Cognitive Habits Linked to Resilience: Surprising Commonalities across the United States

AUTHORS

Carolyn E. Schwartz1,2*, Roland B. Stark1, Bruce D. Rapkin3*, Sophie Selbe4, Wesley Michael5 and Thomas J. Stopka4

1DeltaQuest Foundation, Inc., Concord, MA, USA

2Departments of Medicine and Orthopaedic Surgery, Tufts University Medical School, Boston, MA, USA

3Department of Epidemiology and Population Health, Division of Community Collaboration & Implementation Science, Albert Einstein College of Medicine, Bronx, NY, USA

4Department of Public Health and Community Medicine, Clinical and Translational Science Institute, Tufts University School of Medicine, Boston, MA, USA

5Rare Patient Voice, LLC, Towson, MD, USA



ARTICLE INFORMATION

*Corresponding author: Carolyn Schwartz, Sc.D, DeltaQuest Foundation, Inc., Concord, MA, USA

Received date: 03 March 2020; Accepted date: 13 March 2020; Published date: 17 March 2020.

Citation: Schwartz CE (2020) Cognitive Habits Linked to Resilience: Surprising Commonalities across the United States. J Comm Med Pub Health Rep 1(1): https://doi.org/10.38207/JCMPHR20202

Copyright: © 2020 Schwartz CE. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



ABSTRACT

Background: Research has documented many geographic inequities in health. Research has also documented that the way one thinks about health and quality of life (QOL) affects one’s experience of health, treatment, and one’s ability to cope with health problems.

Purpose: We examined United-States (US) regional differences in QOL appraisal (i.e., the way one thinks about health and QOL), and whether resilience-appraisal relationships varied by region.

Methods: Secondary analysis of 3,955 chronic-disease patients and caregivers assessed QOL appraisal via the QOL Appraisal Profile-v2 and resilience via the Centers for Disease Control Healthy Days Core Module. Covariates included individual-level and aggregate-level socioeconomic status (SES) characteristics. Zone improvement plan (ZIP) code was linked to publicly available indicators of income inequality, poverty, wealth, population density, and rurality. Multivariate and hierarchical residual modeling tested study hypotheses that there are regional differences in QOL appraisal and in the relationship between resilience and appraisal.

Results: After sociodemographic adjustment, QOL appraisal patterns and the appraisal-resilience connection were virtually the same across regions. For resilience, sociodemographic variables explained 26 % of the variance; appraisal processes, an additional 17 %; and region and its interaction terms, just an additional 0.1 %.

Conclusion: The study findings underscore a geographic universality across the contiguous US in how people think about QOL, and in the relationship between appraisal and resilience. Despite the recent prominence of divisive rhetoric suggesting vast regional differences in values, priorities, and experiences, our findings support the commonality of ways of thinking and responding to life challenges. These findings support the wide applicability of cognitive-based interventions to boost resilience.

Abbreviations: MANOVA = Multivariate Analysis of Variance; PCA = principal components analysis; QOL = quality of life; SES = socioeconomic status; US = United States; ZIP = Zone Improvement Plan (postal code)


Keywords: appraisal; resilience; cognitive; quality of life; societal; geographic


Introduction

Over the past three decades, it has become increasingly evident that the way one thinks about health, healthcare, and quality of life (QOL) has a substantial impact on one’s experience of health, healthcare, and QOL [1- 4]. Appraisal assessment provides a lens to understanding patients’ internal resources, including ways of thinking about QOL and goals, experience sampling, standards of comparison, and patterns of emphasis [3,5,6]. By querying people about what they are thinking about when answering QOL questionnaires, appraisal-assessment tools are able to characterize what QOL means to different people, what goals are relevant to people’s sense of QOL, to whom people compare themselves, what types  of experiences they tend to think about when answering questions, and what aspects of all of the above (i.e., QOL definition, goals, standards of comparison, experiences sampled) are emphasized in deriving their responses.

Research on a range of patient groups has revealed that cognitive appraisal processes can mediate or moderate the impact of health-state changes on QOL and well-being [7,8]. While there is no right or wrong way for people to appraise their own QOL, inter-individual differences can obscure the impact of health-state changes and can attenuate evidence for the effectiveness of treatment interventions [9,10]. Appraisal assessment can explain why two patients with the same objective outcome have vastly different perspectives and evaluations of their health or QOL [9,11]. Appraisal can, for example, shed light on differing patient expectations of treatment outcomes or of what “quality of healthcare” means to an individual [4]. Appraisal assessment has provided a tool for helping patients and providers with medical decision-making and end-of-life care planning, by highlighting patient values and goals and examining how the various treatment options fit those values and goals criteria [5].

While researchers generally agree that geography should not determine disease outcomes [12], spatial epidemiological research has helped to highlight disparities in disease- clustering patterns [13], access to public health, and healthcare services [14,15]. Research has also suggested that people from different socioeconomic groups may have very different ways of appraising their QOL [9], health care [4], and treatment-related changes [16]. Studies comparing people living with human immunodeficiency virus who received fee-for-service Medicaid versus a Medicaid program providing greater care management and access show divergences over time in appraisal processes that drove satisfaction ratings. For example, fee-for- service Medicaid patients’ satisfaction ratings were driven by a continued focus on routine medical needs, whereas Care-Management Medicaid patients’ satisfaction ratings were driven by focusing on greater access to specialists [4]. It is also possible that theseappraisal differences reflect variations in health literacy and utilization of preventive healthcare [1]. While this growing evidence base supports the importance of appraisal assessment for medical care and medical decision-making [11], it is not known whether QOL appraisal varies geographically within the United States (US); nor what the impact of such variation might be on resilience to health problems. In this context, “resilience” is the idea that people maintain engagement and functioning despite physical and/or mental health challenges. The present work examines US regional differences in the ways people with chronic conditions and their caregivers appraise QOL, and in the relationship between resilience and appraisal. Specifically, we sought to test two hypotheses: (1) that there are regional differences in QOL appraisal and; that there are regional differences in the relationship between resilience and appraisal.

Materials and Methods

Sample

This secondary analysis utilized data collected in 2016 from 3,955 US respondents from chronic/rare disease panels comprising patients representing about 350 diagnoses and their caregivers. Eligible participants were 18 years of age or older and able to complete an online questionnaire. Participants were excluded from participating if they were less than 18  years of age, and/or if they were unable to provide written informed consent. Participants included US patients and caregivers recruited from panels of Rare Patient Voice, LLC. Participants were invited to participate in this academic study aimed at developing new measures of reserve- building and of appraisal. Normally panel participants are paid for their participation. For this academic study, however, they were not offered compensation.

Procedure

This secondary analysis utilized data from a web-based questionnaire. The study was reviewed and approved by the New England Independent Review Board (NEIRB#15-254). All participants provided written informed consent.

Measures

Cognitive appraisal processes underlying responses to patient-reported outcomes (i.e., measures of health-related QOL and well-being) were assessed using the QOL Appraisal Profile–v2 [17]. This measure includes 85 closed-ended items and enables descriptive analyses of individual differences in frame of reference (e.g., goals they want to achieve, responsibilities they want to let go), ways of recalling experiences (e.g., the most recent, the most upsetting), standards of comparison (e.g., other patients, one’s ideal health), and relative emphasis in reconciling discrepant experiences (e.g., positive versus negative, self-focused versus other- focused). It yields 12 composite scores: Wellness Focus, Health Focus, Recent Challenges, Spiritual Focus, Relationship Focus, Maintenance Roles, Independence, Reduction of Responsibilities, Pursuit of Dreams, Anticipation of Decline, Worry-Free state, and Lightness of Being (see Appendix Table A.1. for definitions).

Resilience was operationalized using residual modelling [3,17,18] with items from the Centers for Disease Control (CDC) Healthy Days Core Module [19]. The first item (also the general health item of the PROMIS-10 described below) queries the respondent’s general health. The second and third items ask the respondent to indicate how many days of the past 30 days their physical (Physical) or mental (Mental) health, respectively, was not good. The fourth item, Activities of Daily Living Impaired (ADL Impaired), asks how many days of the past 30 the respondent’s poor physical or mental health kept them from doing their usual activities, such as self-care, work, or recreation. Resilience is exemplified by an individual endorsing a higher level of functioning or performance than would be expected given their health impairment. For example, one would expect that someone with two days of physical-health impairment and four days of mental-health impairment would have six days of ADL impairment. If they have in fact fewer than six days of ADL, then they would have a higher score on the Resilience metric. Our approach built on a precedent for using residual modeling to study epiphenomena [3,18]. Specifically, we computed a regression model with the CDC Healthy Days ADL Impaired as the dependent variable, and Physical Health, Mental Health, and their interaction as predictors. The residuals from the regression model were saved and multiplied by negative one (-1). Accordingly, a high Resilience score reflects fewer-than-expected days that the respondent is unable to function due to physical or mental health problems or their synergistic effect [19]

Covariates included individual-level and aggregate-level sociodemographic and socioeconomic status (SES) characteristics. These covariates were selected because they are objective indicators to be adjusted in our analyses of subjective variables’ association with region [20]. Individual-level characteristics included age, age at diagnosis, whether the person received help completing the questionnaire, gender, number of comorbidities, marital status, ethnicity, race, income, employment status, occupational complexity (past or present), education, mother’s education, father’s education, and Zone Improvement Plan (ZIP) Code (“postal code”). Aggregate-level characteristics were linked via individual ZIP Codes to the analytic data set using the most recent publicly-available data that wecould obtain from the US Census Bureau’s American Community Survey [21] and the Inter- university Consortium for Political and Social Research [22]. Recency varied by topic: these ZIP Code data included US region, population @ 2013, population density @ 2013, urban-rural characterization @ 2003 [1 = urban, 5 = rural], median household income @ 2013, percent of households below poverty level @ 2017. We also obtained Gini coefficient @ 2010 by state [23] [higher numbers indicate a more unequal distribution of income] [24,25]. We also created a variable to assess relative wealth (“Keeping up with the Joneses”) which reflected the individual’s income relative to the typical level of wealth in their ZIP Code.

Statistical Analysis

Frequency distributions examined the prevalence of ZIP Codes by state in the sample and led to a decision to aggregate at the regional level to examine geospatial differences, to have enough sample size for multivariate comparisons (Figure 1). Descriptive statistics characterized the sample sociodemographic characteristics at the individual and aggregated regional levels. Due to small sample sizes of participants from Alaska and Hawaii (n = 24), data only from the 48 contiguous states were included in the multivariate analyses2.

Figure 1: United States by Region. Image Source. States that are contiguous and within the same region have the same color for ease of distinguishing the regions used in analysis.

 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Principal components analysis with an Oblimin rotation and Kaiser Normalization was used for data reduction of the aggregate-level SES characteristics. Pearson correlation coefficients were used to examine the association between appraisal scores and resilience by region. Multivariate Analysis of Variance (MANOVA) using listwise deletion tested the hypothesis that region (the key independent variable) was associated with appraisal scores (12 dependent variables, after adjusting for individual- and aggregate-level sociodemographic characteristics (covariates). A hierarchical series of general linear models tested the hypothesis that region, appraisal variables, and their interactions explained variance in resilience (dependent variable), after adjusting for individual- and aggregate-level sociodemographic characteristics (covariates). These models were implemented in four stages.

Model I included the individual- and aggregate-level sociodemographic covariates and saved the residuals for use in the subsequent model. Model II included the 12 appraisal scores to predict the covariate residuals from Model I and saved the new residuals for use in the subsequent model. Model III included the categorical variable for region to predict the residuals from Model II and saved the new residuals for use in the subsequent model. Model IV included 12 region-by-appraisal interactions to predict the Model III residuals. Due to the relatively large sample size and the many comparisons considered to test our hypotheses, we decided to focus on effect sizes that were “small” or larger using Cohen’s criteria [26] rather than on p-values. Accordingly, individual predictors’ eta-squared (η2) statistics had to be at least 0.01 (1% of variance explained) for us to consider them noteworthy. Statistical analyses were implemented using IBM SPSS version 26 [27].

Results

Individual-Level Sample Demographics

The analytic sample included between 2,853 and 3,955 people who had complete data on the relevant measures for a given analysis. This sample represented between 68% and 95% of the 4,174 respondents. In other words, due to sporadic missing data, different subsets of the 4,174 were kept in the analyses. Table 1 provides the individual-level sociodemographic characteristics. The sample was comprised mostly of patients (82%), with a mean age of 48 years, and mean age at diagnosis was 41 years. The sample was predominantly female (86%), White (91%), married or cohabitating (67%), and not currently employed (53%). While 53% of respondents had completed college or more education, only about 28% of their parents had. The median income range was $ 50,000-100,000.

                                                 Table 1: Person-Level Demographic Characteristics (N = 3,955), United States, 2016                                                                 

 

Variable

   

Role

Patient

80 %

 

Caregiver

18 %

 

Both

2 %

 

Missing

0 %

Age

Mean (SD)

48.2 (13.3)

Age at diagnosis

Mean (SD)

40.8 (16.9)

Had help completing

questionnaire

 

3 %

Gender

Male

14 %

 

Female

86 %

 

Missing

0 %

Number of comorbidities

0

4 %

 

1

11 %

 

2

14 %

 

3

17 %

 

4

17 %

 

5

13 %

 

 

6

 

11 %

 

7 or more

7 %

 

Missing

0 %

Marital Status

Never Married

14 %

 

Married

61 %

 

Cohabitation/ Domestic

Partnership

6 %

 

Separated

2 %

 

Divorced

12 %

 

Widowed

4 %

 

Missing

1 %

Ethnicity (%)

Not Hispanic or Latino

91 %

 

Hispanic or Latino

5 %

 

Missing

3 %

Race (%)

Black or African American

5 %

 

White

91 %

 

Other

2 %

 

Missing

2 %

Income (%)

Less than $ 15,000

9 %

 

$ 15,001 to $ 30,000

14 %

 

$ 30,001 to $ 50,000

17 %

 

$ 50,001 to $ 100,000

28 %

 

$ 100,001 to $ 150,000

12 %

 

$ 150,001 to 200,000

4 %

 

Over $ 200,000

3 %

 

Missing

0 %

Employment Status

Employed

47 %

 

Unemployed

12 %

 

Retired

13 %

 

Disabled Due to Medical

Condition

26 %

 

Missing

2 %

Work Complexity (past or

present)

Mean (SD), 1-5 scale

3.3 (1.0)

Education

Some high school

2 %

 

High school diploma/GED

25 %

 

Technical or trade school degree

19 %

 

Bachelor's degree

31 %

 

Graduate or professional degree

22 %

 

Missing

2 %

Mother's Education

Some high school

14 %

 

High school diploma/GED

46 %

 

Technical or trade school degree

12 %

 

Bachelor's degree

16 %

 

Graduate or professional degree

9 %

 

Missing

3 %

Father's Education

Some high school

16 %

 

High school diploma/GED

36 %

 

 

 

 

Technical or trade school degree

13 %

 

Bachelor's degree

16 %

 

Graduate or professional degree

13 %

 

Missing

6 %

Some sets of percentages may not add up to 100 % due to rounding.

GED = General Educational Development (i.e., high-school equivalency test) SD = standard deviation

 

Aggregate-Level Sample Demographics

Table 2 provides the aggregate-level sociodemographic characteristics considered in the analysis. Nine of the ten regions had sufficient sample sizes to be retained in subsequent multivariate analyses (i.e., non- contiguous states were excluded from analysis). The majority of respondents lived in a metropolitan area [22], with mean population natural log [Ln] of 9.9 (i.e., about 20,000 people in their ZIP Code) and a mean Ln density of 6.8 (i.e., about 900 people per square mile). The median household income by ZIP Code was about $60,000, and 9% of the people in the ZIP codes included in our sample were below the poverty level. The mean Gini coefficient by state was 0.47, which is mid-range in the worldwide empirical distribution of 0.24-0.63 [24,25], where zero indicates a perfectly uniform distribution of population wealth.

Table 2: Aggregate-Level Demographic Characteristics (N=3,955)

Aggregate-Level Demographic Characteristics (N = 3,955)

Variable

Region

States included

 

 

US Region: N, %

East North Central

Illinois, Indiana, Michigan, Ohio, Wisconsin

755

19 %

 

East South Central

Alabama, Kentucky, Mississippi, Tennessee

218

6 %

 

 

Middle Atlantic

Maryland, New Jersey, New York, Pennsylvania

 

464

 

12 %

 

 

Mountain

Montana, Idaho, Wyoming,

Nevada, Utah, Colorado, Arizona, New Mexico

 

317

 

8 %

 

 

New England

Connecticut, Maine, Massachusetts, New Hampshire,

Rhode Island, Vermont

 

215

 

5 %

 

Non-Contiguous

Alaska, Hawaii

24

1 %

 

Pacific

California, Oregon, Washington

549

14 %

 

 

South Atlantic

Delaware, Florida, Georgia, North Carolina, South Carolina, Virginia, Washington DC, West

Virginia

 

809

 

20 %

 

 

West North Central

Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota

 

286

 

7 %

 

West South Central

Arkansas, Louisiana, Oklahoma,

Texas

342

42 %

 

ZIP-Code-Based Societal

Variables

 

Mean Ln Population, 2013 (SD)

 

NA

 

9.9

 

(0.9)

 

Mean Ln Population Density, 2013 (SD)

NA

6.8

(1.7)

 

Median Urban-Rural Continuum code, 2003 (%),

1 = urban, 9 = rural

 

NA

 

1

 

53 %

 

Median Household income, 2013 (SD)

NA

$ 59,970

(23,067)

 

% of households below poverty level, 2017

NA

9%

 

 

 

NA

 

 

 

State-Based Societal Variable

Mean Gini coefficient (SD)

(range 0%-100%; higher no. indicates worse income inequality)

 

NA

 

0.47

 

(0.02)

Some sets of percentages may not add up to 100% due to rounding.

Ln=natural log; SD=standard deviation

 

The principal components analysis (PCA) yielded four components that we labeled as Wealth, Population, Poverty, and Rural; these explained 65% of the variance (Appendix Table A.2.). Wealth had small correlations with Population (r = 0.21) and Poverty (r = -0.25), and a  medium correlation with Rural (r = -0.32). Poverty was uncorrelated with Population (r = -0.03) and had a small correlation with Rural (r = 0.16).

Regional Differences in Appraisal ?

Table 3 show results of the MANOVA investigating the importance of region (independent variable) in predicting the 12 appraisal scores (dependent variables), after adjusting for individual- and aggregate-level sociodemographic covariates. The overall model explained nearly 34 % of the variance (sum of all partial η2 = 0.339). Among the sociodemographic covariates, significant multivariate effects predicting appraisal were detected for marital status, race, income, being employed, education, mother’s education, number of comorbidities, age, age at diagnosis, and area population (data not shown). In predicting appraisal variables, all of the models had eta-squared coefficients that qualified as “small” using Cohen’s criteria. Over and above the afore mentioned sociodemographic covariates, region had a multivariate effect that was statistically significant (omnibus results for Pillai’s Trace F = 1.81, df = 96, 22,440; p < 0.0001) but practically insignificant.

Table 3: Results of MANOVA investigating regional differences in appraisal (N = 2853)                                                 

 
Table 3. Results of MANOVA investigating regional differences in appraisal (N=2853)  
Multivariate Test            
    F Sig. (Partial) η2*  
Region Effect Pillai's Trace 1.821 0.000 0.008    
Tests of Between-Subjects Effects          
Corrected Model Wellness Focus 8.53 0.00 0.116    
  Health Worries 8.60 0.00 0.116    
  Recent Challenges 6.52 0.00 0.091    
  Spiritual Focus 5.17 0.00 0.073    
  Relationship Focus 3.28 0.00 0.048    
  Maintain Roles 11.30 0.00 0.148    
  Independence 2.60 0.00 0.038    
  Reduce Responsibilities 3.74 0.00 0.054    
  Pursue Dreams 5.39 0.00 0.076    
  Anticipating Decline 4.95 0.00 0.070    
  Worry Free 3.21 0.00 0.047    
  Lightness of Being 2.47 0.00 0.036    
Region Wellness Focus 2.14 0.03 0.006    
  Health Worries 2.55 0.01 0.007    
  Recent Challenges 0.78 0.62 0.002    
  Spiritual Focus 4.55 0.00 0.013    
  Relationship Focus 0.83 0.58 0.002    
  Maintain Roles 0.86 0.55 0.002    
  Independence 0.49 0.86 0.001    
  Reduce Responsibilities 2.89 0.00 0.008    
  Pursue Dreams 2.70 0.01 0.008    
  Anticipating Decline 1.46 0.17 0.004    
  Worry Free 1.02 0.42 0.003    
  Lightness of Being 1.03 0.41 0.003    
Parameter Estimates            
Dependent Variable Region B Sig. Partial η2*  
             
Wellness Focus East North Central 0.01 0.90 0.000    
  East South Central -0.16 0.13 0.001    
  Middle Atlantic -0.15 0.15 0.001    
  New England 0.02 0.88 0.000    
  Pacific 0.08 0.39 0.000    
  South Atlantic 0.05 0.58 0.000    
  West North Central 0.12 0.22 0.001    
  West South Central -0.07 0.46 0.000    
  Mountain  (Referent for Deviation Contrast)  
Health Worries East North Central 0.14 0.06 0.001    
  East South Central 0.17 0.10 0.001    
  Middle Atlantic -0.01 0.96 0.000    
  New England 0.01 0.90 0.000    
  Pacific 0.05 0.58 0.000    
  South Atlantic 0.20 0.01 0.002    
  West North Central 0.00 0.96 0.000    
  West South Central 0.05 0.57 0.000    
  Mountain  (Referent for Deviation Contrast)  
Recent Challenges East North Central 0.10 0.22 0.001    
  East South Central 0.04 0.70 0.000    
  Middle Atlantic 0.16 0.12 0.001    
  New England 0.02 0.83 0.000    
  Pacific 0.03 0.74 0.000    
  South Atlantic 0.07 0.40 0.000    
  West North Central -0.02 0.85 0.000    
  West South Central 0.11 0.28 0.000    
  Mountain  (Referent for Deviation Contrast)  
Spiritual Focus East North Central -0.12 0.12 0.001    
  East South Central 0.21 0.06 0.001    
  Middle Atlantic -0.17 0.10 0.001    
  New England -0.17 0.11 0.001    
  Pacific -0.07 0.43 0.000    
  South Atlantic 0.06 0.49 0.000    
  West North Central 0.00 0.99 0.000    
  West South Central 0.15 0.13 0.001    
  Mountain  (Referent for Deviation Contrast)  
Relationship Focus East North Central 0.09 0.29 0.000    
  East South Central -0.04 0.75 0.000    
  Middle Atlantic 0.12 0.25 0.000    
  New England 0.09 0.39 0.000    
  Pacific 0.04 0.68 0.000    
  South Atlantic 0.02 0.81 0.000    
  West North Central -0.06 0.54 0.000    
  West South Central 0.04 0.70 0.000    
  Mountain  (Referent for Deviation Contrast)  
Maintain Roles East North Central -0.07 0.36 0.000    
  East South Central -0.12 0.27 0.000    
  Middle Atlantic -0.17 0.08 0.001    
  New England -0.18 0.08 0.001    
  Pacific -0.13 0.14 0.001    
  South Atlantic -0.11 0.18 0.001    
  West North Central -0.02 0.86 0.000    
  West South Central -0.02 0.82 0.000    
  Mountain  (Referent for Deviation Contrast)  
Independence East North Central 0.00 0.97 0.000    
  East South Central -0.04 0.74 0.000    
  Middle Atlantic 0.08 0.45 0.000    
  New England 0.04 0.69 0.000    
  Pacific 0.01 0.95 0.000    
  South Atlantic 0.02 0.82 0.000    
  West North Central -0.08 0.40 0.000    
  West South Central -0.05 0.62 0.000    
  Mountain  (Referent for Deviation Contrast)  
Reduce Responsibilities East North Central 0.16 0.05 0.001    
  East South Central -0.05 0.65 0.000    
  Middle Atlantic 0.12 0.22 0.001    
  New England 0.19 0.07 0.001    
  Pacific 0.26 0.00 0.003    
  South Atlantic 0.20 0.02 0.002    
  West North Central 0.14 0.16 0.001    
  West South Central 0.00 0.97 0.000    
  Mountain  (Referent for Deviation Contrast)  
Pursue Dreams East North Central -0.19 0.02 0.002    
  East South Central -0.37 0.00 0.004    
  Middle Atlantic -0.20 0.05 0.001    
  New England -0.01 0.93 0.000    
  Pacific -0.06 0.53 0.000    
  South Atlantic -0.16 0.06 0.001    
  West North Central -0.18 0.07 0.001    
  West South Central -0.22 0.02 0.002    
  Mountain  (Referent for Deviation Contrast)  
Anticipating Decline East North Central 0.02 0.83 0.000    
  East South Central -0.06 0.59 0.000    
  Middle Atlantic 0.11 0.28 0.000    
  New England 0.21 0.04 0.001    
  Pacific 0.08 0.39 0.000    
  South Atlantic 0.09 0.28 0.000    
  West North Central -0.05 0.57 0.000    
  West South Central 0.12 0.24 0.001    
  Mountain  (Referent for Deviation Contrast)  
Worry Free East North Central -0.05 0.51 0.000    
  East South Central 0.02 0.89 0.000    
  Middle Atlantic 0.01 0.90 0.000    
  New England 0.01 0.92 0.000    
  Pacific -0.07 0.43 0.000    
  South Atlantic -0.12 0.17 0.001    
  West North Central -0.04 0.70 0.000    
  West South Central -0.15 0.13 0.001    
  Mountain  (Referent for Deviation Contrast)  
Lightness of Being East North Central 0.05 0.51 0.000    
  East South Central 0.12 0.30 0.000    
  Middle Atlantic 0.03 0.78 0.000    
  New England -0.10 0.36 0.000    
  Pacific 0.00 0.96 0.000    
  South Atlantic 0.04 0.64 0.000    
  West North Central 0.13 0.17 0.001    
  West South Central 0.12 0.23 0.001    
  Mountain  (Referent for Deviation Contrast)  
*Bolded if η2 > .020 for overall model or if partial η2 > .010 for region variable or for individual regions.
                        

Appraisal scores did not differ substantially by region. After adjusting for covariates, no regional appraisal difference accounted for an η2 larger than 0.013. Even for the domain that best distinguished regions (Spiritual Focus), mean differences were so small as to be barely visible, even when regions were sorted by mean (see dotted line of the overall mean in Figure 2).

Figure 2: Spiritual Focus Means by Region Appraisal scores did not differ substantially by region. After adjusting for covariates, no regional appraisal difference accounted for an η2 larger than

0.013. Even for the domain that best distinguished regions (Spiritual Focus), mean differences were so small as to be barely visible, even when regions were sorted by mean (see dotted line of the overall mean in Figure 2).

 
 
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Histogram panel shows the distribution of Spiritual Focus appraisal scores by region in descending order. Contrast results revealed a small effect size (η2 = 0.013) such that compared to the overall US mean East South Central and West South Central had higher Spiritual Focus scores; East North Central, Middle Atlantic, and New England had lower Spiritual Focus scores.

 

Regional Differences in Relationship between Resilience and Appraisal As a basic indicator of the way relationships did or did not differ by region, Table 4 shows correlation coefficients between appraisal and resilience by region, with conditional formatting to indicate the effect size. Of note, Wellness Focus, Health Worries, and Recent Challenges had consistent medium or small correlations across regions with one or two exceptions by region. Relationship Focus and Maintain Roles generally had correlations less than ± 0.10, with a few exceptions that were between 0.10 and 0.30 (i.e., small effect size). The next model, with Model I residuals as a dependent  variable (i.e., resilience adjusted for sociodemographic), explained 17% of the variance by including the 12 appraisal composite scores.  Appraisal    patterns    associated    with    greater    resilience    were characterized by a greater emphasis on Wellness and Spiritual Focus, and less on Health Worries, Recent Challenges, Anticipating Decline, and Being Worry-Free (p < 0.0001 to 0.02). The next model, with Model II residuals as a dependent variable (i.e., resilience adjusted for sociodemographic and appraisal), explained just 0.1% of the variance by including Region. The final model, with Model III residuals (i.e., resilience adjusted for sociodemographic, appraisal, and region) as a dependent variable, explained even less of the variance (0.05%) by including Appraisal-by-Region interactions.

Discussion

The study findings underscore a geographic universality across the contiguous US in the connections between appraisal and resilience. Despite the recent prominence of divisive rhetoric suggesting vast regional differences in values, priorities, and experiences, our findings support the commonality of ways of thinking and responding to life challenges. While our content focuses on health, we believe these findings generalize to other life domains and societal priorities.

The universality we observed in the QOL appraisal-resilience connection has distinct clinical implications. It suggests ways in which cognitive- coaching interventions could help patients and caregivers increase their resilience. Our results support the kind of interventions that help individuals to pursue a calm, healthy lifestyle; practice self-acceptance; and maintain activities that help them remain positive and balanced. Our results also support de-emphasizing rumination about “worst moments.” In parallel, our results support the benefit of a “spiritual focus,” one that prioritizes helping others, leaving a legacy of a positive impact on the world, and finding ways to feel part of something greater than oneself. All these cognitive appraisal processes were distinctly associated with greater resilience in the face of health problems. While the study sample is large and heterogeneous in its illness representation, some limitations must be acknowledged. First, the data are cross-sectional, limiting our ability to make causal inference. Second, the sample disproportionately reflects some demographic characteristics (i.e., middle-aged, white, female, married, and/or living with family members), which may affect external validity. Third, some aggregate-level demographic indicators were  limited by the public unavailability of more recent data. Fourth, it is possible that the listwise deletion in the MANOVA analyses (i.e., from 3,955 to 2,853 cases) biased coefficients. Fifth, our regional comparisons were limited by the available sample sizes, which reduced our power to detect small effect sizes.

Generally speaking, researchers do not like to report null results. In this case, however, our null results underscore important commonalities in appraisal, resilience, and the appraisal- resilience connection across diverse geographic regions.

They also suggest a wide applicability of relatively standardized interventions to support resilience. We did find that resilience was negatively associated with being disabled from work, having more comorbidities, and being older. Such sociodemographic factors as well as SES factors per se can present potent barriers to treatment adherence, which is increasingly the focus of attention among healthcare providers promoting person-centered healthcare [28,29]. Social-service initiatives that can help individuals with such challenges may by extension better enable clinical interventions aimed at strengthening resilience. With pragmatic solutions to such barriers, we see great promise in appraisal-based approaches to helping individuals become more resilient in the face of health challenges.

Table 4: Pearson Correlation Coefficients Summarizing Resilience-Appraisal Association by Region

 

Table 4: Pearson Correlation Coefficients Summarizing Resilience-Appraisal Association by Region
  Wellness Focus Health Worries Recent Challenges Spiritual Focus Relationship Focus Maintain Roles Independence Reduce Responsibilities Pursue Dreams Anticipating Decline Worry-Free Lightness of Being
East North Central 0.41 -0.28 -0.18 0.02 0.06 0.13 0.02 -0.03 0.04 -0.08 -0.04 0.05
East South Central 0.42 -0.37 -0.24 0.04 0.15 -0.01 0.08 0.12 0.03 -0.03 0.07 0.05
Middle Atlantic 0.39 -0.36 -0.13 0.07 0.07 0.16 -0.08 -0.04 -0.03 -0.09 -0.01 0.16
Mountain 0.48 -0.35 -0.20 0.08 0.05 0.17 0.02 -0.02 -0.02 -0.13 -0.01 -0.05
New England 0.44 -0.39 -0.17 0.04 0.02 0.08 -0.03 -0.02 0.06 0.00 0.04 -0.03
Non-Contiguous 0.51 -0.50 -0.27 0.05 0.26 0.16 0.12 -0.15 0.02 0.03 0.23 0.05
Pacific 0.38 -0.37 -0.22 -0.02 0.01 0.08 0.04 -0.01 -0.02 -0.04 0.00 0.05
South Atlantic 0.40 -0.36 -0.24 0.11 0.07 0.03 -0.01 0.02 0.05 -0.03 -0.01 0.05
West North Central 0.30 -0.31 -0.09 -0.04 0.12 0.21 -0.08 0.01 -0.08 -0.03 0.00 0.02
West South Central 0.33 -0.38 -0.24 0.03 -0.01 0.06 0.00 -0.01 0.16 -0.13 0.01 0.09

         

Cohen's Effect Size   small     med     large  

                                            

Table 5. Summary of Results of Hierarchical Series of Regressions Predicting Resilience

Summary of Results of Hierarchical Series of Regressions Predicting Resilience

Model

Dependent variable

Adjusted for

F statistic

df

p-value

Adjusted R2

Cumulative R2

 

I.

 

Resilience

Sociodemographic

Covariates

80.5

16

0.0001

0.255

0.000

II.

Model I residuals

Appraisal Main Effects

64.1

12

0.0001

0.173

0.428

III.

Model II residuals

Region

1.5

8

0.15

0.001

0.429

 

IV.

 

Model III residuals

Appraisal-by-Region

Interactions

1.01

108

0.45

0.000

0.429

 

Declarations

Ethics approval and consent to participate. The study was reviewed and approved by the New England Review Board (NEIRB#15-254), and all participants provided informed consent. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Availability of Data and Material

The study data are confidential and thus not able to be shared.

Competing Interests

All authors declare that they have no potential conflicts of interest and report no disclosures.

Funding

This work was not funded by any external agency.

Authors' Contributions

CES and TJS discussed the idea of looking at associations between appraisal and resilience from a geographic perspective. CES and RBS designed the research study.

WM provided access to the sample.

CES performed the research. CES, RBS, and BDR analyzed the data. CES wrote the paper and WM, RBS, BDR, SS, and TJS edited the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We are grateful to the patients and caregivers who participated in this study.

 

For supplemtary information:

http://www.acquaintpublications.org/SupplementalTable.pdf

http://www.acquaintpublications.org/SupplementalTable2.pdf

 

 

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*Corresponding author: Carolyn Schwartz, Sc.D, DeltaQuest Foundation, Inc., Concord, MA, USA

Received date: 03 March 2020; Accepted date: 13 March 2020; Published date: 17 March 2020.

Citation: Schwartz CE (2020) Cognitive Habits Linked to Resilience: Surprising Commonalities across the United States. J Comm Med Pub Health Rep 1(1): https://doi.org/10.38207/JCMPHR20202

Copyright: © 2020 Schwartz CE. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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