Appendix 1

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Table 1a Results of logistic regression analyses examining the association between debts status at baseline with onset of 12-month mood or anxiety disorder at follow-up, while controlling for substance use disorder and other potential

confounders at baseline, in weighted adjusted odds ratios (aOR) with 95% confidence intervals (95% CI) Total (N=4204) 12-month mood or anxiety disorder at follow-up

Model 1 Model 2

Baseline characteristics n % % aOR (95% CI) aOR (95% CI)

Debts

No 3777 88.4 78.3 REF REF

Yes and easy to pay back 262 7.2 12.6 2.04 [1.23,3.36]** 1.84 [1.13,2.99]*

Yes and difficult to pay back 99 2.9 3.7 1.40 [0.73,2.71] 1.21 [0.62,2.39]

Yes and very difficult to pay back

57 1.5 5.4 4.95

[2.15,11.40]*** 3.64 [1.44,9.19]**

P for trend 0.000*** 0.002**

Substance use disorder 94 3.4 5.0 1.46 [0.62,3.43] 1.21 [0.53,2.78]

REF: Reference category.

Model 1: All odds ratios are adjusted for all variables in the column and additionally for gender and age.

Model 2: All odds ratios are adjusted for all variables in the column and additionally for gender, age, education, living situation, job status, any negative life event, any chronic physical disorder, body mass index and smoking status.

Bold: Significant OR at the 0.05 level, 2-sided test. +: P<.10; *: P<.05; **: P<.01; ***: P<.001

Table 1b Results of logistic regression analyses examining the association between debts status at baseline with onset of 12-month substance use disorder at follow-up, while controlling for mood or anxiety disorder and other potential

confounders at baseline, in weighted adjusted odds ratios (aOR) with 95% confidence intervals (95% CI) Total (N=4491) 12-month substance use disorder at follow-up

Model 1 Model 2

Debts

No 3994 87.0 73.9 REF REF

Yes and easy to pay back 284 7.0 12.3 1.52 [0.60,3.86] 1.23 [0.50,3.01]

Yes and very difficult to pay back 85 2.3 11.2 6.65 [2.34,18.90]*** 4.83 [1.60,14.54]**

P for trend 0.014* 0.063+

Mood or anxiety disorder 381 10.1 16.7 1.21 [0.45,3.24] 1.04 [0.38,2.85]

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Appendix 2

Table 2a Results of logistic regression analyses examining the association between debts status at baseline with persistence of 12-month mood or anxiety disorder at follow-up, while controlling for substance use disorder and other potential confounders at baseline, in weighted adjusted odds ratios (aOR) with 95% confidence intervals (95% CI)

Total (N=414) 12-month mood or anxiety disorder at follow-up

Model 1 Model 2

Debts

No 314 69.0 59.3 REF REF

Yes and very difficult to pay

back 34 10.2 14.8

2.65 [0.96,7.30]+ 2.08 [0.67,6.47]

P for trend 0.019* 0.109

Substance use disorder 33 8.0 8.8 1.19 [0.43,3.33] 0.75 [0.24,2.39]

Table 2b Results of logistic regression analyses examining the association between debts status at baseline with persistence of 12-month substance use disorder at follow-up, while controlling for mood or anxiety disorder and other potential confounders at baseline, in weighted adjusted odds ratios (aOR) with 95% confidence intervals (95% CI)

Total (N=127) 12-month substance use disorder at follow-up

Model 1 Model 2

Debts

No 97 70.9 60.0 REF REF

Yes and very difficult to pay

back 6 5.3 11.8

16.71 [1.87,149.49]* 19.12 [1.50,243.57]*

P for trend 0.115 0.105

Mood or anxiety disorder 33 21.8 21.6 0.73 [0.20,2.63] 0.93 [0.27,3.17]

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Table 3a Results of multinomial logistic regression analyses examining the association between type of mental disorder at baseline with onset of debts at follow-up (reference group is no debts at follow-up), while controlling for potential confounders at baseline, in weighted adjusted relative risk ratios (aRRR) with 95%

confidence intervals (95% CI) Total

(N=4091) Debts at follow-up

Model 1 Model 2

Yes:

easy to pay back

Yes:

difficult to pay back

Yes easy to pay back vs. no debts

Yes difficult to pay back vs. no debts¹

Yes difficult to pay back vs.

no debts¹

Baseline

characteristics n % % % aRRR (95%

CI) aRRR (95%

CI) Mood or

anxiety disorder

314 8.4

13.6 21.6 1.56

[0.86,2.83] 2.62

[1.64,4.17]*** 1.46

[0.80,2.67] 1.78 [1.13,2.80]*

Substance use

disorder 97 3.1

9.5 7.7 2.60

[0.98,6.96]+ 2.27

[0.78,6.66] 2.45

[0.89,6.71]+ 1.50 [0.55,4.10]

1: The category difficult to pay back includes the categories difficult and very difficult to pay back debts.

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Appendix 4

Table 4a Results of multinomial logistic regression analyses examining the association between type of mental disorder at baseline with persistence of debts at follow-up (reference group is no debts at follow-up), while controlling for potential confounders at baseline, in weighted adjusted relative risk ratios (aRRR) with 95% confidence intervals (95% CI)

Total

(N=518) Debts at follow-up

Model 1 Model 2

Yes:

easy to pay back

Yes:

difficult to pay back

Yes difficult to pay back vs. no debts¹

Yes difficult to

pay back vs.

no debts¹

Baseline

characteristics n % % % aRRR

(95% CI) aRRR (95%

CI) aRRR

(95% CI) aRRR (95%

CI) Mood or

anxiety disorder

100 24.0

13.7 37.0 0.57

[0.26,1.23] 2.23

[1.26,3.97]** 0.63

[0.28,1.39] 1.95 [1.07,3.54]*

Substance use

disorder 29 8.2

10.0 7.2 1.09

[0.31,3.93] 1.06

[0.30,3.70] 0.96

[0.31,2.97] 1.02 [0.29,3.58]

1: The category difficult to pay back includes the categories difficult and very difficult to pay back debts.

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Jenkins et al., 2008The second British National Survey of Psychiatric Morbidity, 2000

England, Scotland and Wales

Cross-sectional 8550 adults aged 16-74 years from the general population

CIS-R SCAN AUDIT SAD-Q

– The relationship between low income and CMD is largely mediated by debt – Debt increased risk of CMD after adjustment for income

– Dose–response effect:

more debts, greater risk of CMD

Age, gender, ethnicity, marital status, household size, household tenure, education, social class, employment, urban or rural, region, income

Meltzer et al., 2011

The third British National Survey of Psychiatric Morbidity, 2007

England Cross-sectional 7461 adults aged 16 and over from the general population

ACQ on suicidal ideation

AUDIT SAD-Q

– Debt increased risk of suicidal ideation

– The relationship between debt and suicidal ideation is partially mediated by feelings of hopelessness – Dose–response effect:

more debts from

different sources, greater risk of suicidal ideation

Age, gender, marital status, employment, drinking, gambling, recent stressful life events

Meltzer et al.,

2012 The third British National Survey of Psychiatric Morbidity, 2007

England Cross-sectional 7461 adults aged 16 and over from the general population

CIS-R

SAD-Q – Debt increased risk of all CMD: mood, anxiety and substance use disorders – No differences for source of debt

Age, gender, marital status, employment, household tenure

Dackehag et al., 2019

The Swedish Living Conditions Surveys, 2006-2007;

Prescribed Pharmaceutical register,

2005-2008;

Inpatient register, 1997-2007

Sweden Panel study Two waves:

2006-2007 This study was combined with administrative registers on prescriptions and hospital stays

10950 adults from the general population

ACQ anxiety (1 item)

Registered prescriptions of psychopharmaca

– Payment difficulties is cross-sectionally associated with self- reported anxiety

– Payment difficulties did not increase future use of psychopharmaca – Use of

psychopharmaca increased risk of future payment difficulties

Age, gender, ethnicity, living situation, household tenure, education, urban or rural, income, children

Drentea & The Miami Disability US, Miami Panel study 1463 older adults with CES-D – Debt is cross- Age, gender, ethnicity, marital 5

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Reynolds, 2012 Study, 2000-2003 Two waves:

2000-2001 and 2002-2003

and without physical disabilities

(median age = 59) from the general population

ACQ anxiety (5

items) sectionally associated with depression and anxiety symptoms after adjustment for prior mental health

– Debt more strongly predicted mental health than any other SES indicator

status, household tenure, education, employment, health insurance coverage, income, household assets,

physical disability status, children

Gunasinghe et al.,

2018 The South East London Community Health Study, 2008-2013

England,

London Panel study Two waves:

2008-2010 and 2011-2013

1052 adults aged 16 and over from the general population

CIS-R – Debt increased risk of presence of CMD after adjustment for prior CMD – Among those with a CMD at baseline, debt increased risk of persistence of CMD

Age, gender, ethnicity, relationship status, education, employment, income, number

of residents per household, benefit receipt

Hintikka et al.,

1998 The Finnish Study, 1993-

1995 Finland Cross-sectional 4868 adults

aged 18-74 years from the general population

ACQ on suicidal ideation

GHQ-12

– Those with a current minor mental disorder (GHQ≥3) were likely to have difficulties in repaying debts (37% vs.

16%)

– Difficulty in repaying debts increased risk of suicidal ideation

Age, gender, marital status, employment, minor mental disorder, alcohol abuse

Hojman et al.,

2016 The Chilean Social Protection

Survey, 2006-2009 Chile Panel study Two waves:

2006 and 2009

10,900 adults aged 18 and over from the general population

CES-D short form (8

items) – Persistent debt and increased debt increased risk of depressive symptoms

– Differences for type of debt: the association seems to be driven by consumer credit or late mortgage payments, less for secured debt (secured by collateral)

Socio demographics (age, gender, income, education, employment), family

characteristics (marital status, children), health problems (e.g.

chronic disorder, has been diagnosed with depression), personality traits, assets, personal history, health risk factors (BMI, smoking, drinking)

Sweet et al., 2013 The National Longitudinal Study of Adolescent Health, 2007-2008

US Cross-sectional:

data from the 4^th wave were used

8400 younger adults aged 24-32

from the general

CES-D short form (5

items) – Higher debt-to-asset ratio is cross-sectionally associated with

Marital status, household size, household tenure, education, employment, income, health

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– This also holds for subjective relative debt

Zimmerman &

Katon, 2005 The National Longitudinal

Survey of Youth, 1992 US Cross-sectional:

data from 1992 were used

7278 younger adults aged 27–35

from the general population

CES-D – Higher debt-to-asset ratio increased risk of depressive symptoms

– This holds for both men and women; and for both low and high income groups

Ethnicity, marital status, house tenure, education, employment, insurance, region, children, self- esteem, (past) physical health problem

Zurlo et al., 2014 The Health and Retirement

Study, 2006 US Cross-sectional 5817 middle-aged and

older adults aged 51 and over from the general population

CES-D short form (8

items) – Unsecured debt increased risk of depressive

symptoms

– This holds for the presence as well as the amount of unsecured debt

Age, gender, ethnicity, marital status, education, employment, income, general health, perceived control over finances

Abbreviations:

ACQ: Author Constructed Question(s) aOR: Adjusted Odds Ratio

CESD: Centre for Epidemiological Studies Depression Scale CIS-R: Clinical Interview Schedule – Revised

SCAN: Schedule for Assessment in Neuropsychiatry AUDIT: Alcohol Use Disorder Identification Test SAD-Q: Alcohol Dependence Questionnaire GHQ-12: General Health Questionnaire-12

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