socioeconomic status in danish women with polycystic ovary

University of Southern Denmark

Socioeconomic status in Danish women with polycystic ovary syndrome. A register-basedcohort study

Rubin, Katrine H; Andersen, Marianne skovsager; Abrahamsen, Bo; Glintborg, Dorte

Published in:Acta Obstetricia et Gynecologica Scandinavica

DOI:10.1111/aogs.13514

Publication date:2019

Document version:Accepted manuscript

Citation for pulished version (APA):Rubin, K. H., Andersen, M. S., Abrahamsen, B., & Glintborg, D. (2019). Socioeconomic status in Danish womenwith polycystic ovary syndrome. A register-based cohort study. Acta Obstetricia et Gynecologica Scandinavica,98(4), 440-450. https://doi.org/10.1111/aogs.13514

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DR DORTE GLINTBORG (Orcid ID : 0000-0002-8338-8025) PROFESSOR MARIANNE SKOVSAGER ANDERSEN (Orcid ID : 0000-0002-4603-9504)

Article type : Original Research Article

Socioeconomic status in Danish women with polycystic ovary syndrome. A

register-based cohort study

Katrine H Rubin1, Marianne Andersen

3, Bo Abrahamsen

1,2, Dorte Glintborg

3

1Odense Patient data Explorative Network-OPEN, Department of Clinical Research, University of

Southern Denmark, and Odense University Hospital, Odense, Denmark

2Department of Internal Medicine, Holbaek Hospital, Denmark

3Department of Endocrinology, Odense University Hospital, Odense, Denmark

Corresponding author:

Dorte Glintborg

Odense University Hospital, Kløvervænget 6, 3rd floor, 5000 Odense C, Denmark

Email: [email protected]

Conflict of interest:

The authors have nothing to disclose.


ABSTRACT

Introduction: Low socioeconomic status (SES) could be associated with increased risk of

polycystic ovary syndrome (PCOS) and vice versa. Possible associations between SES,

obesity, and ethnicity in PCOS are undetermined. Material and methods: National register-

based study including women with PCOS aged 25 years or above (PCOS Denmark and an

embedded cohort; PCOS Odense University Hospital (OUH)) and one control population.

PCOS Denmark (N=13,891) included women with PCOS in the Danish National Patient

Register. Women in PCOS OUH underwent clinical examination (N=814). Three age

matched controls were included per patient (N=41,584). The main outcome measure was SES

(Personal income, occupational status, and education). Results: The median (Q1; Q3) age of

women in PCOS Denmark and controls was 33 (29; 39) years. Women with personal income

in the lower tertile had higher probability of a PCOS diagnosis than women in the high-

income tertile (adj. odds ratio OR 1.5 (95% CI; 1.4 to 1.6)). Women being unemployed or on

welfare payment (adj. OR 1.5 (95% CI; 1.4 to1.6)), or being early retired (OR 1.8 (95% CI;

1.7 to 2.0)) had higher probability of a PCOS diagnosis than women affiliated to the labour

market. Women originating from the Middle East more often had PCOS (adj. OR 3.2 (95%

CI 2.8 to 3.7)) compared to women originating from Europe. In PCOS OUH, SES was lower

in obese vs. normal weight women. Conclusions: A diagnosis of PCOS was associated with

lower SES. In PCOS, women of foreign origin and women with obesity, more often had low

SES.

Keywords:

polycystic ovary syndrome, register-based, socioeconomic status, ethnicity, nationwide

Key message

A diagnosis of PCOS was associated with lower socioeconomic status in a Danish register based study

population. Furthermore, in women with PCOS, socioeconomic status was inversely associated with

obesity.


Abbreviations

FG-score: Ferriman Gallwey score

OUH: Odense University Hospital

PCOS: polycystic ovary syndrome

SES: socioeconomic status

OR odds ratio

INTRODUCTION

Polycystic ovary syndrome (PCOS) can be diagnosed in around 10% of premenopausal women (1,2).

PCOS is characterized by irregular menses, polycystic ovaries, and hyperandrogenism (1). Quality of

life may be impaired in PCOS and the risk of depression and development of medical and psychiatric

diseases is increased (3,4).

More than 50% women with PCOS are obese (5). Obesity is associated with a more severe PCOS

phenotype including more irregular menses, infertility, and hirsutism (6,7) and obesity is associated

with impaired quality of life and depression in PCOS (8,9). In the general population, Newton et al

reported a consistent association between lower life course socioeconomic status (SES) and obesity

among women in developed countries (10). Accordingly, obesity was associated with lower income

(11,12) and shorter education (13) in wealthy societies. Apart from obesity, other factors known to

be associated with lower SES (14) are clustered in women with PCOS (figure 1). Women with PCOS

have increased risk of type 2 diabetes (15), hypertension (16) and dyslipidemia (17). Importantly,

obesity increases the risk of type 2 diabetes, cardiovascular disease, and several cancer types in

PCOS (18). Presence of obesity may therefore be associated with lower SES in PCOS. Furthermore,

PCOS is associated with increased risk of medical diseases such as asthma and thyroid disease and

psychiatric diseases including depression, anxiety and eating disorders (19). In accordance, women

with PCOS have more medicine prescriptions than controls (19). Higher morbidity also in young

women with PCOS (9,19) could be associated with low SES.

Women with PCOS could have a more unfavorable lifestyle compared to healthy women. Appetite

regulation may be impaired in PCOS (20) and women with PCOS more often had unhealthy dietary


intake compared to controls (21). We previously reported a higher prevalence of smoking in PCOS

vs. controls (22) and smoking was associated with a more adverse lipid profile (22) and insulin re-

sistance (23) in PCOS. Unhealthy lifestyle including low physical activity (24) could be associated with

low SES in PCOS (25).

Only two previous studies investigated SES in PCOS (26,27). Both studies reported an inverse associa-

tion between SES and BMI in PCOS (26,27), but the studies were of limited size (n<250) and one

study did not include healthy controls (26). The hypothesis that SES is lower in women with PCOS

compared to controls therefore remains to be tested.

Ethnic background affects PCOS phenotype and metabolic risk profile (28,29). Danish women with

PCOS originating from the Middle East had higher Ferriman Gallwey (FG)-score and lower insulin

sensitivity than ethnic Northern European Danish women with PCOS (29). A more adverse metabolic

risk profile and a more severe PCOS phenotype in women with PCOS originating from the Middle

East could be associated with lower SES. Furthermore, low vitamin D levels are common in women

with PCOS originating from the Middle East (30). The possible effect of low vitamin D levels on men-

tal health is debated (31). Previous studies regarding SES in PCOS (26,27) included women from Italy

(26) (ethnic homogeneous) and USA (27) (50% white, 50% black). The hypothesis that ethnic back-

ground could be associated with SES in PCOS was not tested in these studies.

The aim of the present register-based study was to compare SES in women with a diagnosis

of PCOS and controls based on data from Danish national registers. We also investigated

possible associations between SES, obesity, and ethnic background in women with PCOS.

MATERIAL AND METHODS

Design and population

The study design and baseline data for this study cohort have been reported in detail earlier

(19). Data regarding development of type 2 diabetes, cardiovascular disease and incident

fractures (15,32,33) in the study cohort has been published recently. We included two patient

populations with PCOS and one control population (Figure 1). In women with hirsutism, the

prevalence of PCOS is more than 75% (34,35), and women with a diagnosis of hirsutism

were included in the patient cohort. PCOS Denmark included all women in Denmark aged

12-60 years, who were diagnosed through a hospital contact with PCOS (E282) and/or


hirsutism (L680) between January 1st, 1995 and the end of 2012. PCOS Odense University

Hospital (OUH) included an embedded cohort of women with PCOS and/or hirsutism treated

at OUH with available clinical and biochemical information. For each patient in PCOS

Denmark (and PCOS OUH), three control women born in the same year as the patient were

randomly drawn from the Civil Population Register. Controls were assigned the index date

(date of first PCOS diagnosis) of their matched PCOS case and should be alive at the index

date of their PCOS case.

Exclusion criteria were women with the diagnoses E221 (hyperprolactinemia), E220

(acromegaly), E24 (Cushing’s syndrome), E25 (adrenogenital syndrome), and Q96 (Turner

syndrome). Furthermore, women aged <25 years at the index date were excluded.

Variables

Danish National Registries

All Danish individuals are assigned a unique personal identification number. The

identification number can be used to link data from all public registers at an individual level

(36,37). Denmark has a large array of high-quality national registers that cover the entire

population and include consistently coded long-term data (37). Register based data provide a

unique opportunity to perform population-based studies linking information about hospital

diagnoses, socioeconomic data, medicine prescriptions at the individual level with a very

high validity (37,38).We retrieved information from registers about SES, ethnicity and area of

origin, and hospital contacts in the study population.

The Danish Patient Register (NPR) holds data on all inpatient hospital contacts in Denmark

since 1977 and since 1995, outpatient contacts are included in Danish Patient Register. The

diagnostic codes used in the patient register are classified according to the Danish version of

the International Classification of Diseases, version 8 (ICD8: 1977-1993) and, since 1994,

version 10 (ICD10). As previously described (19), Danish Patient Register was used to

identify the PCOS Denmark cohort and to obtain information regarding comorbidity in the

study population.

Socioeconomic status (SES)

Variables on SES included information on personal income, occupational status, and education. Data

were retrieved from Danish registers at Statistics Denmark as described below on the index year for

each woman.


Personal income: Personal income was extracted from the Income Statistics Register (39) and includ-

ed information on the available average income after tax and interest (included salary, retirement

benefits, welfare payment, remuneration, company profits etc.). The income was categorized into

tertiles (high, middle, low).

Occupational status (affiliation to the labour market): Occupational status was also extracted from

the Income Statistics Register (39) and divided into five categories: Affiliated to labour market (em-

ployed or self-employed), education, unemployed or welfare payment, early retirement, and un-

known or missing.

Education: Education was extracted from the Population Education Register and included infor-

mation on the highest completed education based on the International Standard Classification of

Education (40). Population Education Register covers education and training completed in Denmark.

Information on education acquired outside Denmark and information for immigrants is based on

self-reported information on completed education (if available).

Education level was divided into: Master´s or doctoral education, short tertiary education (incl.

bachelor), vocational education, upper secondary school, basic school (primary), and unknown or

missing.

Covariates

We gathered information on ethnicity and area of origin from the Danish Civil Registration System

(36).

Ethnicity: Ethnicity was extracted from the Civil Registration System (36) and divided into Danish,

immigrants, descendants, and unknown or missing.

Area of origin: Area of origin was extracted from Civil Registration System (36) and divided into

women from Europe, Middle East, and other (America, Africa, Oceania, and unknown or missing)

(Countries shown in Supporting Information Table S1).


Comorbidity: The Charlson comorbidity index is based on 19 comorbid conditions (41) and was calcu-

lated from the ICD-10 operationalization by Quan et al.(42). The Charlson comorbidity index was

used to classify comorbid conditions among the respondents from 1995 to index date. To calculate

the Charlson score, we used information from the Danish Patient Register and the score was catego-

rized into no comorbidity (0), and plus one comorbidity (≥1).

Statistical analyses

Descriptive analyses for categorical variables were presented as frequencies and difference

between PCOS and controls was evaluated by Chi-square test. Continuous variables were

tabulated as medians (with quartiles, Q1 and Q3) and nonparametric test on the equality of

medians was used to test for differences between groups.

Conditional logistic regression analyses were used to calculate odds ratios (OR) with 95%

confidence intervals and corresponding p-values, to examine the association between PCOS

and SES, ethnicity, area of origin and comorbidity separately for PCOS Denmark and PCOS

OUH vs. matched controls. The OR was reported unadjusted and mutually adjusted for

education, occupational status, personal income, ethnicity and area of origin.

Logistic regression analyses were performed to analyse the association between 1) BMI

(divided into BMI≥25 kg/m2 and BMI<25 kg/m

2 as reference) and SES and 2) FG-score

(divided into FG-score ≥5 and FG-score<5 as reference) and SES in the PCOS OUH cohort.

The logistic regression analyses were carried out unadjusted and mutually adjusted for age,

education, occupational status, personal income, ethnicity and area of origin.P-values <0.05

were considered statistically significant. Analyses were conducted using STATA 14

(StataCorp 2015).

Sensitivity analyses were performed to explore the possible effect of area of origin. Included

women were divided according to origin from Europe or the Middle East. In these

conditional logistics regressions analyses, analyses were mutually adjusted for education,

occupational status, and personal income.

Sensitivity analyses were repeated in women above age 30 years. The Rotterdam criteria

were introduced in 2003 and include more mild PCOS phenotypes. Therefore, two sensitivity

analyses were performed in women with index date before and after 2003, respectively.


Furthermore, women with idiopathic hirsutism could be included in the diagnosis L680.

Therefore, we performed a sensitivity analysis including only women with the diagnosis

E282 (excluding women with L680).

Ethical approval

The core study was an open register-based cohort study. The study did not need approval

from the local Ethics committee or Institutional Review Board by Danish law. The study was

approved by the Data Protection Agency and by Statistics Denmark, project no 704175.

RESULTS

The flow chart of included women is summarized in Figure 1. A total of 55,475 women

fulfilled the inclusion criteria, 13,891 women with PCOS (PCOS Denmark and the embedded

cohort PCOS OUH, N=814) and 41,584 controls.

Baseline characteristics of the study population are shown in Table 1. The median age was 33

years in all study cohorts (range 25-60 years). Women in PCOS Denmark were more often in

the middle tertile (37 vs. 32%) and low tertile (38 vs. 32%) of personal income than controls.

Women in PCOS Denmark had lower frequency of affiliation to the labour market (70 vs.

80%) and the occupational status was more often unemployed or on welfare payment (13 vs.

7 %) than controls. Women with PCOS more often had basic school (25 vs. 23%) and short

tertiary education (incl. bachelor) (25 vs. 21%) as highest education compared to controls.

Women in PCOS Denmark more often originated from the Middle East (8 vs. 3%) compared

to controls and more women in PCOS Denmark vs. controls were immigrants (15 vs. 10%).

Comorbidity according to Carlson index ≥1 was more often present in women with PCOS vs.

controls (9 vs. 5%).

Women in PCOS OUH differed from the remaining women in PCOS Denmark regarding

personal income, occupational status and education (Supporting Information Table S2).

The conditional logistics regressions analyses (Table 1) showed significant associations

between PCOS diagnosis and SES (personal income, occupational status, and education).

Women with personal income in the low (OR 1.7) and middle tertile (OR=1.7) had a higher

probability of PCOS than women in the high-income tertile in the unadjusted analysis.


Associations between personal income and PCOS remained significant in the analysis

adjusted for education, occupational status, ethnicity and area of origin.

Women under education (OR=1.4), being unemployed or on welfare payment (OR=1.9), or

being early retired (OR=1.9) had higher probability of PCOS than women affiliated to the

labour market. Significant associations between occupational status and PCOS remained

significant in the adjusted analysis with slightly lower OR.

Women with short tertiary education (incl. bachelor) (OR= 1.7), vocational education

(OR=1.2), upper secondary school (OR= 1.4), and basic school (OR= 1.5) as the highest

educational levels more often had PCOS compared to women with master´s or doctoral

education. In adjusted analyses, only the significant association between short tertiary

education (incl. bachelor) and PCOS remained significant.

Immigrants and descendants more often had PCOS compared to women of European

ethnicity (unadjusted OR= 1.6, OR adjusted for SES not significant). Women originating

from the Middle East more often had PCOS (unadjusted OR=3.4 and adjusted OR= 3.2)

compared to women originating from Europe. Similar significant associations were seen in

the conditional logistic regressions analyses in women in PCOS OUH compared to their

controls, but with higher OR.

Sensitivity analyses:

Conditional logistic regressions analyses were repeated in women with PCOS originating

from Europe (N=12,152) and in women with PCOS originating from the Middle East

(N=1,112) compared to their age matched controls (Table 2). The association between SES

and PCOS remained significant when women were divided according to area of origin, but

with higher OR in women with PCOS originating from the Middle East compared to analyses

in the whole study cohort. In women with PCOS originating from the Middle East vs.

controls, women under education had adjusted OR=2.1, unemployed women or women on

welfare payment had adjusted OR=7.1 and women on early retirement had an adjusted

OR=2.6 for PCOS compared to women affiliated to the labour market. In women with PCOS

of European origin and their respective controls, results of logistic regression analyses

resembled analyses in the whole study cohort.


Analyses were repeated in the following sensitivity analyses 1: Women aged above 30 years

(N=38,064), 2: Women with index date before (N=23,417) and after (N=32.058) 2003, and 3:

Women with the diagnosis E282 (excluding the diagnosis L680) (N=28,912). This did not

change the findings, nor did analyses excluding all women with unknown or missing

information on personal income, occupational status and/or education (N=50,464). Results

for the five sensitivity analyses are shown in Supporting Information Table S3.

Table 3 shows associations between SES and obesity in PCOS. Women in PCOS OUH

(N=759) were categorized into BMI< 25 kg/m2

(N=257) and BMI≥25 kg/m2

(N=502).

Women with PCOS and BMI< 25 kg/m2

more often had high personal income (22 vs. 18%),

were affiliated to the labour market (72 vs. 62%) and had master´s or doctoral education (7 vs.

1%) or short tertiary education (incl. bachelor) (25 vs. 22%) compared to women with PCOS

and BMI≥25 kg/m2. In logistic regression analyses, women with PCOS, who were

unemployed or on welfare payment (OR=1.9) and early retirement (OR=2.6) had higher

probability of BMI≥25 kg/m2 than women affiliated to the labour market. Further, women

with PCOS and BMI≥25 kg/m2 more often had basic school as highest completed education

compared to women with PCOS and BMI< 25 kg/m2 (OR=8.3 in the unadjusted and OR=7.0

in the mutually adjusted analyses).

Analyses were repeated in PCOS OUH with women stratified according to FG-score

(Supporting Information Table S4). Women with FG-score ≥5 more often had vocational

education (34 vs. 24%) and basic school (28 vs. 24%) as the highest education level

compared to women with FG-score < 5, they were more often immigrants (19 vs. 8%) and

more often originated from the Middle East (12 vs. 2%). FG-score was not associated with

SES in logistic regression analyses.

DISCUSSION

To our knowledge this is the first nationwide, population-based study that reports lower SES in PCOS

compared to controls. We found that Danish women with PCOS more often had low personal in-

come, they were more often unemployed or on welfare payment and were more often early retired

compared to age matched controls. Furthermore, SES was inversely associated with obesity.


As previously reported (19), participants in our study population were relatively lean (median

BMI in PCOS OUH, 27 kg/m2). Our findings therefore supported that low SES should be

considered even in a relatively lean study population of women with PCOS. Furthermore,

area of origin and ethnicity were closely associated with SES. We found that immigrants and

women with PCOS originating from the Middle East had even higher risk of low SES

compared to women of Danish ethnicity and women of European origin.

The data from the present study expand findings from two previous studies regarding SES in

PCOS (26,27). Merkin et al included 938 American women and investigated childhood and

adult SES and risk of development of PCOS (27). The authors found that low childhood SES

(low education in parents) increased the risk of development of PCOS (27). However, this

association was only significant in women of high education. Furthermore, women with high

education had elevated risk of PCOS compared to women with lower education, which could

support increased attention of PCOS diagnosis in women of higher education (27). In

accordance with findings of the present study, significant associations between SES and risk

of PCOS development became stronger in obese compared to lean women (27). However,

study limitations may apply to the study by Merkin et al; the study was of limited size and

included a population of mixed ethnicity, and the diagnosis of PCOS was based on primarily

self-reported data (27). The use of self-reported data could explain some of the findings by

Merkin as women with higher education could be more likely to recall and report past and

current health conditions (27). Women of higher education could be more likely to use the

healthcare system and be diagnosed with PCOS (27). Furthermore, the authors suggested that

upward socioeconomic mobility; ie women with PCOS of high education having parents with

low education, could suffer from more peripubertal stress, greater incidence of obesity, and

thus early disruption of menstrual cycles (27). In the present study we did not include SES of

the parents, but clearly, more studies are needed regarding possible implications of upward

and downward socioeconomic mobility in women with PCOS.

Di Fede et al investigated possible associations between SES and PCOS phenotype in 244

Italian women with PCOS (26). No controls were included in the study. In accordance with

the present study, low family income and low education was associated with higher BMI

(26). Furthermore, low SES was associated with a more severe PCOS phenotype with higher

degree of anovulation, higher waist circumference, and higher insulin levels compared to

women with PCOS and high SES. These findings support the importance of high BMI for


low SES in PCOS. Obesity is closely associated with PCOS phenotype and infertility and

BMI is inversely associated with quality of life in PCOS (18). Furthermore, obesity is an

important risk factor for development of type 2 diabetes and cardiovascular disease in PCOS

(18). In accordance, we found that high FG-score was associated with lower SES, however

this association did not remain significant in logistic regression analyses. It is possible that

lean women with PCOS represent a separate PCOS phenotype (43,44) and metabolic risk in

lean women with PCOS could be determined primarily by hyperandrogenism (45). The

present study design did not allow us to compare SES in lean women with PCOS compared

to lean controls. The study by Merkin et al (27) supported that low parental education

predicted PCOS development, which could be due to poor nutritional habits (27), but genetic

and environmental factors should also be taken into consideration (46). The present study did

not include data regarding physical activity and smoking habits and therefore, we could not

test hypotheses regarding possible associations between BMI, physical activity, smoking, and

SES in PCOS.

In the present study, we investigated the importance of ethnic background for SES in PCOS.

We found that women of foreign origin had lower SES than women of Danish origin, and the

OR for low SES was higher in women with PCOS originating from the Middle East

compared to analyses in the whole study cohort. Several factors could have an adverse effect

on SES in foreign women. We previously reported that women originating from the Middle

East had higher FG-score and were more insulin resistant than women with PCOS originating

from Northern Europe (29). A more severe PCOS phenotype could have a negative impact

on SES in PCOS. In agreement with this hypothesis, we found that a high FG-score was more

prevalent in foreign women with PCOS compared to women with PCOS of Danish origin.

Furthermore, De Fede et al (26) found an inverse association between PCOS phenotype and

SES. Poor language skills and moving to a country of other culture and/or religion could also

have a negative effect on SES in foreign women with PCOS. The present study design did not

allow us to test this hypothesis. D-vitamin is important for brain development and function

(31). Low D-vitamin levels are associated with obesity, foreign origin, low sun exposure and

dietary intake without fatty fish and dairy products. We did not have data on D-vitamin levels

in the present study, and therefore we could not test possible associations between D-vitamin

levels, mental health and SES in the present study. Future studies are needed in foreign

women with PCOS to determine predictors for low SES and to improve possibilities for

structured intervention.


Strengths and limitations may apply in the present study. The study included a large and well-

characterized group of patients with PCOS, we embedded a clinical cohort into a national register-

based study, and we had access to well-validated register-based data. SES will be dependent on age.

Included women in the present study therefore should be aged at least 25 years. Some women could

finish their education at an older age, and we performed a sensitivity analyses in women aged 30

years or more. Inclusion of women aged 30 years or more did not change significant results. Some

limitations may apply to the present study design (15,32,33). Education level in immigrants could be

underestimated as their education must be individually reported to Statistics Denmark while educa-

tion completed in Denmark is already in the registers. Furthermore, the present matched case-

control design did not allow us to investigate the risk of low SES between foreign women with and

without PCOS. Many patients with PCOS are not seen at the hospital, but are treated by their gen-

eral practitioner or a private gynaecologist. Furthermore, some women in the control group could

have undiagnosed PCOS, which could lead to underestimation of differences in SES between women

with PCOS and controls. We included women from 1995 and onwards, whereas the Rotterdam crite-

ria were introduced in 2003 (1). Therefore, different criteria for PCOS would be applied in the earli-

est entries into the PCOS cohort. We included women with hirsutism in the PCOS population as the

majority of patients with clinical and/or biochemical hyperandrogenism are diagnosed with PCOS

(47-49). However, some women could have idiopathic hirsutism, and the inclusion of these women

in the PCOS cohort could lead to a type 2 error. The results from two sensitivity analyses where we

included only women with index date before 2003 and where we included only women with the

diagnosis E282 (PCOS) and excluded women with L680 (hirsutism) did not change significant results.

Our included study cohort was relatively lean and the ethnic background was relatively homogene-

ous. The findings of the present study therefore need to be confirmed in more obese study popula-

tions of other ethnic background. Our cohort (PCOS OUH) differed from PCOS Denmark in terms of

age, comorbidity and prescription history. However, the differences were small and therefore unlike-

ly to be a major source of bias in our findings.

CONCLUSION

SES was lower in women with PCOS compared to controls and SES was associated with

obesity and ethnic background. More studies are needed regarding the effect of medical and

lifestyle intervention on SES in PCOS.


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Supporting Information legends

Table S1: Area of origin and included countries.

Table S2: General characteristics and comparison of the PCOS OUH and PCOS Denmark cohort

Table S3: Conditional logistic regression analyses in PCOS Denmark cases and the respective control

for 5 different sensitivity analyses.

Table S4: PCOS OUH and FG-score at baseline and association between FG-score and socioeconomic

in PCOS OUH.


Legends tables and figures

Table 1. General characteristics of the study population at baseline and conditional logistic regres-

sion analyses in PCOS Denmark cases and the respective controls (N=55,475) and in PCOS OUH cases

and respective controls (N=3,251).

Table 2: Conditional logistics regression analyses stratified by area of origin.

Table 3. PCOS OUH and BMI at baseline and association between BMI and socioeconomic status in

PCOS OUH.

Figure 1. Possible associations between socioeconomic status (SES) and polycystic ovary syndrome

(PCOS).

Figure 2. Flowchart of the study. PCOS, polycystic ovary syndrome; NPR, Danish Patient Register;

OUH, Odense University Hospital. .


Table 1. General characteristics of the study population at baseline and conditional logistic regression analyses in PCOS Denmark cases and the re-

spective controls (N=55,475) and in PCOS OUH cases and respective controls (N=3,251)

PCOS Den-

mark

N=13,891

Control

N=41,584

PCOS Denmark vs. controls

N=55,475

PCOS OUH

N=814

Control

N=2,437

PCOS OUH vs. controls

N=3,251

N(%) N (%) OR (CI 95%)

Unadjusted

OR (CI 95%)

Adjusteda

N (%) N (%) OR (CI 95%)

Unadjusted

OR (CI 95%)

Adjusteda

Personal income

High tertile 3,467 (25) 14,996 (36) 1 1 152 (19) 928 (38) 1 1

Middle tertile 5,136 (37) 13,331 (32) 1.7 (1.6; 1.7) 1.6 (1.5; 1.7) 315 (39) 813 (33) 2.4 (1.9; 2.9) 2.2 (1.7; 2.7)

Low tertile 5,280 (38) 13,199 (32) 1.7 (1.6; 1.8) 1.5 (1.4; 1.6) 347 (43) 693 (28) 3.1 (2.5; 3.8) 2.2 (1.7; 2.9)

Unknown or missing 8 (0.1) 58 (0,1) 0.6 (0.3; 1.3) 0.4 (0.2; 0.9) 0 3 (0.1) Na Na

Occupational status

Affiliated to the labour market 9,699 (70) 33,038 (80) 1 1 526 (65) 1,991 (82) 1 1

Education 645 (5) 1,525 (4) 1.4 (1.3; 1.6) 1.2 (1.1; 1.4) 57 (7) 71 (3) 3.0 (2.1; 4.4) 2.4 (1.6; 3.5)

Unemployed or welfare payment 1,739 (13) 3,176 (7) 1.9 (1.8; 2.0) 1.5 (1.4; 1.6) 124 (15) 192 (8) 2.4 (1.9; 3.1) 1.7 (1.3; 2.2)

Early retirement 870 (6) 1,529 (4) 1.9 (1.8; 2.1) 1.8 (1.7; 2.0) 47 (6) 59 (2) 3.0 (2.0;4.5) 2.6 (1.7; 3.9)

Unknown or missing 938 (7) 2,316 (6) 1.4 (1.3; 1.5) 1.1 (1.1; 1.3) 60 (7) 124 (5) 1.8 (1.3; 2.5) 1.3 (0.9; 1.9)

Education

Master´s or doctoral education 691 (5) 2,885 (7) 1 1 29 (4) 179 (7) 1 1

Short tertiary education (incl. bachelor) 3,478 (25) 8,532 (21) 1.7 (1.6; 1.9) 1.5 (1.4; 1.7) 181 (23) 535 (22) 2.1 (1.4; 3.2) 1.8 (1.2; 2.8)

Vocational education 4,355 (31) 15,005 (36) 1.2 (1.1; 1.3) 1.0 (0.9; 1.1) 262 (32) 837 (34) 1.9 (1.3; 3.0) 1.5 (1.0; 2.3)

Upper secondary school 1,468 (11) 4,324 (10) 1.4 (1.3; 1.6) 1.0 (0.9; 1.2) 103 (13) 275 (11) 2.3 (1.5; 3.6) 1.4 (0.9; 2.2)

Basic (primary school) 3,461 (25) 9,366 (23) 1.5 (1.4; 1.7) 1.0 (0.9;1.1) 218 (27) 535 (22) 2.5 (1.6; 3.8) 1.4 (0.9; 2.2)

Unknown or missing 438 (3) 1,472 (4) 1.2 (1.1; 1.4) 0.7 (0.6; 0.8) 21 (3) 76 (3) 1.7 (0.9;3.2) 0.8 (0.4; 1.5)

Ethnicity

Danish 11,604 (84) 36,985 (89) 1 1 675 (83) 2,173 (89) 1 1

Immigrants 2,094 (15) 4,197 (10) 1.6 (1.5; 1.7) 1.0 (0.9; 1.1) 129 (16) 239 (10) 1.7 (1.4; 2.2) 1.0 (0.6; 1.6)

Descendants 162 (1) 325 (1) 1.6 (1.3; 1.9) 0.9 (0.7: 1.2) 10 (1) 21 (1) 1.5 (0.7; 3.2) 0.8 (0.3; 1.9)

Unknown or missing 31 (0.2) 77 (0.2) 1.3 (0.8; 2.0) 1.9 (1.1; 3.1) 0 2 (0.2) Na Na

Area of origin

Europe 12,152 (87) 38,901 (94) 1 1 705 (87) 2,278 (93) 1 1

Middle east 1,112 (8) 1,039 (3) 3.4 (3.1; 3.7) 3.2 (2.8; 3.7) 75 (9) 62 (3) 3.9 (2.8; 5.5) 3.3 (1.9; 5.7)

Other (and unknown or missing) 627 (5) 1,644 (4) 1.2 (1.1; 1.3) 1.2 (1.1; 1.4) 34 (4) 97 (4) 1.1 (0.8; 1.7) 1.0 ( 0.6; 1.8)

Comorbidity

Charlson = 0 12,595 (91) 39,363 (95) 1 - 748 (92) 2,319 (95) 1 -

Charlson ≥ 1 1,296 (9) 2,221 (5) 1.8 (1.7; 2.0) - 66 (8) 118 (5) 1.7 (1.3; 2.3) - aMutually adjusted for education, occupational status, personal income, ethnicity and area of origin

PCOS; polycystic ovary syndrome; PCOS OUH; PCOS Odense University Hospital; OR, odds ratio; CI, confidence interval.


Table 2: Conditional logistics regression analyses stratified by area of origin

Cases from Europe and respective controls Cases from Middle East and respective controls


N=48,529


N= 2,815


N=4,443


N=300

OR (CI 95%)

Unadjusted

OR (CI 95%)

Adjusteda

OR (CI 95%)

Unadjusted

OR (CI 95%)

Adjusteda

OR (CI 95%)

Unadjusted

OR (CI 95%)

Adjusteda

OR (CI 95%)

Unadjusted

OR (CI 95%)

Adjusteda

Personal income

High tertile 1 1 1 1 1 1 1 1

Middle tertile 1.6 (1.6; 1.7) 1.7 (1.6; 1.7) 2.3 (1.9; 2.9) 2.2 (1.8; 2.8) 2.7 (2.1; 3.4) 1.6 (1.2; 2.0) 3.0 (1.2; 7.7) 1.8 (0.6; 5.4)

Low tertile 1.5 (1.4; 1.6) 1.5 (1.4; 1.6) 2.6 (2.1; 3.2) 2.1 (1.7; 2.7) 5.6 (4.6; 6.9) 1.6 (1.2; 2.0) 7.6 (3.0; 19.2) 1.9 (0.6; 6.5)

Unknown or missing 0.1 (0.0; 0.6) 0.2 (0.0; 1.6) Na Na Na Na Na Na

Occupational status

Affiliated to the labour

market 1 1 1 1 1 1 1 1

Education 1.3 (1.2; 1.5) 1.2 (1.1; 1.4) 2.8 (1.9; 4.1) 2.2 (1.4; 3.4) 2.9 (2.9; 4.0) 2.1 (1.5; 3.1) 2.9 (0.8; 10.2) 1.4 (0.3; 5.6)

Unemployed or welfare

payment 1.4 (1.3; 1.5) 1.4 (1.3; 1.5) 2.0 (1.6; 2.6) 1.7 (1.3; 2.2) 9.8 (8.1; 11.9) 7.1 (5.7; 8.7) 7.9 (4.0; 15.6) 5.4 (2.3; 12.4)

Early retirement 1.8 (1.7; 2.0) 1.8 (1.6; 2.0) 2.7 (1.8; 4.1) 2.5 (1.6; 3.9) 4.2 (3.1; 5.8) 2.6 (1.9; 3.7) 7.3 (1.7; 31.7) 4.3 (1.0; 19.0)

Unknown or missing 0.8 (0.7; 0.9) 0.9 (0.8; 0.9) 1.0 (0.7; 1.5) 0.9 (0.6; 1.5) 10.2 (8.3; 12.6) 6.8 (5.3; 8.7) 10.0 (4.5; 22.9) 6.1 (2.0; 18.5)

Education

Master´s or doctoral educa-

tion 1 1 1 1 1 1 1 1

Short tertiary education

(incl. bachelor) 1.7 (1.6; 1.9) 1.5 (1.4; 1.7) 2.1 (1.3; 3.3) 1.9 (1.2; 3.0) 1.6 (1.0; 2.4) 1.3 (0.9; 2.1) 1.6 (0.3; 8.4) 0.7 (0.1; 4.8)

Vocational education 1.2 (1.1; 1.4) 1.0 (0.9; 1.1) 2.0 (1.3; 3.2) 1.6 (1.0; 2.6) 1.4 (0.9; 2.1) 1.0 (0.7; 1.6) 1.4 (0.3; 7.4) 0.6 (0.1; 3.4)

Upper secondary school 1.3 (1.2; 1.5) 1.0 (0.9; 1.1) 2.4 (1.5; 3.8) 1.5 (0.9; 2.6) 3.4 (2.2; 5.2) 1.8 (1.1; 2.8) 3.4 (0.6; 19.2) 1.2 (0.2; 8.8)

Basic (primary school) 1.4 (1.3; 1.5) 1.0 (0.9; 1.1) 2.3 (1.5; 3.7) 1.4 (0.9; 2.3) 4.9 (3.3; 7.2) 2.3 (1.5; 3.6) 5.6 (1.2; 27.5) 1.4 (0.2; 9.6)

Unknown or missing 0.5 (0.4; 0.6) 0.4 (0.3; 0.5) 0.5 (0.2; 1.2) 0.3 (0.1; 0.9) 12.4 (7.9; 19.5) 5.3 (3.1; 9.0) 17.1 (3.0; 96.8) 4.4 (0.4; 43.5)

Comorbidity

Charlson = 0 1 - 1 - 1 - 1 -

Charlson ≥ 1 1.8 (1.7; 1.9) - 1.7 (1.3; 2.4) - 2.2 (1.7; 2.8) - 2.4 (1.1; 5.5) - aMutually adjusted for education, occupational status, and personal income

PCOS; polycystic ovary syndrome; PCOS OUH; PCOS Odense University Hospital; OR, odds ratio; CI, confidence interval.


Table 3. PCOS OUH and BMI at baseline and association between BMI and socioeconomic sta-

tus in PCOS OUH

BMI<25 kg/m2

N=257

BMI≥25 kg/m2

N=502

BMI ≥25 kg/m2 versus BMI<25

kg/m2 (reference)

N (%) N (%) OR (CI 95%)

Unadjusted

OR (CI 95%)

Adjusteda

Age, median (Q1;Q3) 33 (28; 37) 33 (29; 38) 1.0 (1.0; 1.0) 1.0 (1.0; 1.0)

Personal income

High tertile 57 (22) 90 (18) 1 1

Middle tertile 102 (40) 196 (39) 1.2 (0.8; 1.8) 1.0 (0.6; 1.5)

Low tertile 98 (38) 216 (43) 1.4 (0.9; 2.1) 1.0 (0.6; 1.6)

Occupational status

Affiliated to the labour market 186 (72) 310 (62) 1 1

Education 20 (8) 29 (6) 0.9 (0.5; 1.6) 1.1 (0.6; 2.2)

Unemployed or welfare payment 27 (11) 86 (17) 1.9 (1.2; 3.1) 1.7 (1.0; 2.9)

Early retirement 8 (3) 35 (7) 2.6 (1.2; 5.8) 1.9 (0.8; 4.3)

Unknown or missing 16 (6) 42 (8) 1.6 (0.9; 2.9) 1.5 (0.7; 3.1)

Education

Master´s or doctoral education 18 (7) 7 (1) 1 1

Short tertiary education (incl. bach-

elor) 63 (25) 109 (22) 4.4 (1.8; 11.2) 4.6 (1.8; 11.8)

Vocational education 76 (30) 169 (34) 5.7 (2.3; 14.3) 5.4 (2.1; 13.8)

Upper secondary school 48 (19) 46 (9) 2.5 (0.9; 6.4) 2.2 (0.8; 6.1)

Basic (primary school) 49 (19) 158 (31) 8.3 (3.2; 21.0) 7.0 (2.7; 18.4)

Unknown or missing 3 (1) 13 (3) 11.1 (2.4; 51.4) 10.2 (2.0; 51.8)

Ethnicity

Danish 219 (85) 417 (83) 1 1

Immigrants 34 (13) 81 (16) 1.3 (0.8; 1.9) 0.9 (0.4; 1.8)

Descendants 4 (2) 4 (1) 0.5 (0.1; 2.1) 0.3 (0.1; 1.5)

Area of origin

Europe 228 (85) 436 (87) 1 1

Middle east 17 (7) 50 (10) 1.5 (0.9; 2.7) 1,3 (0.5; 3.4)

Other (and unknown or missing) 12 (5) 16 (3) 0.7 (0.3; 1.5) 0.6 (0.2; 1.8)

Comorbidity

Charlson = 0 246 (96) 453 (90) 1 -

Charlson ≥ 1 11 (4) 49 (10) 2.4 (1.6; 2.1) aMutually adjusted for age, education, occupational status, personal income, ethnicity and area of origin

PCOS OUH; polycystic ovary syndrome Odense University Hospital; BMI, body mass index; Q,

quartile; OR, odds ratio; CI, confidence interval.


Figure 1. Possible associations between SES and PCOS


Figure 2. Flowchart of the study

socioeconomic status in danish women with polycystic ovary

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