Evaluating disparity of subjective cognitive decline between male veterans and non-veterans in the United States using propensity score matching estimation: A behavioral risk factor surveillance system survey cross-sectional study

Subjective cognitive decline (SCD) is one of the most important early onset symptoms of Alzheimer's Disease. Previous studies consistently reported that SCD is associated with quality of life, socio-economic factors, and related health comorbidities. However, the impact of veteran status on SCD has been little investigated. This study conducted a cross-sectional study to address disproportionate effects in subjective cognitive decline between veterans and non-veterans in the United States. Propensity score matching (PSM) was applied in this observational study to achieve covariate balancing and reduce selection bias, providing a more accurate estimate of the isolated effect of veteran status on SCD. Our study utilized 32,431 forty-five years or older non-institutionalized White, Black or African-American, and Hispanic or Latin-American male population from the 2019 Behavioral Risk Factor Surveillance System data. We first identified 10,685 paired PSM samples for the binary veteran status using the preselected covariates. Next, we performed a logistic regression for modeling the relationship between the veteran status and the SCD status using the PSM samples along with the covariates selected by a BIC-based stepwise selection. Our analyses revealed a statistically significant causal association between veteran status and SCD after PSM (odds ratio (OR): 1.16 and 95% confidence interval (CI): 1.06-1.27). We obtained a similar result before PSM with an OR of 1.20 and 95% CI of 1.10-1.31. When we focused on a minority group (Black or African-American males), we found a significantly increased veterans' risk of SCD, especially after propensity score matching (OR: 1.69, 95% CI: 1.16-2.45). We also found several factors such as employment status, difficulty dressing/walking/running errands, general health status, physical health status, unaffordability of medical costs, mental health status, and comorbid conditions including stroke, blindness, high cholesterol, and arthritis as statistically significantly associated with SCD (P-value < 0.05). Similar to post-traumatic stress disorder and traumatic brain injury, our study demonstrated a causal association between SCD and military-related activities in the United States, which has a disproportionate impact on the minority population. This study sets the groundwork to further research in this domain to diagnose neurological diseases early among veterans.

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PLoS ONE
Published
2024-09-13
DOI
https://doi.org/10.1371/journal.pone.0310102
Citations
1
Primary Topic
Advanced Causal Inference Techniques
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article
Field-Weighted Citation Impact
0.71

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article

Evaluating disparity of subjective cognitive decline between male veterans and non-veterans in the United States using propensity score matching estimation: A behavioral risk factor surveillance system survey cross-sectional study

Man Sik Park, Seong‐Tae Kim, Chinenye Ifebirinachi
1 citations
PLoS ONE
Advanced Causal Inference Techniques
0.71
article

Evaluating disparity of subjective cognitive decline between male veterans and non-veterans in the United States using propensity score matching estimation: A behavioral risk factor surveillance system survey cross-sectional study

Man Sik Park, Seong‐Tae Kim, Chinenye Ifebirinachi
article en
1 citations

Abstract

Subjective cognitive decline (SCD) is one of the most important early onset symptoms of Alzheimer's Disease. Previous studies consistently reported that SCD is associated with quality of life, socio-economic factors, and related health comorbidities. However, the impact of veteran status on SCD has been little investigated. This study conducted a cross-sectional study to address disproportionate effects in subjective cognitive decline between veterans and non-veterans in the United States. Propensity score matching (PSM) was applied in this observational study to achieve covariate balancing and reduce selection bias, providing a more accurate estimate of the isolated effect of veteran status on SCD. Our study utilized 32,431 forty-five years or older non-institutionalized White, Black or African-American, and Hispanic or Latin-American male population from the 2019 Behavioral Risk Factor Surveillance System data. We first identified 10,685 paired PSM samples for the binary veteran status using the preselected covariates. Next, we performed a logistic regression for modeling the relationship between the veteran status and the SCD status using the PSM samples along with the covariates selected by a BIC-based stepwise selection. Our analyses revealed a statistically significant causal association between veteran status and SCD after PSM (odds ratio (OR): 1.16 and 95% confidence interval (CI): 1.06-1.27). We obtained a similar result before PSM with an OR of 1.20 and 95% CI of 1.10-1.31. When we focused on a minority group (Black or African-American males), we found a significantly increased veterans' risk of SCD, especially after propensity score matching (OR: 1.69, 95% CI: 1.16-2.45). We also found several factors such as employment status, difficulty dressing/walking/running errands, general health status, physical health status, unaffordability of medical costs, mental health status, and comorbid conditions including stroke, blindness, high cholesterol, and arthritis as statistically significantly associated with SCD (P-value < 0.05). Similar to post-traumatic stress disorder and traumatic brain injury, our study demonstrated a causal association between SCD and military-related activities in the United States, which has a disproportionate impact on the minority population. This study sets the groundwork to further research in this domain to diagnose neurological diseases early among veterans.

PLoS ONEVol. 19(9)
Sungshin Women's University (KR), North Carolina Agricultural and Technical State University (US)
National Science Foundation
Decent work and economic growth
Openalex Percentile: Top 26%
Advanced Causal Inference Techniques
0.71
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