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Strengths and Limitations of Secondary Data Sources

Demie Alaekwe

DNP in Psychiatric Mental Nursing, Walden University

NURS  8310: Epidemiology and Population Health

Dr. Rodgers

June 8, 2025

Strengths and Limitations of Secondary Data Sources

Growing concerns about mental health in the aging population highlight the urgent need for epidemiological research in this area. Three datasets with relevant findings to investigate trends in mental health in this population are the National Survey on Drug Use and Health (NSDUH), the Behavioral Risk Factor Surveillance System (BRFSS), and the National Health and Nutrition Examination Survey (NHANES). The purpose of this assignment is to discuss relevant variables in these datasets, assess the validity of each datasets, and briefly highlight how researchers have utilized these datasets.

Population Health Problem

Mental health, especially among the elderly, poses a significant public issue. Globally, at least 20 percent of the population aged 60 years and above have some form of diagnosed or undiagnosed mental illness, with depression being the most prevalent (Elshaikh et al., 2023). In the US, older Americans with mental disorders are more likely to experience disparities in accessing care, and factors like race, income, and gender may worsen disparities (US Census Bureau, 2024). In terms of geographic factors, older Americans in urban centers are more likely to report depression due to social isolation, while those in rural areas experience adequate access to psychiatric services. Recent events, such as the COVID-19 pandemic, worsened mental health due to isolation, anxiety, and loss of income. Economic hardships further exacerbate these challenges, highlighting the urgent need for tailored interventions to address the unique concerns of this population. 

Dataset One: National Survey on Drug Use and Health (NSDUH)

NSDUH is a CDC dataset, where data is collected annually with a nationally representative sample. The target population includes adolescents, adults, and older persons, while the areas of interest are mental health, substance use, and access to care (National Survey on Drug Use and Health, 2023). NSDUH estimates inform researchers, policymakers, clinicians, and the public about mental and behavioral health trends, guiding efforts to improve population health.

The Variables of Interest

The variables of interest include depression, anxiety, mental health, suicidal ideation, service utilization, and demographic factors like race, age, gender, and income (National Survey on Drug Use and Health, 2023). When examining trends in mental health, the associations among these variables can reveal useful patterns for policymakers, researchers, and the general public to tailor mental health interventions. In older adults, these variables could capture barriers to access to services, stigma, and lack of providers in certain mental health disorders. 

Validity of the Dataset

NSDUH is considered a valid and reliable source of mental health data due to its large and nationally representative sample. With an annual sample size of more than 70,000 participants, NSDUH data has a high statistical power to detect important trends and disparities in mental health (National Survey on Drug Use and Health, 2023). The recruited sample is also nationally representative, increasing the generalizability of the findings. However, self-reported data among older participants carries the risk of bias. 

Use in Prior Study

Dhinsa et al. (2023) utilized the 2017-2019 NSDUH data to investigate the predictors of mental health and utilization of substance use treatment among adults. The researchers specifically analyzed how demographic factors, such as age, race, and gender, including social determinants, influence access to mental health services. The findings of this study help demonstrate that NSDUH data is useful in assessing how multiple variables may influence access to mental health services.

Dataset Two: Behavioral Risk Factor Surveillance System (BRFSS)

BRFSS is one of the largest continuously conducted health survey systems by the CDC, targeting adults across 50 states in the US. The main objective of this data collection exercise is to monitor health-related risk behaviors, chronic health conditions, and access to preventive services through self-reported surveys (The Centers for Disease Control and Prevention, 2025). Researchers and policymakers could utilize this dataset to track determinants of health, like education, income, and housing stability and their impact on mental health outcomes.

Variables of Interest

BRFSS captures multiple variables, such as clinically diagnosed depression, anxiety, emotional support, and housing stability, which collectively reveal how interrelated factors affect mental health outcomes (The Centers for Disease Control and Prevention, 2025). Clinically diagnosed depression is usually captured through surveys, which are critical in tracking diagnosed cases and investigating disparities in access to treatment. Emotional support is a psychosocial variable that serves as a risk or protective factor, and policymakers can use its data to design tailored interventions to improve mental health outcomes among socially isolated persons.

Validity of the Dataset

BRFSS is considered a valid and reliable source of public health data, especially when studying population-level trends in chronic diseases, mental health outcomes, and health behaviors. Like NSDUH, BRFSS also employs a large sample size, recruited nationally from across the 50 states. This broad coverage improves the external validity or generalizability of the findings among subgroups. The random-digit-dialing (RDD) ensures that the collected data captures insights from diverse households, including the elderly, who usually rely on landline phones (The Centers for Disease Control and Prevention, 2025). However, over-reliance on self-reported surveys introduces the risk of biases, which can compromise validity.  

Use in Prior Study

In their cross-sectional study, Gupta (2022) used Delaware BRFSS data to investigate how ACEs might influence health outcomes among adults and established that young adults, women, racial minorities, and LGBTQ+ report high rates of ACEs with reduced mental health. The study also concluded that low educational attainment is a significant factor contributing to a reduction in mental health outcomes. The findings reveal that BRFSS data is useful for those investigating variables shaping mental health outcomes across the lifespan.

Dataset Three: The National Health and Nutrition Examination Survey (NHANES)

NHANES is a CDC dataset that contains information on what Americans drink, eat, and consume as supplements to determine the amount of nutrients in their diet. When it comes to mental health among older adults, NHANES includes information related to depression screening and other mental disorders. Annually, more than 5,000 adults and children from different communities in the US participate in the NHANES to ensure broad representation (National Health and Nutrition Examination Survey, 2024).

Variables of Interest

The variables of interest include depression screening, physical health measures, cognitive function, and social and behavioral factors. Depression screening typically assesses depressive symptoms, a major indicator of mental status. Physical measures evaluate the presence of diseases and functional limitations that can worsen mental disorders. Cognitive functions such as dementia may mask depression symptoms and can increase social isolation. Collectively, these variables interact to exacerbate or mitigate mental health disorders.  

Validity of the Dataset

The NHANES dataset is valid and reliable and could be used to study mental health disorders. The primary strength is that standardized screening tools for depression are applicable across the population, which also increases generalizability (National Health and Nutrition Examination Survey, 2024). Objective data collection through self-reports and clinical examinations can improve the accuracy of capturing mental health indicators. Behavioral variables, however, rely on self-reports, which bring biases that can limit generalizability.  

Use in Prior Study

Rezaei et al. (2024) utilized NHANES data to investigate the link between dermatologic conditions and multiple health outcomes, including mental health and sleep quality. The findings of this study help to demonstrate that NHANES is a nationally representative dataset and can provide valuable insights into population health. 

Challenges in Identifying a Proper Data Set

A researcher can encounter multiple challenges in identifying an appropriate dataset. First, regulatory and privacy compliance could significantly hinder data accessibility. Data usage agreements, for instance, could place limitations on sharing certain information with other researchers. Second, population representation issues could emerge, as most general population surveys exclude critical subgroups like institutionalized adults. Third, methodological inconsistencies tend to create variability in data collection, limiting generalizability (Yarkoni, 2020). Additionally, researchers may encounter technical and resource barriers, and in some instances, they might be forced to possess advanced statistical skills to analyze data structures. Finally, data fragmentation may limit access to the whole dataset, which creates silos, limiting data sharing and application to the general population.  

Conclusion

In conclusion, high-quality datasets like NSDUH, BRFSS, and NHANES are important for tracking and understanding mental health trends in older adults. Their validity ensures accurate and evidence-based insights, which could guide interventions and shape policy. Reliable data collection and variable consistency remain crucial for improving mental health outcomes and addressing disparities in elderly populations.

References

Dhinsa, J., Roman-Urrestarazu, A., Van Kessel, R., & Humphreys, K. (2023). Understanding predictors of mental health and substance use treatment utilization among US adults: A repeated cross-sectional study. Global Epidemiology, 5, 100109. https://doi.org/10.1016/j.gloepi.2023.100109

Elshaikh, U., Sheik, R., Saeed, R. K., Chivese, T., & Alsayed Hassan, D. (2023). Barriers and facilitators of older adults for professional mental health help-seeking: A systematic review. BMC Geriatrics, 23(1). https://doi.org/10.1186/s12877-023-04229-x

Gupta, S. (2022). First-time exploration of adverse childhood experiences among adults in Delaware using BRFSS data: A cross-sectional study. Public Health in Practice, 3, 100233. https://doi.org/10.1016/j.puhip.2022.100233

National Health and Nutrition Examination Survey. (2024, December 16). About NHANES. https://www.cdc.gov/nchs/nhanes/about/index.html

National Survey on Drug Use and Health (NSDUH). (2023, June 26). Centers for Disease Control and Prevention. https://www.cdc.gov/nchs/hus/sources-definitions/nsduh.htm

Rezaei, S., Chen, M., Kim, J., & Linos, E. (2024). Mental health, sleep and general health among individuals with Dermatologic conditions: A US population‐based study using the National Health and Nutrition Examination Survey (NHANES). Experimental Dermatology, 33(10). https://doi.org/10.1111/exd.15195

The Centers for Disease Control and Prevention. (2025, January 31). About the behavioral risk factor surveillance system (BRFSS) healthy aging data. Alzheimer's Disease and Healthy Aging Data. https://www.cdc.gov/healthy-aging-data/brfss/

US Census Bureau. (2024, August 29). Fewer older than younger adults reported mental health struggles during pandemic but results varied by socioeconomic group. Census.gov. https://www.census.gov/library/stories/2024/08/pandemic-mental-health-struggles.html

Yarkoni, T. (2020). The generalizability crisis. Behavioral and Brain Sciences, 45. https://doi.org/10.1017/s0140525x20001685