Table 2 Associations between comorbid chronic diseases and stigma in BCSs.

From: The association between comorbidities and stigma among breast cancer survivors

Model

Characteristics

Unstandardized coefficient (SE)

t

F

R2

Model 1

Diabetes (11.3%)

0.006 (0.023)

0.252

1.381

0.024

Model 2

Heart and cardiovascular (11.7%)

− 0.034 (0.023)

− 1.454

1.513

0.026

Model 3

Stroke (5.2%)

− 0.083 (0.033)

− 2.535*

1.789*

0.031

Model 4

Respiratory diseases (9.9%)

− 0.036 (0.025)

− 1.436

1.509

0.026

Model 5

Digestive diseases (53.1%)

− 0.035 (0.015)

− 2.389*

1.743*

0.030

Model 6

Musculoskeletal diseases (21.9%)

− 0.046 (0.018)

− 2.535*

1.789*

0.031

Model 7

Number of comorbidities

  

1.972*

0.036

0 (32.2%)

    

1–2 (55.7%)

− 0.038 (0.016)

− 2.330*

  

≥ 3 (12.1%)

− 0.081 (0.025)

− 3.182**

  
  1. SE, standard error. *p < 0.05; **p < 0.01. In all these models, the dependent variable stigma was inverted transformed. And the following confounding factors were considered in each model: age, BMI and time since diagnosis as continuous variables, marital status, surgery, radiotherapy, chemotherapy, endocrine drug therapy, hysterectomy, recurrence and metastasis as dichotomous variables, education level and household per capital income as multi-categorical variables were converted into dummy variables before being included in the regression models. In model 7, the independent variable number of comorbidities was also converted into dummy variable (set dummy variables c1, c2; when number of comorbidities = 0, c1 = 0 and c2 = 0; when number of comorbidities = 1–2, c1 = 1 and c2 = 0; when number of comorbidities ≥ 3, c1 = 0 and c2 = 1). In all these models, all the variation inflation factor (VIF) values were below 10.
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