Using disability-adjusted life years to assess the burden of disease and injury in Rhode Island.

OBJECTIVES: Disability-adjusted life years (DALYs) measure the burden of disease and injury in a population. We tested the feasibility of calculating DALYs to assess the burden of disease and injury in Rhode Island (RI). METHODS: We computed DALYs for the 2008 RI population using methods developed by the World Health Organization, Harvard University, and the World Bank. DALYs are a composite measure that sum years of life lost (YLLs) due to premature mortality with years lived with disability (YLDs). We calculated crude mortality, YLLs, YLDs, and DALYs for 90 major health conditions for RI and stratified them by gender and age. Calculations for YLLs and YLDs were based on five-year averages. We compared our results with U.S. and Los Angeles County, California, estimates. RESULTS: A DALYs ranking produces a different picture of RI's disease and injury burden than does mortality-based ranking. Of 90 major health conditions assessed for RI, six of the top 10 causes for mortality and DALYs were the same, but were ranked differently: ischemic heart disease, cerebrovascular disease, Alzheimer dementia and other dementias, trachea/bronchus/lung cancer, chronic obstructive pulmonary disease, and diabetes mellitus. These six conditions accounted for 59% of deaths but only 35% of DALYs. Causes and rank orders for DALYs differed between males and females and among age groups. CONCLUSIONS: Including nonfatal health conditions in an assessment of population health provides a different picture than traditional mortality-based assessments. This study demonstrates the feasibility and constraints of using DALYs to assess the burden of disease and injury at the state level.

A comparison of depression and mental distress indicators, Rhode Island Behavioral Risk Factor Surveillance System, 2006.

INTRODUCTION: Depression is a public health concern that warrants accurate population estimates. The patient health questionnaire 8 (PHQ-8) offers high sensitivity and specificity for assessing depression but is time-consuming to administer, answer, and score. We sought to determine whether 1 of 3 simpler instruments - the shorter PHQ-2 or 2 single questions from the health-related quality of life (HRQOL) module of the Behavioral Risk Factor Surveillance System (BRFSS) - could offer accuracy comparable to the PHQ-8. METHODS: We compared the depression and mental distress indicators of 2006 Rhode Island BRFSS data by using 4 types of analyses: 1) sensitivity and specificity estimates, 2) prevalence estimates, 3) multivariable logistic regression modeling of the relationship between each of the 4 indicators and 11 demographic and health risk variables, and 4) geographic distribution of prevalence. RESULTS: Compared with the PHQ-8, the 3 other measures have high levels of specificity but lower sensitivity. Depression prevalence estimates ranged from 8.6% to 10.3%. The adjusted odds ratios from logistic regression modeling were consistent. Each of the indicators was significantly associated with low income, being unable to work, current smoking, and having a disability. CONCLUSION: The PHQ-8 indicator is the most sensitive and specific and can assess depression severity. The HRQOL and PHQ-2 indicators are adequate to obtain population prevalence estimates if questionnaire length is limited.

Suicide patterns and association with predictors among Rhode Island public high school students: a latent class analysis.

OBJECTIVES: We analyzed Rhode Island's 2007 Youth Risk Behavior Survey (YRBS) data to investigate suicide patterns and their association with suicide risk predictors among public high school students. METHODS: We used latent class regression analysis of Rhode Island's 2007 YRBS data (from a random sample of 2210 public high school students) to model latent classes of suicide risk and identify predictors of latent class membership. RESULTS: Four latent classes of suicide risk were modeled and predictors were associated with each: class 1 (emotionally healthy, 74%); class 2 (considered and planned suicide, 14%) was associated with being female, having low grades, being gay/lesbian/bisexual/unsure, feeling unsafe at school, having experienced forced sexual intercourse, and self-perceived overweight; class 3 (attempted suicide, 6%) was associated with speaking a language other than English at home, being gay/lesbian/bisexual/unsure, feeling unsafe at school, and forced sexual intercourse; and class 4 (planned and attempted suicide, 6%) was associated with the previously mentioned predictors and with being in 9th or 10th grade and currently smoking. CONCLUSIONS: A single model characterized and quantified 4 patterns of suicide risk among adolescents and identified predictors for 3 at-risk classes. Interventions for high-risk youths may help prevent adolescent suicides.

Adolescent suicide and health risk behaviors: Rhode Island's 2007 Youth Risk Behavior Survey.

BACKGROUND: Suicide is the third-leading cause of death among high school students in the U.S. PURPOSE: This study examined the relationships among indicators of depressed mood, suicidal thoughts, suicide attempts, and demographics and risk behaviors in Rhode Island high school students. METHODS: Data from Rhode Island's 2007 Youth Risk Behavior Survey were utilized for this study. The statewide sample contained 2210 randomly selected public high school students. Data were analyzed in 2008 to model for each of five depressed mood/suicide indicators using multivariable logistic regression. RESULTS: By examining depressed mood and suicide indicators through a multivariable approach, the strongest predictors were identified, for multiple as well as specific suicide indicators. These predictors included being female, having low grades, speaking a language other than English at home, being lesbian/gay/bisexual/unsure of sexual orientation, not going to school as a result of feeling unsafe, having been a victim of forced sexual intercourse, being a current cigarette smoker, and having a self-perception of being overweight. CONCLUSIONS: The strength of associations between three factors (immigrant status, feeling unsafe, and having forced sex) and suicide indicators adds new information about potential predictors of suicidal behavior in adolescents.

Using item response theory to analyze the relationship between health-related quality of life and health risk factors.

Many researchers have presented results of the relationships between health-related quality of life (HRQOL) indicators (outcomes) and health risk factors using either linear or logistic regression modeling. We combined the results of multiple HRQOL models by using item response theory (IRT) to assess the association between multiple correlated HRQOL indicators and multiple demographic and health risk variables as predictors. The data source for the study was Rhode Island's 2004 Behavioral Risk Factor Surveillance System, which had a sample of 3,999 adults aged 18 years or older. We developed a single model for overall HRQOL by using IRT to assess the association between HRQOL indicators and multiple demographic and health risk variables as predictors. The strongest predictors for overall poor HRQOL were lower income, inability to work, unemployment, smoking, lack of exercise, asthma, obesity, and disability. IRT may serve as a solution for modeling multiple correlated outcomes in epidemiology. Application of IRT to epidemiologic data can help identify at-risk subgroups for targeted interventions.

Patterns of health-related quality of life and patterns associated with health risks among Rhode Island adults.

BACKGROUND: Health-related quality of life (HRQOL) has become an important consideration in assessing the impact of chronic disease on individuals as well as in populations. HRQOL is often assessed using multiple indicators. The authors sought to determine if multiple indicators of HRQOL could be used to characterize patterns of HRQOL in a population, and if so, to examine the association between such patterns and demographic, health risk and health condition covariates. METHODS: Data from Rhode Island's 2004 Behavioral Risk Factor Surveillance System (BRFSS) were used for this analysis. The BRFSS is a population-based random-digit-dialed telephone survey of adults ages 18 and older. In 2004 RI's BRFSS interviewed 3,999 respondents. A latent class regression (LCR) model, using 9 BRFSS HRQOL indicators, was used to determine latent classes of HRQOL for RI adults and to model the relationship between latent class membership and covariates. RESULTS: RI adults were categorized into four latent classes of HRQOL. Class 1 (76%) was characterized by good physical and mental HRQOL; Class 2 (9%) was characterized as having physically related poor HRQOL; Class 3 (11%) was characterized as having mentally related poor HRQOL; and Class 4 (4%) as having both physically and mentally related poor HRQOL. Class 2 was associated with older age, being female, unable to work, disabled, or unemployed, no participation in leisure time physical activity, or with having asthma or diabetes. Class 3 was associated with being female, current smoking, or having asthma or disability. Class 4 was associated with almost all the same predictors of Classes 2 and 3, i.e. older age, being female, unable to work, disabled, or unemployed, no participation in leisure time physical activity, current smoking, with having asthma or diabetes, or with low income. CONCLUSION: Using a LCR model, the authors found 4 distinct patterns of HRQOL among RI adults. The largest class was associated with good HRQOL; three smaller classes were associated with poor HRQOL. We identified the characteristics of subgroups at higher-risk for each of the three classes of poor HRQOL. Focusing interventions on the high-risk populations may be one approach to improving HRQOL in RI.

Correlates of the stages of change for physical activity in a population survey.

OBJECTIVES: We sought to identify variables associated with being in a particular stage of change for physical activity-a measure of behavioral intention to engage in regular physical activity. Understanding behavioral intentions can be useful in explaining why individuals are physically inactive or active. METHODS: Data from the Rhode Island 2000 Behavioral Risk Factor Surveillance System were used to evaluate predictors of stage of change for physical activity. There were 3454 observations in the data set, representing a weighted population of 742636 people. Estimates were obtained from polytomous multiple logistic models. RESULTS: Being a woman, Hispanic, non-Hispanic Black, and older than 55 years of age were associated with being in precontemplation and contemplation stages of change rather than maintenance. Self-perceived health status and rarely feeling healthy or full of energy were strongly predictive of stage of change. Having a health limitation was a dichotomous predictor, predicting being in action and precontemplation stages. CONCLUSIONS: Several sociodemographic and health variables were associated with varying patterns of stages of change for physical activity. The complexity of individual intentions for physical activity provides evidence for the potential existence of mediating, effect-modifying, and confounding variables that differ depending on individual characteristics.

Associations between health-related quality of life and demographics and health risks. Results from Rhode Island's 2002 behavioral risk factor survey.

BACKGROUND: Health-Related Quality of Life (HRQOL) has received much attention in recent years. HRQOL indicators have been used to track population trends, identify health disparities, and monitor progress in achieving national health objectives for 2010. Prior studies have examined health risks and HRQOL at the national level as well as at the state level. This paper examines multiple indicators of HRQOL by demographic characteristics and selected health behaviors for Rhode Island adults. METHODS: Data from Rhode Island's 2002 Behavioral Risk Factor Surveillance System (BRFSS), a random digit dialled telephone survey, were used for this study. The state wide sample contained a total of 3,843 respondents ages 18 and older. Multiple Imputation (MI) was applied to handle missing data, and data were modelled for each of 10 HRQOL indicators using multivariable logistic regression. RESULTS: By examining HRQOL through a multivariable approach we identified the strongest predictors for multiple indicators of poor HRQOL as well as predictors for specific indicators of poor HRQOL. Predictors for multiple indicators of poor HRQOL were: disability, inability to work, unemployment, lower income, lack of exercise, asthma, and smoking (specifically associated with poor mental health). CONCLUSION: Using multiple measures of HRQOL can help to assess the burden of poor health in a population, identify subgroups with unmet HRQOL needs, inform the development of targeted interventions, and monitor changes in a population's HRQOL over time. Use of these HRQOL measures in longitudinal and intervention studies is needed to increase our understanding of the causal relationships between demographics, health risk behaviors, and HRQOL.