Steven G. Heeringa

Bio: Steven G. Heeringa is an academic researcher from University of Michigan. The author has contributed to research in topics: Poison control & Population. The author has an hindex of 55, co-authored 170 publications receiving 19561 citations. Previous affiliations of Steven G. Heeringa include Max Planck Society & University of Iowa.

Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization World Mental Health Surveys.

Abstract: Context Little is known about the extent or severity of untreated mental disorders, especially in less-developed countries. Objective To estimate prevalence, severity, and treatment of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) mental disorders in 14 countries (6 less developed, 8 developed) in the World Health Organization (WHO) World Mental Health (WMH) Survey Initiative. Design, Setting, and Participants Face-to-face household surveys of 60463 community adults conducted from 2001-2003 in 14 countries in the Americas, Europe, the Middle East, Africa, and Asia. Main Outcome Measures The DSM-IV disorders, severity, and treatment were assessed with the WMH version of the WHO Composite International Diagnostic Interview (WMH-CIDI), a fully structured, lay-administered psychiatric diagnostic interview. Results The prevalence of having any WMH-CIDI/DSM-IV disorder in the prior year varied widely, from 4.3% in Shanghai to 26.4% in the United States, with an interquartile range (IQR) of 9.1%-16.9%. Between 33.1% (Colombia) and 80.9% (Nigeria) of 12-month cases were mild (IQR, 40.2%-53.3%). Serious disorders were associated with substantial role disability. Although disorder severity was correlated with probability of treatment in almost all countries, 35.5%.to 50.3% of serious cases in developed countries and 76.3% to 85.4% in less-developed countries received no treatment in the 12 months before the interview. Due to the high prevalence of mild and subthreshold cases, the number of those who received treatment far exceeds the number of untreated serious cases in every country. Conclusions Reallocation of treatment resources could substantially decrease the problem of unmet need for treatment of mental disorders among serious cases. Structural barriers exist to this reallocation. Careful consideration needs to be given to the value of treating some mild cases,. especially those at risk for progressing to more serious disorders.

Prevalence of dementia in the United States: the aging, demographics, and memory study.

Abstract: Aim: To estimate the prevalence of Alzheimer’s disease (AD) and other dementias in the USA using a nationally representative sample. Methods: The Aging, Demographics, and Memory Study sample was composed of 856 individuals aged 71 years and older from the nationally representative Health and Retirement Study (HRS) who were evaluated for dementia using a comprehensive in-home assessment. An expert consensus panel used this information to assign a diagnosis of normal cognition, cognitive impairment but not demented, or dementia (and dementia subtype). Using sampling weights derived from the HRS, we estimated the national prevalence of dementia, AD and vascular dementia by age and gender. Results: The prevalence of dementia among individuals aged 71 and older was 13.9%, comprising about 3.4 million individuals in the USA in 2002. The corresponding values for AD were 9.7% and 2.4 million individuals. Dementia prevalence increased with age, from 5.0% of those aged 71–79 years to 37.4% of those aged 90 and older. Conclusions: Dementia prevalence estimates from this first nation

Applied Survey Data Analysis

Abstract: Applied Survey Data Analysis: Overview Introduction A Brief History of Applied Survey Data Analysis Example Data Sets and Exercises Getting to Know the Complex Sample Design Introduction Classification of Sample Designs Target Populations and Survey Populations Simple Random Sampling: A Simple Model for Design-Based Inference Complex Sample Design Effects Complex Samples: Clustering and Stratification Weighting in Analysis of Survey Data Multistage Area Probability Sample Designs Special Types of Sampling Plans Encountered in Surveys Foundations and Techniques for Design-Based Estimation and Inference Introduction Finite Populations and Superpopulation Models Confidence Intervals for Population Parameters Weighted Estimation of Population Parameters Probability Distributions and Design-Based Inference Variance Estimation Hypothesis Testing in Survey Data Analysis Total Survey Error and Its Impact on Survey Estimation and Inference Preparation for Complex Sample Survey Data Analysis Introduction Analysis Weights: Review by the Data User Understanding and Checking the Sampling Error Calculation Model Addressing Item Missing Data in Analysis Variables Preparing to Analyze Data for Sample Subpopulations A Final Checklist for Data Users Descriptive Analysis for Continuous Variables Introduction Special Considerations in Descriptive Analysis of Complex Sample Survey Data Simple Statistics for Univariate Continuous Distributions Bivariate Relationships between Two Continuous Variables Descriptive Statistics for Subpopulations Linear Functions of Descriptive Estimates and Differences of Means Exercises Categorical Data Analysis Introduction A Framework for Analysis of Categorical Survey Data Univariate Analysis of Categorical Data Bivariate Analysis of Categorical Data Analysis of Multivariate Categorical Data Exercises Linear Regression Models Introduction The Linear Regression Model Four Steps in Linear Regression Analysis Some Practical Considerations and Tools Application: Modeling Diastolic Blood Pressure with the NHANES Data Exercises Logistic Regression and Generalized Linear Models (GLMs) for Binary Survey Variables Introduction GLMs for Binary Survey Responses Building the Logistic Regression Model: Stage 1, Model Specification Building the Logistic Regression Model: Stage 2, Estimation of Model Parameters and Standard Errors Building the Logistic Regression Model: Stage 3, Evaluation of the Fitted Model Building the Logistic Regression Model: Stage 4, Interpretation and Inference Analysis Application Comparing the Logistic, Probit, and Complementary Log-Log GLMs for Binary Dependent Variables Exercises GLMs for Multinomial, Ordinal, and Count Variables Introduction Analyzing Survey Data Using Multinomial Logit Regression Models Logistic Regression Models for Ordinal Survey Data Regression Models for Count Outcomes Exercises Survival Analysis of Event History Survey Data Introduction Basic Theory of Survival Analysis (Nonparametric) Kaplan-Meier Estimation of the Survivor Function Cox Proportional Hazards Model Discrete Time Survival Models Exercises Multiple Imputation: Methods and Applications for Survey Analysts Introduction Important Missing Data Concepts An Introduction to Imputation and the Multiple Imputation Method Models for Multiply Imputing Missing Data Creating the Imputations Estimation and Inference for Multiply Imputed Data Applications to Survey Data Exercises Advanced Topics in the Analysis of Survey Data Introduction Bayesian Analysis of Complex Sample Survey Data Generalized Linear Mixed Models (GLMMs) in Survey Data Analysis Fitting Structural Equation Models to Complex Sample Survey Data Small Area Estimation and Complex Sample Survey Data Nonparametric Methods for Complex Sample Survey Data References Appendix: Software Overview

Prevalence of cognitive impairment without dementia in the United States.

Abstract: Results: In 2002, an estimated 5.4 million people (22.2%) in the United States age 71 years or older had cognitive impairment without dementia. Prominent subtypes included prodromal Alzheimer disease (8.2%) and cerebrovascular disease (5.7%). Among participants who completed follow-up assessments, 11.7% with cognitive impairment without dementia progressed to dementia annually, whereas those with subtypes of prodromal Alzheimer disease and stroke progressed at annual rates of 17% to 20%. The annual death rate was 8% among those with cognitive impairment without dementia and almost 15% among those with cognitive impairment due to medical conditions. Limitations: Only 56% of the nondeceased target sample completed the initial assessment. Population sampling weights were derived to adjust for at least some of the potential bias due to nonresponse and attrition. Conclusion: Cognitive impairment without dementia is more prevalent in the United States than dementia, and its subtypes vary in prevalence and outcomes.

Lifetime Prevalence and Age-of-Onset Distributions of DSM-IV Disorders in the National Comorbidity Survey Replication

Abstract: Context Little is known about lifetime prevalence or age of onset of DSM-IV disorders. Objective To estimate lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the recently completed National Comorbidity Survey Replication. Design and Setting Nationally representative face-to-face household survey conducted between February 2001 and April 2003 using the fully structured World Health Organization World Mental Health Survey version of the Composite International Diagnostic Interview. Participants Nine thousand two hundred eighty-two English-speaking respondents aged 18 years and older. Main Outcome Measures Lifetime DSM-IV anxiety, mood, impulse-control, and substance use disorders. Results Lifetime prevalence estimates are as follows: anxiety disorders, 28.8%; mood disorders, 20.8%; impulse-control disorders, 24.8%; substance use disorders, 14.6%; any disorder, 46.4%. Median age of onset is much earlier for anxiety (11 years) and impulse-control (11 years) disorders than for substance use (20 years) and mood (30 years) disorders. Half of all lifetime cases start by age 14 years and three fourths by age 24 years. Later onsets are mostly of comorbid conditions, with estimated lifetime risk of any disorder at age 75 years (50.8%) only slightly higher than observed lifetime prevalence (46.4%). Lifetime prevalence estimates are higher in recent cohorts than in earlier cohorts and have fairly stable intercohort differences across the life course that vary in substantively plausible ways among sociodemographic subgroups. Conclusions About half of Americans will meet the criteria for a DSM-IV disorder sometime in their life, with first onset usually in childhood or adolescence. Interventions aimed at prevention or early treatment need to focus on youth.

Prevalence, Severity, and Comorbidity of 12-Month DSM-IV Disorders in the National Comorbidity Survey Replication

Abstract: Background Little is known about the general population prevalence or severity of DSM-IV mental disorders. Objective To estimate 12-month prevalence, severity, and comorbidity of DSM-IV anxiety, mood, impulse control, and substance disorders in the recently completed US National Comorbidity Survey Replication. Design and Setting Nationally representative face-to-face household survey conducted between February 2001 and April 2003 using a fully structured diagnostic interview, the World Health Organization World Mental Health Survey Initiative version of the Composite International Diagnostic Interview. Participants Nine thousand two hundred eighty-two English-speaking respondents 18 years and older. Main Outcome Measures Twelve-month DSM-IV disorders. Results Twelve-month prevalence estimates were anxiety, 18.1%; mood, 9.5%; impulse control, 8.9%; substance, 3.8%; and any disorder, 26.2%. Of 12-month cases, 22.3% were classified as serious; 37.3%, moderate; and 40.4%, mild. Fifty-five percent carried only a single diagnosis; 22%, 2 diagnoses; and 23%, 3 or more diagnoses. Latent class analysis detected 7 multivariate disorder classes, including 3 highly comorbid classes representing 7% of the population. Conclusion Although mental disorders are widespread, serious cases are concentrated among a relatively small proportion of cases with high comorbidity.