working with bilingual community health worker promotoras ... · a recent study among heart failure...

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Working with Bilingual Community Health Worker Promotoras to Improve Depression and

Self-Care among Latino Patients with Long-Term Health Problems

Kathleen Ell, DSWa ,María P. Aranda, PhDa,b ,Shinyi Wu, PhDa,b,c ,Hyunsung Oh, PhDd ,Pey‐Jiuan

Lee, MSa ,Jeffrey Guterman, MD, MSe,f

a USC Suzanne Dworak‐Peck School of Social Work, University of Southern California, Los Angeles, California b USC Roybal Institute on Aging, University of Southern California, Los Angeles, California c Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California d School of Social Work, Arizona State University, Tempe, Arizona e David Geffen School of Medicine at University of California Los Angeles, California f Los Angeles County Department of Health Services, Research and Innovation, Los Angeles, California

Original Title: A Helping Hand to Activate Patient-Centered Depression Care among Low-Income

Patients (AHH). PCORI ID: AD‐1304‐7364

ClinicalTrials.gov: NCT02147522

HSRproj ID: 20143390

_______________________________

To cite this document, please use: Ell K, Aranda M, Wu S., et al. 2018. Working with Bilingual Community Health Worker Promotoras to Improve Depression and Self-Care among Latino Patients with Long-Term Health Problems Washington, DC: Patient‐Centered Outcomes Research Institute (PCORI).

https://doi.org/10.25302/3.2018.AD.13047364

A Helping Hand—Integrating Promotoras Into LAC-DHS Clinics 2

Table of Contents

ABSTRACT .......................................................................................................................................... 3

BACKGROUND ................................................................................................................................... 5

Participation of patients and other stakeholders in the design and conduct of research ....................... 8

METHODS .......................................................................................................................................... 9

Study design .............................................................................................................................................. 9 Study setting ........................................................................................................................................... 10 Subject recruitment ................................................................................................................................ 11 Intervention ............................................................................................................................................ 13 Promotora training and care- assisted management ............................................................................. 15 Control— – PCMH usual care (UC) .......................................................................................................... 18 Data collection ....................................................................................................................................... 18 Power analysis ......................................................................................................................................... 19 Data analysis ........................................................................................................................................... 20

RESULTS .......................................................................................................................................... 22

Sample characteristics ............................................................................................................................ 22 Intervention service delivery ................................................................................................................. 27 Study attrition ........................................................................................................................................ 30 Primary outcomes on depression symptom improvement and disease management .......................... 30 Secondary outcomes on health care utilization and quality of life Results ............................................ 34 Adverse events ....................................................................................................................................... 42 Qualitative interviews ............................................................................................................................. 42 Qualitative assessments of patients ...................................................................................................... 42 Qualitative assessments of promotoras ................................................................................................ 43 Qualitative interview with LAC-DHS medical providers ......................................................................... 43

DISCUSSION ........................................................................................................................ 47

Decisional context .................................................................................................................................. 47 The study results in context ................................................................................................................... 47 Implementation of study results ............................................................................................................. 48 Generalizability ...................................................................................................................................... 49 Subpopulations ....................................................................................................................................... 49 Study limitations .................................................................................................................................... 49 Future research ...................................................................................................................................... 50

CONCLUSION ....................................................................................................................... 50

REFERENCES ........................................................................................................................ 51

ACKNOWLEDGEMENT .......................................................................................................... 59


Abstract Background: Depression has negative effects on patient self-care and social stress

management. The negative effects of depression disproportionately affect low-income Latino

patients with chronic medical illness. Objectives: To evaluate the effectiveness of A Helping

Hand (AHH; Programa Mano Amiga in Primary Care) for patients with depressive symptoms and

comorbid medical illness. Methods: Patients with significant depressive symptoms (9-item

Patient Health Questionnaire score ≥ 10) and coexisting diabetes or heart disease were

randomized to AHH or usual care (UC) in 3 Los Angeles County Department of Health Services

(LAC-DHS) safety-net clinics that were implementing patient-centered medical home (PCMH)

models. The AHH intervention supported patients, families, and care providers by facilitating

self-care management skills and activating patient communication with clinic medical providers.

Community based, bilingual promotoras delivered the intervention in 6 weekly in-person or

telephone sessions, followed by 3 monthly booster sessions. From April 2014 to May 2015, we

screened 1957 and enrolled 348 depressed patients, of whom 296 (85%) had diabetes, 14 (4%)

had heart disease, and 38 (11%) had both diseases. All participants received care management

materials and community resource information. An interviewer blind to intervention

assignment assessed outcomes at 6 and 12 months. Baseline and outcome data include

depression, mental health assessments, treatment receipt, comorbid illness self-care, social

relationships, and environmental stressor assessments. Results: Study participants were

predominantly female (85%), Latino (99%), and born outside of the United States (91%). Overall

study retention rate was 70% (121 AHH and 121 UC). Baseline characteristics did not vary

significantly between retained and attrition groups. Half of AHH patients received 4 or more

promotora sessions. Promotoras made 12 referrals to LAC-DHS providers and 154 referrals to

community resources (most frequently requested community services:

community/senior/wellness center, 88 occurrences; transportation, 33; food bank, 25). During

the trial period, LAC-DHS activated health care improvements, including adding community

health workers into UC clinics. Depression outcomes did not vary significantly between

intervention and usual care groups (UC as the reference group; at 6 months: mean difference


0.01, 95%CI –1.3 to 1.3; at 12 months: mean difference –1.1, 95%CI –2.5 to 0.2); however, we

found significant improvements in most assessed physical and mental health outcomes for each

study group. Conclusions: No significant differences existed in primary depression outcomes

between the AHH intervention and the PCMH usual care study groups. Limitations and

subpopulation considerations: The challenges were to maximize intervention attendance and

minimize study attrition given the high representation of immigrant, Spanish-speaking, safety-

net population in the sample. The effects of the intervention were confounded by major quality

improvement initiatives in the participating clinics. Future work is needed to provide a more

definitive test of the AHH promotora model, while addressing these potential confounders.


1. Background Activated in 2014, the Affordable Care Act (ACA) health insurance mandate included an

aim to significantly reduce racial and ethnic disparities.1 Unfortunately, there is a concern that

Latinos and African Americans will continue to have problems accessing and using high-quality

health care, especially in states that are not expanding Medicaid eligibility as provided by the

ACA.2-4 In the United States, public health safety-net organizations are facilitating integrated

behavioral health services through biopsychosocial team preferences, language proficiency, and

self-care skills. It is noteworthy that California is expanding community health centers and

promoting patient-centered medical home (PCMH) models.

Depression is a common mental health disorder among low-income patients; depression

with concurrent comorbid chronic illnesses can increase patient depression relapse and

recurrence, morbidity, and mortality. At the same time, depression can negatively impact

patient self-care. Difficulty in managing concurrent comorbid illness can also trigger

depression.5,6 For example, low-income patients with diabetes are at high risk of clinically

significant depression over time, and depression can negatively affect both depression and

diabetes self-care management.5,7-9 A recent study among heart failure patients found that

depression independently predicted increased use of health care resources and mortality, and

suggested improved management of depression may improve outcomes.10-14 However, among

minority heart failure patients, depression is often persistent and severe, whereas perceived

emotional and informational support is associated with better self-care maintenance and self-

care management.15 Depression and anxiety have been related to an increased risk of mortality

in coronary heart disease patients.16

Integrated biopsychosocial team care is increasingly recognized as essential for patients

with depression and other chronic illnesses.17,18 Unfortunately, depression care that is tailored

to low-income, ethnically diverse patient populations is not readily available.19 Safety-net

patients with major depression plus a concurrent chronic illness face significant barriers to

patient depression care receipt, motivation, skills, and confidence that equip patients to

become actively engaged in their health care.20 Patients with poorer health literacy, lower

levels of social support, and more severe depression are also more likely to have poorer

medical illness outcomes. Concurrent self-care challenges include (1) managing overlapping


concurrent symptoms (e.g., depressed mood, pain, anxiety, fatigue, adverse reactions to

medications, overall medication management); (2) making daily decisions that affect overall

health, including potential negative effects on concurrent illness treatment management and

adherence; (3) addressing sociocultural and economic influences, such as day-to-day coping

demands,21 ongoing or intermittent social relationship distress or abuse,22-24 and economic

stress; and (4) managing family and caregiver communication regarding depression and chronic

disease symptoms management, depression treatment preferences and stigma concerns,

depression potential negative effects on comorbid concurrent chronic illness treatment uptake,

and dietary and exercise management. Disparities in patient-centered care management are

troubling given evidence that depression care for low-income, minority patients is effective in

reducing depression symptoms and increasing self-care adherence and depression treatment

retention, including among those with concurrent physical illness.5,6,25-30

Safety-net populations are also likely to encounter significant self-care management

stressors triggered by patient difficulties in communicating with primary care providers,27

navigating multiple specialist care providers, managing uncoordinated treatment plans, and

navigating existing community organization resources.28 At the same time, time-pressured

safety-net medical providers are responsible for synthesizing multiple and complex health-

related information, managing prescriptions, and communicating with multiple providers,

including specialists and hospital/ER providers. Primary care providers often find that engaging

patients with major depression is a significant challenge and, not surprisingly, is even more

difficult when the depression is accompanied by a concurrent chronic illness, because

engagement requires conducting initial and follow-up routine depression assessment and

management over time.29,30

Biopsychosocial care models include medical, psychological, and social emotional stress,

such as depression, whereas the social component investigates how different social factors

such as socioeconomic status, culture, poverty, technology, and religion can influence health

and depression.31 In a philosophical sense, the biopsychosocial model states that the workings

of the body can affect the mind, and the workings of the mind can affect the body.4 This means

both a direct interaction between mind and body as well as indirect effects through


intermediate factors. Health is best understood in terms of a combination of factors rather than

solely in biological terms. This is in contrast to the biomedical model of medicine that suggests

every disease process can be explained in terms of an underlying deviation from normal

function, such as a virus, a gene or developmental abnormality, or an injury.

Low-income patient self-care is also affected by culture, literacy, and language and

financial barriers that often exceed provider skills in patient communication.32 Among Latinos (a

significant majority in Los Angeles County Department of Health Services [LAC-DHS] clinics),

group orientation/harmony in families can encourage or deter depression or physical illness

care.33,34 In Latino culture, depression treatment is more effective if it aligns with contextual

stress-related needs and cultural values and if it considers preferences for counseling over

depression medication.17 The project A Helping Hand (AHH) assessed the comparative

effectiveness of 2 diverse multiple-team providers: AHH integrated thoroughly trained English–

Spanish bilingual promotoras into the standard Department of Health Services (DHS)-PCMH

team of physician, nurse, and medical assistant. We evaluated the effects of promotora-

mediated patient assistance in reducing patient depression symptoms, activating overall health

management over time, reducing depression and comorbid illness treatment barriers, and

optimizing patient care uptake and satisfaction in low-income Latinos. This study is the first to

incorporate promotoras in a public health safety-net care system to reduce racial and ethnic

disparities in depression and self-care management among Spanish-speaking patients with

diabetes and/or heart disease.

Promotora is a commonly used Spanish term for what is referred to in English as a

community health worker (CHW), navigator, outreach worker, lay health advisor, patient

advocate, etc. His or her expertise is commonly used in health care, although not exclusively, as

a promotora can be deployed to other workforce areas—e.g., housing, education, social

services, environmental advocacy. Like other CHWs, promotoras are commonly deployed in

areas with high representation of limited-English speakers, in low-income and racially and

ethnically diverse communities, in rural areas, in communities with shortages of health

providers; in places with migrant groups, and in communities regarded as “hard-to-reach” due

to public distrust of formal health care providers.35,36 Thus, the integration of promotoras has


strong appeal across several types of target areas and communities. Implementation of

promotoras in health care presents the same funding issues that any new service or program

encounters. Funding for promotoras beyond grant-funded research is an ongoing challenge and

the most frequently cited barrier to sustaining promotora-led programs.37 Promotoras and

CHWs are prepared to engage patients and enhance their health literacy, address a wide range

of community health issues, and provide advocacy and leadership development; however,

significant DHS safety-net system challenges exist, including costs related to community health

planning, management, and policies that include developing sustainable strategies (e.g., they

have recently activated a focus on preventive care). DHS PCMH is a path toward health equity,

as it provides care that does not vary in quality due to patient characteristics—i.e., gender,

ethnicity, socioeconomic status, and geographic location—and that focuses on community

engagement. The Los Angeles County DHS PCMH model (LAC-DHS-PCMH) continues to activate

and encourage multiple practice and research opportunities. These opportunities are aimed at

reducing fragmented care via bio-psychosocial health care providers, patients, and family

member stakeholders, and are subsequently aiming to implement and disseminate PCMH

effective care management throughout LAC-DHS care network. In the United States, patient-

centered care is emerging as a key element for improving the quality of health care, as it

strengthens the patient–clinician relationship via coordinated care.

2. Participation of patients and other stakeholders in the design and conduct of research

The University of Southern California and the Los Angeles County Department of Health

Services (USC-DHS) research teams have previously conducted comparative effectiveness

randomized and quasi-experimental clinical trials as well as qualitative studies on major

depression. We conducted A Helping Hand (AHH) in collaboration with the DHS Ambulatory

Care Network, the second largest safety-net care system in the United States. AHH is based on

previous USC-DHS depression studies among DHS patients with diabetes, heart failure, and

coronary heart disease.5,6,25,27-29

Engaged community stakeholders included nonstudy patients from previous DHS trials

who were similar to the current study patients; California community organization Visión y


Compromiso (VyC) promotoras and leadership; and DHS Ambulatory Clinics providers and

administrative staff, including physicians, nurses, and clinic directors. Initially, the research

investigators met with nonstudy patients (who were recruited from the same subject pool via

telephone), provided promotora training, and held meetings with the clinic directors and their

staff.

As a result of patient and stakeholder participation, we developed a community

resources list of available services, programs, and products. All participants were

socioeconomically disadvantaged, and many were facing significant financial strain,

immigration issues, and daily life pressures. Other study design changes included augmenting

the availability of completing intervention and interview sessions in the home rather than on

site in the clinics. In addition, this participation informed the qualitative interview guide

questions regarding quality of interactions with clinic staff and providers and degree to which

matched-provider ethnicity and language availability was important to the patients.

The study population consisted of low-income, predominantly Latino patients (n = 348)

with depression and concurrent diabetes and/or heart disease from 3 DHS safety-net

community clinics. After completion of the baseline assessment, study participants were

randomly assigned into AHH enhanced innovative promotora patient depression and care self-

management support (AHH) or standard clinic depression treatment provided by the DHS

PCMH multiple provider primary care model clinic team usual care (UC). Thus, the AHH trial

compared 2 diverse multiple-team providers: AHH, which added a thoroughly trained

promotora provider team member, to the standard DHS clinic usual care team of providers with

its PCMH physician, nurse, and medical assistant.

3. Methods 3.1. Study design

Be based this randomized controlled trial (A Helping Hand/Programa Mano Amiga in

Primary Care) on the assumption that personal, socioeconomic, cultural, and health systems

processes are key elements in predisposing, reinforcing, and influencing patient-centered

depression and chronic illness self-care management. Patient self-care management and


treatment adherence are influenced by individual choice, cultural beliefs, stigmas, practical

barriers, and physical health in combination with contextual supports/barriers (e.g., missed

work time, transportation to clinic costs, difficulty in communicating with providers who do not

speak Spanish, low health literacy). Generally, low-income patients have fewer resources to

support treatment participation.38 Culturally normative patterns of physical and psychological

symptom definition, response, and help-seeking are critical antecedents of self-management

behavior and care utilization, but they are not independent of environmental and care

provider/system factors. Evidence exists that patient self-efficacy and provider–patient

communication influences self-care management and treatment adherence. Culturally

competent/tailored self-management education may enhance treatment adherence and care

management.39 Thus, the study provided to both AHH and UC participants and their families

culturally adapted depression educational booklets, including a photo storybook fotonovela40

and brochures on specific chronic illness treatments and self-care. All educational materials

were available in English and in Spanish.

For the primary outcomes, we hypothesized reduced depression symptoms at 6 and 12 months

among AHH versus UC patients, as well as improved concurrent illness self-care management.

The secondary research question addressed medical care utilization: whether AHH reduced

hospitalizations and ER visits and improved patient care satisfaction and quality of life. We also

conducted qualitative assessments of patients, promotoras, DHS medical providers, and clinic

organizational leaders.

3.2. Study setting

The study was approved by the University of Southern California-Health Sciences

Institutional Review Board and conducted in collaboration with LAC-DHS at 3 community clinics

within similar geographic neighborhoods and with similar demographics (i.e., race/ethnicity

predominantly Latino with more than 50% monolingual Spanish speakers, 90% younger than

age 65, low household income with a significant number living below the poverty rate, and 30%

to 35% receiving Medicaid as health insurance). Inclusion criteria were aged 18 years and older,

English or Spanish speaking, can communicate by phone, screened with clinically significant


depression (i.e., having at least 1 cardinal symptom of depressed mood or loss of interest more

than half the days to nearly every day plus the 9-item Patient Health Questionnaire (PHQ-9)

score of 10 or more), and concurrent diabetes and/or heart disease (coronary heart disease or

heart failure). Exclusion criteria were current suicidal ideation; a score of 2 or greater on the

CAGE alcohol assessment,41 recent use of lithium or antipsychotic medication, and cognitive

impairment precluding informed consent.

3.3. Subject recruitment

Bilingual study recruiters trained in cultural competence described the study to eligible

patients and obtained written informed consent at designated private areas in the recruitment

clinics near the waiting room. From April 2014 to May 2015, 1957 patients were screened, 421

met study inclusion criteria, and 348 completed enrollment with study baseline assessment

(See CONSORT Figure 1). The study recruiters recorded depression, chronic illness status, study

enrollment, and baseline interview data via an iPad linked to the study’s secure online data

center. The study group assignment was randomized following completion of the baseline

interview. We used the common computer-assisted randomization to generate equal

probabilities of the study groups. For patients to feel included in the process of study group

assignment, we asked patients to pick a number from 1 to 4 that were internally coded as 2 UC

and 2 AHH in a random order. The coded group information was concealed from the

interviewer and participant. A new randomization of the groups was generated at each study

group inquiry. After the patient chose a number, the corresponding group information was

revealed and displayed on the iPad screen, and the patient was informed of his or her group

assignment.


Figure 1. Study CONSORT

170 (48.9%)PCMH Usual Care

178 (51.1%)AHH Intervention

1973 Patients Identified with Diabetes (95.5%)

and/or Heart Disease (4.5%)

16 Unscreened ─11 Language/Communication barrier

5 Refused screening

1957 Screened for Depression72.8% women, 27.2% men

421 Study Eligible349 women, 72 men 67 Excluded ─ Refused to participate

46 not interested6 refused to sign study consent4 no need help3 currently receiving depression care 3 no private phone3 planned to move out of area soon2 having other health condition

1518 Excluded ─ Not met inclusion criteria for major depressive disorder

354 (84.1% of eligible) Agreed to Participate and Provided Consent

298 (85.4%) women, 56 (77.8%) men

439 (22.4% of screened) Met Inclusion Criteria for Clinically

Significant Depression359 (25.2%) women, 80 (15%) men

18 Excluded ─ Met exclusion criteria2 Failed to pass alcohol screen

14 Other psychiatric condition2 Acute suicidality

348 Completed Enrollment with Baseline Assessment

85.3% with Diabetes3.7% with Heart Disease

10.9% with Diabetes and Heart Disease

6-Month Interview133 Completed

37 Lost to follow-up28 unlocatable

8 declined1 hospitalized


48 Lost to follow-up38 unlocatable

9 declined1 in nursing home


48 Lost to follow-up35 unlocatable11 declined

1 hospitalized1 deceased


56 Lost to follow-up45 unlocatable10 declined

1 in nursing home

Randomization


In view of known barriers to participation in clinical trials among low-income minority

populations, we consistently made the following efforts to facilitate recruitment and

acceptance of the intervention and to minimize study attrition: (1) Spanish-speaking promotora

staff and study and intervention materials in Spanish were adapted to varied literacy levels and

language idioms; (2) interventions were offered through in-person or telephone visits and

allowed for evening or weekend appointments; (3) outcome data were collected by telephone;

and (4) a $10 gift card incentive was given to each participant for each outcome interview

completion.

3.4. Intervention

Although recent studies have assessed and proposed diverse forms of self-management

in patients with chronic illness, few studies have facilitated depression care management

among patients with concurrent chronic illnesses. Based on the biopsychosocial care model,

multiple chronic illnesses management requires multiple and complex components, such as

patient–provider and provider–provider interactive management to facilitate patient self-

management. To improve patient-centered care, safety-net medical and behavioral health

providers must operate as multidisciplinary provider teams. Previous studies found that

racial/ethnic minorities with lower socioeconomic status were less likely to provide essential

information about progress of diseases and unusual symptom change during clinics visits.5,6,25

Failure to inform physicians might contribute to poorer patient health outcomes such as

diabetes complications and increased mortality among heart patients. Therefore, we developed

a culturally competent practice model utilizing bilingual promotoras in safety-net clinics

AHH intervention was contextually guided by the Chronic Care Model,42 in which

patient-centered needs, health care–provider delivery, and patient outcomes are seen as a

product of the interaction between the patient, provider, and 7 health care process

components: 1)delivery system design, 2) patient self-management support, 3) patient care

management preferences, 4) community navigation resource linkages 5) provider team and

decision support, 6) shared clinical information system, and 7) health system organization. DHS

patients have relatively high rates of depression (about 20%), concurrent chronic illnesses (up

to 80%), limited health literacy, and high rates of contextual sources of depression and overall


medical stressors. However, few clinical trials among diverse populations have simultaneously

examined racial/ethnic patient self-management care,30,32 health literacy limitations, patient

preferences and uptake plus the patient-provider care management relationship and

communication.43-46 In the study we followed a problem-solving framework using self-care

management of chronic care conditions as a specific skill-building focus. Thus, the framework

closely followed a key skill-building strategy involved in problem-solving treatment for primary

care, which was the rational problem-solving component.47-49 The AHH intervention provided

problem-solving modeling and opportunities for patients to use to activate self-care and

communication with their medical provider about treatment access, health care concerns, and

self-care management. AHH aimed to assess the effects of promotora-mediated patient

assistance in reducing patient depression, activating overall health management over time,

reducing depression and comorbid illness treatment barriers, and optimizing patient care

uptake and satisfaction.42


Figure 2. A Helping Hand (AHH)—Participant Flow

3.4.1. Promotora training and care-assisted management

The intervention protocol called for 6 sessions (on average about 45 minutes each) to be

provided during the active intervention phase, with 3 brief booster sessions (on average about

15 minutes via telephone; see Figure 2). The first session included a rapport-building

introduction followed by psychoeducational elements. Psychoeducation included the provision

and review of written materials on depression, diabetes, and/or cardiovascular disease tailored

to the participant’s individual presenting symptoms and provider recommendations. The first

session also included an introduction of problem solving, the rationale for the intervention,

expectations, and an interactive session to develop an action plan. All subsequent sessions


included evaluation of the action plan and development of a new plan for the subsequent

week. All sessions included a participant-identified pleasant activity to activate along with their

action plans. Community resource referrals were also provided when needed.

We recruited a total of 3 female bilingual AHH promotoras in close partnership with the

host promotora organization, Visión y Compromiso (VyC), a leading nonprofit organization

dedicated to promotora-assisted quality health care services.37, 50-58 The study promotoras

received an initial orientation from VyC. The VyC-sponsored training was meant to introduce

the role of the promotora from an empowerment perspective in order to value this role and its

contributions to the health of the community. In the initial training, promotoras engaged in

knowledge building, skill development, and consciousness raising based on popular education

for marginalized groups.59 Hiring eligibility criteria included the following: (1) at least a high

school education; (2) 1 year of prior promotora work experience in a health promotion

program; and (3) fluency in verbal and written English and Spanish.

Each promotora received group-administered (1) orientation and intensive training, (2)

booster training sessions, and (3) supervision throughout the active intervention study phase

from the second author (MPA), a licensed clinical social worker and national trainer in problem-

solving treatment for the bilingual/bicultural behavioral health workforce sector. Promotoras

were trained to engage patients at home or in the clinics and to enhance their health literacy,

address a wide range of community health issues, and provide advocacy and leadership

development.37, 50-55 Held over several days, the promotora training included intensive training

on patient engagement, the rationale for problem-solving therapy (PST) and its components,

study protocol and procedures, and interventionist self-care. Learning formats encompassed

the following: didactic lectures; role playing; videos; reading material; self-study; practice with

patients; and guest lectures. The same trainer provided booster trainings on an ongoing basis to

support any content or skill development needs.

Promotora supervision sessions were provided weekly or biweekly to offer booster

training, sessions on promotora self-care, and a review of participant caseloads and progress.

Ongoing in-person supervision sessions were dedicated to case presentations whereby the

promotoras presented information about each of their assigned patients, the process and


content of their interactions, integration of sociocultural strategies, and recommendations for

each patient’s upcoming session. Supervision sessions also involved electronic research records

which the promotoras documented verbatim for each participant’s problem list, action plan

and pleasant activity. Any corrections during the supervision sessions were reviewed in the

following session to evaluate changes in process or approach and need for additional training or

enhancements.

Guided by our earlier studies, we based promotora training on (1) introductory modules

that can be used across all patients (“getting to know one another”; introduction to depression

and chronic disease); (2) wellness modules based on overlapping symptom management (that

cut across diagnoses) such as dysphoric mood, pain, fatigue; (3) problem-solving and action-

planning modules that serve as core activation strategies for depression and multiple chronic

illness management and medical and community provider resource navigation issues; (4) a

personalized menu of modules based on concurrent chronic illness-specific symptoms or

consequences; and (5) training on research study protocols, human subjects protections, and

documentation. Written health education materials were distributed based on patient health

conditions and guideline-concordant national standards for illness self-management.

Promotoras scheduled to meet with patients over 6 visits to engage in 6 tasks:

engagement (i.e., initial rapport building); problem formulation (i.e., targeted problem list);

education (i.e., self-care management strategies and health information); action planning (i.e.,

developing action steps and implementation, community resource navigation, referrals to

providers); and evaluation (i.e., feedback). Sessions were administered primarily face-to-face in

the patient’s home or other preferred setting, which was aimed at fostering an engaged and

supportive relationship with patients, followed by 3 monthly booster sessions. At each

intervention session, promotoras documented patient progress in building problem-solving

skills, and, where needed, any medical care concerns and referrals to care provider and

community resources. Either during these sessions or by follow-up via telephone or email,

promotoras could inform the AHH promotora supervisor or the PCMH team about a patient’s

worsening symptoms, medication side effects, and provider access concerns.


All participants continued to have access to depression care services from their health

care providers. Because this was a real-world trial, the AHH intervention was not meant to

modify patients’ usual and typical receipt of care at the county clinics, which might include

psycho-pharmacology and specialized mental health services.

3.5. Control—PCMH usual care (UC)

Participants in the UC arm received DHS PCMH clinic team usual care from their

respective county health clinic providers. The DHS PCMH usual care multiple-providers care

team included its PCMH physician, nurse, and medical assistant. The PCMH model had available

DHS medical providers and social workers for depression care, who referred patients, when

indicated, to community mental health clinics. Problem-solving therapy was available in some

of the participating clinics. During the trial period, LAC-DHS activated health care

improvements, including adding community health workers (CHWs) into UC clinics. The CHW

had a similar role as that of the AHH promotora.

3.6. Data collection

Study recruiters collected study participants’ demographic data (i.e., age, ethnicity/race,

nativity, years in United States, marital status) at baseline. In addition, we collected patient

mental health and chronic illness history, socioeconomic stress, and health literacy assessments

at baseline before study group randomization. An independent study interviewer who was

blind to patient study groups conducted the 6- and 12-month follow-up interviews. Promotoras

logged their contacts with patients on service delivery and intervention session adherence, as

well as patient self-care concerns and referrals to community resources, if any.

We assessed the primary depression outcomes using the 9-item Patient Health

Questionnaire (PHQ-9) and the 20-item Symptom Checklist (SCL-20) depression scores. The

PHQ-9, which establishes provisional depressive disorder diagnosis as well as grades depressive

symptom severity, scores each of the 9 DSM-IV criteria as 0 (not at all) to 3 (nearly every day),

with possible scores ranging from 0 to 27. PHQ-9 scores of 5, 10, 15, and 20 represent the

thresholds for mild, moderate, moderately severe, and severe depression. We used a validated

Spanish version of the PHQ-9.60-62 We used the SCL-20 depression scale because it has been

shown to be sensitive to change over time in primary care trials of depression (Cronbach α =


0.91).63,64 The Self-efficacy for Managing Chronic Disease Scale is a 6-item self-reported

measure that assesses symptom control, role function, emotional functioning, and

communicating with physicians.65 The short form Patient Activation Measure is a 13-item

instrument that assesses patient self-reported knowledge, skills, and confidence for self-

management of one’s health or chronic condition.66,67

Secondary outcomes included the following: To capture the extent of health service

utilization, patients were asked to respond to questions about their outpatient (medical and

mental health), inpatient, and ER service utilization, including use outside of DHS as well as

depression care and treatment receipt. In addition to self-reported health care utilization, we

determined service utilization using data from DHS web registry outpatient, pharmacy, ER, and

hospitalization visits. We assessed patient satisfaction with care using a single-item ordinal

scale of 1 to 5 (indicating poor to excellent). We assessed patient use, acceptance, and

satisfaction with promotora assistance in the AHH group. To measure overall functioning and

quality of life, we calculated the MOS Short-Form Health Survey (SF-12)68 Physical and Mental

Component Summary norm-based scores standardized to the general US population with a

mean of 50 and a standard deviation (SD) of 10. The Sheehan Disability Scale assessed illness

interference that was derived as an average of 10-point Likert scales (10 indicating inability to

carry out any activity) in 3 domains: work, family life and home responsibilities, and social life.69

We also assessed anxiety using GAD-770; somatic symptoms using PHQ-1571; depression

remission using the Remission Evaluation and Mood Inventory Tool72; social and economic

stresses using selected items from the Hispanic Stress Inventory73 modules (e.g., financial,

employment, family, community violence worry); stigma using the Latino Scale for

Antidepressant Stigma74,75; chronic pain defined as having pain most of the time for 6 months

or longer in the past year; and pain impact items from the Brief Pain Inventory.76

3.7. Power analysis

Power calculation in the planning of the study to estimate sample sizes needed to

adequately evaluate the trial effects of AHH versus UC as a comparison condition was based on

our previous studies; in which small to medium effect sizes were detected for depression


outcomes in SCL-20 and PHQ-9 scores. Because the AHH system was a pioneering work and its

main effect would be investigated in this project, we assumed a small to moderate size of the

treatment effects in calculating statistical power. Given the health conditions of the study

population, we estimated an attrition rate of 10% at each follow-up phase or 20% over the 12-

month study duration, based on our previous studies on depression and diabetes or cancer DHS

populations. Power to evaluate program effects was determined with G*Power (V3.1)

software.77 We conducted all power calculations at 2-tailed alpha = .05. We aimed for power

= .80 or higher, and were guided by Cohen’s conventional standards for effect size (f index

measuring the ratio of population SDs in analysis of variance) with f = 0.1 for small effect size

and f = 0.25 for medium effect size. With the proposed sample size of 350 at baseline and 20%

overall attrition rate, the final analytical sample of 280 would have sufficient statistical power

to detect a small effect size around f = 0.14 of the AHH intervention.

3.8. Data analysis

We used simple descriptive statistics to summarize participants’ demographics and

baseline characteristics, with count and percentage presented for categorical variables and

mean and SD for continuous variables. To examine comparability of study groups and

investigate potential bias to group randomization, we conducted bivariate analyses to compare

demographics and clinical characteristics between intervention and usual care patients at

baseline, using a chi-square test for categorical data and t test for continuous data. To

investigate risk factors to study attrition, we compared baseline values between patients who

completed the 12-month end-of-trial assessment versus those who did not complete it.

To assess the intervention effect over the 12-month study duration, we conducted an

intention-to-treat analysis of repeated measures (baseline, 6-month and 12-month follow-ups)

for each dependent variable. We fitted mixed-effects regression models implemented in SAS

Proc Mixed procedures for continuous variables and mixed-effects logistic regression models

implemented in SAS Proc GLIMMIX procedures for dichotomous variables. We converted the

logit model-predicted outcomes through an inverse link into predicted probabilities for ease of

interpretation. The mixed-effects model allows time-dependent, within-cell covariates and


operates on incomplete (i.e., missing data) matrices. We accounted for missing values in the

mixed model, which uses a likelihood-based estimation procedure. This resulted in in nonbiased

estimates by imputation of missing responses based on the surrounding responses and the

modeled covariance structure. We specified the unstructured covariance in the mixed-effects

models to account for within-patient correlations of repeated observations over time, and we

examined fixed effects of time, study group, and their interactions. In addition, because of the

high attrition in the study population, we conducted a sensitivity analysis on outcomes using

the last-observation-carried-forward method. Because LAC-DHS activated health care

improvements, including adding community health workers (similar role as that of AHH

promotoras) into UC clinics during the trial period, we also examined outcomes over time for

each study group individually. We conducted all statistical analyses at 0.05 significance level (2-

tailed) using SAS software version 9.3 (Cary, NC: SAS Institute).

Promotoras provided timely documentation of each encounter with study participants

for research purposes and for participant caseload follow-up. The promotoras electronically

entered intervention problem-solving session notes. We coded and analyzed each data entry

using the summated content analysis method,78 which allows for identifying, quantifying, and

summating words or context from text to understand individuals’ responses to particular

questions or prompts based on a priori areas in the existing literature. A trained bilingual

qualitative interviewer reviewed all action plan entries and manually coded the responses,

which were then coded into larger categories or classifications. Based on content analysis, we

summarized the data extracted from the open-ended fields that reflected verbatim responses

of the following fields for each participant: (1) the problem (or situation) selected by the

participant to be the focus of the session and, as a result, the focus of the practice homework;

(2) the action plan to practice for the upcoming week; and (3) the pleasant activity to activate

for the upcoming week.


4. Results 4.1. Sample characteristics

Of 1973 target patients identified from April 2014 to May 2015, 1957 (99%) were

screened (96% diabetes, 3% heart disease, and 1% both illnesses) and 22% met criteria for

clinically significant depression (female 25% versus male 15%; chi-square = 22.96; p < .0001).

Excluding 10 women and 8 men who met exclusion criteria, a total of 354 patients agreed to

participate and provided study consent (enrollment rate = 84%; female 85% versus male 78%; p

= 0.11). Of them, 6 patients did not complete the baseline assessment. The recruitment yielded

an analytical study sample of N = 348, with 178 (51%) randomized to the AHH intervention

group and 170 (49%) to the PCMH usual care group (Figure 1).

Tables 1 to 3 present sample demographic and baseline characteristics in each study

group. At baseline, demographics and baseline values did not vary significantly between study

groups. Study participants were predominantly female (296; 85%), Latino (344; 99%), and born

outside of the United States (318; 91%), and were an average age of 56.7 years old (SD = 8.3).

Most of them were from Mexico (238; 68%), El Salvador (32; 9%), and Guatemala (25; 7%); had

been living in the United States for 10 years or more (93% of immigrants); preferred to speak

Spanish (315; 91%); and had not completed high school (261; 75%). More than 60% of patients

(225; 65%) were overweight with body mass index (BMI) 30 or greater, and more than 40% had

poor blood sugar levels (A1C more than 9%; see Table 1).

Table 1. Sample Demographic and Baseline Physical Characteristics

PCMH Usual Care, N = 170

AHH Intervention, N = 178

Female 144 (84.7) 152 (85.4)

Age, mean (SD) 56.5 (7.7) 56.8 (8.8)

Latino 168 (98.8) 176 (98.9)

Foreign born 152 (89.4) 166 (93.3) Living in the United States 10 years or longer 140 (92.1) 155 (93.4)

Primary language, Spanish 151 (88.8) 164 (92.1)

Education, years of schooling • < 6 105 (61.8) 111 (62.4) • 7-11 21 (12.4) 24 (13.5) • 12 (high school grad or equivalent) 17 (10.0) 16 (9.0) • > 12 27 (15.9) 27 (15.2)


BMI ≥ 30 114 (67.1) 111 (62.4)

A1C > 9, the latest test before study enrollmenta 60 (42.0) 62 (45.6)

Study inclusion diagnosis

• Diabetes only 143 (84.1) 154 (86.5) • Heart disease only 5 (2.9) 8 (4.5) • Both diseases 22 (12.9) 16 (9.0)

Depression score and severity at study enrollment

• 10-14 moderate 62 (36.5) 71 (39.9) • 15-19 moderately severe 73 (42.9) 74 (41.6) • 20+ severe 35 (20.6) 33 (18.5)

Self-rated health

• Poor 68 (40.0) 53 (29.8) • Fair 82 (48.2) 99 (55.6) • Good 19 (11.2) 26 (14.6) • Very good 1 (0.6) 0 (0)

Having chronic pain 79 (46.5) 83 (46.6)

Sheehan disability scale, Mean (SD) (possible range 0 to 10, higher = worse functioning)

5.5 (3.1) 5.3 (3.2)

Physical summary score, Mean (SD) (on a theoretical 0- to 100-point scale, higher = better health)

38.3 (11.1) 38.3 (10.5)

Data are frequency (%) unless otherwise specified. a Based on 143 UC and 136 AHH who had an A1C test. BMI= Body Mass Index


Regarding medical conditions, 297 (85%) had diabetes, 13 (4%) had heart disease, and 38 (11%)

reported both chronic illnesses. More than two-thirds of patients had other comorbid medical

conditions including arthritis, retinopathy, gastrointestinal problems, kidney disease, lung

disease, stroke, cancer, and urinary tract or prostate problems. Nearly half (162; 47%) of the

study participants had chronic pain, and 87% patients rated their health as fair (181; 52%) or

poor (121; 35%). The average Sheehan Disability Scale was 5.4 (SD = 3.2), and the physical

summary score was 38.3 (SD = 10.8) as assessed by the SF-12 health survey with score profiles

standardized to the general US population with a mean of 50 (SD = 10).

At baseline, depression severity assessed by the PHQ-9 was moderate (score 10–14) for

38% of the sample; 147 patients (42%) scored moderately severe (score 15–19), and 68 patients

(20%) scored severe depression (score 20 and higher). Self-report depression history indicated

that 147 patients (42%) had a history of major depressive disorder, and only 26% had been

prescribed antidepressant medication. More than half of the study population had a cut-point

of 10 or higher (moderate to severe) on GAD-7 anxiety (185; 53%) and PHQ-15 somatic

symptoms (216; 62%). This population’s average mental component summary of SF-12 was

30.9 (SD = 9.2) versus mean value 50 for the US general population. Participants’ psychometric

measures did not vary significantly between study groups at baseline (Table 2).


Table 2. Psychological Health at Baseline



Psychometric measures, mean (SD)

Depression score (Patient Health Questionnaire-9, possible

range 0-27)

16.0 (4.0) 15.8 (4.0)

Depression symptom scale (Symptom Checklist-20,

possible range, 0-4)

1.6 (0.7) 1.7 (0.6)

Depression remission assessment (Remission Evaluation

and Mood Inventory Tool, possible range 0-20, lower

score = better remission)

10.0 (3.8) 10.1 (3.7)

Anxiety score (Generalized Anxiety Disorder-7, possible

range 0-21)

10.1 (4.9) 10.3 (4.9)

Somatic symptom score (Patient Health Questionnaire-15,

PHQ-15, possible range 0-30) a

11.1 (4.8) 12.0 (5.1)

Mental summary score, mean (SD) (on a theoretical 0- to

100-point scale, higher = better health)

30.5 (9.2) 31.2 (9.2)

Patient self-report, N (%)

Dysthymia 113 (66.5) 104 (58.4)

History of diagnosed major depression 78 (45.9) 69 (38.8)

Already been prescribed antidepressant before study

enrollment

49 (28.8) 43 (24.2)

a One UC patient did not respond to this assessment.

Table 3 presents patients’ self-care management and social relationships at baseline

assessment. Again, we found no significant group difference. For the Self-Efficacy for Managing

Chronic Disease scale, on a scale of 1 to 10, group average was 6.2 in AHH versus 5.8 in PCMH

UC (p = 0.17). The group mean score was 3.3 in AHH versus 2.9 in PCMH UC (p = 0.23) for the

Latino Scale for Antidepressant Stigma, with a possible scale range of 0 to 14. The other

baseline measures were similar between the AHH and PCMH UC study groups. We transformed


the social support measure scored by the 8-item modified Medical Outcomes Study Social

Support Survey to a 0 to 100 scale, with higher scores indicating more support. The study group

had a mean score of 49.9 (SD = 33.7) in instrumental support, 54.7 (SD = 31.5) in emotional

support, and 52.3 (SD = 28.9) in overall social support. This study population had an average of

3.6 (SD = 2.0) stressors assessed from the 10-item Hispanic Stress Inventory. Most patients

(322; 93%) self-reported having financial problems, having difficulty in paying bills, or having no

money left over at the end of the month. Other summary stress domains were

work/employment (69%), marital/family conflicts (64%), and cultural conflicts and immigration

issues (26%). In summary, we found no significant group difference with respect to any

assessed variables at baseline.

Table 3. Care Management, Social Support, and Stress Assessment at Baseline



Mean (SD) Mean (SD) Self-care Self-efficacy for Managing Chronic Disease 5.8 (2.6) 6.2 (3.1) Latino Scale for Antidepressant Stigmaa 2.9 (3.1) 3.3 (3.7) Patient Activation Measure 75.0 (18.6) 74.8 (20.9)

Stages Believes active role important 82.0 (22.1) 82.0 (23.0) Confidence and knowledge to take action 77.7 (19.8) 77.0 (20.5) Taking actionb 67.6 (25.5) 68.4 (29.8) Staying the course under stressb 70.1 (30.0) 70.2 (30.3)

Social support

Medical Outcomes Social Support Survey, 8 items 52.6 (28.4) 52.0 (29.5) Subscales

Instrumental support 50.9 (33.6) 48.9 (33.8) Emotional support 54.3 (31.6) 55.1 (31.5)

Life stress

Number of stressors, 10-item Hispanic Stress Inventory 3.5 (1.9) 3.6 (2.0) Four summary domains N (%) N (%) Work and (un)employment 116 (68.2) 125 (70.2) Financial problem and distress 160 (94.1) 162 (91.0) Marital conflicts; family issues, problems, or burden 102 (60.0) 121 (68.0) Cultural conflicts and immigration issues 46 (27.1) 43 (24.2)

a Three UC patients did not complete this measure. b One UC patient did not complete this measure.


4.2. Intervention service delivery

Over the course of intervention—i.e., 6 weekly sessions followed by 3 boosters—113

(63%) patients started an initial session with a study promotora and 98 (55%) continued

participating in the AHH intervention; 89 (50%) received 4 or more sessions (Table 4). Of the 65

patients who did not begin the intervention, 32 declined (9 refused promotora sessions, 8 were

out of the state or country, 7 had no time, 2 had work conflicts, 2 were busy as a caregiver to a

family member, 2 were seeing another therapist, 1 preferred follow-up with the doctor, 1

claimed not depressed); 21 could not be contacted by phone (12 not locatable from no answers

and no return calls, 4 with wrong number, 3 phone out of service, 2 hung up the phone); and

another 12 passively declined (6 with broken session appointments; 5 failed to set up

appointment; 1 refused by daughter).

Table 4. Receipt of AHH Intervention (N = 178)

Number of Patients Number of Intervention Sessions Completed

0 1 2 to 3 4 and More Intervention status 65 (36.5%) 15 (8.4%) 9 (5.1%) 89 (50%)

Graduated 66 (37.1%) 66 (37.1%) Refused 39 (21.9%) 32 (18%) 4 (2.2%) 2 (1.1%) 1 (0.6%) Passive refusal 16 (9%) 12 (6.7%) 4 (2.2%) Loss to follow-up 57 (32%) 21 (11.8%) 7 (3.9%) 7 (3.9%) 22 (12.4%)


A total of 755 problem-solving sessions were provided throughout the active

intervention phase of the project. Our participants were actively engaged in their problem-

solving sessions, as evidenced by the extensive documentation of their problem-solving

components: problem formulation, action plans, and pleasant activities. Overall, we analyzed

1348 entries for problem formulation, 949 entries for action plans, and 753 entries for pleasant

activities using content analysis. The sessions were person centered, wherein each session was

tailored to the participant’s unique needs, preferences, and learning style. Thus, the presenting

problems covered a vast array of situations and problems that affected the participants’

psychological and physical health. These centered on medical and psychiatric functioning;

interpersonal conflicts, primarily with family; day-to-day problems such as financial woes; and

the navigation required to apply for—and receive—benefits and community resources, to name

the most frequently endorsed categories. Turning to the action plans, participants focused on

applying for benefits and services, followed by communicating their needs to their provider or

their health care team. In order to address their medical illnesses, they turned to following

medical instructions or advice, taking care of themselves through changing their nutrition or

diet, and increasing exercise or physical activity. They also addressed their low economic status

by seeking employment or making extra money on the side. Communicating their needs to

family members was also a priority. Participants understood the difference between action

plans and pleasant activities; thus they were readily able to provide a behavioral action activity

that fit their individual preferences—i.e., engaging in an activity that used to bring them joy and

contentment. They typically engaged in activities that did not entail a cost (or had a minimal

cost), and preferred such activities as going to church, engaging in exercise/physical activity,

socializing with family, gardening, dancing and listening to music, arts and crafts, and going out.

In sum, the intervention was feasible, acceptable, and easily understood by participants and

promotoras. Engagement with the providers and health care team continues to be a challenge

for this population; many reported getting appointments and information about their medical

care from health care providers as chronic challenges.


Table 5. Referrals Made by Promotoras

Referral Frequency DHS provider 12 Community resource 154 Type of community resource*

• Community/senior/family/wellness center 88

• Transportation 33 • Food bank 25

• Immigration/legal/Social Security/citizenship 18

• Financial 17 • Mental health service/DMH, emotional support group 15

• Support group 15

• Housing 10

• Free cell phone 9

• Dial 211 and 311 8

• Other disease-specific organization (arthritis, cancer) 6

• Dept. Public Social Services 5

• Vision 5

• Job info/training, computer class 4

• Suicide Crisis hot line 3

• Other resource for family member 3

• Al-Anon (drinking problem) 2

• Narcotics Anonymous (drug problem) 2

• Diabetes class/care 2

• Domestic violence 1

• Family counselor 1

• Insurance 1 * Not mutually exclusive

During the intervention period, the most-requested assistances and referrals were

community resources (frequently inquired: community, senior, or wellness center, 88

occurrences; transportation, 33; food bank, 25; see Table 5). The project protocol included a

component for promotora-clinic provider communication, but due to the fact that the clinic

providers’ workloads doubled with their transition to the patient-centered medical home

model and new electronic health record implementation, minimal linkage was established


between the providers and the project’s promotoras. Promotoras made 12 referrals to LAC-DHS

providers for patients’ unmet needs, such as no longer receiving prescription for

antidepressants, needing to see a psychiatrist, and a long wait for a surgery or an exam.

4.3. Study attrition

The study sample consisted of 348 patients with depression and concurrent diabetes

and/or heart disease from 3 DHS safety-net community clinics. Consistent efforts were made to

minimize attrition given the high representation of immigrant, Spanish-speaking, safety-net

population in the sample. The proportions lost to follow-up were not significantly different

between study groups (Figure 1). Study attrition of those who did not complete the follow-up

interview at 12 months was 30% (AHH 31% versus UC 28%; p = 0.51). A total of 104 patients

were lost to follow-up, including 80 (23%) whom we were unable to locate due to phone issues

(phone not in service, number disconnected, wrong number); 21 (6%) patients declining further

study participation; 1 in a nursing home and unable to take calls; 1 due to a medical reason (in

and out of hospital, now on dialysis); and 1 death. We compared demographic and baseline

clinical characteristics, including depression and quality-of-life measures, between patients who

completed the 12-month interview and those who did not complete in the AHH and UC

combined sample. We did not find any baseline variable associated with study attrition (Table

6).

4.4. Primary outcomes on depression symptom improvement and disease management

We evaluated the over-time group differences in depression and disease self-care

management scores in linear mixed-effects regression models (Table 7). There was no

significant over-time group difference in these primary outcomes as reflected by the

nonsignificant p value for time by group interaction, as well as no significant cross-sectional

group difference at each follow-up wave. However, AHH patients had a slightly better group

mean in lower depression at 12 months and better self-care management at each follow-up

wave compared with UC patients. These improvements were not statistically or clinically

significant.


Table 6. Patient Baseline Characteristics and Study Attrition

Retention, N = 244

Attrition, N = 104 P*

n (%) n (%)

AHH intervention group 122 (50.0) 56 (53.8) 0.511 Female 208 (85.2) 88 (84.6) 0.880 Diabetes 235 (96.3) 100 (96.2) 0.943 Heart disease 33 (13.5) 18 (17.3) 0.361 Other illnessa 228 (93.4) 98 (94.2) 0.782 BMI ≥ 30 155 (63.5) 70 (67.3) 0.499 Chronic pain 107 (43.9) 55 (52.9) 0.122 Dysthymia 145 (59.4) 72 (69.2) 0.084 Have major depressive disorder

diagnosis before trial 100 (41.0) 47 (45.2) 0.467

Already received antidepressant before trial

60 (24.6) 32 (30.8) 0.232

Mean (SD) Mean (SD)

Age 56.5 (8.5) 57.0 (7.7) 0.591 Physical health Self-rated health 1.8 (0.7) 1.8 (0.7) 0.984 Sheehan disability scale 5.3 (3.3) 5.6 (3.0) 0.310 Physical component summary of SF-

12 score 38.6 (10.7) 37.5 (11.1) 0.391

Psychological health Depression PHQ-9 score 15.8 (4.1) 16.3 (3.8) 0.283 Depression SCL-20 score 1.6 (0.7) 1.7 (0.6) 0.225 Depression remission assessment

REMIT-5 score 9.8 (3.7) 10.5 (3.9) 0.109

Anxiety GAD-7 score 10.0 (5.0) 10.8 (4.7) 0.176 Somatic symptom PHQ-15 scoreb 11.4 (4.9) 11.8 (5.0) 0.543 Mental component summary of SF-

12 score 30.7 (9.5) 31.3 (8.5) 0.603

Self-care management Self-efficacy for Managing Chronic

Disease 6.0 (2.8) 6.0 (3.1) 0.822

Latino Scale for Antidepressant Stigma)c

3.1 (3.3) 3.0 (3.6) 0.728

Patient Activation Measure 75.1 (19.9) 74.4 (19.6) 0.763 Stress and social support


Retention, N = 244

Attrition, N = 104 P*

n (%) n (%)

Medical Outcomes Social Support Survey

51.7 (29.4) 53.8 (27.8) 0.544

Number of stressors, 10-item Hispanic Stress Inventory

3.6 (1.9) 3.5 (2.0) 0.637

* Chi-square test for categorical variables; t test for continuous variables.

a Arthritis, retinopathy, gastrointestinal, kidney disease, lung disease, stroke, cancer, urinary tract, or prostate problem. b Missing data for 1 retention patient. c Missing data for 3 retention patients. BMI =Body Mass Index


Table 7. Primary Outcomes on Depression Score and Disease Self-care Management Analyzed in Linear Mixed-effects Models

PCMH Usual

Care, N = 170 AHH Intervention, N

= 178 Group Comparison

Reference Group = PCMH

Least Squares

Mean (SE) Least Squares

Mean (SE) Difference

Mean (95% CI) P Time by Group

Interaction, P Depression score PHQ-9 (possible range 0-27) 0.277

Baseline 16.0 (0.4) 15.8 (0.4) –0.2 (–1.4-1.0) 0.750 6 month 8.7 (0.5) 8.7 (0.5) 0.01 (-1.3-1.3) 0.994

12 month 9.4 (0.5) 8.2 (0.5) –1.1 (–2.5-0.2) 0.103 SCL-20 (possible range 0-4) 0.116

Baseline 1.6 (0.1) 1.7 (0.1) 0.1 (–0.1-0.2) 0.451 6 month 1.1 (0.1) 1.0 (0.1) –0.1 (–0.2-0.1) 0.593

12 month 1.2 (0.1) 1.0 (0.1) –0.1 (–0.3-0.1) 0.159 Disease self-care management Self-efficacy for Managing Chronic Disease 0.971

Baseline 5.8 (0.2) 6.2 (0.2) 0.4 (–0.2-1.0) 0.168 6 month 6.8 (0.2) 7.2 (0.2) 0.3 (–0.3-1.0) 0.325

12 month 6.8 (0.3) 7.2 (0.3) 0.4 (–0.3-1.1) 0.234 Patient Activation Measure (PAM-13) 0.443

Baseline 75.0 (1.4) 74.8 (1.3) –0.1 (–3.9-3.7) 0.951 6 month 83.5 (1.5) 84.8 (1.5) 1.3 (–3.0-5.6) 0.549

12 month 84.1 (1.6) 87.2 (1.6) 3.1 (–1.3-7.5) 0.169

Abbreviations: PHQ-9 = Patient Health Questionnaire-9; SCL-20 = 20-item Symptom Checklist.


4.5. Secondary outcomes on health care utilization and quality of life

We obtained medical records from LAC-DHS electronic medical services records for 168

UC and 174 AHH patients who authorized permission to release their health information.

Health care facility utilization in clinic visits, ER, or hospital admission was similar between

study groups (Table 8).

Although the LAC-DHS electronic health record (EHR) Orchid system had been

implemented for more than 1 year, medical records had not been completely integrated into

the new system. Some data were unavailable when we made requests, including outpatient

visit clinic name and activity, mental health referral, and class with social worker. Thus, we

reported mental health care receipt based on patients’ self-reports. Trial data fitted in the

mixed-effects logistic model showed that patient depression treatment acceptance increased

over the 12-month trial (38%, 63%, and 68% in AHH and 46%, 65%, and 70% in UC at baseline, 6

months, and 12 months, respectively), and that more patients had been prescribed

antidepressants since baseline (21%, 36%, and 33% in AHH and 26%, 33%, and 31% in UC at

baseline, 6 months, and 12 months, respectively). The leading barriers to depression care

reported in this population were difficulty in finding a depression care provider who spoke their

language (23%), difficulty in finding a facility in the community for depression care (18%), and

difficulty with clinic hours (7%). Further, fear of addiction to antidepressant medicine was a

barrier reported among UC patients who did not seek help for depression care; that concern

was not a barrier to the AHH patients.

More than half of the study patients had PHQ-9 depression scores of less than 10 (a

cutoff score for minor depression) at follow-up assessment (AHH 60% at 6 months, 65% at 12

months; UC 56% at both 6 and 12 months), and about half of participants in both study arms

reached clinical significant improvement of depressive symptoms defined as a 50% or more

score reduction since baseline (AHH 53% at 6 months, 55% at 12 months; UC 51% at 6 months,

49% at 12 months).


Table 8. Secondary Outcomes on Health Care Utilization Analyzed in Mixed-effects Models

PCMH Usual


= 178 Group Comparison


Medical records* Least Squares




Interaction, P Number of clinic visits 0.781

Baseline 8.4 (0.5) 8.0 (0.5) –0.4 (–1.8-1.0) 0.574 6 month 9.0 (0.5) 8.4 (0.5) –0.6 (–2.0-0.8) 0.368

12 month 6.4 (0.5) 6.3 (0.5) –0.1 (–1.5-1.3) 0.889

Binary outcome Probability (SE) Probability (SE) Odds Ratio (95% CI) P Interaction, P Use of emergency room 0.822

Baseline 0.162 (0.029) 0.117 (0.025) 0.685 (0.362-1.297) 0.245 6 month 0.174 (0.030) 0.157 (0.029) 0.884 (0.488-1.602) 0.683

12 month 0.109 (0.025) 0.094 (0.023) 0.852 (0.416-1.745) 0.660 Hospital admission 0.170

Baseline 0.029 (0.013) 0.045 (0.016) 1.579 (0.499-4.996) 0.436 6 month 0.059 (0.018) 0.062 (0.019) 1.071 (0.435-2.634) 0.882

12 month 0.053 (0.017) 0.017 (0.010) 0.311 (0.082-1.183) 0.086 Patient self-report Seeking professional help for depression 0.749

Baseline 0.457 (0.042) 0.382 (0.040) 0.737 (0.459-1.181) 0.204 6 month 0.652 (0.045) 0.630 (0.046) 0.912 (0.524-1.588) 0.745

12 month 0.697 (0.045) 0.683 (0.046) 0.936 (0.518-1.690) 0.826 Has been prescribed antidepressant medication 0.532

Baseline 0.261 (0.041) 0.212 (0.036) 0.760 (0.421-1.374) 0.363 6 month 0.331 (0.051) 0.357 (0.053) 1.120 (0.591-2.121) 0.728

12 month 0.311 (0.052) 0.334 (0.053) 1.114 (0.570-2.176) 0.752 * Medical records were obtained for 168 PCMH UC and 174 AHH intervention patients who had authorized to release their health information from health care providers.


Table 9. Secondary Outcomes on Physical Well-beings, Stress, and Social Support Analyzed in Linear Mixed-effects Models

PCMH Usual


= 178 Group Comparison,


Least Squares




Interaction, P Physical condition Sheehan disability scale 0.956

Baseline 5.5 (0.3) 5.3 (0.2) –0.2 (–0.9-0.5) 0.643 6 month 4.0 (0.3) 3.9 (0.3) –0.1 (–0.8-0.7) 0.879

12 month 4.3 (0.3) 4.3 (0.3) –0.1 (–0.8-0.7) 0.899 SF-12 physical 0.664

Baseline 38.3 (0.8) 38.3 (0.8) 0.0 (–2.3-2.3) 0.990 6 month 36.5 (0.9) 37.6 (0.9) 1.1 (–1.5-3.7) 0.411

12 month 37.2 (1.0) 37.2 (1.0) –0.0 (–2.7-2.6) 0.985 Pain level 0.122

Baseline 3.0 (0.3) 3.1 (0.3) 0.1 (–0.7-1.0) 0.759 6 month 4.1 (0.3) 4.8 (0.3) 0.7 (–0.2-1.6) 0.135

12 month 4.4 (0.3) 4.0 (0.3) –0.4 (–1.4-0.5) 0.384 Stress and social support Medical Outcomes Social Support Survey 0.769

Baseline 52.6 (2.2) 52.0 (2.1) –0.6 (–6.7-5.4) 0.839 6 month 73.4 (2.4) 74.9 (2.5) 1.5 (–5.3-8.3) 0.672

12 month 83.5 (2.5) 85.5 (2.5) 2.0 (–5.1-9.0) 0.580 Number of stressors (of a list of 10) 0.344

Baseline 3.5 (0.2) 3.6 (0.1) 0.2 (–0.3-0.6) 0.444 6 month 3.2 (0.2) 3.2 (0.2) 0.0 (–0.4-0.5) 0.914

12 month 3.3 (0.2) 3.1 (0.2) –0.2 (–0.7-0.3) 0.343


Table 9 presents quality-of-life outcomes on physical well-being, stress, and social

support. We did not find statistically significant group differences at any time point nor in time

by group interactions.

Because LAC-DHS activated health care improvements, including adding community

health workers (similar role as that of AHH promotoras) into UC clinics during the trial period,

we also examined outcomes over time for each study group individually. We found significant

improvements at 6-month and 12-month follow-ups in almost all assessed outcomes (i.e.,

depression, physical health, self-care management, psychometric measures, socioeconomic

stress, and social support) in each individual group (Table 10 for UC and Table 11 for AHH), and

both study groups performed evenly well. Because of the high attrition in the study population,

we also conducted a sensitive analysis on outcomes using the last-observation-carried-forward

(LOCF) method. We found similar results with LOCF.

Comparing lab test results before and after trial, both study groups had a statistically

significant improvement in blood sugar level A1C (AHH reduced 0.51% in average absolute

change, p = 0.0001; UC reduced 0.37%, p = 0.01). In both AHH and UC, systolic and diastolic

blood pressures and all lipid profiles except HDL cholesterol level declined (Table 12).


Table 10. Over-time Changes Analyzed in Linear Mixed-effects Models for Patients in the PCMH Usual Care Group (N = 170)

Actual Responded Data at Each Wave The Last Observation Carried Forward

Change Score Since Baseline


Least Squares

Mean (SE) Trend,

P Mean (95% CI) P* Least Squares

Mean (SE) Trend,

P Mean (95% CI) P* Depression

PHQ-9 score < .0001 < .0001 Baseline 16.0 (0.4) 16.0 (0.5) 6 month 8.7 (0.5) –7.4 (–8.4- –6.3) < .0001 10.3 (0.5) –5.7 (–6.6- –4.8) < .0001

12 month 9.3 (0.5) –6.7 (–7.7- –5.6) < .0001 10.5 (0.5) –5.5 (–6.4- –4.5) < .0001 SCL-20 score < .0001 < .0001

Baseline 1.6 (0.1) 1.6 (0.1) 6 month 1.1 (0.1) –0.6 (–0.7- –0.4) < .0001 1.2 (0.1) –0.5 (–0.6- –0.3) < .0001

12 month 1.2 (0.1) –0.5 (–0.6- –0.3) < .0001 1.2 (0.1) –0.4 (–0.5- –0.3) < .0001 Disease self-care management

Self-efficacy for Managing Chronic Disease < .0001 0.0001

Baseline 5.8 (0.2) 5.8 (0.2) 6 month 6.8 (0.2) 1.0 (0.5-1.5) 0.0001 6.6 (0.2) 0.8 (0.4-1.2) 0.0002

12 month 6.8 (0.2) 1.0 (0.5-1.5) 0.0001 6.6 (0.2) 0.8 (0.4-1.2) 0.0001 Patient Activation Measure < .0001 < .0001

Baseline 75.0 (1.4) 75.0 (1.4) 6 month 83.5 (1.5) 8.6 (5.1-12.0) < .0001 81.7 (1.4) 6.7 (3.9-9.6) < .0001

12 month 84.1 (1.6) 9.2 (5.6-12.7) < .0001 82.8 (1.4) 7.9 (5.0-10.7) < .0001 Physical condition Sheehan disability scale < .0001 < .0001

Baseline 5.5 (0.3) 5.5 (0.3) 6 month 4.0 (0.3) –1.5 (–2.0- –0.9) < .0001 4.3 (0.3) –1.2 (–1.6- –0.7) < .0001



12 month 4.3 (0.3) –1.1 (–1.7- –0.5) 0.0002 4.5 (0.3) –0.9 (–1.4- –0.5) 0.0001 SF-12 physical 0.186 0.313

Baseline 38.3 (0.9) 38.3 (0.9) 6 month 36.5 (1.0) –1.8 (–3.7-0.1) 0.069 37.1 (0.9) –1.2 (–2.7-0.4) 0.133

12 month 37.2 (1.0) –1.0 (–3.0-0.9) 0.305 37.5 (0.9) –0.7 (–2.3-0.8) 0.338 Pain level 0.0003 < .0001

Baseline 3.0 (0.3) 3.0 (0.3) 6 month 4.1 (0.3) 1.1 (0.4-1.8) 0.002 4.0 (0.3) 1.0 (0.5-1.6) 0.0005

12 month 4.4 (0.3) 1.4 (0.7-2.1) 0.0002 4.3 (0.3) 1.3 (0.7-1.9) < .0001 Stress and social support Medical Outcomes Social Support Survey < .0001 < .0001

Baseline 52.6 (2.2) 52.6 (2.3) 6 month 73.4 (2.5) 20.8 (15.3-26.3) < .0001 69.0 (2.3) 16.3 (11.7-20.9) < .0001

12 month 83.5 (2.6) 30.8 (25.2-36.5) < .0001 76.4 (2.3) 23.7 (19.1-28.4) < .0001 Number of stressors (of a list of 10) 0.355 0.377

Baseline 3.5 (0.2) 3.5 (0.2) 6 month 3.2 (0.2) –0.3 (–0.7-0.1) 0.167 3.3 (0.2) –0.2 (–0.5-0.1) 0.184

12 month 3.3 (0.2) –0.2 (–0.6-0.2) 0.337 3.3 (0.2) –0.2 (–0.5-0.1) 0.301 * Baseline is the reference group.


Table 11. Over Time Changes Analyzed in Linear Mixed-Effects Models for Patients in the AHH Intervention Group (N = 178)




Least Squares

Mean (SE) Trend,

P Mean (95% CI) P* Least Squares

Mean (SE) Trend,

P Mean (95% CI) P* Depression

PHQ-9 score < .0001 < .0001 Baseline 15.8 (0.4) 15.8 (0.4) 6 month 8.7 (0.5) –7.1 (–8.1- –6.2) < .0001 10.6 (0.4) –5.2 (–6.0- –4.4) < .0001

12 month 8.2 (0.5) –7.6 (–8.5- –6.7) < .0001 10.0 (0.4) –5.8 (–6.6- –5.0) < .0001 SCL-20 score < .0001 < .0001

Baseline 1.7 (0.1) 1.7 (0.1) 6 month 1.0 (0.1) –0.7 (–0.8- –0.6) < .0001 1.2 (0.1) –0.5 (–0.6- –0.4) < .0001

12 month 1.0 (0.1) –0.7 (–0.8- –0.5) < .0001 1.2 (0.1) –0.5 (–0.6- –0.4) < .0001 Disease self-care management

Self-efficacy for Managing Chronic Disease 0.0004 0.001

Baseline 6.2 (0.2) 6.2 (0.2) 6 month 7.2 (0.2) 0.9 (0.4-1.5) 0.001 6.8 (0.2) 0.6 (0.2-1.0) 0.007

12 month 7.2 (0.3) 1.0 (0.4-1.6) 0.001 7.0 (0.2) 0.8 (0.4-1.2) 0.0004 Patient Activation Measure < .0001 < .0001

Baseline 74.8 (1.3) 74.8 (1.4) 6 month 84.8 (1.5) 10.0 (6.6-13.4) < .0001 82.2 (1.4) 7.4 (4.7-10.0) < .0001

12 month 87.2 (1.6) 12.4 (8.9-15.8) < .0001 84.5 (1.4) 9.6 (7.0-12.2) < .0001 Physical condition Sheehan disability scale < .0001 < .0001

Baseline 5.3 (0.2) 5.3 (0.2) 6 month 3.9 (0.3) –1.4 (–2.0- –0.8) < .0001 4.3 (0.2) –1.0 (–1.4- –0.5) < .0001



12 month 4.3 (0.3) –1.0 (–1.6- –0.4) 0.001 4.5 (0.2) –0.8 (–1.3- –0.4) 0.0004 SF–12 physical 0.509 0.335

Baseline 38.3 (0.8) 38.3 (0.8) 6– month 37.6 (0.9) –0.7 (–2.5-1.1) 0.451 37.7 (0.8) –0.6 (–2.0-0.8) 0.402 12 month 37.2 (0.9) –1.1 (–2.9-0.8) 0.262 37.2 (0.8) –1.0 (–2.4-0.3) 0.141

Pain level < .0001 < .0001 Baseline 3.1 (0.3) 3.1 (0.3) 6 month 4.8 (0.3) 1.7 (1.0-2.4) < .0001 4.4 (0.3) 1.3 (0.7-1.8) < .0001

12 month 4.0 (0.3) 0.8 (0.1-1.6) 0.023 3.9 (0.3) 0.8 (0.2-1.4) 0.005 Stress and social support Medical Outcomes Social Support Survey < .0001 < .0001

Baseline 52.0 (2.1) 52.0 (2.3) 6 month 74.9 (2.4) 22.9 (17.7-28.1) < .0001 68.6 (2.3) 16.6 (12.3-20.9) < .0001

12 month 85.5 (2.5) 33.4 (28.2-38.7) < .0001 76.9 (2.3) 24.9 (20.6-29.2) < .0001 Number of stressors (of a list of 10) 0.004 0.004

Baseline 3.6 (0.1) 3.6 (0.1) 6 month 3.2 (0.2) –0.4 (–0.8- –0.1) 0.024 3.3 (0.1) –0.4 (–0.6- –0.1) 0.012

12 month 3.0 (0.2) –0.6 (–1.0- –0.2) 0.001 3.2 (0.1) –0.4 (–0.7- –0.2) 0.002 * Baseline is the reference group.


Table 12. Before and After Trial Lab Test Results in Each Individual Study Group

Mean (SD)

Lab Test N Before Trial After Trial Change P PCMH usual care A1C 143 8.85 (2.20) 8.46 (2.06) –0.37 (1.72) 0.011 Total cholesterol 117 184.6 (51.7) 179.0 (46.8) –3.2 (40.1) 0.388 HDL cholesterol 117 48.5 (14.7) 48.7 (13.9) 0.6 (6.7) 0.346 LDL cholesterol 115 101.7 (42.9) 97.8 (36.1) –2.3 (37.4) 0.517 Triglycerides 117 179.5 (132.2) 169.4 (112.2) –6.5 (116.9) 0.552 Systolic blood pressure 134 132.7 (16.5) 130.3 (17.6) –2.8 (17.4) 0.066 Diastolic blood pressure 134 70.1 (9.9) 69.3 (10.0) –1.1 (10.2) 0.233 AHH intervention A1C 136 8.84 (2.25) 8.42 (1.89) –0.51 (1.50) 0.0001 Total cholesterol 114 186.3 (46.8) 187.5 (49.2) –1.4 (33.7) 0.650 HDL cholesterol 114 48.9 (10.8) 49.2 (11.4) 0.2 (7.2) 0.764 LDL cholesterol 113 104.2 (40.8) 104.4 (39.3) –0.9 (28.2) 0.722 Triglycerides 114 168.9 (105.9) 169.0 (108.6) –6.8 (89.4) 0.416 Systolic blood pressure 144 131.2 (18.3) 130.4 (17.2) –1.1 (19.6) 0.484 Diastolic blood pressure 143 69.9 (9.7) 68.7 (9.4) –1.1 (9.9) 0.176

4.6. Adverse events

Two patients were hospitalized (1 UC, 1 AHH), 1 AHH patient was admitted into a

nursing home, and 1 UC patient death was reported by a family member when the outcome

interviewer called for outcome interviews; otherwise, no adverse events existed.

4.7. Qualitative interviews

4.7.1. Qualitative assessments of patients

We conducted a total of 25 in-depth interviews by telephone from a random sample of

intervention and usual care patients. A trained bilingual/bicultural (English–Spanish)

interviewer followed a prepared interview guide that included questions across the following

domains: general knowledge of depression and depression treatment; stigma and disclosure

related to depression; overall impressions and experience with the promotora-mediated

counseling sessions; relational issues with health system providers; and language and cultural

factors related to satisfaction of care. A review of these qualitative interviews indicates that our


patient participants experienced changes in their health care due to changes in the system (e.g.,

pharmacy/medication pick-up procedures, delays in receiving clinical behavioral health

services, changes in designated primary care providers, unsatisfactory treatment experiences).

The review of these qualitative interviews did not indicate impact on study operations and

study quality (e.g., recruitment and retention, study outcomes and findings).

4.7.2. Qualitative assessments of promotoras

Promotoras engaged very well with patients, but few clinic physicians had time to

communicate with them in light of time pressures in the clinics; also, the physicians had begun

taking on responsibility in adopting the PCMH model with other stakeholders (e.g., nurses,

CHWs).

4.7.3. Qualitative interview with LAC-DHS medical providers

We conducted postintervention stakeholder interviews with the study clinic providers—

including approximately 25 physicians, 20 nurses and care managers, and a clinic medical

director—to obtain feedback on our study. Specifically, we presented our study results for

comments and discussed what changes were implemented in the clinics during the study period

(e.g., competing interventions) for chronic disease self-management or services for depression.

We also discussed challenges the providers foresee in addressing patients’ health care needs

(e.g., multiple chronic conditions, income and other resource needs, population health

management, EHR, DHS mandates, ACA mandates, integrated care mandates, lack

of community or family support for patients).

When presented with our finding that both the intervention group and the control

group improved significantly over time, the clinic providers responded that they had

implemented “a lot” of quality improvement initiatives during the study period (i.e., 2014-

2016). Based on the qualitative data, many competing interventions to the AHH promotoras

intervention may have led to the positive outcomes for both groups. First and foremost, the

study clinics have been implementing the patient-centered medical homes (PCMHs) using

team-based care for about 2 to 3 years, during roughly the same time of the AHH study. Their


Cerner (called Orchid in LAC-DHS) electronic health record system was implemented about 1.5

years ago. These clinics also have linked each patient is to a primary care provider (PCP),

although the actual implementation found somewhat inaccurate or missing information, such

as the name of patient’s PCP not displayed at the Cerner system.

The clinic has a large number of diabetes patients (with comorbidities such as coronary

heart disease and hypertension), and an estimated 70% of visits are for patients with diabetes.

Similarly, the vast majority of AHH study participants have diabetes. In the past few years, the

clinics have been highly focused on diabetes care and supporting patient needs. The

information system will allow the clinics to patient health and identify high-risk patients. The

clinics send their patients to the diabetes clinic for intensive treatment, and are conducting

many other initiatives. For example, they send high-risk diabetes patients to a clinical

pharmacist to review medications and educate them about compliance. They made 3 referrals

for the high-risk patients—to (1) a nutritionist, (2) a health educator, and (3) a family resource

center. The providers follow up with patients to determine if they made it to the referrals and

encourage them to do so. They also screen patients for depression and actively prescribe

antidepressants. The clinics have been testing a community health worker program since 2014-

2015 that is very similar to the AHH promotoras’ roles. Patients are randomly assigned to the

program to evaluate its effectiveness.

Additionally, there are clinic-wide care management activities for diabetes patients. For

patients with an A1C value of 10 or higher, care managers (with RN qualification) manage these

patients and follow up with them in 2-week intervals through a face-to-face nurse visit or via

telephone calls. If the patient’s A1C is greater than 8 but less than 10, caregivers in the clinic

follow up with patients so the workload is more manageable for the care managers. Care

managers are the leads for these caregivers. In the PCMH model, there is also SMART goal

setting for patients to set up a plan for exercise, weight loss, etc. Patient workshops in the clinic

teach them how to cook diabetic meals and how to perform certain physical activities, using

relatable approaches so they can learn to make these behavioral changes. In these ways, the

care management team is helpful with patient diabetes self-management, and has facilitated

better communication with PCPs.


Because of the implementation of the Cerner electronic medical records system, the

study clinic’s providers can report a patient’s A1C level to care managers in near real time. The

care managers follow up with patients to ensure they are educated about diabetes, receive

their medication, and follow recommended diet and exercise programs. The care managers are

“very efficient,” according to one physician.

In summary, for diabetes patients (which make up the majority of our sample), the

providers believe that they are on a journey of continuously improving health outcomes

because the clinic has been highly active in improving patient care.

Since the implementation of PCMH and ACA, which requires providing preventive

services such as depression screening, cancer screening, etc. (National Committee for Quality

Assurance [NCQA] requirement), in the past 2 to 3 years, the clinics have also had a great focus

on depression care. Providers are required to screen patients for depression. They first use the

PHQ-2, which has been implemented for 3 to 4 years. As of 2016, they are also required to use

PHQ-9 if a patient’s PHQ-2 score is high. Once a patient is screened and identified as having

depression, providers engage in conversations with patients about their condition and

prescribe medication and/or refer patients to mental health services.

The providers prefer the same kind of support for depression as that currently available for

diabetes. The difficulty is that the providers screened patients using both PHQ-2 and later PHQ-

9 but couldn’t provide adequate care for because they lacked the supporting workforce or

referral channels. Their training and time can afford them to only screen patients and prescribe

medications, not to complete intakes, counsel patients, for mental health, or link patients with

social or community resources. Providers need help to manage patients with depression and

other mental health problems. Many LAC-DHS patients with depression also have comorbid

conditions such as bipolar disorder, anxiety, other severe mental illnesses; drug addiction;

incarceration experiences; poor social support; and poor family dynamics. Because the

providers do not have the time to “open the can of worms,” they tend to send the patients to

public mental health clinics to treat their nonmedical issues. Providers are concerned that if

they begin to deal with depression-related problems, each medical encounter could easily take


them 2 hours, rather than the expected 15 minutes (with half of that time dedicated to charting

in the Orchid EMR system—an increase from about 3 minutes in the old electronic medical

record system).

The providers do recall the AHH team coming to the clinic to introduce the study, and

they recall the study recruiters and had good experiences working with them. However,

providers were unaware of the study promotoras; they did not have contact with them or hear

about them from their patients. The providers strongly felt that the promotoras might be the

helping hand that they need to take care of patients with depression. They view the

promotoras as the personnel they need to engage patients, formulate problems, educate

patients how to rely on self-help strategies using down-to-earth approaches, hold educational

workshops on depression and diabetes in the clinics for easy access, help patients determine

plan actions and pleasant activities, help patients deal with their mental distress, and follow up

with patients regularly.

Unfortunately, because the providers’ workloads doubled with the PCMH and Orchid

EHR implementation, no linkage was established between the providers and our AHH

promotoras. The providers also do not think consumer health technology, such as mobile appss,

would be helpful to only a small number of their patients because of the population’s poor

access to technology and low health literacy.

The providers identified the following strategies to facilitate higher-quality depression care:

1. Providers need more training about PHQ-9 screening. The scale is subjective, and the

score can fluctuate—even for the same patient on the same day with different

providers. Physicians and staff need more training on how to use this assessment and

how to interpret the score.

2. The same number of staff are needed to help patients with depression as the number to

of staff to help people with diabetes.

3. The referral process must be made easier, and it must allow direct access to mental

health services.


4. The diabetes clinic should provide patients with a mental health educational

workshop—similar to the cooking classes—and use down-to-earth approaches to teach

patients how to deal with mental stress.

5. Discussion 5.1. Decisional context

The trial outcomes did not vary significantly between the promotora-assisted and LAC-

DHS usual care groups, and both groups fared better over the active study period on most

measures. The null result was likely a function of high refusal and drop-out rates in the AHH

intervention arm as well as the enhanced LAC-DHS PCMH model, which was enacted during the

study period as a result of the ACA and Medicaid expansion. These clinical improvements

included (1) depression screening by the primary care provider; (2) referral to clinic staff

including community health workers; (3) behavioral health specialty care referrals, which

included receipt of PST in some clinics by social workers; and (4) activated patient care

management. In essence, components of the experimental design may have been offered to

the usual care group as part of regular practice, thus decreasing the potential significance of the

experimental, promotora-assisted group (which trended better on many outcome measures).

Furthermore, the high subject drop-out rate may have prompted a differential response to the

intervention, depending on who remained in the center and who dropped out.

As a result of these 2 factors and perhaps others, the study was not a strong test of the

promotoras model. The decisional context for informing patients, clinicians, and other key

stakeholders about the model is not certain at this time. Additional research is required to

arrive at a definitive judgment about the value of the AHH promotora model in primary care.

5.2. The study results in context

This study is the first to incorporate promotoras in a public health safety-net care

system in order to reduce racial and ethnic disparities in depression and self-care management

among Spanish-speaking patients with diabetes and/or heart disease. It evaluated promotora-

assisted depression and self-care management among patients with diabetes and/or heart


disease based on a 12-month randomized clinical trial within LAC-DHS community clinics.

Promotoras are increasingly at the front line of health care organizations.

It is important to note that the group comparisons involved depression management in

the context of a patient-centered medical home with or without the addition of promotoras

who were trained to provide counseling. The PCMH model with or without promotoras has the

potential to promote better health, functioning, and quality of life among Latino safety-net

health care consumers.

This study took place while the LAC-DHS safety-net care system was experiencing

unprecedented changes brought on by the ACA and by LAC-DHS uptake of the PCMH model,

including increasing disease management within its primary care clinics and, recently, enhanced

health information technology system to better track how patients are doing and to identify

high-risk patients. LAC-DHS also began to include depression screening, based on the ACA and

implementation of the 2008 Mental Health Parity Act, which requires insurers to cover

treatment for depression just as they would cover treatment for a physical illness.

5.3. Implementation of study results

Given that our study did not yield a strong test of the model at this time, we advise

additional research to determine the comparative effectiveness of the promotora model in

public-sector health care before our study results are implemented.

Nevertheless, there remains a significant need to engage Latinos in public safety-net

health care that is consistent with patient preferences and personalized care planning.79 The

challenges to implementation are significantly and uniquely relevant among diverse low-

income populations as well as among safety-net providers, community organizations, and

stakeholders. Patient perceptions of care coordination problems are associated with both

poorer self-care activation and health outcomes. This is particularly relevant to the complexity

of patient self-care that is inherent to depression and concurrent chronic illness management.80

Safety-net patients with major depression plus a concurrent chronic illness face significant

barriers to patient activation, motivation, skills, and confidence—the very attributes that equip

patients to become actively engaged in their health and health care.5,6,26,81-84 Aside from health


system financing barriers, the study encountered barriers to locating a study population that

was financially impoverished, highly mobile, highly stressed due to unemployment, and

experiencing poor access to public benefits and community resources.

5.4. Generalizability

The sample itself is reflective of highly impoverished patients with historically low rates

of health care access. Whether the results are generalizable to other non-Latino populations—

or even to the retained study population—is unknown.

5.5. Subpopulations

The study results cannot be used at this point to differentiate among specific subgroups

or subpopulations in the sample.

5.6. Study limitations

Study limitations include the following:

(1) challenges to maximize intervention attendance and minimize study attrition among

a predominantly immigrant and Spanish-speaking safety-net population;

(2) the co-implementation of AHH promotoras and PCMH CHWs in usual care clinics

making it difficult to distinguish which of them contributed to the intervention effect;

(3) the reliance on self-reported secondary outcome measures while DHS electronic

medical health records were being slowly updated;

(4) the LAC-DHS clinic staff at all sites having limited interaction with promotoras due to

the heavy burden of dealing with high workloads and clinic duties; and

(5) the limited generalizability to primary care clinics lacking a PCMH model.

High (30%) attrition rates in both arms of the study led to uncertainty about how to

interpret the results, since those participants who remained in the study may have had

different study outcomes than those who dropped out of the study. Future studies should

include strategies to address patient attrition at all observation points.


5.7. Future research

Future research should consider a more robust test of the promotora model, taking into

account dynamic changes in the health care system of interest and addressing potential drop-

out or attrition rates of the sample with innovative retention strategies. The drop-out rate in

particular could be a signal for a misleading study result. To avoid these problems, future

research should employ study procedures such as more frequent communication with the

PCMH team through the EMR portal and more intensive education at the informed-consent

phase about study participation expectations.

6. Conclusion Promotora-assisted depression and chronic disease self-management care was not

superior to usual care for any of the primary or secondary outcomes. We found that both the

AHH promotora intervention and the LAC-DHS patient-centered medical home model were

associated with significant improvements over time. The result of nonsignificant differences

between the 2 intervention groups is likely to be a function of the LAC-DHS PCMH model, which

emerged with several patient-centered care improvements, as well as the high dropout rate.

Because our study is not a definitive test of the promotora model, our results should not be

invoked as a reason to abandon the model altogether. Our study calls for additional studies of

the promotoras model in depression care; these studies should integrate innovative strategies

for subject engagement into the study design in order to avoid these potential confounders.


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Acknowledgment

This study is supported by the Patient-Centered Outcomes Research Institute (PCORI) and

activated by Principal Investigator, K. Ell. Trial Registration: NCT02147522, clinicaltrials.gov/ct.

The corresponding author at the Suzanne Dworak-Peck School of Social Work, University of

Southern California is Kathleen Ell ([email protected]). Coinvestigators, Drs. María Aranda and Shinyi

Wu, had significant study roles. Dr. Hyunsung Oh assisted while working on his doctoral

dissertation, and Pey-Jiuan Lee provided data management and analysis. Dr. Jeffrey Guterman,

the chief of research and innovation officer in the Ambulatory Care Network of the LAC-DHS,

provided consultation. Disclosure: No conflict of interest, financial or otherwise, exists

Copyright © 2018 University of Southern California. All Rights Reserved.

Disclaimer:

The [views, statements, opinions] presented in this report are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee.

Acknowledgement:

Research reported in this report was [partially] funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (AD-1304-7364).

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