Article Information

Corresponding author: Carolyn E. Schwartz

Article Type : Research Article

Volume : 1

Issue : 1

Received Date : 03 Mar ,2020


Accepted Date : 13 Mar ,2020

Published Date : 17 Mar ,2020


DOI : https://doi.org/10.38207/jcmphr2020010101

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/jcmphr2020010101

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

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

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

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.

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

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)

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)

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)

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 > 0.010 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

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

Pearson Correlation Coefficients Summarizing Resilience-Appraisal Association by Region

 

Wellness Focus

Health Worries

Recent Challenges

Spiritual Focus

Relationship Focus

Maintain Roles

 

Independence

Reduce Responsibilitie

s

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

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.

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Appendix Table A.1. Description of QOLAPv2 Appraisal Component Scores*

 

Second-Order Component Name

 

Meaning of QOL

 

Goals

 

Experience Sampling

 

Standards of Comparison

 

Combinatory Algorithm

 

First-Order Components Included

PCA

Variance Explained

 

1

 

Wellness Focus

 

 

x

 

x

 

x

 

Calm, healthy lifestyle, self acceptance, keep up activities and health care, focused on improvements, used to how things are, remain positive and balanced - do not think of the worst moments

 

6.6

 

2

 

Health Worries

 

 

x

 

x

 

x

 

Health worries - concern about what doctors say, high frequency of social comparison

 

6.1

 

3

 

Recent Challenges

 

 

x

 

x

 

 

x

Recall relevant episodes and recent challenges, accept people, let go of self- expectations, make multiple comparisons

 

5.9

4

Spiritual Focus

x

x

 

 

 

Faith and generativity

5.1

 

5

 

Relationship Focus

 

x

 

x

 

 

 

 

Romance improved relationships, self-acceptance

 

4.7

 

6

 

Maintain Roles

 

x

 

x

 

 

 

 

Accomplishments and maintaining community and work roles (versus getting rid of family problems, self-acceptance, calm, no regrets)

 

4.6

 

7

 

Independence

 

x

 

x

 

 

 

Independence - resolve problems - stay at home - no regrets, resolve recent money problems and other negative circumstances, keep active and fully participate

 

4.5

 

8

Reduce Responsibilities

 

x

 

x

 

x

 

 

Let go of responsibilities for house, others, self-expectations, spend time with family, influence by questionnaire

 

4.2

 

9

 

Pursue Dreams

 

 

x

 

 

x

 

Pursue dreams and goals, change living situation versus focus on comparisons to others my age and stay in current living situation

 

4.2

 

10

 

Anticipating Decline

 

 

x

 

 

 

x

Prepare loved ones and living situations for declines - ups and downs, compare self to what MD told them

 

4.0

 

 

11

 

 

Worry-Free

 

 

 

x

 

 

 

x

 

Compare to others without health limits versus those who have had similar illness, be worry free, solve money, living, practical problems versus accept people and roles, let go of self-expectations

 

 

3.9

 

12

 

Lightness of Being

 

 

 

x

 

x

 

Spontaneous - not complain - how I saw myself before illness, how others see me

 

3.8

*Adopted with permission from Rapkin et  al., [17].                                                                 Total 57.6  

 

Appendix Table A.2. Pattern Matrix of Aggregate-level SES Principal Components Analysis

Component Loadings

 

Wealth

Population

Poverty

Rural

% of Households >=$200,000

0.9

 

 

 

Mean Income

0.8

 

 

 

Median Income

0.7

 

-0.4

 

% of Households $150,000 to $199,999

0.7

 

 

 

% of Households $100,000 to $149,999

0.5

 

-0.4

 

% of Households $35,000 to $49,999

-0.5

 

 

 

% of Households $25,000 to $34,999

 

 

 

 

Population

 

1

 

 

Households, 2017

 

1

 

 

Ln Population

 

0.8

 

 

Ln Population Density

 

0.5

 

-0.4

% of Households <$10,000

 

 

0.7

 

% of Households with Income in the past

12 months below poverty level, 2017

 

 

 

0.7

 

% of Households $50,000 to $74,999

-0.5

 

-0.6

 

% of Households $10,000 to $14,999

 

 

0.6

 

% of Households $75,000 to $99,999

 

 

-0.6

 

% of Households $15,000 to $24,999

 

 

0.5

 

Urban Influence code, 2003

 

 

 

1

% of Commuters Working in Metropolitan

Areas

 

 

 

 

-1

Urban-Rural Continuum code, 2003

 

 

 

0.9

Extraction: Principal Components. Rotation: Oblimin with Kaiser Normalization.

Eigenvalue

6.76

3.46

1.57

1.30

Total % of variance explained

 

 

 

65.4