Explore the following data resources and select datasets based on key areas of interest. Researchers can request a consultation with our team to help in the process. Get in touch here.
How do I use this website?
Say you have a research question that you want to answer, but you don’t have the right data on hand to do so. You could go out and collect some new data, but doing so will take time, can be incredibly costly, and pose many challenges, especially for investigators early in their careers. Fortunately, there are many high-quality publicly available data sources that collect information on depression and mental health that provide a cost- and time-efficient means of answering research questions.
However, even when using existing datasets researchers must still contend with several challenges: 1) While the data may be publicly-available at no cost, it can take a significant amount of time to sort through the available options and find the right one to answer your question; and 2) once you have identified the appropriate data source, the learning curve can be significant and not all early career investigators may feel prepared to dive into the data themselves. The Data and Design Core is focused on addressing these two issues.
How do I choose the right dataset for my research question?
That depends entirely on the question that you are trying to answer!
There is no perfect dataset. Instead, each data source will have their own strengths and drawbacks. For example, a dataset may have a rigorous, clinically-valid measure of depression, but a limited and non-representative sample which reduces generalizability and statistical power. Alternatively, a study may have a very large, representative sample but the measure of depression may be just a couple of items. How you navigate these trade-offs is a key part of conducting rigorous scientific research.
We have organized the following resources to help you select your dataset based on key areas of interest. If you have any questions, you can also request a consultation with our team to help you in the process.
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Health and Retirement Study
The Health and Retirement Study is a longitudinal panel study of Americans over age 50 that seeks to describe the experiences of America’s aging population, including healthcare, income, assets, employment and other needs. Approximately 20,000 participants are interviewed every two years, dating back to 1992. Biomarker data has been collected since 2012.
Study design: Nationally representative, longitudinal cohort design
Data collection methods: In-person interviewing; biomarker data
Measures of depression: CES-D scale (8-item scale); Past-year history of MD from the Composite International Diagnostic Interview-Short Form. Also: Self-report physician diagnosis of emotional or mental health problems; Treatment of depression
Other measures: Vision; Hearing; Hypertension; Diabetes; Cancer; Lung problems; Cardiovascular programs; Stroke; Arthritis; Memory; Alzheimer’s & dementia; Diabetes; Sleep; Oral health; Alcohol & tobacco use; Psychiatric issues; Anxiety; Physical activity; Cognition; Healthcare utilization; Healthcare expenditures; Functional limitations; Family structure & relationships; Housing; Assets & debt; Income; Employment; Social Security & Disability; Relationship history; Internet use; Social relationships; Hobbies; Life satisfaction; Height; Weight; Walking speed; Balance; Blood pressure; Hearing; Hand strength; Saliva sample
Notable publications using this data:
- M. Lohman et al: Sex Differences in the Construct Overlap of Frailty and Depression: Evidence from the Health and Retirement Study
- A.N. Niles et al: Gender differences in longitudinal relationships between depression and anxiety symptoms and inflammation in the health and retirement study
- K. Oi: Inter-connected trends in cognitive aging and depression: Evidence from the health and retirement study
- P.G. Lee et al: The Co-Occurrence of Chronic Diseases and Geriatric Syndromes: The Health and Retirement Study
UK Biobank
Study design: Longitudinal cohort design
Data collection method: Online survey; physical measurements; genomic sequencing; biophysical samples
Measures of depression: CIDI-SF
Notable publications using this data:
- D.J. Smith et al: Prevalence and Characteristics of Probable Major Depression and Bipolar Disorder within UK Biobank: Cross-Sectional Study of 172,751 Participants
- D.M. Howard et al: Genetic stratification of depression in UK Biobank
- Y. Milaneschi: Association of inflammation with depression and anxiety: evidence for symptom-specificity and potential causality from UK Biobank and NESDA cohorts
- B.I. Nicholl et al: Chronic multisite pain in major depression and bipolar disorder: cross-sectional study of 149,611 participants in UK Biobank
Notes: Access to the UK Biobank dataset requires additional registration. Contact our team if you are interested in using this dataset, and we can assist you with access.