Differential Role of Social Connectedness in Geriatric Depression Among Southeast Asian Ethnic Groups.

BACKGROUND: There is little empirical evidence on the effects of social connectedness on geriatric depression for Southeast Asians. Studies have rarely examined interethnic differences in the resilience factors for depression in this diverse population. Greater understanding is needed as the number of older Southeast Asians in the United States increases. OBJECTIVES: We sought to examine the association between social connectedness and depressive symptoms in Vietnamese, Laotian, and Cambodian elders. METHODS: Using a community-based participatory research (CBPR) approach, bilingual/bicultural staff collected demographics, the Lubben Social Network Scale, and the Geriatric Depression Scale (GDS). Univariate and multivariate regression models were constructed for the full aggregated sample and for each ethnic subgroup. RESULTS: In the full aggregated sample analysis, Southeast Asian elders were at increased risk for depression and had low social connectedness. Marriage and English proficiency were resilience factors, whereas social connectedness did not play a significant role. In disaggregated analyses, high social connectedness, marriage, and younger age were resilience factors for Vietnamese elders. English proficiency was the only significant resilience factor for Laotians, and age at the time of immigration was the only significant resilience factor for Cambodian elders. CONCLUSIONS: This study underscores the need for researchers to disaggregate data for ethnic subgroups in the Asian American population. Southeast Asian elders are at increased risk for depression and have low social connectedness. There exist important interethnic differences in resilience factors for geriatric depression, suggesting the need for more studies and interventions that are sensitive to subtle cultural differences among Southeast Asian subgroups.

Surveying linguistically challenged Southeast Asian American populations: use of a community-partnered methodology.

Southeast (SE) Asians suffer from health disparities often caused by lack of preventive care, exacerbated by linguistic barriers which exclude many from surveys that fail to include minorities. This survey of linguistically-challenged SE Asians captured health status indicators, behaviors and access. Items were based upon Public Health Management Corporation's Household Health Survey. Participants were recruited by a community-based organization serving SE Asians; interviews were conducted in nine languages. Data were weighted to match age-sex distributions of Asians in Philadelphia. Of 527 interviews completed, 57% were U.S. citizens, 48% uninsured, 23% had gone without care due to cost. English was the main language at home for 3%, yet 53% reported their primary health care site did not have an interpreter. Nearly half reported health as fair/poor, and 22% had a chronic health condition. This community survey illuminates the need to profile the health care needs of these immigrant populations.

Partnering to collect health services and public health data in hard-to-reach communities: a community-based participatory research approach for collecting community health data.

BACKGROUND: Community-based participatory research (CBPR) approaches are increasingly used in behavioral studies but also may allow public health and health services researchers to study determinants of health in hard-to-reach communities for whom existing data are missing. OBJECTIVES: We describe our experience with a CBPR project in diverse communities of limited English proficient minorities. METHODS: The process included relationship building, recognition of community needs, resource investment by community and academic partners, and shared decision-making. Community-driven implementation, academic-driven analysis, and joint reporting took advantage of diverse layers of expertise. RESULTS/LESSONS LEARNED: We mobilized eighteen community interviewers, involved researchers from three institutions, and collected public health/health services data from 525 adults in seven weeks using a 117-item survey in nine languages. CONCLUSION: A CBPR approach can make the process of collecting public health and health services data in hard-to-reach communities not only viable, but also highly successful.

Potentially inappropriate medication prescribing for elderly outpatients in Emilia Romagna, Italy: a population-based cohort study.

BACKGROUND: In the US, a growing body of epidemiological studies has documented widespread potentially inappropriate medication prescribing among the elderly in outpatient settings. However, only limited information exists in Europe. OBJECTIVE: To evaluate the prevalence of potentially inappropriate medication prescribing among elderly outpatients in Emilia Romagna, Italy and to investigate factors associated with potentially inappropriate medication prescribing in that setting. METHODS: Retrospective cohort study using the Emilia Romagna outpatient prescription claims database from 1 January 2001 to 31 December 2001 linked with information (age, sex and other variables) available from a demographic file of approximately 1 million Emilia Romagna residents aged >or=65 years. The cohort comprised 849 425 elderly patients who had at least one drug prescription during the study period. The prevalence of potentially inappropriate medication prescribing, as defined by the 2002 Beers' criteria, was measured together with predictors associated with potentially inappropriate medication prescribing. RESULTS: A total of 152 641 (18%) elderly Emilia Romagna outpatients had one or more occurrences of potentially inappropriate medication prescribing. Of these, 11.5% received prescriptions for two medications of concern and 1.7% for three or more. Doxazosin (prescribed to 23% of subjects) was the most frequently occurring potentially inappropriate prescribed medication, followed by ketorolac (20.5%), ticlopidine (18.3%) and amiodarone (12.6%). Factors associated with greater likelihood of potentially inappropriate medication prescribing were older age, overall number of drugs prescribed and greater number of chronic conditions. The odds of receiving potentially inappropriate prescribed medications were lower for females, subjects living in more urban areas and subjects with a higher income level. CONCLUSIONS: This study provides strong evidence that potentially inappropriate medication prescribing for elderly outpatients is a substantial problem in Emilia Romagna. Focusing on the prevalence of potentially inappropriate medication prescribing and associated predictors can help in the development of educational programmes targeting outpatient practitioners to influence prescribing behaviour and, therefore, reduce potentially inappropriate medication prescribing.

Severity of illness and ambulatory care-sensitive conditions.

This study describes how severity of illness may refine the definition of ambulatory care-sensitive conditions, or ACSCs. Hospital discharge abstract data from Philadelphia were combined with census data to develop population-based adjusted rates of hospitalization for diabetes and asthma, two ACSCs. By stratifying ACSC hospitalization by severity of illness, variations were observed by age, race, and gender. Minority groups may be at higher risk for less access to outpatient primary care and were observed to have higher rates of more severely ill, Stage 3 hospitalization. Geographic map displays indicated wide ranges of age-sex-adjusted rates for high-severity hospitalization in the five-county Philadelphia region. This refined ACSC measure may help to identify specific groups and clinical conditions within a population to assist health care planners estimate health care resources such as facilities, manpower, and programs, as well as to evaluate their outcomes.

Modeling risk-adjusted capitation rates for Umbria, Italy.

We studied the potential effect of refining per capita financing in Italy by risk adjustment using severity of illness as well as age and gender. Data were drawn from hospital, pharmaceutical, and demographic files for the entire population of the Umbrian region of Italy in 1997 and 1998. Hospitalization data from 1997 were used to classify patients into severity of illness categories which were hypothesized to be at risk for higher health services costs in 1998. Data on costs in 1998 were developed from hospital and pharmaceutical administrative data. Coefficients from 1997 models were used to develop predicted 1998 costs. Predicted costs in 1998 were compared to observed costs. Disease Staging models identified 155 unique clinical risk adjustment categories. These categories included 5.3% of the Umbrian population in 1997, who accounted for 21.6% of costs in the next year. In prediction models of future year costs using Umbrian data, R(2) values for Disease Staging models were 0.16, compared to values of 0.07 for a risk adjustment model used by Medicare. By identifying groups within the overall population who were more severely ill and who used more resources, these models can be used to assist health care planners estimate health care resources such as facilities, manpower, and programs.