category
| Rate of Service Receipt: Category 1 (No Regular Care) | |||
| Marginal Increase for Category 2 (Site Continuity) over Category 1 (No Regular Care) | |||
| Marginal Increase for Category 3 (Provider Continuity) over Category 2 (Site Continuity) | |||
| Characteristic | |||
| % | |||
| % (95% CI) | |||
| % (95% CI) | |||
| Influenza vaccination (Age 55+) | 31.3 | 11.4 (5.8 to 17.1)† | 12.3 (7.5 to 17.2)† |
| Mammogram (Women, age 50+) | 29.5 | 20.6 (14.9 to 26.2)† | 5.7 (0.8 to 10.6)* |
| Smoking cessation advice (Smokers, age 18+) | 39.0 | 4.9 (−0.6 to 10.3) | 5.3 (0.7 to 9.9)* |
P < .05.
P < .001.
CI, confidence interval.
Table 3 shows results adjusted for the predisposing, need, and enabling factors. The linear trend test for preventive care receipt by continuity category was significant for each test (P < .05; data not reported in the tables). Linear contrasts showed that compared to those in Category 1 (no regular care), subjects in Category 2 (site continuity) reported having significant increases of 10.4% (95% CI, 4.5% to 16.3%) for influenza vaccination and 12.6% (95% CI, 5.1% to 20.1%) for mammography, but the increase of 5.6% (95% CI, −1.7% to 12.8%) for smoking cessation advice was not significant. Further, compared to those in Category 2 (site continuity), subjects in Category 3 (provider continuity) were 6.0% (95% CI, 1.4% to 10.5%) more likely to report having received influenza vaccination and 6.2% (95% CI, 0.4% to 11.9%) more likely to report having received mammography, but the 2.5% (95% CI, −2.3% to 7.3%) increase in smoking cessation advice was not significant. These findings were nearly identical when models were run that excluded nonsignificant factors.
Table 3.
Adjusted Annual Percentage of Service Receipt by Care Category *
| Rate of Service Receipt: Category 1 (No Regular Care) | |||
|---|---|---|---|
| Marginal Increase for Category 2 (Site Continuity) over Category 1 (No Regular Care) | |||
| Marginal Increase for Category 3 (Provider Continuity) over Category 2 (Site Continuity) | |||
| Characteristic | |||
| % | |||
| % (95% CI) | |||
| % (95% CI) | |||
| Influenza vaccination (Age 55+) | 37.5 | 10.4 (4.5 to 16.3)§ | 6.0 (1.4 to 10.5)‡ |
| Mammogram (Women, age 50+) | 36.0 | 12.6 (5.1 to 20.1)§ | 6.2 (0.4 to 11.9)† |
| Smoking cessation advice (Smokers, age 18+) | 39.7 | 5.6 (−1.7 to 12.8) | 2.5 (−2.3 to 7.3) |
Five percent of patients had missing data and were omitted from the adjusted analyses.
P < .05.
P < .01.
P < .001.
CI, confidence interval.
For subjects in Categories 2 and 3, the survey permitted analyses of the roughly 90% of the sample with unchanging site of care during the preceding 12 months. These analyses produced findings that were similar to the comparisons of Categories 2 and 3 presented above.
No significant interactions (P < .05) were identified.
DISCUSSION
Having a regular site of medical care, such as an outpatient setting where one can go to receive primary care, is a commonly used measure of access to care. Using data from a large, nationally representative survey, we examined the association between 3 types of care (no regular care, site continuity, and provider continuity) and 3 preventive care measures (influenza vaccination, mammography, and smoking cessation advice). We hypothesized that having provider continuity would increase receipt of these services beyond having site continuity. Results reached statistical significance for 2 of the 3 preventive services. Compared to subjects in Category 2 (site continuity), those in Category 3 (provider continuity) reported a significantly increased likelihood of having received influenza immunizations and mammography, after adjustment for predisposing, enabling, and need factors plus office visits during the year. Also, a significant linear trend for having received smoking cessation advice by continuity care category was observed.
There are several potential mechanisms that could account for the observed effects. Provider continuity is associated with greater trust,16 and trust may be linked to greater adherence to physician recommendations.17,18 In addition, continuity relationships may also allow providers to sort more effectively through the multiple competing demands posed by patients 19,20 and adequately address prevention.21 Why the effects reached significance for influenza vaccination and mammography but not smoking cessation advice is not clear, though this may reflect the relative simplicity of ordering an immunization or radiograph compared to providing smoking cessation advice. Further study is needed to address this question and to determine whether trust, competing demands, or other factors mediate the relationship between provider continuity and receipt of preventive care.
This study is subject to several limitations. The CTS is a cross-sectional survey, so causal relationships cannot be proven. Unmeasured confounding, particularly differences between patients who achieve continuity and those who do not, may account for the findings. For example, persons who seek a continuity relationship with a single provider may also be more predisposed to obtain preventive services. While we adjusted for numbers of visits to control for the relationship between visits and both receipt of preventive services and the increased opportunity to develop a continuity relationship with more visits, only a prospective, controlled trial could prove a causal connection between personal continuity per se and preventive service receipt. Also, the CTS did not contain items allowing the measurement of continuity based on utilization patterns.22 However, data from the RAND Health Insurance Experiment were used to define a “personal” primary care provider as being the person who received the results of the subject's screening examination.23 This measure of provider continuity remained more stable over time than the most visited provider and represented a generalist who provided care for 87% to 90% of visits for selected primary care problems. A national study showed that 79% of Americans could identify a “regular” doctor by name and that 76% of these doctors were believed to be general or family practitioners, internists, or pediatricians.24 Finally, reports of receipt of preventive services are subject to reporting bias. Studies suggest that respondents tend to underestimate the time interval since they received preventive services and use of preventive services is consistently overreported based on medical record confirmation.25,26 However, it seems unlikely that the presence or absence of provider continuity would bias self-reporting of preventive care.
Primary care practice has placed a progressively greater emphasis on health promotion. Our observation that provider continuity is beneficially related to influenza vaccination and mammography, coupled with studies indicating that provider continuity may be related to improved outcomes including satisfaction 27,28 and reductions in preventable utilization,29–32 lends support to policies aimed at increasing provider continuity. Prospective, controlled studies of interventions designed to improve provider continuity, examining outcomes including the receipt of preventive services, health status, costs, and satisfaction, are warranted.
Acknowledgments
Funded in part by The Robert Wood Johnson Foundation under its Changes in Health Care Financing and Organization Initiative and the American Academy of Family Physicians Advanced Research Training Grant Program.
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