Analytic hierarchy process: An innovative technique for culturally tailoring evidence-based interventions to reduce health disparities.

Latinos in the United States represent a disproportionate burden of illness and disease and face barriers to accessing health care and related resources. Culturally tailored, evidence-based interventions hold promise in addressing many of these challenges. Yet, ensuring patient voice is vital in the successful development and implementation of such interventions. Thus, this paper examines the application of analytic hierarchy process (AHP) to inform the augmentation and implementation of an evidence-based chronic disease self-management programme for underserved Latinos living with both minor depression and chronic illness. The process of AHP allows for direct input from the individuals that would utilize such a programme, including afflicted individuals, their family members and the health educators/promotores that would be responsible for implementation. Specifically, 45 participants, including 15 individuals with chronic disease, 15 family members/caregivers and 15 promotores, partook in the Stakeholder Values Questionnaire, which elicited preferences and values regarding major goals, processes and content for the intervention. AHP was employed to analyse pairwise comparison ratings and to determine differences and similarities across stakeholder groups. This analytical technique allowed for the adaptation of the EBI to stakeholders' specific priorities and preferences and facilitated complex decision-making. Findings not only shed light on similarities and differences between stakeholder groups, but also the magnitude of these priorities and preferences and allowed the intervention to be driven by the participants, themselves. Applying AHP was a unique opportunity to optimize the decision-making process to inform cultural adaptation of an EBI while considering multiple viewpoints systematically.

Application of mixed-methods design in community-engaged research: Lessons learned from an evidence-based intervention for Latinos with chronic illness and minor depression.

The purpose of this paper is to share lessons learned from a collaborative, community-informed mixed-methods approach to adapting an evidence-based intervention to meet the needs of Latinos with chronic disease and minor depression and their family members. Mixed-methods informed by community-based participatory research (CBPR) were employed to triangulate multiple stakeholders' perceptions of facilitators and barriers of implementing the adapted intervention in community settings. Community partners provided an insider perspective to overcome methodological challenges. The study's community informed mixed-methods: research approach offered advantages to a single research methodology by expanding or confirming research findings and engaging multiple stakeholders in data collection. This approach also allowed community partners to collaborate with academic partners in key research decisions.

Caring for Individuals with Chronic Illness and Minor Depression: Latino Perceptions of Caregiver Burden.

Informal caregiving can be fundamental to disease management. Yet, the psychosocial, physical, and financial burden experienced by caregivers can be significant. In the US, Latinos experience increasing rates of chronic conditions, the highest uninsured rates in the country, and a growing dependence on informal caregivers. This article explores the impact of caregiving on caregivers of individuals with comorbid chronic disease and depression. Findings highlight the impact of caregiving on financial insecurity, balancing competing demands, increased emotional distress, and community supports. Findings support the inclusion of caregivers in disease management programs to enhance psychosocial outcomes for both caregivers and their patients.

Translating Research into Practice: Employing Community-Based Mixed Methods Approaches to Address Chronic Disease and Depression Among Latinos.

The co-occurrence of depression and chronic diseases is often under-recognized, under-treated, and under-studied. Among Latinos, complex structural and cultural barriers exist which complicate the translation of chronic disease self-management programs (CDSMP) for this population. To better understand those barriers and deliver a CDSMP designed to best meet local needs, a community-based, mixed methods study was designed. Formative research was conducted through focus groups with Latinos with chronic illness and minor depression (ICD) and family members to obtain insight into perceived needs and interviews with stakeholders to assess barriers and facilitators to the adoption of CDSMPs. Analytic Hierarchy Process was employed to determine core elements of a CDSMP for ICDs, family members, and the promotores who deliver these programs. Findings guided the transcreation of a CDSMP. This study offers a promising model for enhancing the effects of evidence-based interventions and emphasizes the importance of meeting differing needs within the local population.

Primary care clinicians' recognition and management of depression: a model of depression care in real-world primary care practice.

BACKGROUND: Depression is prevalent in primary care (PC) practices and poses a considerable public health burden in the United States. Despite nearly four decades of efforts to improve depression care quality in PC practices, a gap remains between desired treatment outcomes and the reality of how depression care is delivered. OBJECTIVE: This article presents a real-world PC practice model of depression care, elucidating the processes and their influencing conditions. DESIGN: Grounded theory methodology was used for the data collection and analysis to develop a depression care model. Data were collected from 70 individual interviews (60 to 70 min each), three focus group interviews (n = 24, 2 h each), two surveys per clinician, and investigators' field notes on practice environments. Interviews were audiotaped and transcribed for analysis. Surveys and field notes complemented interview data. PARTICIPANTS: Seventy primary care clinicians from 52 PC offices in the Midwest: 28 general internists, 28 family physicians, and 14 nurse practitioners. KEY RESULTS: A depression care model was developed that illustrates how real-world conditions infuse complexity into each step of the depression care process. Depression care in PC settings is mediated through clinicians' interactions with patients, practice, and the local community. A clinician's interactional familiarity ("familiarity capital") was a powerful facilitator for depression care. For the recognition of depression, three previously reported processes and three conditions were confirmed. For the management of depression, 13 processes and 11 conditions were identified. Empowering the patient was a parallel process to the management of depression. CONCLUSIONS: The clinician's ability to develop and utilize interactional relationships and resources needed to recognize and treat a person with depression is key to depression care in primary care settings. The interactional context of depression care makes empowering the patient central to depression care delivery.

Reinvention of depression instruments by primary care clinicians.

PURPOSE: Despite the sophisticated development of depression instruments during the past 4 decades, the critical topic of how primary care clinicians actually use those instruments in their day-to-day practice has not been investigated. We wanted to understand how primary care clinicians use depression instruments, for what purposes, and the conditions that influence their use. METHODS: Grounded theory method was used to guide data collection and analysis. We conducted 70 individual interviews and 3 focus groups (n = 24) with a purposeful sample of 70 primary care clinicians (family physicians, general internists, and nurse practitioners) from 52 offices. Investigators' field notes on office practice environments complemented individual interviews. RESULTS: The clinicians described occasional use of depression instruments but reported they did not routinely use them to aid depression diagnosis or management; the clinicians reportedly used them primarily to enhance patients' acceptance of the diagnosis when they anticipated or encountered resistance to the diagnosis. Three conditions promoted or reduced use of these instruments for different purposes: the extent of competing demands for the clinician's time, the lack of objective evidence of depression, and the clinician's familiarity with the patient. No differences among the 3 clinician groups were found for these 3 conditions. CONCLUSIONS: Depression instruments are reinvented by primary care clinicians in their real-world primary care practice. Although depression instruments were originally conceptualized for screening, diagnosing, or facilitating the management of depression, our study suggests that the real-world practice context influences their use to aid shared decision making-primarily to suggest, tell, or convince patients to accept the diagnosis of depression.

Modifying ICD-9-CM coding of secondary diagnoses to improve risk-adjustment of inpatient mortality rates.

OBJECTIVE: To assess the effect on risk-adjustment of inpatient mortality rates of progressively enhancing administrative claims data with clinical data that are increasingly expensive to obtain. Data Sources. Claims and abstracted clinical data on patients hospitalized for 5 medical conditions and 3 surgical procedures at 188 Pennsylvania hospitals from July 2000 through June 2003. METHODS: Risk-adjustment models for inpatient mortality were derived using claims data with secondary diagnoses limited to conditions unlikely to be hospital-acquired complications. Models were enhanced with one or more of 1) secondary diagnoses inferred from clinical data to have been present-on-admission (POA), 2) secondary diagnoses not coded on claims but documented in medical records as POA, 3) numerical laboratory results from the first hospital day, and 4) all available clinical data from the first hospital day. Alternative models were compared using c-statistics, the magnitude of errors in prediction for individual cases, and the percentage of hospitals with aggregate errors in prediction exceeding specified thresholds. RESULTS: More complete coding of a few under-reported secondary diagnoses and adding numerical laboratory results to claims data substantially improved predictions of inpatient mortality. Little improvement resulted from increasing the maximum number of available secondary diagnoses or adding additional clinical data. CONCLUSIONS: Increasing the completeness and consistency of reporting a few secondary diagnosis codes for findings POA and merging claims data with numerical laboratory values improved risk adjustment of inpatient mortality rates. Expensive abstraction of additional clinical information from medical records resulted in little further improvement.

Responding to the challenge of co-occurring disorders: suggestions for future research.

This special issue consolidates some recent research findings and scientific thought on co-occurring disorders from both the substance abuse and mental health fields. This summary article recaps and synthesizes the main findings and themes, then considers additional issues in the field today to arrive at an agenda for future co-occurring disorders research. Plans must: (1) encourage and assist further development of treatment programs that respond to an array of types and severities of co-occurring disorders while taking into account the limited resources typically available; (2) continue the development and testing of continuing care models by exploring strategies that will sustain the recovery of treated individuals who remain vulnerable to relapse; and (3) contribute to our understanding of the mechanisms and processes that enable new interventions and practices to be adopted, implemented, and sustained. "Co-occurring disorders" is a relatively new area of research; this special issue illustrates the productivity of work to date and indicates the potential for advances to come.

Enhancement of claims data to improve risk adjustment of hospital mortality.

CONTEXT: Comparisons of risk-adjusted hospital performance often are important components of public reports, pay-for-performance programs, and quality improvement initiatives. Risk-adjustment equations used in these analyses must contain sufficient clinical detail to ensure accurate measurements of hospital quality. OBJECTIVE: To assess the effect on risk-adjusted hospital mortality rates of adding present on admission codes and numerical laboratory data to administrative claims data. DESIGN, SETTING, AND PATIENTS: Comparison of risk-adjustment equations for inpatient mortality from July 2000 through June 2003 derived by sequentially adding increasingly difficult-to-obtain clinical data to an administrative database of 188 Pennsylvania hospitals. Patients were hospitalized for acute myocardial infarction, congestive heart failure, cerebrovascular accident, gastrointestinal tract hemorrhage, or pneumonia or underwent an abdominal aortic aneurysm repair, coronary artery bypass graft surgery, or craniotomy. MAIN OUTCOME MEASURES: C statistics as a measure of the discriminatory power of alternative risk-adjustment models (administrative, present on admission, laboratory, and clinical for each of the 5 conditions and 3 procedures). RESULTS: The mean (SD) c statistic for the administrative model was 0.79 (0.02). Adding present on admission codes and numerical laboratory data collected at the time of admission resulted in substantially improved risk-adjustment equations (mean [SD] c statistic of 0.84 [0.01] and 0.86 [0.01], respectively). Modest additional improvements were obtained by adding more complex and expensive to collect clinical data such as vital signs, blood culture results, key clinical findings, and composite scores abstracted from patients' medical records (mean [SD] c statistic of 0.88 [0.01]). CONCLUSIONS: This study supports the value of adding present on admission codes and numerical laboratory values to administrative databases. Secondary abstraction of difficult-to-obtain key clinical findings adds little to the predictive power of risk-adjustment equations.

Treatment patterns and outcomes of depressed medically ill and non-medically ill patients in community psychiatric practice.

The prevalence of depression among the medically ill, the recognition of depression in general medical practice, and the association between depression and medical illness have all been a focus for research in recent years. Less is known about the process and outcomes of depression care in the medically ill compared with the non-medically ill, but some studies suggest that those with concomitant physical illness have poorer outcomes. In a study of community psychiatric practice, a sample of 53 patients with no medical comorbidity (NMI) was compared with 50 patients, categorized by higher (HMI) or lower (LMI) levels of physical comorbidity, approximately 5 months after beginning treatment for a current episode of major depression. No differences were found in treatments received or in mental health outcomes between the three groups. The HMI group showed greater impairment in social and occupational functioning at baseline and significantly greater improvement in these variables at follow-up. Since medical comorbidity does not appear to adversely affect treatment decisions or outcomes in community psychiatric practice, depressed, physically ill patients should be encouraged to seek treatment, regardless of their medical condition or level of disability.

The acceptability of treatment for depression among African-American, Hispanic, and white primary care patients.

BACKGROUND: Ethnic minority patients are less likely than white patients to receive guideline-concordant care for depression. It is uncertain whether racial and ethnic differences exist in patient beliefs, attitudes, and preferences for treatment. METHODS: A telephone survey was conducted of 829 adult patients (659 non-Hispanic whites, 97 African Americans, 73 Hispanics) recruited from primary care offices across the United States who reported 1 week or more of depressed mood or loss of interest within the past month and who met criteria for Major Depressive Episode in the past year. Within this cohort, we examined differences among African Americans, Hispanics, and whites in acceptability of antidepressant medication and acceptability of individual counseling. RESULTS: African Americans (adjusted OR, 0.30; 95% CI 0.19-0.48) and Hispanics (adjusted OR, 0.44; 95% CI, 0.26-0.76) had lower odds than white persons of finding antidepressant medications acceptable. African Americans had somewhat lower odds (adjusted OR, 0.63; 95% CI, 0.35-1.12), and Hispanics had higher odds (adjusted OR, 3.26; 95% CI, 1.08-9.89) of finding counseling acceptable than white persons. Some negative beliefs regarding treatment were more prevalent among ethnic minorities; however adjustment for these beliefs did not explain differences in acceptability of treatment for depression. CONCLUSIONS: African Americans are less likely than white persons to find antidepressant medication acceptable. Hispanics are less likely to find antidepressant medication acceptable, and more likely to find counseling acceptable than white persons. Racial and ethnic differences in beliefs about treatment modalities were found, but did not explain differences in the acceptability of depression treatment. Clinicians should consider patients' cultural and social context when negotiating treatment decisions for depression. Future research should identify other attitudinal barriers to depression care among ethnic minority patients.

Do family physicians and internists differ in knowledge, attitudes, and self-reported approaches for depression?

OBJECTIVES: The purpose of this investigation was to assess the relationship of primary care specialty training with self-assessed skill, knowledge, attitudes, and behavior toward depression recognition and management. METHOD: A baseline self-report questionnaire was administered to 184 internists and 138 family physicians participating in a multisite depression intervention study. RESULTS: There were no marked differences in knowledge of internists and family physicians regarding depression, in attitudes about the effectiveness of specific therapies, or in barriers to providing optimum treatment for depression. However, compared to internists, family physicians rated themselves as more skilled in the management of depression. When considering management of patients with moderate to severe depression, family physicians were more likely to report that they prescribed a selective serotonin-reuptake inhibitor (relative odds (RO) = 3.51, 95 percent Confidence interval (CI) [2.19, 5.60] and to personally counsel patients (RO = 1.97, 95 percent CI [1.16, 3.38]) more than half the patients, but were less likely to refer to a specialist in mental health (RO = 0.52, 95 percent CI [0.33, 0.82]) than were internists. Additional potentially influential characteristics did not wholly account for the reported differences in practice according to specialty. Physicians of both specialties expressed considerable uncertainty in their knowledge of psychotherapy and in their evaluation of the effectiveness of other strategies for the prevention of recurrence of depression. CONCLUSION: Strategies to improve mental health care should account for the orientation of primary care physicians to mental health issues.

Research development mechanisms.

Studies designed to reduce the burden of affective disorders should apply and develop theories and methods from diverse social sciences that could strengthen current interventions. A series of papers from diverse fields, such as quality engineering, behavioral economics, etc. might be a needed first step. Methodological research on design strategies such as group-level randomized trials, or instrumental variables analyses are needed. Finaly, qualitative studies to understand diverse stakeholder views are also needed. To pursue these areas, interdisciplinary training programs are needed to develop skilled researchers to study communities and community-based delivery settings. New research infrastructures are needed to support community and research collaborations, as well as supporting development of new technologies to enable diffusion of care.