Are Physicians Less Likely to Recommend Preventive Services to Low-SES Patients?

26, 350–357 (1997) PM970150

PREVENTIVE MEDICINE ARTICLE NO.

Are Physicians Less Likely to Recommend Preventive Services to Low-SES Patients?1 Leif I. Solberg, M.D.,*,2 Milo L. Brekke, Ph.D.,† and Thomas E. Kottke, M.D.‡ *Group Health Foundation, Minneapolis, Minnesota 55440-1309; †Brekke Associates, Minneapolis, Minnesota 55410-2252; and ‡Mayo Clinic and Foundation, Rochester, Minnesota 55905

Background. Do low-SES adult patients visiting private primary care clinics differ from higher SES adult patients in their need for eight preventive services or in receiving either a recommendation for or the needed services? Methods. Randomly identified adult patients were surveyed within 2 weeks of a visit to 22 clinics in the Minneapolis–St. Paul area. Questions assessed patient recollection of the latest receipt of eight services and whether needed services had been recommended during the visit or received then soon after. Results. Of those surveyed, 4,245 patients (1,650 low SES) responded (84.3%), showing that low SES patients were less likely to be up to date for cholesterol measurement, Pap smear, mammography, breast exam, and flu or pneumonia shots (P < 0.004), but not for blood pressure measurement. Low-SES patients needing services received recommendations to have them and actually received them at the same rate as higher SES patients. Conclusions. The 22 primary care clinics studied appear to be recommending and providing needed preventive services to visiting patients at the same rate regardless of income or insurance status. The reasons for differences in prevention status by SES are complex but the low proportion of all patients receiving recommendations for needed services suggests the need to take advantage of all visits for updating prevention needs. © 1997 Academic Press Key Words: preventive medicine; health; low SES; physician behavior. INTRODUCTION

It is clear that populations with lower socioeconomic status (SES) have disproportionately higher levels of mortality and morbidity [1–8]. In fact, Pappas’ recent 1 This project was supported by Grant RO1 HS08091 from the Agency for Health Care Policy and Research 2 To whom reprint requests should be addressed at the Group Health Foundation, 8100 34th Avenue South, P.O. Box 1309, Minneapolis, MN 55440–1309. Fax: (612) 883–5022. E-mail: [email protected].

review of data from several national surveys [3] and Fein’s literature review [5] both suggest that this overall worse health among low-SES populations has increased in the United States over the past 50 years, and Marmot found the same for England [8]. Although both Feinstein’s and Marmot’s reviews make clear that the reasons for this difference in health status are very complex and poorly understood [1,8], preventive services offer the potential to help reduce this problem. However, many studies have demonstrated that the poor have lower rates of most preventive services [1,9–11]. That seems to be true despite a higher frequency of many risky behaviors in this same group [6–7,12–14]. In addition, it has also been shown that the uninsured are only half as likely as their insured peers to have received a variety of preventive services [15]. It has often been assumed that this lower level of preventive services among people of lower SES is due in part to physician behavior, i.e., that physicians are less likely to recommend or order these services for patients less able to afford them [16] or with other more urgent health care priorities [17]. This assumption has been fed by physician surveys that list reimbursement problems as a barrier to their doing a better job of providing patients with preventive services [18]. In the course of conducting a randomized controlled trial of the use of continuous quality improvement as a means to improve the delivery of preventive services to adults in primary care practices (IMPROVE) [19–20], we have had an opportunity to gather information about this problem. One of the specific aims of this study is to test whether clinics in the trial will improve their preventive services equally for their low-SES patients and their other patients. Although we believed that the private primary care clinics participating in the IMPROVE trial would make systems changes that would benefit all classes of patients equally, our hypothesis was that at baseline, low-SES patients would not be equally up to date. In keeping with the above literature, we expected that the level of previous preventive services as well as preven-

350 0091-7435/97 $25.00 Copyright © 1997 by Academic Press All rights of reproduction in any form reserved.

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tive actions by clinicians at the time of visits would be lower for low-SES adults. The 44 clinics in the trial are all in the greater metropolitan area of Minneapolis and St. Paul and all contract with one or both of the two managed care plans that are sponsors for the trial funded by the Agency for Health Care Policy and Research. They were divided into intervention and comparison groups by a stratified randomization process involving size and measurement of prevention readiness and the quality level of organizational culture. This study of preventive services in relation to socioeconomic status is only being performed in the 22 clinics randomized to the intervention condition, since the specific aim is to determine whether the effect of the intervention varies systematically by SES of patients. One component of the trial’s evaluation is a mail survey of a random sample of patients who had recently been seen by primary care providers in each of the trial clinics. This survey at baseline provided us with an opportunity to answer the questions posed above. METHODS

Clinic Participation and Attributes Participating clinics were recruited from those meeting four criteria: 1. Contract to provide primary care services to either or both of the sponsoring HMOs for this trial (Blue Plus and HealthPartners). 2. Location within 50 miles of the center of the Minneapolis–St. Paul area. 3. Management interest in participating in this trial. 4. Ability to complete preparticipation tasks, including a detailed questionnaire about their clinic. Seventy-one medical groups practicing at 164 sites met the first two criteria and 33 of these groups (47%) agreed to participate and completed the tasks for 44 of their sites. Their completed questionnaires revealed that the 22 intervention clinics in this study contain an average of 6.7 ± a standard deviation of 4.2 adult primary care physicians (mostly family physicians) and 1.5 ± 2.3 midlevel practitioners. They see 762 ± 693 patients per week. Although on average 41% of these patients have prepaid health insurance, only 21% are in the two HMOs sponsoring the trial. An average of 11.1 ± 13.1% of their patients are on medical assistance. Instrumentation, Sampling, and Data Collection The main purpose of the Patient Recent Visit Survey is to determine visiting patients’ self-reported need for the eight preventive services targeted in this study (see Table 1) as well as their report of clinic activities related to those services during the visit. The eight targeted services were selected as those with the most

TABLE 1 Preventive Services Delivery Goals Preventive service

Target group and frequency

Blood pressure Total serum cholesterol

Age 20+ every year. Age 20+ every 5 years.

Tobacco use

Pap smear

Age 20+ users identified. Age 20+ users advised to quit at each visit. Age 50+ women every 2 years. Age 50+ women every 2 years. Age 20+ women every 2 years.

Influenza immunization Pneumococcus immunization

Age 65+ every year. Age 65+ once.

Clinical breast exam Mammogram

general agreement as important per Healthy People 2000 and the U.S. Preventive Services Task Force [21,22]. The target age groups and intervals were chosen in part because of widespread agreement with at least these groupings and in part by the need to focus on a feasible and common interval wherever possible. These varying services and age/gender targets suggested the need for five different survey instruments. However, after extensive pretesting in both clinic and nonclinic settings, we decided to use only two questionnaires: one for men and one for women with subsections for the specific age groups. The questionnaires contained 119 questions for females and 88 questions for males, focusing primarily on when the subject had last received each of the preventive services and whether at the visit the service was recommended or provided. In addition, there were questions about demographic characteristics, reason for visit, relationship to the clinic, satisfaction, and self-perceived health status (from the SF-36) [23–25]. Patients eligible for sampling and receipt of a survey were those ù20 years of age who came to the clinic for a visit with a clinician on days randomly identified during the period between August 1 and September 9 of 1994. For purposes of this study, low SES patients were defined as those who were in any of three categories: ● medical assistance, ● no health insurance, or ● self-reported household income below 150% of the 1993 federal poverty level for household size. The design called for the same number of patients to be included in each of the five age/gender cells sampled from each clinic regardless of the relative size of the clinic or the relative age/gender distribution of their patients. These age/gender cells are women 20–49, 50– 64, and 65+ and men 20–64 and 65+. Although the study reported here was part of the

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original design, the relative proportions of optimal sample sizes for the various cells were different from what they might have been for this study alone. Therefore, for the larger comparison study, we calculated that for adequate statistical power we would need 175 people per clinic, divided into the above cells in the numbers of 70, 20, and 27 for the three female cells, respectively, and 40 and 18 for the two male cells. Based upon preliminary clinic estimates that an average of only 11% of their patients were on medical assistance or had no health insurance, we calculated that an oversample of those on medical assistance or uninsured would be needed from each clinic to add another 0 to 54 patients per clinic (depending upon each clinic’s estimated proportion of these patients). Just prior to the beginning of the intervention in September of 1994, we drew names from appointment logs provided by these clinics for randomly selected days (with the request for a specific day’s names coming at the end of that day). The names of patients who had seen a clinician were consecutively drawn until enough had been obtained to fill each of five age/gender cells related to the eight preventive services in the study. When the number of patient names on a particular log was greater than the number needed for a particular age/gender cell, the names of patients on that log were selected at random until the precise number of names needed to fill the category was reached. Then an oversample was added to each cell from those identified as having medical assistance or no health insurance (since income levels could not be known until the survey was returned) until the calculated requirements were satisfied. However, in some clinics with very skewed age or SES distributions, some of these desired limits could not be met entirely. Questionnaires were distributed and followed-up following the recommendations of Dillman [26]. For further details about survey methodology, see Ref. [27]. Data Management and Analysis After careful verification of data integrity, survey responses were aggregated by clinic and were age/gender adjusted (i.e., weighted) within each clinic to the age and gender proportions in the United States population as defined by the 1990 U.S. Census [28]. This weighting allows the reader to compare rates with a standard population. It also balances the initial between-clinic imbalances in both sampling and rate of response by age and gender. When statistical tests for subgroup differences were performed using these weighted data, the numbers of respondents were also rescaled in the weighted proportions so that they would be equal to the original numbers of respondents. Thus the degrees of freedom for the statistical tests were not changed by this weighting calculation.

The rates reported for each service are based on the denominators of individuals who are eligible by age and gender for the service being reported. However, for the sake of simplicity, data pertaining to tobacco use are limited to current users only. These data do, however, closely reflect clinic activities reported by both the users and nonusers who responded. Although the reported SES group rates are means of clinic means, these rates were not found to be substantively different from the rates for the classes of individual patients as a whole. Mean clinic rates were chosen not only because of the overall need to make group comparisons, but also in recognition of the fact that clinics may create more homogeneous clustering of activities and patients than occurs across all clinics. In addition, our sampling was cluster sampling in which patients were randomly sampled within randomly assigned clinics. Comparisons between mean clinic rates for those in the low SES categories versus the balance of the respondents were tested for significance using two-tailed t tests with the clinic as the unit of analysis. However, in reporting performance if recommended, population comparisons were made because of the low numers with recommendations. The potentially confounding factors in Table 2 were used in backward stepwise lo-

TABLE 2 Characteristics of Low-SES and Non-Low-SES Patients—Clinic Mean (SD) Adjusted to U.S. Population Characteristic

Low SES

Non-low SES

Pa

n Age (years) Male gender (%) Race (%) Asian Black American Indian White Other Hispanic ancestry (%) Household size (%) 1 2 3 4–6 >6 Mean size Household income <$14,000/year (%) Current tobacco use (%) Borderline/high BP (%) Borderline/high cholesterol (%) Overall health status (%) Poor/fair Good Very good/excellent

1,684 44.9 (2.1) 38.2 (1.3)

2,561 46.7 (1.5) 48.8 (5.2)

0.003 0.000

2.0 (2.5) 2.0 (4.6) 3.3 (2.9) 89.9 (8.1) 2.8 (3.3) 2.2 (2.9)

0.2 (0.5) 0.9 (1.2) 0.8 (0.9) 97.1 (2.0) 1.0 (1.1) 1.3 (1.6)

0.003 0.28 0.001 0.001 0.02 0.21

20.4 (7.6) 28.6 (7.3) 21.0 (5.0) 27.4 (5.7) 2.5 (3.4) 2.9 (0.3)

16.4 (5.3) 38.0 (4.1) 17.4 (4.8) 27.5 (6.4) 0.8 (0.9) 2.7 (0.2)

0.05 0.000 0.016 0.98 0.03 0.08

54.9 (13.6) 37.9 (7.5) 19.8 (6.1)

3.2 (1.7) 29.1 (5.8) 21.8 (4.7)

0.000 0.000 0.17

17.9 (7.1)

23.9 (7.2)

0.008

21.9 (5.6) 40.5 (6.3) 37.5 (9.3)

13.1 (5.1) 36.5 (4.6) 50.4 (8.5)

0.000 0.019 0.000

a

t test, two-tailed.

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gistic regression analyses with the patient as the unit of analysis (first age and gender in step 1; then clinic, race, health status, purpose for visit, length of time as a patient, and number of times visited in step 2; and finally SES status in step 3) in order to determine whether any differences attributed to SES as a result of the t tests might be due to these other factors. RESULTS

The number of usable responses for these 22 clinics was 4,245 (1,684 in the low-SES categories and 2,561 others). Thus the response rate was 84.3% overall and 76.4% from low-SES patients. The mean clinic response rates by age/gender cell for females were 86.8% for ages 20–49, 90.4% for ages 50–64, and 86.3% for 65+. Male rates were 80.9% for ages 20–64, and 86.3% for 65+. The median interval from date of visit to date that the respondents indicated they completed the questionnaire was 24 days. As defined above, the low-SES respondents consisted of 1,684 people: 585 (34.7%) who have medical assistance, 649 (38.5%) who have no insurance, 167 on Medicare (9.9%), and 283 (16.8%) with other insurance. The non-low-SES group of 2,561 contained 392 on Medicare and the balance had other insurance. From the 88.8% who answered the question, the overall percentage of each of these groups that reported household incomes below 150% of the 1993 federal poverty level were 74.7% of those on medical assistance, 25.8% of those with no health insurance, 32.2% of those on Medicare, and 12.4% of those with other insurance (overall average of 25.5%). Table 2 provides a description of the characteristics of these two populations (low SES vs non-low SES). As TABLE 3 Clinic Relationship/Visit Status—Clinic Mean (SD) Adjusted to U.S. Population Variable Clinic patient >3 years (%) Number of visits in past 2 years First visit (%) Overall satisfaction with care (%) Poor/fair Good Very good/excellent Mean satisfaction (1–5 scale) Reason for visit (%) Checkup Urgent visit Chronic care Follow-up Other a

t test, two-tailed.

Low SES

Non-low SES

Pa

51.2 (19.4)

57.0 (15.2)

0.28

7.9 (1.9) 10.8 (5.8)

7.6 (1.6) 8.0 (5.1)

0.63 0.10

6.8 (4.9) 26.5 (8.8) 66.7 (10.5) 3.84 (0.22)

7.0 (3.9) 25.3 (7.1) 67.7 (9.5) 3.87 (0.21)

0.91 0.63 0.74 0.68

18.5 20.7 21.5 19.4 19.8

18.9 21.7 19.9 20.6 18.9

0.84 0.65 0.41 0.58 0.54

(6.4) (8.5) (7.2) (6.5) (5.3)

(4.6) (6.1) (5.4) (7.1) (4.7)

TABLE 4 Self-Reported Prevention Status Up To Date—Clinic Mean % (SD) Adjusted to U.S. Population Preventive service

Age group

Interval (years)

Low SES

Non-low SES

BP check Cholesterol Tobacco use Pap Mammogram Breast exam Flu shot Pneum. shot

20+ 20+ 20+ 20+ F 50+ F 50+ F 65+ 65+

1 5 Every visit 2 2 2 1 Ever

80.4 (6.3) 54.9 (9.7) N/A 71.2 (7.8) 44.8 (17.7) 52.9 (19.1) 48.4 (22.2) 26.5 (14.8)

80.6 (5.8) 68.5 (8.3) N/A 78.4 (7.8) 65.4 (11.1) 71.5 (7.8) 65.3 (9.1) 36.0 (11.0)

a

Pa 0.92 0.000 0.002 0.000 0.001 0.004 0.004

t test, two-tailed.

one might expect, there are many differences between them, with presence of high blood pressure, some household sizes, and proportions of some races being the only characteristics that are similar. Table 3 compares these groups by reason for the visit and by time/ visit relationships with the clinic. In contrast with the patient characteristics, none of these differences came close to achieving statistical significance. In order to assess attitudinal differences about preventive services between the groups, we analyzed their responses to two questions: 1. ‘‘I wish my clinic would leave me alone about my health habits,’’ 5.0% low SES agreed/strongly agreed vs 3.2% non-low SES. 2. ‘‘I wish my clinic would do more about making sure I stay healthy,’’ 35.9% low SES agreed/strongly agreed vs 33.2% non-low SES. These differences were statistically significant (P 4 0.006 for 1 and P 4 0.000 for 2 based on Pearson x2), but they were extremely small. Table 4 compares the groups by patient-reported need for the eight preventive services (i.e., whether they had received each of the target services within the specified intervals prior to that visit). With the exception of blood pressure, low-SES patients reported significantly lower rates of being up to date on these services. Their mean clinic rates were 19.8% lower for a cholesterol test within 5 years, 9.2% for Pap smears within 2 years, 31.5% lower for a mammogram within 2 years, 26.0% lower for a breast exam within 2 years, 25.9% lower for a flu shot within the past year, and 26.4% lower for ever having a pneumonia shot. Table 5 compares the patient-reported actions by the clinic at the visit to those who were in need of those actions on the basis of their own report. In contrast with the findings in Table 4, all of the services needed by these patients were reported to be recommended at essentially the same rates, regardless of their SES. The only differences were for tobacco identification and advice, and in those cases low SES patients reported more frequent service. Finally, Table 6 compares the frequency with which

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TABLE 5 Self-Reported Clinic Action if Service Needed—Clinic Mean % (SD) Adjusted to U.S. Population Preventive service First BP check in 1 year Regular BP appointments in high-BP patients Recommend cholesterol Recommended Pap Asked smokers if they smoke Advised smokers to quit Recommended breast exam Recommend mammogram Recommend flu shot Recommend pneum. shot a

Low SES

Non-low SES

Pa

87.3 (11.3)

89.3 (9.2)

0.19

42.3 (20.2) 8.5 (8.5) 25.2 (14.4)

42.3 (14.1) 9.4 (8.2) 29.8 (11.1)

0.99 0.69 0.28

62.1 (16.6) 56.8 (17.1)

54.9 (13.0) 46.4 (13.3)

0.05 0.01

10.8 (9.9)

14.1 (14.4)

0.38

18.9 (15.0) 25.3 (23.9) 3.9 (8.0)

23.0 (11.9) 25.7 (14.8) 7.1 (6.8)

0.31 0.94 0.10

t test, two-tailed.

recommended services were reported to be performed at or soon after the office visit. Again, there was no difference between SES groups in this action for any service that could have been performed. (Note that it was the wrong time of year to give flue shots.) Because of the finding that only 25.8% of those with no health insurance had incomes below 150% of the poverty line, we calculated the rates of preventive services being up to date and recommended when needed for all those who provided income information showing that they were below this household income level. Despite the fact that many individuals changed their SES group status with this recalculation, all of the group differences and similarities reported above were maintained. Finally, controlling for the potential confounding effects of correlated variables using logistic regression analyses failed to change the statistical significance or nonsignificance of any of the SES group differences in being up to date or having needed services recommended.

likely to report that they received them. Also, SES does not appear to make any difference in the likelihood that hypertensive patients report having regular appointments to manage it. These findings are robust. Adjusting for potential confounding effects of covariates does not change any of the above conclusions. The results also seem to be similar for those without health insurance compared with those on medical assistance, despite apparent differences between these groups in out-of-pocket costs of medical services. Using a different definition of low SES (based entirely on income) also did not have any effect on these findings. This should not be surprising given the literature showing that the uninsured are much less likely to have received preventive services. However, the Behavioral Risk Factor Surveillance System report confirming that fact also showed that only 18% of the uninsured report having a usual place of medical care versus 41% of the insured [15]. Our findings suggest that having a usual care source still does not solve the problem for these people. It is interesting to note (Table 3) that the low-SES patients are just as likely as the non-low-SES patients in this sample to have clinic and visit factors that might affect both previous and current preventive service actions by the clinic. These include the fact that low-SES patients are just as likely to have been longterm patients of the clinic and to have had just as many visits (opportunities for prevention). They are also just as likely to be at this visit for a check-up/physical exam. Another analysis of this data set demonstrated that patients presenting for a check-up are twice as likely to be offered needed preventive services as those visiting a clinic for other reasons [28]. The magnitude of the differences in needing these preventive services between the low-SES patients and the others in this study is substantial (20–30% less up to date except for Pap smears at 9% lower). If these differences are not due to differential efforts by physicians to encourage patients to obtain these services or to actually perform them, what might account for it? This study cannot answer that question, but the answer probably is complex.

DISCUSSION

The principal finding from this study is rather surprising to us. Despite the fact that the low-SES patients visiting these private primary care clinics are less likely to be current for all but blood pressure checks, low-SES patients in need of these services are just as likely to report that they received recommendations and received them as are non-low-SES patients. They also appear to have a similar level of interest in the services. The only services that were statistically significantly different between these two groups were tobacco identification and advice to quit, and in these cases the low-SES patients were more

TABLE 6 Self-Reported Performance of Services if Recommended— Total Population % (SD) Adjusted to U.S. Population Preventive service Cholesterol Pap smear Breast exam Mammogram Pneumonia shot a

t test, two-tailed.

Low SES

Non-low SES

Pa

89.3 84.4 92.8 83.8 92.1

82.9 89.3 92.7 89.0 82.6

0.29 0.43 0.99 0.50 0.56

LOW SES PREVENTIVE SERVICES

Others have suggested that differences in receipt of preventive services by those without health insurance or low SES are not due to limited access to medical services. Woolhandler’s study of National Health Interview Survey data showed that the lower rates for four screening tests among the uninsured were (like our data) unrelated to differences in the numbers of physician visits [29]. Hayward reported the same thing for clinical breast exam and mammography in a phone survey of 4,600 women [30] and Kleinman found that 75% of those with no recent Pap had at least one physician visit in the past 2 years [31]. Hutchins’ interviews of the parents of 972 inner city children with measles found that 53% of those age-eligible for measles vaccine had missed at least one opportunity to be immunized during health care visits [32]. Although these and other studies raise doubts that different rates of preventive services are due to limited access to medical care, they all provide support for the idea that there are a great many clinical encounters where the opportunity to provide preventive services is missed. Our data show the same thing. Low- and nonlow-SES patients may all be receiving preventive service recommendations at the same rates, but in both groups the rates are in great need of improvement. Except for blood pressure checks and tobacco advice, only 7–30% of those in need of one of these services were recommended to receive it during the opportunity of a clinic visit (see Table 5). Thus, any deficiencies in being up to date will take a very long time to correct and, due to new people becoming in need of services all the time, will never be corrected. Although it is common in U.S. studies to assume that the differences in rates relative to the presence of health insurance would be helped by providing insurance, Schauffler and Rodriguez’s review of the literature on this topic concludes that ‘‘payment alone may not be enough to increase utilization to appropriate levels’’ [33]. They note that payment for preventive care in the RAND Health Insurance Experiment increased the use of preventive services, but not to recommended levels [34]. The INSURE project also found that ‘‘even when preventive care is covered by insurance, utilization rates average only 30–40%’’ [35]. Schauffler and Rodriguez suggest a variety of mechanisms that managed care plans might use to address the combined financial and nonfinancial barriers to preventive care [33]. Studies in other countries also cast doubt on the idea that financial coverage will solve the problem. Marsh reported a study of 587 pairs of patients in an English general practice that were matched by high vs low SES in which they could examine medical records over time in a system with no differential patient costs for medical care [36]. He found that the low-SES patients had more morbidity, hospital admissions, and emergency visits, but much less of some preventive services. Na-

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varro-Rubio’s survey in Spain showed the same pattern of a strong relationship between SES and children having received preventive services in a country with universal health coverage where 90% of the population seeks care in the official government-run care system [37]. Finally, Katz used 1990 survey data to show that women in the United States and in Ontario, Canada, had the same rates of Pap smears and clinical breast exams while mammography rates were two to three times as high in the United States [38]. There was still an income differential in being up to date in both countries, but the Ontario system covers these services fully, unlike the system in the Unite States. Our data complement the macroanalytic data by suggesting that the problem does not emanate from the physicians’ failure to advise low-SES patients to have services or to provide them when needed. What all this suggests is that there may be a variety of factors related to being of low-SES status that make it more difficult or less desirable to make use of preventive services. Some of these factors may be addressable by more proactive efforts on the part of care providers, managed care organizations, and public health and community groups. However, others have suggested that some may be based in culture and attitudes that will be difficult to affect. For example, Mechanic and Cleary have suggested that ‘‘positive health behavior is part of a wider life orientation including a sense of psychological and physical well-being and a sense of harmony with the dominant social milieu’’ [39]. Similarly, Mayer-Oakes found lower SES patients not only had lower mammography rates, but reported less use of various self-care behaviors [seat belt use, regular exercise, regular Paps/checkups/flu shots (odds ratio 4 2.13)] [40]. Another example of this potential SES-related difference in attitude toward prevention is evidenced in surveys of patient attitudes. David and Boldt found that, whereas only 12 and 15% of their high- and middle-SES respondents agreed with the statement ‘‘a doctor’s main job is to cure an illness you already have rather than prevent one from developing,’’ 38% of those of low SES agreed [41]. Similarly, Price reported that substantially lower percentages of patients of lower education level agreed that physicians should give health promotion services and advice to all who need it [42]. In contrast with these published suggestions of different attitudes toward prevention by those of low SES, our own survey showed only a very small (2%), though statistically significant, difference between SES groups in response to two questions about attitudes toward clinic efforts to change their health habits or to stay healthy. These small differences were in opposite directions for the two questions and certainly do not seem to explain the wide differences we found in being up to date on preventive services in this study.

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There are several potential weaknesses in the current study. First, the clinics participating in this trial may not be representative of all clinics in this area or in other geographic regions. Second, the data are all based on patient self-report, and there is reason to doubt the precision and accuracy of such reports [43– 45]. However, since the principal findings in this report concern within-study differences, any such effect should not impact the findings unless low-SES patients are selectively more likely to overreport need for services or advice and receipt than are non-low-SES patients. Moreover, the advantage of self-report data is that we have recorded the patients’ perceptions of whether or not they received advice, information not reliably available from a chart audit. Fortunately, we have also performed chart audits on these patients. When those data are available, we should be able to clarify any issues about differential reporting by those of different SES statuses. Even though we cannot necessarily generalize these findings to all primary care clinics (even in this geographic region), there are several reasons to believe that these clinics are not unusual for this area. First, the 44 clinics participating in this trial are from medical groups that represent 46.5% of medical groups eligible to participate. In this region, nearly all primary care practices that have survived the changes of the past 10 years try to participate with as many managed care plans as possible, so contracting medical groups are the rule. For example, 83% of the clinics in this trial contract with both Blue Plus and HealthPartners. Second, although it is likely that clinics volunteering to participate in this trial have a greater than usual level of interest in preventive services, their low-SES patients are, nevertheless, still less up to date. This is true despite the evidence that these clinics appear to be just as likely to provide needed services to these patients as they are to those patients of higher SES. Unfortunately, the proportion of racial minorities is so low in this region and these clinics that we cannot adequately evaluate the question of whether there is any racial bias in the provision of clinical preventive services (even the low-SES patients are 90% white). Despite that, this information suggests that even in the absence of racial differences, low SES appears to confer a lower likelihood of being up to date on these preventive services. CONCLUSION

This is the first study to examine whether low-SES patients are being offered and provided needed preventive services during primary care clinic visits at rates different from those of higher SES patients. Although the results demonstrate no differences in the rates of recommendation and provision in this set of clinics, they confirm that low-SES patients are still significantly less likely to be up to date on many services.

However, in the exam room, clinicians do not appear to be distinguishing among their patients by SES in terms of recommending needed preventive services or of performing them. There are no differences in their clinic relationship, reason for visit, or attitudes that might explain this preexisting gap in the likelihood that low-SES patients will be up to date compared with their higher SES peers in the same clinic. The reason(s) for the gap may represent some special barriers faced by these low-SES patients that are external to the health services system. Addressing these barriers effectively will probably require special efforts by clinics as well as managed care organizations and the community’s public health resources. In keeping with many other recent studies, however, the results confirm that there are many missed opportunities to address important preventive services needs during the clinic visits of all patients. Although further studies will be needed to discover why low-SES patients are less up to date despite clinic recommendations and performance, there is an urgent need to identify practical strategies to take advantage of every clinic visit for updating the preventive service needs of every patient seen in primary care settings. The IMPROVE study is designed to test whether a quality improvement and systems strategy can produce that result. ACKNOWLEDGMENTS We are grateful for the efforts of the following individuals who contributed in important ways to the information contained in this article: Kathy Schaivone and Anne Book for managing identification of patients, Jerry Amundson for managing the data system, Brian Harmon for conducting the analysis runs, and Carol Westrum for coordinating the survey and its phone follow-up. We are especially grateful for the people at the following intervention group clinics who participated in this trial and data collection and whose interests and efforts to improve preventive services are the heart of the messages in this study: Aspen Medical Group–W. St. Paul, Aspen Medical Group–W. Suburban, Chanhassen Medical Center, Chisago Medical Center, Creekside Family Practice, Douglas Drive Family Physicians, Eagle Medical, Fridley Medical Center, Hastings Family Practice, Hopkins Family Practice, Interstate Medical Center, Metropolitan Internists, Mork Clinic–Anoka, North St. Paul Medical Center, Ramsey Clinic–Amery, Ramsey Clinic–Baldwin, River Valley Clinic– Farmington, River Valley Clinic–Northfield, Southdale Family Practice, Stillwater Clinic, and United Family Medical Center. REFERENCES 1. Feinstein JS. The relationship between socioeconomic status and health: a review of the literature. Milbank Q 1993;71:279– 322. 2. Angell M. Privilege and health—what is the connection? N Engl J Med 1993;329:126–7. 3. Pappas G, Queen S, Hadden W, Fisher G. The increasing disparity in mortality between socioeconomic groups in the United States, 1960 and 1986. N Engl J Med 1993;329:103–9. 4. Bucher HC, Ragland DR. Socioeconomic indicators and mortal-

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