Linking Adverse Childhood Experiences and Other Risk Factors to Subjective Cognitive Decline in an Aging Population

Hailey Voyer, MPH¹; Semra Aytur, PhD, MPH²; Nicole Tanda, MPH¹ (View author affiliations)

Suggested citation for this article: Voyer H, Aytur S, Tanda N. Linking Adverse Childhood Experiences and Other Risk Factors to Subjective Cognitive Decline in an Aging Population. Prev Chronic Dis 2023;20:230182. DOI: http://dx.doi.org/10.5888/pcd20.230182.

PEER REVIEWED

Abstract

Introduction

The Centers for Disease Control and Prevention’s Healthy Brain Initiative (HBI) encourages an interdisciplinary approach to addressing the burden of subjective cognitive decline (SCD) among the aging US population as that population continues to increase. Our study is one of the first to evaluate associations between SCD and adverse childhood experiences (ACEs) and other modifiable risk factors to support implementation of the initiative.

Methods

We used multivariate logistic regression to assess data from the 2020 Behavioral Risk Factor Surveillance System survey to evaluate associations between SCD and ACEs scores and sociodemographic, behavioral, and clinical risk factors. Models were weighted to account for the complex survey design.

Results

Approximately 8.1% of survey respondents reported experiencing SCD within the past 12 months. Adjusted regression analysis showed that conditions such as depression (AOR, 2.85; 95% CI, 2.29–3.55), arthritis (AOR, 1.30; 95% CI, 1.05–1.60), and diabetes (AOR, 1.33; 95% CI, 1.05–1.68) were significantly associated with SCD. SCD was also associated with experiencing more than 3 falls per year (AOR, 2.95; 95% CI, 2.13–4.09), sleeping more than 9 hours per night (AOR, 2.06; 95% CI, 1.37–3.09), and physical inactivity (AOR, 1.32; 95% CI, 1.03–1.68). Two or more ACEs also significantly increased the odds of SCD (AOR, 1.69; 95% CI, 1.36–2.10).

Conclusion

Findings from our study can be used to inform policy, environment, and systems change efforts aimed at addressing modifiable risk factors to support healthy aging. The role of ACEs as determinants of brain health across the life course should also be considered in the design of clinical and community-based interventions.

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Introduction

In recent years, the aging population in the United States has increased following the baby boom of the 1940s to 1960s and a rising life expectancy (1). More than 16 million US adults are living with some form of cognitive impairment, costing $206 billion in annual Medicare and Medicaid payouts alone (2,3). From 1996 to 2014, the prevalence of cognitive impairment increased annually among adults aged older than 50 years by an average of 0.7% for women and 1.0% for men (4).

In 2005, the Centers for Disease Control and Prevention (CDC) established the Healthy Brain Initiative (HBI), which uses an interdisciplinary approach to address the challenges associated with cognitive decline and to promote overall brain health (5). As part of this initiative, a subjective cognitive decline (SCD) module was added to the Behavioral Risk Factor Surveillance System (BRFSS) survey in 2011 (6). Data from this module provide information on the burden of SCD, as well as risk and protective factors (6).

The objective of our study was to examine sociodemographic, behavioral, and health-related risk factors for SCD by using recent BRFSS data to support the implementation of HBI through changes to clinical practice and policy. We used the lens of the socioecological model to explore how changes at multiple levels (individual, interpersonal, community, organizational, environmental, and policy) may be used to address SCD and dementia as a complex systems issue (7).

Previous studies have used BRFSS data to identify risk factors associated with SCD, such as physical inactivity, hypertension, diet, and smoking status (8,9). Felitti et al extensively described a dose–response relationship between adverse childhood experiences (ACEs) and various health risk behaviors and chronic diseases in adulthood (10). Our study aimed to expand on prior literature by examining associations between ACEs and other risk factors for SCD to inform prevention initiatives.

We used data from the 2020 BRFSS survey to examine the independent relationship between ACEs and SCD above and beyond other known risk factors (11). We provide examples of policy, environment, and systems changes that can address risk factors across the life course in support of HBI implementation.

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Methods

Study sample

BRFSS is a cross-sectional telephone survey collected at the state level that asks US residents questions about health-related risk behaviors, chronic health conditions, and preventive services (12). Participants are noninstitutionalized adults aged 18 years or older located in all 50 states, the District of Columbia, and 3 US territories (12). States are required to ask a set of core component questions and may choose to add optional modules, including the SCD and ACEs modules (12). In 2020, the average BRFSS response rate was 47.9%, comparable to other national telephone surveys (13). A total of 103,610 participants were included in our study from the 18 states that opted to include the SCD common module. Five hundred and forty-seven survey participants responded “don’t know/not sure” or refused to answer the first SCD screening question, 30,306 were excluded for being under the age of 45, and 8,905 were not asked all the SCD questions or had missing data (n = 63,852). Only 9 states participated in both the SCD and ACEs modules, and 22,434 participants answered all 11 ACEs questions. Our final analytic sample consisted of 17,042 observations for which no data were missing for any variable in the model. CDC publishes a list of modules used, by state (14).

Measures

Our primary outcome of interest was SCD. Survey respondents were asked if they had “experienced confusion or memory loss that is happening more often or is getting worse” in the past 12 months (15). If a participant responded affirmatively, they were identified as having SCD and were then asked a series of 5 additional questions regarding their level of difficulty with day-to-day activities, whether they needed help with these activities, whether they were able to get help when needed, whether SCD interfered with socialization, and whether they had discussed their confusion or memory loss with a clinician.

Guided by prior research, we examined a total of 11 behavioral, clinical, and environmental factors in the 2020 BRFSS that were hypothesized to increase risk of SCD (8,11). These included having 2 or more ACEs; being a current or former smoker; physical inactivity; having diabetes, obesity, arthritis, coronary heart disease (CHD), stroke, or depression; having a history of recurrent falls; and the average number of hours of sleep per night (16).

Binary variables were coded as either “yes” or “no”. For variables where the survey question was asked on a Likert scale, responses of “always,” “usually,” or “sometimes” were coded as yes, and responses of “rarely” or “never” were coded as no, to be consistent with prior literature (8).

Other variables were derived and coded as follows:

ACEs score. Eleven questions (Box) regarding ACEs were converted into a summed ACEs score variable. The median ACEs score was 2. A binary variable representing an ACEs score of 2 or more was then used as a cut point for analysis.

Obesity. Obesity was defined as a body mass index (weight in kg divided by height in m²) greater than or equal to 30.

Falls. The number of falls in the last year were grouped into 3 categories: 1) none, 2) between 1 and 3 falls, and 3) more than 3 falls.

Sleep. Hours of sleep per night were grouped into 3 categories based on prior research (16): 1) short duration (<7 h/night), 2) adequate sleep (7–9 h/night), and 3) long duration (>9 h/night).

Physical inactivity. Physical inactivity was defined as no physical activity in the last 30 days, other than that associated with the respondent’s regular job.

Five sociodemographic variables were also analyzed. These were race or ethnicity (Non-Hispanic White, Non-Hispanic Black, Hispanic, or Other Non-Hispanic), age (<65 y vs ≥65 y), education (high school or less, some college or technical school, and graduated from college or technical school), sex (male or female), and poverty status (at or below the federal poverty level of $25,000 annual income for a family of 3). The BRFSS survey contains de-identified, publicly available data that did not meet the definition of research involving human subjects and thus did not require institutional review board review.

Statistical analyses

We used the SAS 9.4 statistical program (SAS Institute Inc) for all analyses. Appropriate sampling weights were applied to all analyses. BRFSS uses a 3-tiered weighting system to account for differences in probability of selecting geographic strata, density of telephone numbers in a given block, and the number of adults who use a particular telephone number (17). This system serves as a blanket adjustment for noncoverage of an area and nonresponse (17). We conducted descriptive univariate and bivariate analyses first, calculating frequencies and prevalence rates of risk factors to compare respondents with and without SCD. We used Wald χ² tests to calculate associated P values.

Next, we used the SAS SURVEYLOGISTIC procedure to conduct multivariate modeling. The first model examined associations between SCD and potential demographic, behavioral, and health-related risk factors except ACEs. A second model assessed the associations between SCD and all risk factors, including ACEs. Adjusted odds ratios (AORs) and 95% CIs were calculated. Multicollinearity was assessed by examining variance inflation factors and correlation coefficients. A factor of less than 10 and a correlation coefficient of less than 0.8 was observed, suggesting that multicollinearity was not a major concern in the analysis.

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Results

Of the 63,852 respondents who participated in the SCD module, 5,443 (8.1%, percentage weighted) reported having experienced SCD in 2020. Of these, 41.2% reported having given up day-to-day household activities or chores because of their symptoms, and 38.0% reported needing assistance with these day-to-day activities (Table 1). Of the 38.0% who reported needing assistance, 87.4% said they were able to get the help they needed from family members or friends. Additionally, 35.9% of respondents with SCD said their confusion and memory loss had interfered with their ability to engage in social activities outside the home. Less than half (45.6%) of respondents with SCD had discussed their symptoms with their health care provider.

Demographics

Compared with those without SCD, respondents who self-reported SCD were more likely to be aged 65 years or older, have incomes below the federal poverty level, and have a lower education level (Table 2).

In both the intermediate (AOR,1.46; 95% CI, 1.14–1.86) and final (AOR, 1.44; 95% CI, 1.13–1.84) models, having an income below the federal poverty level was associated with increased odds of reporting SCD compared with having an income above the federal poverty level (Table 3). Similarly, being aged 65 years or older was significant in the intermediate model (AOR, 1.35; 95% CI, 1.09–1.62) and after adjustment for ACEs in the final model (AOR, 1.45; 95% CI, 1.17–1.79). Race, sex, and education level were not significant risk factors for SCD in any model.

Health conditions

Respondents who self-reported SCD were more likely than those without to have any of the 6 health conditions measured (Table 2). After adjusting for ACEs in the final model, respondents who reported having arthritis were 30% more likely to have SCD (AOR, 1.30; 95% CI, 1.05–1.60) as were those who reported having depression, who were almost 3 times as likely to report SCD (AOR, 2.85; 95% CI, 2.29–3.55) (Table 3) as those without either condition. Having diabetes (AOR, 1.33; 95% CI, 1.05–1.68) or CHD (AOR, 1.37; 95% CI, 1.02–1.84) were also associated with an increased risk of SCD after adjusting for ACEs in the final model. Having had a stroke or having obesity were insignificant in the final model.

Health behaviors

Compared with those without SCD, those with SCD were more likely to report behavioral risk factors (Table 2). Of the 4 health behaviors examined, 3 were significantly associated with increased odds of reporting SCD after adjusting for ACEs in the final model (Table 3). Both insufficient and excessive sleep habits resulted in increased odds of reporting SCD: those sleeping less than 7 hours a night had 1.35 times the odds (AOR, 1.35; 95% CI, 1.08–1.68) and those sleeping more than 9 hours a night had more than 2 times the odds (AOR, 2.06; 95% CI, 1.37–3.09) of reporting SCD compared with those who slept between 7 and 9 hours a night in the final model. Repeated falls were strongly associated with SCD in the final model; respondents who had between 1 and 3 falls in the last year were 68.0% more likely (AOR, 1.68; 95% CI, 1.34–2.10) to report SCD than those who reported no falls, and those with more than 4 falls in the last year were almost 3 times more likely (AOR, 2.95; 95% CI, 2.13–4.09). Physical inactivity also increased a person’s odds of SCD by 32.0% (AOR, 1.32; 95% CI, 1.03–1.68) in the final model. Being a former or current smoker was not a significant risk factor for SCD in either model.

Adverse childhood events

In fully adjusted models that included ACEs controlling for other risk factors, respondents who reported 2 or more ACEs had 1.69 times greater odds of reporting SCD (AOR, 1.69; 95% CI, 1.36–2.10) compared with those who reported fewer than 2 ACEs (Table 3).

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Discussion

The literature examining risk factors for SCD has progressed in recent years to include consideration of ACEs as well as other exposures across the life course (11). Our study explored relationships between ACEs, SCD, and other risk factors by using 2020 BRFSS data collected at the height of the COVID-19 pandemic. In 2020, 8.1% of adults participating in the BRFSS SCD module reported having experienced SCD. Compared with prior years, a lower prevalence of SCD was self-reported in 2020 (18). In multivariate models, the most significant predictors of SCD were a history of depression, repeated falls, physical inactivity, and sleeping more than 9 hours per night. These modifiable risk factors could be managed appropriately if disclosed to a health care provider. For example, physical inactivity was associated with a 32.0% increased risk of SCD, consistent with prior research (9). Also consistent with prior studies, chronic conditions such as diabetes, arthritis, and CHD were found to be significant predictors of cognitive decline (1,8). Prior studies also found that adults with more than 1 comorbid chronic disease were more likely to have associated functional difficulties (1). In turn, worsening memory, as seen in SCD, makes managing these illnesses more difficult (1). Fewer than half of the respondents in the present study reported that they had discussed their SCD symptoms with a health care provider, comparable to prior years (18). This suggests an opportunity to educate both patients and clinicians about the importance of discussing SCD, reducing stigma, and framing SCD in the context of general wellness.

Lifestyle medicine is an evidence-based practice that has extensively demonstrated its ability to provide cost-effective solutions that may prevent and improve neurocognitive impairment (19). The “six pillars of lifestyle medicine for healthy aging” described by Jaqua et al include plant-forward diets (with an emphasis on plant-based foods), physical activity, stress management, avoiding substances such as alcohol and tobacco, restorative sleep, and maintaining social connections (19). Many of these 6 pillars correlate with risk factors identified in our study and should be considered fundamental to any prevention initiative addressing SCD.

We also found a significant association between SCDs and higher levels of childhood adversity, defined as experiencing 2 or more ACEs, independent of other risk factors. In the 2020 BRFSS, having experienced 2 or more ACEs was associated with a 69.0% increased risk of SCD later in life. This finding aligns with prior literature (11). Terry et al combined 2019 and 2020 BRFSS data and found that those with ACEs scores of 3 or more were more likely to report SCD as well as specific types of ACEs (sexual, physical, psychological, and environmental) (11). There is evidence to suggest that the stress induced by childhood adversity affects the development of executive functions, leading to decreased capacity for cognitive flexibility and working memory (20). This downstream effect of childhood adversity may partly explain the association between higher ACE scores and risk of SCD observed in our study.

We found no significant differences in SCD prevalence by race or education level in fully adjusted models. This finding contradicts prior research that demonstrated higher SCD prevalence among non-White groups and groups with lower levels of educational attainment (21). In contrast, poverty was associated with a 44.0% greater likelihood of SCD. This aligns with research conducted by Peterson on the effects of socioeconomic status across the life course and its effects on SCD (22). Peterson posits that social exposures are cumulative over time, and a high quality and quantity of diverse experiences over the life course may offer protection against SCD (22). Included in these experiences are factors such as education, income, and geographic area, which play a role in cognitive preservation (22). Additionally, lower educational attainment and income have been linked to lower health literacy — an individual’s ability to use information to make sound health-related decisions (23). Studies show that people with low health literacy skills are more likely to delay accessing care and lack a primary care physician (24). Care access plays a critical role in prevention or early identification of SCD. Policies that provide incentives to health care providers to screen for social determinants of health and that aim to increase opportunities for social and economic advancement may decrease the incidence of SCD among these socioeconomically vulnerable populations (25).

Furthermore, policies that support healthy behaviors across the life course, such as walkable built environments and interventions that address ACEs early in life, may help to prevent cognitive decline in later life (Table 4) (9). Chronic conditions such as obesity and diabetes are significant predictors of future cognitive impairment. Thus, policies targeting ACEs and chronic disease prevention could be framed as levers for HBI implementation, because they may help reduce the incidence of SCD and improve health outcomes.

Beyond clinical interventions, policy and systems changes can help create environments that promote healthy behaviors among older adults, reducing their risk of SCD, falls, and associated functional difficulties (Table 4).

Older adults face unique challenges related to their social, physical, and economic environments (26). For example, retirement can impose distinctive financial limitations, and transportation becomes a barrier because adults tend to drive less frequently, or not at all, as they age (26). In response to these unique challenges, 4 states have adopted Master Plans for Aging — a collection of comprehensive state and local policy approaches to address the needs of older adults and their caregivers (Table 4) (26). For example, Massachusetts Executive Order 576 (2017) established the Governor’s Council to Address Aging, which released a blueprint outlining 28 recommendations and 67 action steps across 5 areas: caregiving, employment, housing, transportation, and innovation and technology (27). The overarching goals of this blueprint aim to ensure that aging is embedded in all policies with input from older adults, residents have the resources to live a meaningful life in the community of their choice, and people of all ages have access to health and social support (27). Not only is Massachusetts heavily tackling the lifestyle medicine pillar of social connection, but it is also attempting to lessen the economic burdens of aging, increase access to care, and promote independence and mobility in an effort to prevent chronic disease. This blueprint is an important example of a policy approach that addresses the downstream influences of healthy aging.

Limitations

Our study has several limitations that warrant mention. First, BRFSS survey data are self-reported, which may challenge the validity of our results because of recall bias, potentially resulting in an underestimated prevalence of SCD. Second, survey participants are noninstitutionalized adults, excluding those in nursing homes or other long-term care facilities. Third, the BRFSS survey is conducted through random digit dialing of landlines and cellular telephones (12). Systematic biases may be introduced by excluding those who do not have access to a telephone. Nevertheless, the BRFSS survey includes a representative sample of the US population and is largely generalizable.

Another limitation is that the cross-sectional design of our study precludes causal inferences. The risk factors are particularly subject to a reverse causality effect in which the presence of cognitive decline could lead to feelings of depression, being more prone to frequent falls because of functional difficulties, or experiencing an increase in hours of sleep per night. Self-selection bias and recall bias also may affect the interpretation of the results.

Lastly, all models excluded observations in which data were missing for any given variable included in the analysis. Response bias may have been conferred by including only those who completed the ACEs questions. Previous studies found that participants without missing ACEs data may be more affluent compared with other participants (28). This reduced the final analytic sample size, potentially resulting in a loss of power. Methods such as multiple imputation can be used to address missing data; however, these methods have only recently been applied to ACEs because of their complexity and challenges in achieving model convergence. Houtepen et al applied a pragmatic imputation strategy in a recent longitudinal ACEs study, offering a method that could be explored in future research (29). Additionally, future research should examine mediation and moderation with respect to specific ACEs, other risk factors, and SCD.

An unexpected finding in our analysis was the lower prevalence of survey respondents who reported SCD in 2020 (8.1%) compared with prepandemic years. Historically, SCD prevalence has hovered around 11.0 %, as it did in 2019 (18). This may be due to a healthy worker effect, a form of selection bias, resulting from the COVID-19 pandemic (30). Respondents in 2020 were less likely to be employed, more likely to be working from home, and more likely to report good or better health than in 2019 (18). Participants who answered the survey in 2020 may have been relatively healthy individuals who would otherwise have been working outside the home as in prepandemic years. This argument is supported when comparing the prevalence of other chronic conditions in 2020 versus 2019. For example, the number of respondents who reported having had a prior stroke was 4.5% in 2019 compared with 3.9% in 2020 (15,18). Similar trends were observed for other diseases, such as diabetes, cancer, and chronic obstructive pulmonary disease (18). Future research should assess whether these trends continue in postpandemic years.

In conclusion, the results of our study can be used to support the HBI by informing primary and secondary prevention interventions and policies that address modifiable risk factors across the life course. Initiatives such as Master Plans for Aging, as well as those that address ACEs, can provide critical synergistic frameworks for policy, environment, and systems change that engage communities in reducing disparities faced by aging populations.

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Acknowledgments

Study findings described in this article were presented at the American Public Health Association (APHA) Annual Conference in Boston, Massachusetts, in November 2022. We thank APHA Roundtable discussants in the Healthy Aging section for their constructive feedback. We thank Marcia Johnson, MPH, for assistance with the policy research in this article. The authors declare that they have no conflict of interest. We received no financial support related to this study. No copyrighted materials were used in this research or article.

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Author Information

Corresponding Author: Hailey Voyer, MPH, The Dartmouth Institute for Health Policy and Clinical Practice, 125 Jenckes Hill Road, Lincoln, RI 02865 (Haileyv1997@gmail.com).

Author Affiliations: ¹The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, Hanover, New Hampshire. ²University of New Hampshire, Health Management and Policy, Durham, New Hampshire.

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References

1. Centers for Disease Control and Prevention. Subjective cognitive decline — a public health issue. Published March 25, 2019. Accessed May 16, 2022. https://www.cdc.gov/aging/data/subjective-cognitive-decline-brief.html
2. Centers for Disease Control and Prevention. Cognitive impairment: a call for action, now! Published online February 2011. Accessed May 16, 2022. https://www.cdc.gov/aging/pdf/cognitive_impairment/cogimp_poilicy_final.pdf
3. Alzheimer’s Impact Movement. Cost of Alzheimer’s to Medicare and Medicaid. alzimpact.org. Published online March 2020. Accessed May 16, 2022. https://act.alz.org/site/DocServer/2012_Costs_Fact_Sheet_version_2.pdf?docID=7161#:~:text=skyrocket%20to%20%24777%20billion%20(in%202020%20dollars)&text=Alzheimer’s%20will%20total%20%24584%20billion,of%20total%20estimate%20Medicare%20spending.&text=increase%20278%25%20between%20now%20and,reach%20%24194%20billion%20in%202050
4. Hale JM, Schneider DC, Gampe J, Mehta NK, Myrskylä M. Trends in the risk of cognitive impairment in the United States, 1996–2014. Epidemiology. 2020;31(5):745–754. PubMed doi:10.1097/EDE.0000000000001219
5. Centers for Disease Control and Prevention. Healthy Brain Initiative. Alzheimer’s disease and healthy aging. Centers for Disease Control and Prevention. Published July 30, 2020. Accessed May 16, 2022. https://www.cdc.gov/aging/healthybrain/index.htm
6. Centers for Disease Control and Prevention. BRFSS statistical brief: cognitive decline optional module. National Center for Chronic Disease Prevention and Health Promotion. Published March 2020. Accessed May 16, 2022. https://www.cdc.gov/aging/publications/BRFSS-cognitive-decline-brief-508.pdf
7. Aytur SA, Carlino S, Bernard F, West K, Dobrzycki V, Malik R. Social-ecological theory, substance misuse, adverse childhood experiences, and adolescent suicidal ideation: applications for community-academic partnerships. J Community Psychol. 2022;50(1):265–284. PubMed doi:10.1002/jcop.22560
8. Adams ML, Grandpre J, Katz DL, Shenson D. Cognitive impairment and cardiovascular disease: a comparison of risk factors, disability, quality of life, and access to health care. Public Health Rep. 2020;135(1):132–140. PubMed doi:10.1177/0033354919893030
9. Miyawaki CE, Bouldin ED, Kumar GS, McGuire LC. Associations between physical activity and cognitive functioning among middle-aged and older adults. J Nutr Health Aging. 2017;21(6):637–647. PubMed doi:10.1007/s12603-016-0835-6
10. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, et al. . Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. Am J Prev Med. 1998;14(4):245–258. PubMed doi:10.1016/S0749-3797(98)00017-8
11. Terry RM, Schiffmacher SE, Dutcher AA, Croff JM, Jelley MJ, Hartwell ML. Adverse childhood experience categories and subjective cognitive decline in adulthood: an analysis of the Behavioral Risk Factor Surveillance System. J Osteopath Med. 2022;123(3):125–133. PubMed doi:10.1515/jom-2022-0140
12. Centers for Disease Control and Prevention. Behavioral Risk Factors Surveillance System. About BRFSS. Published February 9, 2019. Accessed May 10, 2022. https://www.cdc.gov/brfss/about/index.htm
13. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. 2020 Summary data quality report. Published August 2, 2021. Accessed May 10, 2022? https://www.cdc.gov/brfss/annual_data/2020/pdf/2020-sdqr-508.pdf
14. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. CDC – BRFSS – Questionnaires 2020 modules by state by data set & weight. Published August 31, 2021. Accessed May 10, 2022. https://www.cdc.gov/brfss/questionnaires/modules/state2020.htm
15. Centers for Disease Control and Prevention. LLCP 2020 Codebook Report. Overall version weighted with LLCPWT. Behavioral Risk Factor Surveillance System. Published online August 6, 2021. Accessed May 10, 2022. https://www.cdc.gov/brfss/annual_data/2020/pdf/codebook20_llcp-v2-508.pdf
16. Xie B. Association between sleep duration and cognitive impairment: a cross-sectional study using the BRFSS Database. IEEE 2020 International Conference on Public Health and Data Science (ICPHDS), November 20–22, 2020. Guangzhou, China. Pp 108–115. [REMOVED IF= FIELD]
17. Centers for Disease Control and Prevention. Weighting the BRFSS data. 2017. Accessed September 6, 2022. https://www.cdc.gov/brfss/annual_data/2017/pdf/weighting-2017-508.pdf
18. Centers for Disease Control and Prevention. LLCP 2019 codebook report. Overall version data weighted with _LLCPWT. July 31, 2020. Accessed May 19, 2022. https://www.cdc.gov/brfss/annual_data/2019/pdf/codebook19_llcp-v2-508.HTML
19. Jaqua E, Biddy E, Moore C, Browne G. The impact of the six pillars of lifestyle medicine on brain health. Cureus. 2023;15(2):e34605. PubMed doi:10.7759/cureus.34605
20. Ji S, Wang H. A study of the relationship between adverse childhood experiences, life events, and executive function among college students in China. Psicol Reflex Crit. 2018;31(1):28. PubMed doi:10.1186/s41155-018-0107-y
21. Gupta S. Racial and ethnic disparities in subjective cognitive decline: a closer look, United States, 2015-2018. BMC Public Health. 2021;21(1):1173. PubMed doi:10.1186/s12889-021-11068-1
22. Peterson R. Lifecourse contexts and policy opportunities to reduce cognitive aging and dementia disparities. Virtual interview presented on March 15, 2022. Oregon State University, Zoom recording. Accessed May 23, 2022. https://media.oregonstate.edu/media/t/1_g7szuymd
23. Paasche-Orlow MK, Parker RM, Gazmararian JA, Nielsen-Bohlman LT, Rudd RR. The prevalence of limited health literacy. J Gen Intern Med. 2005;20(2):175–184. PubMed doi:10.1111/j.1525-1497.2005.40245.x
24. Levy H, Janke A. Health literacy and access to care. J Health Commun. 2016;21 (suppl 1):43–50. PubMed doi:10.1080/10810730.2015.1131776
25. Bradywood A, Leming-Lee TS, Watters R, Blackmore C. Implementing screening for social determinants of health using the Core 5 screening tool. BMJ Open Qual. 2021;10(3):e001362. PubMed doi:10.1136/bmjoq-2021-001362
26. National Conference of State Legislatures. Comprehensive policy approaches to support the aging population. Updated May 3, 2021. Accessed April 23, 2023. https://www.ncsl.org/health/comprehensive-policy-approaches-to-support-the-aging-population
27. Executive Office of Health and Human Services. Aging in Massachusetts: governor’s council to address aging in Massachusetts. Blueprint recommendations. Presented October 12, 2018. Accessed April 23, 2023. https://www.mass.gov/doc/governors-council-to-address-aging-in-massachusetts-blueprint-recommendations-december-2018/download
28. Howe LD, Tilling K, Galobardes B, Lawlor DA. Loss to follow-up in cohort studies: bias in estimates of socioeconomic inequalities. Epidemiology. 2013;24(1):1–9. PubMed doi:10.1097/EDE.0b013e31827623b1
29. Houtepen LC, Heron J, Suderman MJ, Tilling K, Howe LD. Adverse childhood experiences in the children of the Avon Longitudinal Study of Parents and Children (ALSPAC). Wellcome Open Res. 2018;3:106. PubMed doi:10.12688/wellcomeopenres.14716.1
30. Chowdhury R, Shah D, Payal AR. Healthy worker effect phenomenon: revisited with emphasis on statistical methods – a review. Indian J Occup Environ Med. 2017;21(1):2–8. PubMed doi:10.4103/ijoem.IJOEM_53_16
31. Compass MP. Points on the Compass. Adverse Childhood Experiences Response Team (ACERT). Manchester Proud. Published 2023. Accessed April 23, 2023. https://manchesterproudcompass.org/agencies/adverse-childhood-experiences-response-team-acert
32. Enhance Fitness. What is Enhance Fitness? Accessed April 23, 2023. https://projectenhance.org/enhancefitness/
33. Romanova M, Liang LJ, Deng ML, Li Z, Heber D. Effectiveness of the MOVE! multidisciplinary weight loss program for veterans in Los Angeles. Prev Chronic Dis. 2013;10:E112. PubMed
34. Ahrendt AD, Kattelmann KK, Rector TS, Maddox DA. The effectiveness of telemedicine for weight management in the MOVE! Program. J Rural Health. 2014;30(1):113–119. PubMed doi:10.1111/jrh.12049
35. van den Brink AC, Brouwer-Brolsma EM, Berendsen AAM, van de Rest O. The Mediterranean, Dietary Approaches to Stop Hypertension (DASH), and Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) Diets are associated with less cognitive decline and a lower risk of Alzheimer’s Disease — a review. Adv Nutr. 2019;10(6):1040–1065. PubMed doi:10.1093/advances/nmz054
36. Aytur SA, Ray KL, Meier SK, Campbell J, Gendron B, Waller N, et al. . Neural mechanisms of acceptance and commitment therapy for chronic pain: a network-based fMRI approach. Front Hum Neurosci. 2021;15:587018. PubMed
37. Sexton CE, Sykara K, Karageorgiou E, Zitser J, Rosa T, Yaffe K, et al. . Connections between insomnia and cognitive aging. Neurosci Bull. 2020;36(1):77–84. PubMed doi:10.1007/s12264-019-00401-9
38. Zubatsky M, Berg-Weger M, Morley J. Using telehealth groups to combat loneliness in older adults through COVID-19. J Am Geriatr Soc. 2020;68(8):1678–1679. PubMed doi:10.1111/jgs.16553
39. da Rosa MC, Rosa CB, Boff RM, Oliveira MS, Schwanke CHA. Transtheoretical model for lifestyle changes in older persons: a systematic review protocol. Ann N Y Acad Sci. 2022;1508(1):172–177. PubMed doi:10.1111/nyas.14714
40. MaineHealth. Fall prevention: a matter of balance. Published 2023. Accessed April 23, 2023. https://www.mainehealth.org/care-services/older-adult-care-geriatric-medicine/fall-prevention-matter-balance

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Tables

^a Taken from respondents to the Subjective Cognitive Decline module.
^b Participant answered “always,” “usually,” or “sometimes.”
^c Participant answered “rarely” or “never.”

Abbreviation: ACE, adverse childhood event.
^a Taken from respondents to the Subjective Cognitive Decline module.
^b Values calculated by using appropriate weighting system and by restricting observations to those for which ACEs is not missing.
^c P value calculated by using Wald χ² test.
^d Annual income below $25,000 for a family of 3.
^e Physical inactivity defined as participant reporting no physical activity within the past 30 days.
^f Score reflects the number of “yes” answers to the eleven ACEs questions for an individual.

^a Taken from respondents to the Subjective Cognitive Decline module.
^b Crude and intermediate odds ratios calculated by restricting observations to those for whom ACEs score is not missing.
^c Adjusted for all variables in the table except for ACEs score.
^d Adjusted for all variables in the table.
^e Significant at P < .001.
^f Significant at P < .05.
^g Significant at P < .01.

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