AMERICA S HEALTH CENTERS (HCs) are

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1 J Ambulatory Care Manage Vol. 35, No. 1, pp Copyright C 2012 Wolters Kluwer Health Lippincott Williams & Wilkins US Primary Care Delivery After the Health Center Growth Initiative Comparison of Health Centers, Hospital Outpatient Departments, and Physicians Offices Leiyu Shi, DrPH, MBA, MPA; Lydie A. Lebrun, PhD, MPH; Li-Mei Hung, PhD; Jinsheng Zhu, MEc; Jenna Tsai, EdD Abstract: We compared patient management during primary care visits in 3 settings (health centers, hospital outpatient departments, and physicians offices) and investigated racial/ethnic and insurance-based disparities in the wake of the recent health center program expansion. Within health centers, there were few differences in patient management across racial/ethnic or insurance groups. In contrast, the other settings displayed more racial/ethnic and insurance disparities in patient management during visits. Health centers performed processes of care with comparable or higher occurrence, relative to physicians offices. Health care disparities were also attenuated in health centers, compared with other primary care settings. Key words: disparities, organization and delivery of care, primary care, safety-net systems Author Affiliations: Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health (Drs Shi and Lebrun and Ms Zhu) and Johns Hopkins Primary Care Policy Center (Dr Shi) Baltimore, Maryland; and Hungkuang University, Taichung County, Taiwan (Drs Tsai and Hung). Financial support for this study was provided by the Bureau of Primary Health Care, Health Resources and Services Administration, Department of Health and Human Services. The views expressed in this publication are the opinions of the authors and do not necessarily reflect the official policies of the US Department of Health and Human Services or the Health Resources and Services Administration, nor does mention of the department or agency imply endorsement by the US government. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Correspondence: Leiyu Shi, DrPH, MBA, MPA, Johns Hopkins Primary Care Policy Center, Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, Room 452, Baltimore, MD (lshi@jhsph.edu). DOI: /JAC.0b013e31823abf07 AMERICA S HEALTH CENTERS (HCs) are federally supported health care sites that provide comprehensive primary health care regardless of patients ability to pay (Taylor, 2004). Health centers are primary care safety-net providers because they aim to meet the needs of underserved populations in the United States, including the poor, uninsured, homeless, and minority populations. In 2009, they provided care to nearly 19 million patients and handled more than 74 million patient encounters including more than 70% at or below the federal poverty level, nearly 40% uninsured, about 6% homeless, and about two-thirds racial/ethnic minorities (Health Resources and Services Administration, 2010). In addition to HCs, other major primary care providers in the United States include physicians offices (POs), hospital outpatient departments (OPDs), and community clinics. However, one major difference between these mainstream primary care providers and HCs is that they do not explicitly serve 60

2 Primary Care Delivery 61 vulnerable populations as HCs do and do not receive federal funding for that purpose. From a policy perspective, it is important to understand whether safety-net providers such as HCs provide comparable care to their targeted vulnerable populations as mainstream providers such as POs and OPDs do to their largely insured populations. Any significant differences in favor of mainstream providers would cause concerns about a 2-tiered system in which the poor and uninsured obtain second-class care compared with the welloff and the insured. Reducing and eliminating disparities is a top priority for the United States, and to the extent that HCs provide comparable care and overcome disparities in care, they could serve as a model for other providers to improve primary care delivery and reduce health disparities. Improving the primary care system is critical, as recent studies have shown that continuous and comprehensive delivery of primary care can significantly improve health outcomes (Macinko et al., 2007; Shi et al., 2002; Starfield et al., 2007). Especially in the midst of ongoing health care reform implementation, there is a need for empirical evidence that can inform policymakers about care delivery models that are effective and efficient, while also providing a safety-net for vulnerable populations and reducing health care disparities. Previous research has documented numerous differences in processes of care across various health care settings (Kerr et al., 2004; Powers et al., 2009; Werner et al., 2008). Comparisons of HCs with POs and OPDs, specifically, have found that HCs generally perform better regarding a range of process measures (eg, timely follow-up care, provision of well-child care, complete medical records, access to needed care for specific conditions; Shields et al., 2002; Starfield et al., 1994; Stuart et al., 1995). Conceptually, provision of recommended processes of care is linked to improved patient outcomes (Donabedian, 1966). Thus, investigating the presence or absence of certain procedures during primary care visits is of interest to health care providers and policy makers seeking to improve health care quality. A decade ago, Forrest and Whelan (2000) conducted a comprehensive, nationally representative study, using 1994 data to compare the mix of patients and the types of services received across 3 primary care settings: HCs, POs, and OPDs. The results showed that HCs served a larger proportion of racial/ethnic minorities, Medicaid-insured or uninsured individuals, and rural dwellers compared with the other 2 settings. In addition, there was a large disparity in the overall number of primary care visits, with ethnic minorities having significantly fewer annual visits than non- whites. Among their conclusions, the authors stated that doubling the number of HCs would significantly reduce the primary care disparities between non- whites and minorities. In the years since this study was published, the HC program has grown rapidly, with much of the expansion occurring during the Health Center Growth Initiative, which began in 2002 and ended in During this time, federal spending on the HC program nearly doubled; more than 1200 HCs now operate 6000 service delivery sites, representing a 56% increase in the number of patients treated by HCs (Bureau of Primary Health Care, 2008). However, few studies have comprehensively examined the patient and service mix across primary care settings since this expansion, and there have not been many investigations of the extent to which racial/ethnic and insurance-based disparities in patient management currently occur in these settings. We drew on Forrest and Whelan s article as motivation for conducting new analyses, using recent data to describe current patterns in patient mix and service provision across the 3 primary care settings. We also conducted additional analyses for specific racial/ethnic and insurance groups to determine the extent of disparities in the wake of the HC program expansion. Specifically, we sought to answer 3 main research questions: (1) How are sociodemographic characteristics of primary care patient visits currently distributed across HCs, POs, and OPDs? (2) Are there differences across settings regarding patient management during primary

3 62 JOURNAL OF AMBULATORY CARE MANAGEMENT/JANUARY MARCH 2012 care visits? (3) What is the magnitude of racial/ethnic disparities and insurance-based disparities in patient management in each of the 3 primary care settings? The first question confirms that HCs indeed target more vulnerable populations as warranted by their safety-net status. The second question compares primary care services and focuses on an important primary care component, that is, disease management, since chronic illness is prevalent among primary care visits and disease management is integral to treating chronic conditions. The third question examines disparities in care and targets racial/ethnic and insurance-related disparities; a preponderance of research demonstrates that these are the most significant disparities concerning health care access. On the basis of previous research and our knowledge of HC performance, we hypothesized that HCs would display better patient management and reduced disparities compared with the other 2 mainstream primary care providers. METHODS Data Visit data for POs and HCs were obtained from the 2006 National Ambulatory Medical Care Survey (NAMCS; National Center for Health Statistics, 2007). This is a nationally representative annual survey, which reports on visits to nonfederal, office-based physicians, excluding radiologists, pathologists, and anesthesiologists (58.9% response rate). In 2006, the NAMCS also included an oversampling of 104 HCs to provide more reliable data on HC patient visits. Health centers were selected from a list provided by the Health Resources and Services Administration s Bureau of Primary Health Care Uniform Data System and the Indian Health Service. Within each sampled HC, a sample of health care providers was selected to report on a sample of patient visits during a randomly assigned weeklong period. A total of 150 HC physicians and 1185 office-based physicians submitted patient record forms for the 2006 NAMCS. The 2006 National Hospital Ambulatory Medical Care Survey (NHAMCS) was used to acquire visit data from nonfederal, short-stay OPDs (National Center for Health Statistics, 2007). Visits to emergency departments (EDs) were excluded from the analyses because EDs were not considered mainstream primary care providers. Questionnaires were completed by hospital staff on the basis of patient records for a sample of visits within a random 4-week sampling period (72.7% response rate). A total of 235 OPDs submitted patient record forms for the 2006 NHAMCS. Primary care visits were identified, and other types of visits were excluded from analyses. For HCs and POs, visits to physicians with specialties in general and family practice, internal medicine, pediatrics, and obstetrics and gynecology were identified to obtain the sample of primary care visits. For OPDs, primary care visits were identified as those that took place in general medicine, pediatric, or obstetrics and gynecology clinics. This methodology was similar to that used in previous research (Forrest & Whelan, 2000). Measures The selection of dependent variables was informed by Donabedian s model of the structure, process, and outcomes of care, as well as Starfield s primary care model (Donabedian, 1966; Starfield, 1979). We focused on process of care measures to directly assess the actual content of care provided during physician-patient encounters. As noted earlier, patient disease management is critical in treating chronic conditions, which are predominant in primary care settings. These measures included specific procedures performed during primary care visits (ie, laboratory tests, imaging studies, blood pressure checks, and medication prescribed), as well as patient disposition after primary care visits (ie, referral to specialty care, no follow-up). These indicators were selected because they reflect activities that occur during visits, namely problem recognition, diagnosis formulation, management, and reassessment (Starfield, 1979). The primary independent variable was primary care setting, categorized as HCs, POs,

4 Primary Care Delivery 63 and OPDs. Race/ethnicity and health insurance type were also included as independent variables of interest, primarily for disparity analyses. Racial/ethnic groups included non- white; non- black/african American; or Latino; and non- Asian, Native Hawaiian, or other Pacific Islander. Because of small sample size, our analyses did not include American Indians or Alaska Natives and individuals reporting more than 1 race. Insurance coverage type was categorized into uninsured, private insurance, Medicare, and Medicaid/State Children s Health Insurance Program. Measures of patient presentation were also considered as covariates to account for factors that may have an impact on processes of care that take place during visits. Indicators included type of visit (routine/preventive care vs other type of care), type of patient (new vs established), and the Johns Hopkins Adjusted Clinical Groups (ACG) case-mix index (Weiner et al., 1991). The ACG case-mix system measures morbidity burden among patients on the basis of disease patterns, age, and gender.itusesinternational Classification of Diseases, Ninth Revision, Clinical Modification diagnostic information from clinical and insurance records to accurately categorize patients illness patterns and estimate health care needs. Higher index scores indicate sicker patients and greater medical complexity, which may influence the type of patient management services provided during health care visits. ACG case-mix index was used to adjust for patients severity of illness, which is related to the intensity of the provision of primary care services. Other patient sociodemographic characteristics included as covariates were age group (0-17 years, years, 65 years and older), gender, and rural residence (ie, nonmetropolitan statistical areas). These were included because of their established association with health care utilization. Analyses Across the 3 primary care delivery sites, we compared patient sociodemographic and disease management characteristics during visits. Sampling weights were used to account for multistage sampling design and nonresponse to obtain nationally representative estimates of patient visits. Frequencies, proportions, and rates of visits (number per patient per year) were provided for each delivery site. For patient disease management measures, logistic regression models were conducted to obtain odds ratios and 95% confidence intervals. These models adjusted for age, gender, rural residence, race/ethnicity, health insurance type, severity of illness, and patient presentation. Visits to POs were used as the reference group for the comparative analyses across the 3 delivery sites because they represent the dominant mainstream primary care providers that serve most Americans. We also examined primary care disparities among racial/ethnic groups and insurance coverage types. Patient disease management indicators within each delivery site were reanalyzed separately for each racial/ethnic and insurance group. whites were used as the reference group for racial/ethnic comparisons, consistent with the disparities literature. The uninsured were used as the reference category for insurance coverage comparisons because this group represents the largest proportion of patients served by HCs nationwide (Health Resources and Services Administration, 2008). Finally, we used a difference-in-differences (DD) approach to estimate the effect of primary health care setting on the disparities in patient management (Harrell, 2001). Specifically, we used racial/ethnic and insurance disparities in the comparison group (HCs) to net out the disparities remaining in the other 2 settings. For example, to identify the relative effect of POs versus HCs on differences in patient management between non- black/african American and non- white patients, we would start by estimating a POs regression for the outcome of interest and obtaining predicted probabilities for non- blacks/african Americans and non- whites, holding other covariates constant. We would then estimate an HC regression and obtain the predicted probabilities for non-

5 64 JOURNAL OF AMBULATORY CARE MANAGEMENT/JANUARY MARCH 2012 blacks/african Americans and non- whites. Finally, we would subtract the difference in the measure of interest between non- blacks/african Americans and non- whites in HCs from the difference in the measure between non- blacks/african Americans and non- whites in POs. This DD is captured in the following equation: DD = (% POs, non- black/african American % POs, non- white) (% HCs, non- black/african American % HCs, non- white) Thus, this approach allows us to examine the relative magnitude of black-white disparities in disease management in POs, compared with disparities in HCs. RESULTS Volume of primary care patient visits In 2006, there were a total of million health care visits to HCs, POs, and OPDs, averaging 3.4 annual visits per patient. Out of these visits, million (60.6%) were primary care visits, a rate of 2.1 annual visits per patient (Table 1). Health centers were the delivery site for 2.3% of all primary care visits, while POs were responsible for 84.1% and OPDs for 13.6% of such visits. The annual primary care visit rate per patient was much lower for HCs and OPDs compared with POs. Demographic characteristics of primary care patient visits Table 1 provides the frequency, percentage distribution, and annual rates of primary care visits according to patient characteristics for the 3 delivery sites. Overall, the majority of visits were from patients aged 18 to 64 years (53.3%). In addition, a greater proportion of visits were from females than from males (61.8% vs 39.2%). Compared with non- whites, the overall rate of annual primary care visits was 6.0% lower for non- blacks/african Americans and 4.8% lower for s/ Latinos. In POs, annual per person visit rates were 22% lower for non- blacks/african Americans and 14% lower for s/latinos, relative to non- whites. On the other hand, HCs and OPDs had higher visit rates for minorities than non- whites: annual per person visit rates for non- blacks/african Americans were 160% higher than those for non- whites in HCs and 100% higher in OPDs. Similarly, annual per person visit rates for s/latinos were 300% higher than non- whites in HCs and 32% higher in OPDs. Both POs and OPDs had a higher proportion of visits covered by private payers than other types of insurance (57.2% and 36.3%, respectively). Health centers provided primarily Medicaid-covered visits (50.0%). Among the 3 health care delivery sites, HCs also had the highest proportion of primary care visits from uninsured patients (10.6%), followed by OPDs (7.0%) and POs (3.9%). Rates of primary care visits from rural residents were similar in POs and OPDs (16.6% and 15.5%, respectively); in contrast, HCs had only 6.3% of visits from rural residents. Patient disease management during primary care visits Table 2 compares patient disease management intervention and disposition indicators for primary care visits made to the 3 delivery settings. In adjusted analyses, HCs had 1.38 times higher odds of prescribing a medication during a visit, compared with POs. In addition, there were 1.68 times higher odds of blood pressure checks during visits in HCs versus POs. Outpatient departments and HCs had higher odds of ordering laboratory tests compared with POs. Outpatient departments also had 68% higher odds of performing imaging studies than POs. Outpatient departments referred a greater proportion of primary care visits to specialty care (15.8%), compared with HCs (9.9%) and POs (8.5%). The adjusted odds of a specialty referral in OPDs were almost 2 times higher than those in POs. There were

6 Primary Care Delivery 65 Table 1. Frequencies and Demographic Characteristics of Primary Care Patient Visits Across Delivery Sites, the United States 2006 a Overall Health Centers Physicians Offices Hospital Outpatient Departments All visits (100.0) (1.4) (88.3) (10.2) 34.7 Primary care visits (100.0) (2.3) (84.1) (13.6) 28.1 Demographic characteristics of primary care patient visits Age, y (29.0) (27.7) (29.7) (24.5) (53.3) (61.6) (52.1) (59.6) (17.7) (10.7) (18.2) (15.9) 36.9 Gender Female (61.8) (60.2) (61.8) (62.4) 34.3 Male (38.2) (39.8) (38.2) (37.6) 21.6 Race/ethnicity (68.9) (35.2) (71.5) (58.1) 24.1 white (11.7) (16.3) (9.9) (21.6) 49.8 black/african American /Latino (14.6) (30.9) (13.8) (17.1) (4.8) (17.6) (4.8) (3.2) 19.8 Asian, native Hawaiian, or other Pacific Islander Health insurance payer Private (53.2) (10.4) (57.2) (36.3)... Medicare (15.8) (11.4) (16.2) (14.1)... Medicaid/SCHIP (20.4) (50.0) (17.7) (32.0)... Uninsured (4.5) (10.6) (3.9) (7.0)... Other payment (6.1) (17.6) (5.0) (10.6)... (continues)

7 66 JOURNAL OF AMBULATORY CARE MANAGEMENT/JANUARY MARCH 2012 Table 1. Frequencies and Demographic Characteristics of Primary Care Patient Visits Across Delivery Sites, the United States 2006 a (Continued) Hospital Outpatient Departments Overall Health Centers Physicians Offices Rural residence Yes (16.2) (6.3) (16.6) (15.5) 26.0 No (83.8) (93.7) (83.4) (84.5) 28.6 a From National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, b Visits counts were multiplied by sampling weights to account for the multistage sampling design and nonresponse of in-scope practitioners to obtain national estimates. c Based on the US Bureau of the Census estimates of the noninstitutionalized US civilian population as of July 1, Figures are consistent with the downloadable series, Monthly Postcensal Civilian InstitutionalPopulation,by single year ofage,gender,race,and/latinoorigin (NC-EST2007-ALLDATA-N-File14.csv) available at the Census Internet site: (accessed October 23, 2008). Figures are consistent with the downloadable series Monthly National Population Estimates: April 1, 2000, to November 1, 2007 (NA-EST ) available at the Census Internet site: EST html (accessed October 23, 2008). no differences in specialty referrals between HCs and POs. Patient disease management during primary care visits according to racial/ethnic groups Table 3 presents characteristics of primary care visits in each of the 3 delivery sites, grouped by racial/ethnic categories. In adjusted analyses, visits by non- Asians in POs had 41.1% lower odds of including medication prescription compared with visits by non- whites. In OPDs, visits by s/latinos had 23.3% lower odds of including medication prescription compared with visits by non- whites. Racial/ethnic differences were also found for laboratory tests in OPDs, with visits from non- blacks/african Americans having 1.51 times higher adjusted odds of receiving the tests compared with visits from non- whites. For blood pressure checks, both POs and OPDs showed variations between racial/ethnic groups. In both settings, non- whites had the highest proportion of visits that included blood pressure checks, followed closely by non- blacks/african Americans and then s/latinos and non- Asians. However, adjusted odds of receiving blood pressure checks were not statistically significantly different across racial/ethnic groups. In OPDs, non- whites had the highest proportion of patient visits that did not lead to a follow-up visit (8.4%), followed by s/latinos (8.0%), non- Asians (5.6%), and non- blacks/african Americans (5.0%). Visits by non- blacks/african Americans had 43.6% lower odds than visits by non- whites of receiving no follow-up. Similarly, in HCs, visits by non- Asians had 72.9% lower adjusted odds of not receiving follow-up compared with visits by non- whites (in other words, visits from non- Asian patients were more likely to lead to follow-up care than visits from non- white patients). There were no other racial/ethnic differences in patient management indicators within HCs.

8 Primary Care Delivery 67 Table 2. Patient Management Measures During Primary Care the United States, 2006 a,b Health Centers Physicians Offices Hospital Outpatient Departments Interventions during visit Laboratory test c Unadjusted, % d ( ) e ( ) d Imaging study Unadjusted, % d ( ) ( ) d Blood pressure check Unadjusted, % ( ) f ( ) Medication prescribed Unadjusted, % ( ) f ( ) Disposition after visit Specialty referral Unadjusted, % d ( ) ( ) d No follow-up Unadjusted, % ( ) ( ) Abbreviations: CI, confidence interval; OR, odds ratio. a From National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, b Logistic regression models adjusted for patient age, gender, race/ethnicity, insurance type, rural residence, visit type, patient type, and case-mix index. c Laboratory tests include blood test (complete blood cell count, electrolytes, glucose, glycohemoglobin [HbA1C], cholesterol, prostate-specific antigen [PSA], and other blood test), biopsy, Chlamydia test, Papanicolaou test conventional, Papanicolaou test liquid-based, Papanicolaou test unspecified, HPV DNA test, EKG/ECG, spirometry/pulmonary function test, and urinalysis. d P <.001. e P <.01. f P <.05. Patient disease management during primary care visits according to insurance type Table 3 also presents characteristics of primary care visits in each of the 3 delivery sites, grouped by type of insurance. Both POs and OPDs showed insurance-based differences regarding the proportion of patient visits that included medication prescriptions. Physicians offices prescribed medication in 88.7% of Medicare visits, 83.9% of uninsured visits, 75.9% of Medicaid visits, and 75.4% of privately insured visits. Within this setting, privately insured visits had 40.8% lower adjusted odds than uninsured visits of being prescribed medication. In OPDs, Medicare visits once again had the highest proportion of medication prescription (86.3%), followed by privately insured visits (81.2%), Medicaid visits (77.3%), and uninsured visits (75.7%); none of these differences were statistically significant in adjusted logistic regression modeling. Laboratory testing in POs and OPDs also showed differences across insurance types. In visits to POs, 41.6% of Medicare patients received laboratory tests compared with 31.8% of the privately insured, 24.0% of Medicaid patients, and 23.2% of the uninsured. The

9 68 JOURNAL OF AMBULATORY CARE MANAGEMENT/JANUARY MARCH 2012 Table 3. Patient Management Measures During Primary Care Visits, by Racial/Ethnic Group and Insurance Type, the United States 2006 a Race/Ethnicity b white Health Centers Physicians Offices Hospital Outpatient Departments black/ African American / Latino Asian white black/ African American / Latino Asian white black/ African American / Latino Interventions during visit Laboratory test c Unadjusted, % d 45.1 d 40.8 d 43.7 d ( ) 1.23 ( ) 0.83 ( ) ( ) 1.05 ( ) 0.93 ( ) d ( ) 1.28 ( ) Imaging study Unadjusted,% ( ) 1.17 ( ) 0.65 ( ) ( ) 0.84 ( ) 1.06 ( ) ( ) 1.23 ( ) Blood pressure check Unadjusted, % f 72.0 f 63.3 f 64.3 f 77.0 f 75.6 f 71.5 f 68.2 f ( ) 1.16 ( ) 1.51 ( ) ( ) 0.97 ( ) 0.74 ( ) ( ) 0.96 ( ) Medication prescribed Unadjusted, % e 77.9 e 72.4 e 68.5 e 82.1 d 78.4 d 73.9 d 77.7 d ( ) 1.05 ( ) 1.78 ( ) ( ) 0.81 ( ) 0.59 f ( ) ( ) 0.77 f ( ) Disposition after visit Specialty referral Unadjusted, % ( ) 0.66 ( ) 0.70 ( ) ( ) 0.86 ( ) 1.12 ( ) ( ) 1.26 ( ) No follow-up Unadjusted, % e 5.0 e 8.0 e 5.6 e ( ) 0.71 ( ) 0.27 f ( ) ( ) 1.25 ( ) 0.74 ( ) e ( ) 0.83 ( ) Asian 1.37 ( ) 1.57 e ( ) 0.68 ( ) 0.81 ( ) 1.16 ( ) 0.62 ( ) (continues)

10 Primary Care Delivery 69 Table 3. Patient Management Measures During Primary Care Visits, by Racial/Ethnic Group and Insurance Type, the United States 2006 a (Continued) Health Centers Physicians Offices Hospital Outpatient Departments Insurance b Uninsured Private Medicare Medicaid Uninsured Private Medicare Medicaid Uninsured Private Medicare Medicaid Interventions during visit Laboratory test c Unadjusted, % d 31.8 d 41.6 d 24.0 d 36.1 d 34.1 d 46.5 d 40.1 d ( ) 0.84 ( ) 0.82 ( ) e ( ) 0.98 ( ) 1.27 ( ) ( ) 0.79 ( ) Imaging study Unadjusted, % e 11.1 e 12.3 e 7.4 e ( ) 0.70 ( ) 0.74 ( ) ( ) 0.48 d ( ) 0.70 ( ) ( ) 0.82 ( ) Blood pressure check Unadjusted, % 90.2 e 79.3 e 90.9 e 70.0 e 80.3 d 71.6 d 92.2 d 52.3 d 79.4 d 75.1 d 84.3 d 69.1 d ( ) 0.33 ( ) 0.38 f ( ) ( ) 0.29 d ( ) 0.67 ( ) ( ) 0.31 d ( ) Medication prescribed Unadjusted, % d 75.4 d 88.7 d 75.9 d 75.7 d 81.2 d 86.3 d 77.3 d ( ) 1.06 ( ) 1.15 ( ) e ( ) 0.74 ( ) 0.73 ( ) ( ) 1.10 ( ) Disposition after visit Specialty referral Unadjusted, % e 8.4 e 11.2 e 6.2 e ( ) 0.82 ( ) 1.00 ( ) ( ) 1.04 ( ) 0.88 ( ) f ( ) 1.07 ( ) No follow-up Unadjusted, % e 9.2 e 4.4 e 10.9 e ( ) 1.29 ( ) 0.41 e ( ) ( ) 0.77 ( ) 0.82 ( ) ( ) 1.29 ( ) 1.23 ( ) 1.07 ( ) 0.89 ( ) 1.14 ( ) 1.52 f ( ) 0.78 ( ) Abbreviations: CI, confidence interval; OR, odds ratio. a From National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, b Logistic regression models adjusted for age, gender, insurance type, rural residence, visit type, patient type, and case-mix index. c Laboratory tests include: blood test (complete blood count, electrolytes, glucose, HbA1C (glycohemoglobin), cholesterol, PSA (Prostate specific antigen), other blood test), Biopsy, Chlamydia test, Papanicolaou test conventional, Papanicolaou test liquid-based, Papanicolaou test unspecified, HPV DNA test, EKG/ECG, Spirometry/Pulmonary function test, Urinalysis. d P <.001. e P <.01. f P <.05.

11 70 JOURNAL OF AMBULATORY CARE MANAGEMENT/JANUARY MARCH 2012 adjusted odds of receiving a laboratory test were 56.6% higher for privately insured visits than uninsured visits. Outpatient departments also provided more laboratory tests during Medicare-insured visits (46.5%), followed by Medicaid visits (40.1%), uninsured visits (36.1%), and privately insured visits (34.1%); again, none of these differences were statistically significant in adjusted logistic regression modeling. For imaging studies in POs, uninsured and Medicare visits included the imaging studies most frequently (12.4% and 12.3%, respectively), followed closely by privately insured (11.1%) and then Medicaid visits (7.4%). However, in adjusted regression models, Medicare visits had 52.4% lower odds of receiving an imaging study than uninsured visits. Blood pressure checks showed significant differences across insurance coverage types for all 3 delivery sites. In HCs, Medicareinsured and uninsured visits included blood pressure checks most frequently (90.9% and 90.2%, respectively), while privately insured and Medicaid visits trailed behind (79.3% and 70.0%, respectively). The trends were similar in POs and OPDs. In POs, a smaller proportion of Medicare-insured visits received no followup (4.4%) compared with Medicaid-insured visits (10.9%), uninsured visits (10.1%), and privately insured visits (9.2%). Conversely, a higher proportion of Medicare visits received specialty referrals (11.2%) compared with uninsured and privately insured visits (both 8.4%) and Medicaid visits (6.2%). However, there were no statistically significant differences in adjusted odds of receiving no followup or specialty referrals. Within OPDs, there were statistically significant differences in specialty referrals by insurance type, with Medicaid visits and privately insured visits having higher odds of referral than uninsured visits. DD across primary care settings, by racial/ethnic group and insurance type Table 4 presents results of the DD analyses. For relative differences in racial/ethnic disparities across settings, a negative value indicates comparatively lower adjusted probability of an indicator among non- black/african American patient visits (or /Latino or non- Asian) versus non- white patient visits in POs (or OPDs) compared with HCs. Conversely, a positive value indicates comparatively higher probability among non- black/african American patient visits (or /Latino or non- Asian) versus non- white patient visits in POs (or OPDs) compared with HCs. The interpretation is similar for relative differences in insurance-based disparities across settings. For racial/ethnic disparities, results revealed comparatively wider disparities in general between minorities and non- whites in POs than HCs. For instance, visits from non- blacks/african Americans, s/latinos, and non- Asians had relatively lower adjusted probabilities of including medication prescriptions compared with visits from non- whites (DD = 3.0%, 3.9%, and 15.5%, respectively). Visits from s/latinos had relatively lower probabilities of including laboratory tests or imaging studies and a higher probability of lacking follow-up compared with visits from non- whites (DD = 3.6%, 3.1%, and 3.0%, respectively). Visits from non- Asian patients also had a relatively lower probability of including a blood pressure check compared with visits from non- white patients (DD = 8.1%). There were also a few cases where relative differences favored minority patient visits over non- white patient visits. For example, visits from non- Asian patients had a relatively higher probability of including imaging studies (DD = 3.4%) compared with visits from non- white patients; minorities also had relatively higher probabilities of receiving a specialty referral during a visit (DD = 2.6% to 4.6%). There were also comparatively wider disparities between minorities and non- whites in OPDs than HCs. Visits from minorities had a relatively lower probability of including medication prescriptions compared with visits from non- whites (DD = 4.4% to 9.8%), and visits from

12 Primary Care Delivery 71 Table 4. Difference-in-Differences Estimates of Patient Management Indicators: Comparing Physicians Offices and Hospital Outpatient Departments With Health Centers, by Racial/Ethnic Group and Insurance Type, the United States 2006 a,b Relative Difference in Adjusted Probability of Patient Management Indicator POs vs HCs OPDs vs HCs blacks/ African Americans vs Race/Ethnicity whites Interventions during visit Laboratory test s/ Latinos vs whites Asians vs whites s/ Latinos vs whites Imaging study Blood pressure check Medication prescribed Disposition after visit Specialty referral No follow-up blacks/ African Americans vs whites Asians vs whites POs vs HCs OPDs vs HCs Private vs Medicare vs Medicaid vs Private vs Medicare vs Medicaid vs Insurance Uninsured Uninsured Uninsured Uninsured Uninsured Uninsured Interventions during visit Laboratory test Imaging study Blood pressure check Medication prescribed Disposition after visit Specialty referral No follow-up Abbreviations: HC, health center; OPD, hospital outpatient department; PO, physicians office. a From National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, b Adjusted probabilities are based on logistic regression models adjusted for insurance type (for race/ethnicity disparities analyses), race/ethnicity (for insurance disparities analyses), age, gender, rural residence, visit type, patient type, and case-mix index. Continuous variables were set to median values and categorical variables were set to mode values. non- Asians also had a lower probability of including a blood pressure check (DD = 9.7%). On the other hand, some relative differences favored minority patient visits over non- white patient visits in OPDs relative to HCs. For instance, visits from non- blacks/african Americans had a relatively lower probability of lacking follow-up compared with visits from non- whites (DD = 4.3%), and visits

13 72 JOURNAL OF AMBULATORY CARE MANAGEMENT/JANUARY MARCH 2012 from non- Asians had a higher relative probability of including imaging studies (DD = 10.0%). For insurance-based disparities, results also showed comparatively wider disparities in general across insurance groups in POs versus HCs, as well as OPDs versus HCs. Relative differences in adjusted probabilities of patient disease management indicators favored both the insured groups and the uninsured, depending on the outcome of interest. DISCUSSION We used data from 2 nationally representative surveys collected in 2006, the most current year available for our analyses of interest. Our analyses indicate that 1 in 44 primary care visits occurs in HCs. As expected, larger proportions of visits in HCs are made by racial/ethnic minorities, Medicaid-insured or uninsured, compared with the other 2 settings. Across all delivery settings, racial/ethnic disparities in primary care delivery persist, although the gaps appear to have closed significantly since Forrest and Whelan s study (2000). They reported that in 1994 the rates of annual primary care visits were 33% lower for non- blacks/african Americans and 20% lower for s/latinos compared with non- whites; this study found that the rates are now 6.0% and 4.8% lower, respectively. However, since we did not seek to exactly replicate the methodology of Forrest and Whelan s study, we caution against making direct comparisons between these estimates. Furthermore, our data sources are cross-sectional in nature; therefore, we cannot confirm whether the recent HC program expansion is indeed responsible for bridging racial/ethnic disparities in primary care or whether other factors explain the reduced gap. Findings confirmed that HCs performed better than POs on some, but not all, process of care measures, indicating that there is still room for improvement in the delivery of care in HCs. For instance, OPDs reported more specialty referrals than POs or HCs, indicating that hospital linkages may provide easier access to specialty providers. Previous studies have documented these same difficulties among HCs, and the lack of specialists in HCs presents a problem for patients who need consultations and treatments beyond primary health care services (Cooks et al., 2007; Gusmano et al., 2002). However, a recent study reports that those HCs with close affiliations to hospitals have fewer problems obtaining specialty care for their patients (Doty et al., 2010). This finding suggests a potential avenue for action, but further investigation is needed to determine other solutions for removing barriers to specialty care in HC settings. Ensuring specialty referral is critical if HCs wish to strive to become accountable care organizations (ACOs). Accountable care organizations have recently been thrust into the spotlight as a result of health care reform; these health care delivery systems are purported to facilitate coordination and cooperation among various health care providers including primary care physicians, specialists, and hospitals to improve the quality of care and reduce unnecessary costs (Fisher et al., 2007). As part of current reform efforts, ACOs have thus been promoted as model delivery systems and will be included in the Medicare program on a voluntary, pilot-program basis starting in 2012, with ACOs receiving financial rewards for good performance based on quality and spending measures. However, it is essential that ACOs have the capacity to provide and manage a continuum of care across various settings (eg, ambulatory, inpatient hospital, postacute care) and it is here that HCs need to focus their efforts (Devers & Berenson, 2009). Findings also revealed no major differences in patient disease management during primary care visits to HCs across racial/ethnic and insurance groups. In contrast with HCs, both POs and OPDs displayed higher variation in delivery across racial/ethnic groups and insurance types. Future research efforts should focus on elucidating the factors that contribute to HCs performance, as well as

14 Primary Care Delivery 73 variations across HCs, so that the HC model can be duplicated in other primary care settings, and perhaps integrated into ACO delivery systems to reduce health care disparities. There were a number of limitations with this study. Not all primary care delivery sites were included in this study (eg, EDs, Veterans Affairs), and these exclusions may have impacted the results, particularly since a growing number of patients receive primary care in EDs. In addition, the NAMCS and NHAMCS surveys did not determine whether primary care physicians performed specialty care services, nor did they survey specialists to determine whether they were serving as primary care physicians for any patients. The study used 2 different surveys, which have different data collection methods: the NHAMCS obtains information from medical records, while the NAMCS uses questionnaires filled out by physicians. These reporting differences may have resulted in discrepancies in comparing visits in OPDs with those in POs and HCs. We also focused on general patient disease management measures rather than diseasespecific process of care measures. Although we would have favored examining the latter, we were unable to do so because small sample sizes within HC visits prevented us from detecting significant differences across primary care settings. Finally, some of our results suggest that there may have been sampling issues regarding HCs in the NAMCS data set. Specifically, we reported that about 10% of HC visits were made by uninsured patients; other sources indicate that closer to 40% of HC patients are uninsured (Bureau of Primary Health Care, 2008). We also estimated that about 6% of HC visits were from patients in rural areas, which contrasts with other sources reporting that about half of HC grantees are located in rural areas (Taylor, 2004). One possible explanation for these differences is that our analyses were at the visit level, rather than the patient (or grantee) level; another possibility is that despite oversampling, the total number of HCs included in the NAMCS was still rather small. It is possible that the HC sample included in the data set was not in fact completely nationally representative. Despite these limitations, the findings of our study are helpful for informing policy decisions. Safety-net providers play a continual role in this country s health care delivery system. Even with successful health insurance reform, millions of Americans will still be uninsured and many of the newly insured will be more vulnerable than the average population. Therefore, a safety-net remains critical for individuals who will need access to health care providers with relevant experience serving at-risk populations. A well-organized and adequately funded safety-net system has the potential to provide a reasonable level of care to individuals, even to those without health insurance coverage. There is a dearth of solid evidence concerning the effectiveness of safetynet providers, but this study contributes to building a case that HCs can successfully provide primary care services. One of this nation s ongoing health priorities is to reduce and eliminate disparities in health and health care (U.S. Department of Health and Human Services, 2000). Our finding that HCs provide more comparable care across racial/ethnic and insurance groups, compared with other primary care providers, further supports the appropriateness of the HC delivery model. Mainstream providers may not have the expertise or resources to adequately address the needs of vulnerable populations; however, HCs provide mandated enabling services (eg, outreach, eligibility assistance, transportation, language interpretation, health education), which greatly facilitate access to primary care for these groups. In conclusion, this study provides evidence that HCs generally perform processes of care with comparable or higher occurrence, relative to POs and OPDs, 2 mainstream primary care providers. In addition, health care disparities are significantly attenuated if not eliminated in HCs compared with POs and OPDs. The HC model should thus be regarded as an effective delivery model for providing primary health care not only to vulnerable populations but also to all Americans.

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