Sarah L. Goff, MD

Team-based primary care programs have the potential to address healthcare disparities. We evaluated the impact of a multidisciplinary team-based enhanced primary care program on vaccination and cancer screening rates at a community health center serving a low-income Hispanic community.

 In this controlled before-and-after study, data were extracted from the electronic health record for the following groups of patients aged 18 to 64 years: (1) those enrolled in the program between August 2011 and January 2012 and (2) randomly selected controls cared for during this time. Outcomes included eligible patients’ receipt of (1) influenza and/or pneumococcal vaccines and (2) breast, cervical, and/or colon cancer screens over a 12-month period following enrollment. We used an adjusted difference-in-differences (DID) analysis to estimate the effect of the intervention.

A total of 134 intervention and 393 control patients were included in the analysis. Unadjusted influenza vaccination rates increased from 47.3% to 63.6% in the control group and from 48.5% to 63.4% in the intervention group in adjusted models, a nonsignificant DID of —1.4 percentage points (95% CI, –14.1 to 8.5; 

 = .66). Pneumococcal vaccination rates fell for the control (63.7% to 57.4%) and intervention (53.9% to 42.6%) groups, with a nonsignificant DID of —4.7 percentage points (95% CI, –16.5 to 7.0; 

 = .45). There was no difference in changes in cancer screening rates.

A newly implemented culturally and linguistically concordant enhanced primary care program had no measurable effect on preventive care outcomes for a low-income, largely Hispanic population. These findings suggest a need for a deeper examination of both the implementation process of similar programs and their long-term effectiveness.

Many team-based systems of care, such as case management programs, peer health coaching, and patient-centered medical home certification, seek to enhance the delivery of primary care services.

 Healthcare organizations and insurance companies have both played a role in developing and funding these types of programs in an effort to improve primary care services and reduce costs.

 The term “enhanced primary care” refers to a team-based care model that aims to improve care processes and patient outcomes for both preventive care services and chronic disease management.

Enhanced primary care programs are often directed at populations at highest risk for underutilization of preventive services, overutilization of acute care services, and health disparities. Disparities in preventive care services related to race/ethnicity, language, and socioeconomic status contribute to a disproportionate risk of disease among vulnerable populations in the United States.

 An enhanced primary care team may improve access to care through increased personalized care and may develop more trusting relationships, potentially benefitting patients who experience disparities in care the most. In this study, we aimed to determine the impact of a newly implemented, insurance company—sponsored, team-based enhanced primary care program (Buena Salud) on vaccination and cancer screening rates in a low-income, largely Hispanic population with high rates of limited English proficiency.

Buena Salud is a bilingual enhanced primary care program for Medicaid managed care patients in western Massachusetts. The program was financed by the Health New England insurance company and implemented by Brightwood Health Center (BHC). BHC is an urban community health center that serves a predominantly Hispanic population (88%) who are insured primarily by either Medicaid (59%) or Medicare (28%). More than 50% of BHC patients prefer Spanish as their spoken language. Buena Salud was started in an effort to improve compliance with preventive care, provide support for chronic disease management, and optimize utilization of healthcare services. Most patients were referred to Buena Salud by their primary care provider, but patients also were enrolled through a periodic auto-enrollment process or self-referral. The Buena Salud team consisted of 2 registered nurse care managers, 2 medical assistants trained as community health workers, and a case worker. The care team differed from many other managed care teams in that each team member was bilingual (Spanish/English) and was from the same racial/ethnic group as the majority of BHC patients. The Buena Salud team offered education and coaching for disease self-management, provided additional support for patients in the clinic and at home, used electronic health registries to identify patients in need of care and services such as vaccination and cancer screenings, and provided linkage to community-based support services. A more detailed description of the program is included in the 

). Each Buena Salud nurse was expected to actively manage up to 50 patients at any given time. Care intensity varied depending on individual patients’ needs.

We reviewed eligible patients’ electronic health records (EHRs), extracting data pertinent to the study aims. Eligibility for the intervention arm of the study was defined as all BHC patients aged 18 to 64 years who (1) were newly enrolled in the Buena Salud program between August 1, 2011, and January 31, 2012 (intervention period); (2) were eligible for at least 1 of the outcomes measured; and (3) remained a patient at BHC for 12 months following enrollment in Buena Salud. Eligibility criteria for controls included all BHC patients aged 18 to 64 years who (1) had been seen for a clinical encounter at BHC in the intervention period, (2) were not enrolled in Buena Salud, and (3) remained a patient at BHC for 12 months following enrollment in the study. From the list of eligible controls, we randomly selected 3 patients who had been seen in the clinic in the same month that an intervention patient had been enrolled per 1 intervention patient (3:1 matching) using a random number generator. We selected controls based on when they were seen in the clinic rather than matching on variables such as age, race/ethnicity, or gender because (1) the population attending the clinic is relatively homogeneous for these measurable variables and (2) selecting patients seen in a similar time frame reduces the potential for differences in unmeasured confounders related to temporal changes in practice at the health center. Intervention patients and controls must have had at least 1 visit at BHC in the 12 months prior to the enrollment/index visit so that baseline data could be extracted. The methodology is similar to that used in a study of the impact of the Buena Salud program on outcomes for patients with diabetes.

 This study was approved by the Baystate Institutional Review Board, which waived informed consent.

Time Period Studied and Outcome Variables 

The primary study outcome was the change in the percentage of eligible patients who received recommended vaccinations and cancer screenings prior to implementation of the Buena Salud program to 12 months post implementation. Eligibility for influenza and pneumococcal vaccinations and breast, cervical, and colon cancer screenings was based on national guidelines from the following organizations: (1) CDC for vaccines,

 (2) United States Preventive Services Task Force (USPSTF) guidelines for breast and colon cancer screening,

 and (3) American College of Obstetrics and Gynecology guidelines for cervical cancer screening.

 Patients were considered up to date on vaccination and/or cancer screenings at baseline as follows: (1) influenza vaccination in the 12 months prior to enrollment, (2) pneumococcal vaccine consistent with CDC guidelines depending on patient’s comorbidities and age, (3) mammogram to screen for breast cancer in the 2 years prior to enrollment for patients 50 years or older, (4) Pap smear to screen for cervical cancer in the 3 years prior to enrollment for patients 21 years or older, and (5) any of the colon cancer screening modalities consistent with USPSTF guidelines for patients 50 years or older. We chose the time period following enrollment (12 months) so that we could include the maximum number of intervention patients in the study. In addition to vaccination and cancer screening data, we also extracted data on potential confounders (eg, comorbidities and the number of years a patient received care at BHC) from participants’ EHRs (

We first oriented data extractors to the study’s data dictionary and extraction protocol, which included where to locate pertinent data in the EHR. After establishing baseline consistency using standardized extraction forms, 2 extractors independently reviewed 20 randomly selected health records to assess interextractor consistency at 6 and 18 months into the course of data extraction to test whether consistency was maintained.

 Study data were collected and managed using REDCap electronic data capture tools.

Participant characteristics are presented as means and SDs for continuous variables and frequencies and percentages for categorical variables. To estimate differences between the groups studied for vaccinations, we used a difference-in-differences (DID) approach (the difference between the pre-post change in the

intervention group compared with the control group). Outcomes were modeled using generalized estimating equations with exchangeable correlations and robust standard errors (clustering on patient). Outcomes were modeled using the logit link and binomial family. Models were estimated with main effects for the intervention group and time period, with an interaction term between these 2 representing the DID. Because participants in the intervention group were frequency matched to controls based on enrollment month, we used enrollment month as an indicator variable in the model. This term was not significant in the models, so it was therefore removed. Statistical significance was set at an α of .05. Multivariable models included potential confounders (eg, demographic data, comorbidities, and number of years receiving care at BHC). Using Wald tests, models were reduced to include variables that were significant at the .05 level. To control for possible residual confounding and for face validity, we retained age, mental health, and substance use in all models. The analysis was conducted using Stata version 13.1 (StataCorp LP; College Station, Texas).

A total of 527 patients were included in the study: 134 in the intervention group and 393 in the control group. The median age was 50 years (range, 20-65), 72.7% were female, and Spanish was the preferred language for 56.7% (Table 1). Most of those in the intervention group (estimated 90%) were referred to the program by a healthcare provider, with fewer (estimated 10%) enrolled via auto-enrollment or self-referral. Baseline differences between the groups included the following: The control group had a slightly higher median age compared with the intervention group (50 vs 49 years), were more likely to have HIV (10.2% vs 1.5%), were more likely to have been prescribed buprenorphine or naloxone (6.6% vs 2.2%), were less likely to be homeless (1.0% vs 2.2%), and were less likely to have been diagnosed with anxiety or depression (25.7.8% vs 29.9%) (Table 1). The intervention group had a slightly higher number of visits with a primary care provider (0.7 vs 1.5 visits; DID, 0.8; 

 = .03) in the 12 months following enrollment. All other variables were similar at baseline. Extractor agreement was estimated at 93%.

All patients were eligible for influenza vaccination. Overall, influenza vaccination rates increased from 47.6% in the year prior to enrollment to 63.6% at the end of the study period. The increase was similar for controls (47.3% to 63.6%) and intervention patients (48.5% to 63.4%). The adjusted difference in the change in percentage points (—2.8; 95% CI, –14.1 to 8.5; 

= .66) between groups was not significant (

Of the 527 patients included in the study, 432 were eligible for pneumococcal vaccination (317 control and 115 intervention). Overall, the percentage of eligible patients who were up to date on pneumococcal vaccination decreased from 61.6% prior to inception of the Buena Salud program to 53.5% 15 months following the start of the program. The unadjusted vaccination rate for controls went from 63.7% to 53.4% and from 53.9% to 42.6% for the intervention group. The adjusted difference in change between the groups was not significant, at —4.7 percentage points (95% CI, –16.5 to 7.0; 

 Of the 527 patients included in the study, 178 were eligible to be screened for breast cancer (131 control and 47 intervention). Of these, 147 (82.6%) were up to date on breast cancer screening at the time of enrollment (baseline) and 82.0% were up to date at the end of the study period. The control group’s unadjusted screening rates went from 86.3% to 85.5% and the intervention group’s rate did not change from 72.3%, resulting in an adjusted difference of —0.7 percentage points (95% CI, –9.2 to 10.6; 

Of the 527 patients included in the study, 153 were eligible for cervical cancer screening (113 control and 40 intervention). Of these, 72.6% were up to date on screening at the time of enrollment and 79.6% were up to date at the end of the study period. Control patients’ unadjusted screening rates increased from 72.6% to 81.4% and intervention arm rates went from 72.5% to 75.0%, giving a nonsignificant adjusted difference in rate changes between groups of —6.3 percentage points (95% CI, –22.5 to 9.9; 

Of the 527 patients included in the study, 273 were eligible for colon cancer screening (202 control and 71 intervention). Overall, 50.9% of patients in the study were up to date on colon cancer screening at baseline and 52.8% were up to date at the end of the study period. Unadjusted screening rates increased from 54.0% to 56.4% in the control group and did not change from 42.3% in the intervention group, for a DID of —2.5 percentage points (95% CI, –14.8 to 9.8; 

In this controlled before-and-after study, we found that patients enrolled in an ethnically and culturally concordant team-based enhanced primary care program implemented in a community health center serving a population of lower-income, largely Hispanic patients did not experience significantly different changes in rates of vaccination or cancer screenings compared with patients not enrolled in the program. The study demonstrates some of the complexities of programs designed to enhance care delivery processes in underserved communities and raises questions about the most appropriate outcomes to measure in the early stages of interventions that are intended to improve patient outcomes and decrease costs in a racially and ethnically diverse population.

Racial and ethnic disparities in vaccination rates and cancer screenings persist in the US healthcare system.

 Explanations for these disparities are likely multifactorial, including system-, provider-, and patient-level factors. Prior interventions to reduce disparities in cancer screening have taken both population-and clinic-based approaches. Interventions that have demonstrated effectiveness include a cluster-randomized trial of 150,417 patients in the United Kingdom. This study found a reduction in the socioeconomic status gradient in screening with an enhanced reminder letter.

 A systematic review of patient navigators also showed improved screening rates for breast, cervical, and colon cancer for patients with limited English proficiency,

 and tracking and outreach at the clinic level have been effective for increasing influenza vaccination in an inner-city population.

 Enhanced primary care teams, particularly teams staffed by a racially/ethnically concordant staff, have the potential to reduce disparities in preventive care services through outreach and by addressing issues related to limited English proficiency, mistrust in the healthcare system, and implicit bias, similar to patient navigator interventions. Our study’s results do not demonstrate differences between intervention and control groups for the outcomes measured in the current study. Rather than conclude that the program is ineffective, we reflected on whether the outcomes that were measured, which are common measures of enhanced primary care programs’ effectiveness, were the best measures of intervention effectiveness in the context of a new program implemented in a natural setting. The following provides some insights into these reflections.

Interventions to improve both vaccination and cancer screening rates have shown success in experimental settings, but the enhanced primary care model we studied was implemented outside of a clinical trial and there may be a learning curve for the enhanced care team.

 We followed patients for a relatively brief period (15 months) after enrollment in the program, and it may take more time for team members to develop trusting relationships with care recipients, thus limiting the early effects on these “hard” outcomes. We learned through interviews with the Buena Salud team that there was no systematic process for documenting the team’s interactions with patients during the time period studied. This meant that we could not accurately measure the intervention “dose” individuals received, meaning some patients enrolled may have had no “touch” by the team. In a small study such as this, variation in expertise among the Buena Salud team members also could have influenced the outcomes observed.

This study’s strengths included the following: comparison with a control group, use of a DID analysis that adjusted for secular trends in care and outcomes, and risk adjustment using a broad array of clinical and demographic data. The latter allowed us to address the nonrandom assignment of patients to intervention and control groups. This study should also be considered in light of its limitations. First, this was an observational study and not a randomized clinical trial. The Buena Salud program sought to provide support for the sickest patients, as evidenced by the measured baseline differences found between Buena Salud patients and controls; there may have been other unmeasured important differences not accounted for in our risk adjustment. Second, this study evaluated patients from 1 health center. Although this allowed us to focus on the population of interest, the intervention might have different effects in other populations or in other health centers with a similar population. Third, we were unable to assess whether patients were offered vaccination or cancer screenings but declined. Fourth, screening guidelines vary somewhat, and it is possible that application of guidelines other than those used in this study could result in different estimates of uptake. Finally, enrollment in the Buena Salud program was associated with a slightly greater number of office visits with a primary care provider; these additional visits may have presented an additional burden for the intervention group.

A team-based enhanced primary care program delivered by a multidisciplinary bilingual team that was linguistically and culturally concordant with the majority of the underserved patients enrolled in the program did not have statistically significant effects on vaccination and cancer screening rates in its first 15 months of existence. Care should be taken in drawing conclusions from the outcomes assessed in the first year of a new program because there is likely a learning curve to engaging and partnering with patients in this context. Additional outcomes, such as measures of patient experience, trust, and satisfaction with care, may yield more meaningful insight into a program’s effectiveness. Studies that examine the implementation and longitudinal effectiveness of these programs will contribute additional important information to our understanding of the potential benefits of enhanced primary care team interventions for at-risk patient populations.

University of Massachusetts Medical School—Baystate (SLG, AK, HG-K, AG, PKL), Springfield, MA; Western New England Renal and Transplant Associates (LM), Springfield, MA.

This study was funded by an incubator grant from Baystate Medical Center. Dr Goff was supported by the National Institute for Child Health and Human Development of the National Institutes of Health under award number K23HD080870. Dr Lindenauer was supported by grant K24HL132008 from the National Heart, Lung, and Blood Institute.

 Dr Goff, Ms Murphy, Mr Knee, Ms Guhn-Knight, Dr Guhn, and Dr Lindenauer are or were employed by Baystate Health, which owns Health New England, a health insurance company that financially supports the Be Healthy program.

Concept and design (SLG, LM, AG, PKL); acquisition of data (SLG, LM, HG-K, AG); analysis and interpretation of data (SLG, AK, PKL); drafting of the manuscript (SLG, AK, HG-K); critical revision of the manuscript for important intellectual content (SLG, LM, AK, HG-K, AG, PKL); statistical analysis (SLG, AK); provision of study materials or patients (AG); obtaining funding (SLG); administrative, technical, or logistic support (HG-K); and supervision (SLG, PKL).

 Sarah Goff, MD, University of Massachusetts Medical School—Baystate, 759 Chestnut St, Springfield, MA 01199. Email: Sarah.goffmd@baystatehealth.org.

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Articles

This natural experiment compared rates of indicated preventive care for low-income Hispanic patients enrolled in an enhanced primary care program with those of patients receiving usual care.

Effectiveness of Enhanced Primary Care on Preventive Health Services

Massachusetts has insurance rates similar to those projected under the Affordable Care Act, but many of the state's patients are insured through private insurance plans with high out-of-pocket costs. We aimed to explore the relationship between insurance type (private vs public) and delays in care due to cost, stratified by income. 

We conducted a study of English-speaking adults recruited from the waiting rooms of the emergency department or outpatient clinics of a large healthcare system in western Massachusetts. Our primary outcome was the association between insurance type and cost-related delay in care, stratified by income. 

Of 800 individuals approached, 619 (77%) completed the survey. Participants were 60.6% male and 40.2% white, 37.2% Hispanic, and 12.6% black. The majority (61.4%) of those surveyed had public insurance, 34.1% had private insurance, and 4.5% were uninsured. Overall, 13.3% reported delays in seeking care that were related to cost. The impact of insurance on delay of care differed significantly by income tertile (P = .02): in the middle-income group ($12,500 to <$25,000 per person annually), privately insured respondents were more likely to delay care due to cost compared with publicly insured subjects (15.6% vs 8.1%; odds ratio [OR], 4.4; 95% confidence interval [CI], 1.9-10.2, unadjusted; OR, 2.2; 95% CI, 0.9-5.8, adjusted). 

Cost-related delays in care are prevalent despite the presence of an insurance mandate. Middle-income, privately insured patients report more cost-related delays in care compared with publicly insured patients with similar incomes. 

We surveyed patients and visitors in a large emergency department and outpatient clinic about type of insurance, out-of-pocket costs, and self-reported cost-related delays in receiving medical treatment. Middle-income respondents with private insurance reported more cost-related delays in care than middle-income respondents with public insurance, although adjustment attenuated these results. 

These findings suggest that national healthcare policies that have pushed middle-income patients toward high-deductible healthcare plans with high out-of-pocket costs may have the unintended consequence of increasing cost-related delays in care for this group.

Due to the worse health outcomes of the uninsured, the Affordable Care Act (ACA) aimed to expand health insurance coverage through multiple interventions and mandates: expanding Medicaid coverage; incentivizing employers to provide health insurance; and, for those who do not have employer- or government-provided coverage, implementing an individual mandate for the purchase of private health insurance on a regulated health insurance marketplace.

 Decreasing the number of uninsured aimed to improve population health by decreasing financial barriers to healthcare.

 Patients newly insured under the ACA are a heterogeneous group that includes those with employer-sponsored insurance, those with Medicaid, and those who purchased private individual plans from state and federal exchanges.

 Because of this heterogeneity, there is a risk that some types of health insurance will not allow patients to fully access needed care. This concern is all the more relevant however, given that the most rapidly expanding insurance types under the ACA are private plans with high levels of cost sharing.

 &shy;&shy;&shy;Although these plans offer more affordable monthly premiums,

 they are likely to have higher co-pays and deductibles than public insurance,

 which may put some patients at risk for reduced access to care due to cost.

Since 2006, Massachusetts has had a mandate requiring individuals to carry health insurance, resulting in less than 5% of the population being uninsured.

 This creates insurance coverage that is similar to the coverage patterns expected nationally after full implementation of the ACA.

 We hypothesized that individuals in Massachusetts with private insurance would be more likely to experience cost-related delays in care than participants with public insurance, and that this association would be related to household income and co-pays. To test this hypothesis, we surveyed patients and visitors in a large health system in Massachusetts to explore the relationship between insurance type and delays in healthcare due to cost.

We identified a convenience sample of adults recruited from the waiting rooms of an emergency department (ED) and 2 outpatient primary care clinics in western Massachusetts. The ED serves a diverse population, with 110,000 patient visits annually. The outpatient clinics in the study health system serve primarily low-income patients, with 49,000 visits annually. We approached adults who were not visibly in distress in the waiting rooms of the ED and the clinics. After confirming that they were at least 18 years old and English-speaking, we invited patients and those accompanying them to participate in this study. The Baystate Medical Center Institutional Review Board approved the study protocol; informed consent was waived given that no protected health information was collected other than zip code. Data were collected between February 2014 and May 2014.

Using previously validated instruments as a guide,

 we developed a questionnaire to measure participants’ healthcare utilization and their reported impact of cost on utilization of healthcare (eAppendix A [

]). Demographic data consisted of age, sex, race, education, employment status, annual pre-tax household income, zip code, and household size.

Our exposure variable, insurance type, was categorized as uninsured, private insurance, or public insurance. Participants were also asked to specify whether they had an individual or a family health insurance plan. We further asked whether private insurance was self-purchased or employer-sponsored, as self-purchased insurance is more likely to have higher out-of-pocket (OOP) costs.

 Public insurance included Medicare and Medicaid (referred to as “MassHealth” in Massachusetts), as well as a subsidized public—private insurance partnership called “Commonwealth Care.” We categorized both Medicare and Medicaid as public insurance because both have much lower premiums and less cost sharing than private insurance (although they have different patient populations and payment methods).

Until January 30, 2015, Commonwealth Care was a state-subsidized plan available to individuals whose income was not low enough to qualify for Medicaid, but for whom private insurance at market rates would be a significant financial burden. Under the ACA, there is no clear comparator with Commonwealth Care, but as Commonwealth Care was highly subsidized by the state, with co-pays comparable with Medicaid’s in the lowest-income population, we opted to categorize it under public insurance.

 This decision was supported by the fact that Commonwealth Care respondents’ demographics were more similar to Medicaid/Medicare patient demographics than to the privately insured group.

The questionnaire also included questions about health and access to healthcare. We collected participants’ self-reported number of chronic medical conditions and state of health,

 as well as a variety of previously validated measures of access to care, including number of outpatient visits in the past year,

 For those who responded “yes” to delays of medical care, we asked additional questions about type of medical care that was delayed and the reasons for delaying care.

The primary outcome was delay in care due to cost, defined as voluntary delay or refusal of care by the respondent due to the cost of care involved. That is, we defined respondents as such if they affirmatively answered the question, “In the past year, have you ever delayed or avoided getting any kind of medical care?” and then selected the reason, “It cost too much money” when asked, “Why did you delay or avoid medical care?”

We first pilot-tested the survey with a sample similar to the target audience using cognitive interviewing techniques.

 The goals of pilot testing were to ensure clarity and completeness of the questions and to assess administration and completion time. We then revised the questionnaire based on the feedback we received. No data obtained from the pilot testing were included in the analysis. The survey administration details can be reviewed in eAppendix B.

We generated descriptive statistics as proportions for categorical factors and medians, with interquartile range (IQR) for ordinal variables. We next calculated per-person household income (PPHI) by dividing the median value of self-reported pre-tax household income category by the number of household residents. For respondents with missing PPHI, multiple imputation was used to estimate the value using available education, interview site (ED vs clinic), self-rated health, and self-rated OOP costs as predictors, chosen for their relatively high correlation with income in the complete sample.

 We then divided PPHI into tertiles. We included uninsured respondents in descriptive analyses and in calculations of income tertiles to ensure a representative sample. However, we excluded the uninsured from multivariable models as this group was too small to draw meaningful conclusions.

Because we hypothesized that those with lower incomes would be more sensitive to high co-pays and deductibles than those with higher income, we tested an “income-level by insurance category interaction term” using the Likelihood Ratio (LR) test. If the 

 value for the LR test of the interaction term was ≤0.2, a stratified approach would be used and separate estimates for the association between delay of care and insurance type would be presented for each income level. Final model fit was examined using the Hosmer-Lemeshow Goodness-of-Fit test. After multivariable adjustment, we wished to quantify the extent to which OOP costs and outstanding bills “explained” differences in delay of care that remained after multivariable adjustment. To do so, we added each variable to the fully adjusted model and noted the change in the insurance variable’s beta coefficient.

 We used Stata version 13.1 (StataCorp; College Station, Texas) for all analyses.

Of 800 individuals approached, 704 responded to the survey (88%). Of these, we excluded 85 (12%) questionnaires that were missing insurance category information or for which there was no response to the question regarding delayed care. Thus, we had 619 surveys completed of the 800 subjects originally approached, giving us a final response rate of 77%. Compared with those included in the study, the 85 excluded participants (who failed to complete the survey) were generally older (median age = 49.5 vs 39.5 years; 

= .02), less educated (median years of education 12 vs 14; 

= .001), had higher median comorbidity counts (7 vs 2; 

<.001), and were more likely to be surveyed in a clinic versus the ED (68.2% vs 23.4%; 

Of the 619 participant responses analyzed, 474 participants (77%) were recruited from the ED and 145 (23%) from 2 primary care clinics. Participants’ characteristics appear in Table 1.

Tertiles of the pre-tax PPHI distribution corresponded to cut points from the 2014 MassHealth Income Standards and Federal Poverty Guidelines

: (tertile 1: <$12,500; tertile 2: $12,500 to <$25,000; tertile 3: ≥$25,000). Household income (HHI) was missing for 111 respondents (17.9%), and we were able to impute HHI for all but 16, who were excluded from the multivariable analysis.

The median number of visits to a provider within the past 12 months was 3 (IQR = 2-8). Eight percent reported delays in filling prescriptions, and 32% reported outstanding medical bills. One-third of those analyzed reported OOP costs upwards of $500 annually (32.8%) (Table 1). Overall, 189 of the 619 subjects (30.5%; 95% confidence interval [CI], 27.0%-34.3%) reported any delay in care in the 12 months prior to completing the survey (data not shown). Of the 189 who delayed care for any reason, 82 (43%) did so due to cost. Other nonmutually exclusive reasons for delay are shown in Figure 1.

Forty-three percent had more than 1 chronic medical condition, with 9% reporting upwards of 5 chronic medical conditions for which they receive prescriptions. In terms of self-reported health, 35.4% reported their health to be very good or excellent and 25.7% reported their health to be fair or poor (Table 1).

The uninsured were similar to the publicly insured in terms of race and income level, but were younger overall (Table 1). The uninsured had higher OOP costs than those with public insurance but less than those with private insurance (Figure 2).

As shown in Table 1, demographic characteristics and annual OOP spending varied by insurance type. For example, compared with publicly insured participants, those with private insurance were more likely to be employed (76.8% vs 30.8%; 

 <.001) and to have family insurance plans (64.5% vs 26.6%; 

 <.001). They were also more likely to identify as white (62.6% vs 28.7%), have income greater than $100,000 per year (11.9% vs 1.6%), have a graduate degree (14.2% vs 4.7%), and to have been surveyed in the ED as opposed to the clinic (89.1% vs 68.9%). Compared with publicly insured participants, those with private insurance were less likely to report 5 or more chronic conditions requiring medication (2.8% vs 12.9%). Respondents with public insurance were significantly less likely than those with private insurance to have OOP expenditures greater than $500 per year (Figure 2).

Characteristics of respondents who delayed care due to cost are shown in the eAppendix Table. Delay in care due to cost was highly correlated with a delay in filling prescriptions (41.5% of those who delayed care avoided filling a prescription vs 3.4% who did not delay care; 

<.001), but was not significantly associated with frequent ED use for healthcare (17.1% vs 11.7%; 

 = .21). Delay of care due to cost was significantly associated with white race/ethnicity (whites composed 54.9% of those who delayed care vs 38.0% of those who did not), being employed (64.6% in the delay group vs 44.7% in the nondelay group), and having outstanding medical bills (72.0% vs 26.6%). Annual OOP costs less than $500 were associated with fewer reported delays in care (42.7% vs 67.8%).

In the multivariable model, examining delay of care by income level and insurance type, we found that the income tertile by insurance type interaction term was statistically significant (

= .02), indicating the need for a stratified approach. The association between delay of care and insurance type, by income stratum, is shown in Table 2. Table 2 also includes standardized effect sizes so that the magnitude of the difference between groups can be interpreted without respect to sample size,

 because the stratified approach resulted in small subgroups and multivariable adjustment necessitated exclusion of respondents with missing covariates.

In unadjusted models, privately insured subjects in the middle-income group were 4.4 times (95% CI, 1.9-10.2; 

= .001, unadjusted) more likely to delay care compared with publicly insured subjects. After adjustment, privately insured middle income patients were 2.2 times (95% CI, 0.9-5.8; 

= .10, adjusted) more likely to delay care due to cost compared with publicly insured subjects (adjusted proportions = 15.6% vs 8.1%; Cohen’s d, +0.4, moderate).

Although the association was no longer statistically significant after adjustment (95% CI, 0.9-5.8), we refer to the effect size rather than the statistical significance because multivariable adjustment (and exclusion of respondents with missing covariates) may have affected statistical power. Of note, lower-income, privately insured respondents were less likely than publicly insured respondents to delay care due to cost (adjusted proportions 3.1% vs 14.1%; odds ratio 0.2; 

= .21; Cohen’s d, —0.9, large), although this finding did not achieve statistical significance in adjusted or unadjusted models. We noted only very small differences in delay of care between private and public insurance in the highest income tertile.

After multivariable adjustment, we wished to quantify the extent to which OOP costs and outstanding bills “explained” differences in delay of care. In the lowest-income tertile, adjustment for OOP costs increased the protective effect of private insurance by 16%, whereas outstanding bills did so by 1%. In the middle-income group, adjustment for OOP costs removed 27% of the risk for delay of care for privately insured subjects, whereas outstanding bills removed 11%. This suggests that some of the observed association between delay of care in privately insured, middle-income respondents is due to higher OOP costs and more outstanding bills compared with their publicly insured counterparts.

In a large convenience sample in Massachusetts, we found that one-third of patients reported a delay in care in the year prior to the survey. About half of the time, this delay was due to cost. We also found that, after stratifying by income, middle-income tertile ($12,500 to <$25,000 PPHI) participants with private insurance were more likely to report cost-related delays in care compared with participants in the same income group with public insurance. Adjustment for demographic confounders—race, employment status, and self-rated health&mdash;attenuated but did not eliminate the association, as evidenced by a medium effect size even after multivariable adjustment. Annual OOP costs (eg, co-pays, deductibles) partially mediated the association between insurance type and delay due to cost.

Our findings may, at first, appear to contradict findings from a recent study conducted by McCormick et al that found Medicaid was associated with more delays in care compared with private insurance or Commonwealth Care.

 However, there are several key differences between their study and ours. First, they collected data only from patients in the ED, which might indicate that they were more likely to sample patients without a regular source of care. In contrast, we also sampled visitors in outpatient clinics, thus including those who were not directly using the healthcare system at the time the survey was completed. Second, they did not stratify by income, and, as we have noted, income appears to be an effect modifier in the relationship between insurance type and delays in care. Third, they did not include Medicare patients, meaning that their “Medicaid” group was different from our “public” insurance group. To assess the extent to which this contributed to the difference in our results, we conducted a sensitivity analysis by excluding patients 65 or older (eAppendix C). We found that results did not change appreciably, supporting the original model. Finally, we included Commonwealth Care under the publicly insured group, which makes the studies less comparable. It is notable that both studies show similar overall proportions of participants who delayed care.

 Another study, by Bernard et al, surveyed workers who declined employer-sponsored health insurance and found that those with public insurance had similar access to care compared with those with private insurance.

 Again, the authors did not stratify by income, which could be important since employed workers who declined insurance are likely to be a self-selected group. Notably, a study that did collect income data, by Wisk et al, reported that the healthcare-related financial burden in families with children was dependent on income.

A 2015 Gallup survey reported that, in the prior year, 31% of Americans delayed seeking healthcare because of cost.

 In contrast, we found that 13% of our sample delayed care because of cost in the year prior to the survey. We suspect that there are 2 reasons why we reported fewer cost-related delays in care than were identified in the Gallup survey. First, Massachusetts—with insurance rates above 90%&mdash;is more reflective of the post-ACA rather than pre-ACA world. Thus, our study may be an indicator of upcoming trends: if the ACA remains law, it is possible that, in the near future, we will see fewer overall delays in care, but an increasing number of middle-income patients with private insurance who are experiencing delays in care.

 Second, we surveyed subjects who were in a healthcare setting, either in an ED or an oupatient clinic waiting room. This population may be different than the randomly dialed national sample obtained by the Gallup poll.

There are several limitations to our study. First, we relied on an English-speaking convenience sample in a single geographical area—western Massachusetts&mdash;which may not be representative of the rest of the United States. We attempted to address this limitation by conducting surveys in an ED (which serves a large regional catchment area) and clinics that serve a lower-income clientele (who are primarily publicly insured). We included only individuals who were in the process of accessing medical care directly or indirectly (as visitors or companions), which is a population that may differ from the overall population. However, the demographic characteristics of our sample population were comparable with those of the state overall.

 Because the ACA was based on Massachusetts’ 2006 healthcare reforms, local studies, such as ours, can inform projections about the insurance landscape under the ACA in the rest of the country, assuming that the ACA remains intact in the future.

A second limitation of our study is the fact that insurance information was self-reported, preventing us from collecting details regarding insurance plans and coverage. For example, there were 3 types of Commonwealth Care that had different levels of cost sharing, but most patients were not aware of these differences. We attempted to address this limitation by asking separate questions about OOP costs. We also included Commonwealth Care, Medicare, and Medicaid in a single public insurance category. Another limitation is the fact that we collected income information in a categorical fashion and imputed some income data. Although categories were designed to improve response rates and increase respondent comfort with providing delicate information, their categorical nature limited our ability to assign PPHI that corresponded with federal poverty guidelines. Nevertheless, this method of reporting income has been reported to have acceptable accuracy.

Finally, given that the majority of patients sampled were in the ED, this can bias the overall population to those with less access to care (those with a usual provider would be less inclined to go to the ED for low-acuity issues). However, given that we not only sampled patients, but also family and friends, we hoped to include a population that was not directly accessing the healthcare system. Future studies should target outpatient clinics in areas with varied demographics to better delineate the effect of insurance type on access to care, with income as an effect modifier.

In spite of these limitations, our study also has important implications. Our study results confirm that patients with health insurance have more access to care than those without insurance. Thus, higher insurance rates achieved under the ACA will likely improve access for patients who were previously uninsured. However, our study also highlights that one-third of patients with health insurance still experience delays in care. Privately insured patients in our study had higher OOP costs than those without insurance, and we found that many people who were privately insured and living above the poverty line (the middle-income group) frequently experienced recent delays in care due to cost. This may be because this group has the means to purchase private insurance, but may not have sufficient disposable income to cover large unexpected medical bills.

A potential unintended consequence of an insurance mandate is that employers and patients will increasingly embrace plans with high rates of cost sharing, resulting in large numbers of privately insured patients who are unable to afford recommended care. To reduce the chance that this could happen, our study results indicate that policy makers should pay close attention to the regulation of insurance plans. In particular, efforts should be made to ensure robust minimum coverage and to maintain or expand current limitations on OOP expenses. Most importantly, our findings suggest that there are great risks to current political efforts aimed at loosening minimum healthcare coverage and reducing limitations on OOP medical expenses. If these efforts succeed and plans with even greater cost sharing are allowed to flood the market, the result could be widespread increases in cost-related delays in care in susceptible sociodemographic groups, specifically middle-income beneficiaries with private insurance.

Proposed changes to healthcare law (eg, the repeal of the ACA and replacement with an alternative plan) would likely result in a smaller proportion of the population who can obtain public insurance (because of cuts to Medicaid), and, for those with private insurance, a loss of guaranteed minimum healthcare coverage and fewer limitations on OOP spending. This study is an important addition to the ongoing policy debate, as the results suggest that these changes would result in more cost-related delays in access to care for middle-income populations with private insurance (compared with similar populations with public insurance). Policy makers considering such changes to current law should be aware that higher OOP costs are likely to increase delays in access to healthcare, especially for populations with private insurance.&ensp;

 Department of Internal Medicine, Divison of Primary Care, Tufts Medical Center (SAR), Boston, MA; Center for Value-Based Care Research, Medicine Institute, Cleveland Clinic (MBR), Cleveland, OH; Department of Internal Medicine, Gallup Indian Medical Center (BJ), Springfield, Gallup, NM; Department of Internal Medicine, Berkshire Medical Center (MAM), Pittsfield, MA; Cigna HealthCare (JF), Bloomfield, CT; Department of Emergency Medicine, San Jose Regional Medical Center (JM), San Jose, CA; Center for Quality of Care Research, Department of Medicine, Tufts University School of Medicine (SLG, TL), Boston, MA; Baystate-University of Massachusetts Medical School (TL), Springfield, MA.

 Dr Lagu is supported by the National Heart, Lung and Blood Institute of the National Institutes of Health under Award Number K01HL114745.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

In a survey of patients and visitors to a large academic medical center, middle-income respondents with private insurance reported more cost-related delays in care than those with public insurance.

The Association Between Insurance Type and Cost-Related Delay in Care: A Survey

To evaluate the effectiveness of Buena Salud, a multidisciplinary enhanced primary care program for Medicaid Managed Care patients at a community health center serving a low-income Hispanic community.

Controlled before-and-after observational study. 

We extracted data from the electronic health record for patients aged 18 to 64 years with a) type 2 diabetes (T2D) enrolled in the Buena Salud program between August 2011 and January 2012 and b) randomly selected control patients with T2D who had been seen at the study health center during the same time frame. Outcomes included process measures (eg, number of glycosylated hemoglobin measures in a year), target lab and blood pressure values, and utilization measures (eg, emergency department visits). Demographics and other potential confounders were also extracted. We used a difference-in-differences (DID) analysis to estimate the effect of the intervention.

A total of 72 Buena Salud patients with T2D and 247 control patients with T2D were included in the analysis. The Buena Salud group had a greater increase in the percentage of patients with guideline-concordant measurement of microalbumin/creatinine (DID = 22.2%; 

 = .008), a trend toward fewer hospitalizations than controls, and a mean rise in diastolic blood pressure. We did not find differences in other outcome or utilization measures.

A recently implemented enhanced primary care program had minimal impact on T2D process, outcome, and utilization measures for patients in this study. However, there were some promising trends, and it is possible that the intervention may demonstrate more of an effect as the program matures.

As risk for population health is increasingly being shared, insurance companies are exploring the use of interdisciplinary care teams in primary care to improve chronic disease and population health management. In this study, we evaluated the outcomes of a new insurance company—sponsored enhanced primary care program, Buena Salud, in a controlled before-and-after study. Buena Salud targeted Hispanic patients with diabetes at a community center serving a mostly low-income population. We found limited effects on diabetes outcomes, including process, utilization, and patient outcome measures, in the program’s first year of existence. 

		Implementation studies of team-based primary care programs may enhance program effectiveness outside of an experimental setting. 

		Policy makers may want to allow more time for programs implemented in a natural setting to mature before determining effectiveness.

		Policies that address the social determinants of health may be necessary for enhanced primary care programs to have sustained substantial impact.

Case management, peer health coaching, and other team-based systems of care, such as the patient-centered medical home, have been implemented throughout the United States in an effort to improve care coordination and patient outcomes in the ambulatory setting.

 These types of programs are often created and implemented by healthcare delivery systems, but insurance companies have played an increasing role in developing and funding strategies to improve chronic disease management as well.

 The term "enhanced primary care” refers to a team-based care model that aims to improve care processes and outcomes.

 This multi-disciplinary team model includes the use of clinical tools such as practice guidelines, patient monitoring and tracking systems, and measures of resource use. 

Team-based care models often focus on management of chronic diseases, such as type 2 diabetes (T2D), because of the higher morbidity, mortality, and costs associated with chronic disease.

 T2D has proven particularly challenging to manage in the primary care setting, with less than 20% of patients with T2D achieving targets for glycated hemoglobin (A1C), low-density lipoprotein cholesterol (LDL-C), and blood pressure.

 Although team-based care coordination programs for patients with T2D have shown promise,

 disease management remains suboptimal, particularly for vulnerable populations.

Racial, ethnic, and economic disparities in T2D prevalence, care, and outcomes in the United States cause a disproportionate burden of disease in vulnerable populations.

 Arguably, patients with the highest risk for poor outcomes may benefit most from the additional resources provided by an enhanced primary care team. In this study, we aimed to determine the impact of a newly implemented insurance company—sponsored team-based enhanced primary care program (Buena Salud) on process measures, patient outcomes, and healthcare utilization for a low-income racial and ethnic minority population of patients with T2D. 

We conducted a controlled before-and-after study to assess the effect of the Buena Salud program on T2D process, outcome, and utilization measures. Although the Buena Salud team provided support for patients with other chronic diseases and promoted preventive care, this analysis is limited to patients with T2D.

Buena Salud is a bilingual enhanced primary care program for Medicaid Managed Care patients. The program was financed by the Health New England insurance company and implemented by Brightwood Health Center (BHC), an urban community health center with a largely Hispanic population (88%) insured primarily by either Medicaid (59%) or Medicare (28%). More than 50% of BHC patients prefer Spanish as their spoken language, and 17% of adult BHC patients have T2D. Patients were generally enrolled in Buena Salud through a referral from their primary care provider, but they also could have been enrolled through a periodic auto-enrollment process or through self-referral. 

The Buena Salud enhanced primary care team consisted of 2 registered nurse care managers, 2 medical assistants trained as community health workers, and a caseworker. The care team was unusual compared with many managed care teams in that each team member was bilingual (Spanish/English) and from the same racial/ethnic group as the majority of BHC patients. The Buena Salud intervention was based on the Chronic Care Model, which focuses on 6 areas that can optimize chronic disease care: self-management support, healthcare delivery system design, decision support, use of clinical information systems, organization of healthcare, and community support.

The Buena Salud team offered patients education and coaching for disease self-management, provided additional contact with patients in the clinic and at home, interfaced with diabetes specialists for decision support, used electronic health registries to identify patients in need of care and services, and utilized linkage to community-based support services (

A total of 450 patients were enrolled in the Buena Salud program between August 2011 and January 2012. Each Buena Salud nurse was expected to actively manage up to 50 of these patients at any given time, not all of whom had T2D. Care intensity varied depending on individual need, but was not formally documented by the team. 

We reviewed eligible patients’ electronic health records (EHRs) to extract data pertinent to the study aims. Eligibility was defined as follows: 1) all BHC patients aged 18 to 64 years with T2D who were newly enrolled in the Buena Salud program between August 1, 2011, and January 31, 2012 (intervention); and 2) all BHC patients aged 18 to 64 years who had T2D, had been seen for a clinical encounter at BHC between August 1, 2011, and January 31, 2012, and were not enrolled in Buena Salud (potential controls). All intervention and control patients were cared for in the same clinical setting. From the list of potential controls, we randomly selected 3 patients with T2D who had been seen in the clinic in the same month that a Buena Salud patient had been enrolled. We selected controls in this manner rather than matching on variables such as age, race/ethnicity, or gender for 2 reasons: 1) the population attending the clinic is relatively homogeneous for these measurable variables and 2) selecting patients seen in a similar time frame reduces the potential for differences in unmeasured confounders related to temporal changes in practice at the health center. Buena Salud patients and controls also must have had at least 1 visit to BHC in the 12 months prior to the enrollment/index visit so that baseline data could be extracted. Because there were fewer patients with T2D enrolled in Buena Salud than anticipated, we had slightly higher than a 3:1 ratio. This study was approved by the Baystate Institutional Review Board, which waived informed consent.

We identified target clinical outcomes, care processes, and healthcare utilization measures for patients with T2D using the national American Diabetes Association diabetes care guidelines that were in place in 2011 when the study was designed and systematic reviews of studies that included diabetes process and outcome measures.

 Clinical outcome measures included values for A1C, LDL-C, systolic blood pressure, and diastolic blood pressure (DBP). Care processes included the number of times A1C, microalbumin/creatinine ratio, and lipids were measured. Utilization measures included emergency department (ED) visits and unplanned hospitalizations, defined as any hospitalization other than for a nonemergent procedure (

). Baseline data were collected from the year prior to enrollment/index visit, beginning with the first day of the month in which they were enrolled, and the intervention period was defined as a window ranging from 12 to 15 months following the enrollment/index month. Timing of this window varied slightly for different outcomes in order to account for the earliest time after enrollment that the intervention would have been expected to affect the outcome measured and to provide grace periods for guidelines requiring a certain frequency of a measure (Figure). The overall duration of follow-up was chosen so that we could capture the maximum number of Buena Salud patients in the study. In addition to T2D process and outcomes data, we also extracted demographic information and data regarding potential confounders (eg, comorbidities and the number of years a patient received care at BHC) (

We first oriented data extractors to the study’s data dictionary and extraction protocol, which included where to locate pertinent data in the EHR. After establishing baseline consistency using standardized extraction forms, 2 extractors also independently reviewed 20 randomly selected EHRs to assess inter-extractor consistency at 6 and 18 months into the course of data extraction to test whether consistency was maintained. Study data were collected and managed using REDCap electronic data capture tools.

Participant characteristics are presented as means and standard deviations (SDs) for continuous variables and as frequencies and percentages for categorical variables. To estimate differences between the groups studied, we used a difference-in-differences (DID) approach (the difference between the pre-post change in the Buena Salud group compared with the control group). Study outcomes were modeled using generalized estimating equations with exchangeable correlations and robust standard errors (clustering on patient). Continuous outcomes were modeled using the identity link, Gaussian family binary outcomes were modeled using the logit link and binomial family, and count outcomes were modeled using the log link and negative binomial family. Models were estimated with main effects for the intervention group and time period, with an interaction term between these 2 representing the DID. Because Buena Salud participants were frequency-matched to controls based on enrollment month, we used enrollment month as an indicator variable in the model. This term was not significant in the models, so it was removed. 

Predicted outcomes and 95% confidence intervals (CIs) are presented in their original metrics (eg, mm/Hg for blood pressure) using Stata’s “-margins-” postestimation command. Statistical significance was set at an alpha of <0.05. Multivariable models included potential confounders (eg, demographic data, comorbidities, and number of years receiving care at BHC). Using Wald tests, models were reduced to include variables that were significant at the <0.05 level. To control for possible residual confounding and for face validity, we retained age, mental health, and substance use in all models. Original power calculations estimated that if at least 100 Buena Salud patients and 175 controls were included, this sample size would provide >85% power to detect a medium effect size (Cohen’s d = 0.40) at an alpha of <0.05. Clinically, this would be the equivalent of a DID estimate for A1C of 0.75, assuming a pooled SD of change of 1.70. Our achieved sample size was less than the estimated 100, but there was still 85% power to detect the same effect size. The analysis was conducted using Stata version 13.1 (StataCorp LP, College Station, Texas). 

A total of 319 patients with T2D were included in the study: 72 in the Buena Salud (intervention) group and 247 in the control group. The median age was 53 years (interquartile range, 45-59 years), 63.6% were female, and Spanish was the preferred language for 57.7% (Table 1). Most (estimated 90%) of the Buena Salud patients were referred to the program by a healthcare provider, with fewer (estimated 10%) enrolled via auto-enrollment or self-referral. Baseline differences between the groups included the following: control group patients were older (aged 54 vs 50 years), more likely to be infected with HIV (11.3% vs 2.8%), and less likely to have been diagnosed with anxiety or depression (49.8% vs 73.6%) (Table 1). Baseline A1C was also lower in the control group (7.8%; SD = 2.1) compared with the Buena Salud group (8.1%; SD = 2.2), and the baseline number of ED visits per person per year for those with any visit was lower in the control group (2.1 per year; SD = 3.5) compared with Buena Salud (3.5 per year; SD = 3.7). Baseline unplanned hospitalizations differed, with 17.8% of controls and 26.4% of Buena Salud participants having had at least 1 hospitalization in the year preceding enrollment. All other variables were similar at baseline. Extractor agreement was greater than 90%.

There was no difference in the change in A1C values between intervention and control patients in either unadjusted or adjusted models (absolute DID = 0.38; 95% CI, —0.13 to 0.88; 

). The difference in the change in the percent of patients achieving the target A1C was —0.9% (95% CI, –10.4% to 8.6%; 

 = .85) in unadjusted models and —1.4% (95% CI, –10.8% to 8.1%; 

With the exception of DBP, we found no differences in the change in hemodynamic or lipid profiles between the control and Buena Salud groups in unadjusted or adjusted models (Table 2). For DBP, there was a significant difference of 2.5 mm/Hg (95% CI, 0.8-4.3; 

 = .004) in both the adjusted and unadjusted models. This reflected a rise in mean DBP for the Buena Salud group and a fall for controls (Table 2). 

We found that the percentage of Buena Salud patients having A1C measures did not change during the study period and that although the percent of controls with the recommended number of measures dropped, the change between the 2 groups was not statistically significant (

). Similarly, Buena Salud saw a 4.2% increase in patients with guideline concordant LDL-C measures while controls dropped by 5.7%, for a DID of 9.8%, but this was also not a significant change (Table 3). There was a significant difference in the percent of patients with the recommended number of microalbumin/creatinine ratio measures: the Buena Salud group increased by 25% (95% CI, 11.4%-38.8%) compared with a 2.8% increase (95% CI, —4.6% to 10.2%) among controls (

 <.01). Change in the annual rate of ED visits did not differ between groups, but unplanned hospitalization rates decreased by 2.8% (95% CI, —13.7% to 8.1%) in the Buena Salud group and increased by 8.9% (95% CI, 2.9%-15.0%) among controls, resulting in a DID of 11.7% (

 = .06). The difference remained the same in adjusted models, but the 

In this controlled before-and-after study, we found that a team-based enhanced primary care program, Buena Salud, did not appreciably improve T2D process, outcome, or utilization measures for low-income Hispanic patients during the program’s first 15 months of existence compared with patients with T2D who did not participate in the program. Positive effects included a greater increase in the percent of patients with the appropriate number of measurements of microalbumin/creatinine ratios. There was also a trend toward fewer unplanned hospitalizations for Buena Salud patients compared with controls.

Diabetes affects nearly 10% of the US population and generated $245 billion in healthcare costs in 2012, a 41% increase from 2007.

 Numerous studies have tested innovative approaches to improving care for the patients from populations with the worst T2D outcomes. For example, investigators assessed the effectiveness of a computer-based support system in the context of primary care team-based management of T2D in a controlled natural experiment. Similar to the current study, which also could be categorized as a natural experiment, the investigators found improvement in process measures, such as rates of microalbumin/creatinine and A1C testing in the intervention group (n = 435) compared with controls (n = 435) after 12 months, but a limited effect on patient outcomes or healthcare costs.

In another study conducted with 165 Mexican American patients in rural Texas, the investigators tested whether the addition of a nurse case manager to a diabetes education and self-management program improved patient outcomes by addressing sociocultural barriers to accessing the successful self-management program.

 The study used a pre-post controlled design, similar to the current study except that it was a prospective cohort. The outcomes included changes in A1C, fasting blood sugar, lipids, blood pressure, diabetes-related knowledge, health behaviors, and body mass index over a 6-month time period. The study found no difference in changes in outcome measures between groups. 

An insurance company—sponsored enhanced primary care program had little effect on selected outcomes for low-income patients with diabetes. 

Sarah L. Goff, MD

Articles

Effectiveness of Enhanced Primary Care on Preventive Health Services

The Association Between Insurance Type and Cost-Related Delay in Care: A Survey

Effects of an Enhanced Primary Care Program on Diabetes Outcomes