Skip to main content
SpringerLink
Log in

A Fay-Herriot Model for Estimating Subjective Cognitive Decline among Military Veterans

  • Original Research
  • Published:
The Journal of Prevention of Alzheimer's Disease Aims and scope Submit manuscript

Abstract

Background

Although studies have examined the geographic distribution of dementia among the general population in order to develop geographically targeted interventions, no studies have examined the geographic distribution of subjective cognitive decline (SCD) among military veterans specifically.

Objectives

To map the geographic distribution of subjective cognitive decline from 2011–2019 in the United States among military veterans.

Design

Cross-sectional.

Setting

United States.

Participants

Individuals reporting previous service in the United States Armed Forces.

Measurements

Using 2011 Behavioral Risk Factor Surveillance System (BRFSS) data, which is last year for which geocoded SCD data is publicly available, we estimated the survey-weighted county-level prevalence of veteran SCD for counties with >30 veterans (43 counties in 7 states). We then developed a Fay-Herriot small area estimation linear model using auxiliary data from the Census, with county-level veteran-specific covariates including % >65 years old, % female, % college educated, and median income. Following model validation, we created beta-weighted predictions of veteran SCD for all USA counties for 2011–2019 using relevant time-specific Census auxiliary data. We provide choropleth maps of our predictions.

Results

Results of our model on 43 counties showed that county-level rates of SCD were significantly associated with all auxiliary variables except annual income (F = 1.49, df = 4, 38). Direct survey-weighted rates were correlated with model-predicted rates in 43 counties (Pearson r = 0.32). Regarding predicted rates for the entire USA, the average county-level prevalence rate of veteran SCD in 2011 was 13.83% (SD = 7.35), but 29.13% in 2019 (SD = 14.71) — although variation in these rates were evident across counties.

Conclusions

SCD has increased since 2011 among veterans. Veterans Affairs hospitals should implement plans that include cognitive assessments, referral to resources, and monitoring patient progress, especially in rural areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2

Similar content being viewed by others

Availability of data and material: https://www.cdc.gov/brfss/annual_data/annual_2011.htm

Code availability: Not applicable.

References

  1. Alzheimer’s Association. 2018 Alzheimer’s disease facts and figures. Alzheimer’s and Dementia. 2018;14:367–429 1. https://doi.org/10.1016/j.jalz.2018.02.001.

    Article  Google Scholar 

  2. Crous-Bou M, Minguillón C, Gramunt N, et al. Alzheimer’s disease prevention: From risk factors to early intervention. Alzheimer’s Research and Therapy. 2017; 9:71 2. https://doi.org/10.1186/s13195-017-0297-z.

    Article  Google Scholar 

  3. Alzheimer’s Association. 2021 Alzheimer’s disease facts and figures. Special report. Race, ethnicity and Alzheimer’s in America. Chicago, IL: Alzheimer’s Association. Retrieved 03/292021 from https://www.alz.org/media/Documents/alzheimers-facts-and-figures.pdf.

  4. Rajan KB, Weuve J, Barnes LL, McAninch EA, Wilson RS, Evans DA. Population estimate of people with clinical AD and mild cognitive impairment in the United States (2020–2060). Alzheimer’s & Dementia. In press.

  5. Alzheimer’s Association. Fact sheet. 2020. Retrieved 03/29/2021 from https://www.alz.org/media/Documents/subjective-cognitive-decline-in-us.pdf

  6. Centers for Disease Control and Prevention. Subjective cognitive decline — a public health issue. 2019. Retrieved 03/29/2021 from https://www.cdc.gov/aging/agingdata/docs/subjective-cognitive-decline-508.pdf

  7. Taylor CA, Bouldin ED, McGuire LC. Subjective cognitive decline among adults aged >45 years — United States, 2015–2016. Morbidity and Mortality Weekly Report. 2018;67(27):753–757 7. https://doi.org/10.15585/mmwr.mm6727a1.

    Article  Google Scholar 

  8. Weiner MW, Friedl KE, Pacifico A, et al. Military risk factors for Alzheimer’s disease. Alzheimer’s and Dementia. 2013;9(4):445–451 8. https://doi.org/10.1016/j.jalz.2013.03.005.

    Article  Google Scholar 

  9. Kirson NY, Meadows ES, Desai U, Smith BP, Cheung HC, Zuckerman P, Matthews BR. Temporal and geographic variation in the incidence of Alzheimer’s disease diagnosis in the US between 2007–2014. Journal of the American Geriatrics Society. 2019;68(2): 346–353 9. https://doi.org/10.1111/jgs.16262.

    Article  Google Scholar 

  10. Fowler M, DiBlasio C, Crowe M, Kennedy RE. Geographic disparities in county-level prevalence of Alzheimer’s disease across the United States. Innovations in Aging. 2019;3(1):S782.

    Article  Google Scholar 

  11. Centers for Disease Control and Prevention. BRFSS 2011 survey data and documentation. 2011. Retrieved 1/2/2021 from https://www.cdc.gov/brfss/annual_data/annual_2011.htm.

  12. Fay RE, Herriot RA. Estimates of income for small places: An application of James-Stein procedures to census data. Journal of the American Statistical Association. 1979;74:269–277 12. https://doi.org/10.2307/2286322.

    Article  Google Scholar 

  13. Li H, Lahiri P. An adjusted maximum likelihood method for solving small area estimation problems. J Multivariate Analysis. 2010;101(4):882–982 13. https://doi.org/10.1016/j.jmva.2009.10.009.

    Article  Google Scholar 

  14. Parker JD, Talih M, Malec DJ, et al. National Center for Health Statistics data -presentation standards for proportions. National Center for Health Statistics. Vital Health Statistics. 2017;2(175):1–22 14. https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.

    Google Scholar 

  15. United States Census Bureau. Advanced search. 2021. Retrieved 1/5/2021 from https://data.census.gov/cedsci/advanced.

  16. Evans DA, Hebert LE, Beckett LA, Scherr PA, Albert MS, Chown MJ, Pilgrim DM, Taylor JO. Education and other measures of socioeconomic status and risk of incident Alzheimer disease in a defined population of older persons. Archives of Neurology. 1997;54:1399–1405. 16. https://doi.org/10.1001/archneur.1997.00550230066019

    Article  CAS  Google Scholar 

  17. VanHoorhis CRW, Morgan BL. Understanding power and rules of thumb for determining sample sizes. Tutorials in Quantitative Methods for Psychology. 2017;3(2):43–50. 17. https://doi.org/10.20982/tqmp.03.2.p043

    Google Scholar 

  18. Zhang X, Holt JB, Yun S, Lu H, Greenlund KJ, Croft JB. Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the Behavioral Risk Factor Surveillance System. American Journal of Epidemiology. 2015;182(2):127–137. 18. https://doi.org/10.1093/aje/kwv002

    Article  Google Scholar 

  19. Barnett JH, Lewis L, Blackwell AD, Taylor M. Early intervention in Alzheimer’s disease: A health economic study of the effects of diagnostic timing. BMC Neurology. 2014;14,101. 19. https://doi.org/10.1186/1471-2377-14-101

  20. Lopez C, Caputo A, Liu F, Riviere ME, Rouzade-Dominguez ML, Thomas RG, Langbaum JB, Lenz R, Reiman EM, Graf A, Tariot PN. The Alzheimer’s prevention initiative generation program: Evaluating CNP520 efficacy in the prevention of Alzheimer’s disease. Journal of the Prevention of Alzheimer’s Disease. 2017;4(4):242–246. 20. https://doi.org/10.14283/jpad.2017.37

    Google Scholar 

  21. Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C. Potential for primary prevention of Alzheimer’s disease: An analysis of population based data. Lancet Neurology. 2014;13:788–794. 21. https://doi.org/10.1016/S1474-4422(14)70136-X

    Article  Google Scholar 

  22. United States Department of Veterans Affairs. MOVE! Weight management program. 2021. Retrieved 3/9/2021 from https://www.move.va.gov/.

  23. Haun JN, Paykel J, Alman AC, Patel N, Melillo C. A complementary and integrative health group-based program pilot demonstrates positive health outcomes with female veterans. EXPLORE. 2020;16(2):85–89. 23. https://doi.org/10.1016/j.explore.2019.08.001

    Article  Google Scholar 

Download references

Funding

Funding: No funding was received for this study.

Author information

Authors and Affiliations

  1. Southern Illinois University Carbondale, Carbondale, USA

    Justin T. McDaniel, R. J. McDermott & T. Schneider

Authors
  1. Justin T. McDaniel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. J. McDermott
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Justin T. McDaniel.

Ethics declarations

Conflicts of interest/Competing interests: The authors have no conflicts of interest to disclose.

Ethical standards: Because this study used de-identiied data freely available on the web, this study was considered exempt from IRB review.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

McDaniel, J.T., McDermott, R.J. & Schneider, T. A Fay-Herriot Model for Estimating Subjective Cognitive Decline among Military Veterans. J Prev Alzheimers Dis 8, 457–461 (2021). https://doi.org/10.14283/jpad.2021.28

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.14283/jpad.2021.28

Key words

Search

Navigation