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Nordic Journal of Psychiatry

ISSN: 0803-9488 (Print) 1502-4725 (Online) Journal homepage: https://www.tandfonline.com/loi/ipsc20

Gender differences in healthcare management ofdepression: aspects of sick leave and treatmentwith psychoactive drugs in a Swedish setting

Per Lytsy, Johan Hallqvist, Kristina Alexanderson & Annika Åhs

To cite this article: Per Lytsy, Johan Hallqvist, Kristina Alexanderson & Annika Åhs (2019)Gender differences in healthcare management of depression: aspects of sick leave and treatmentwith psychoactive drugs in a Swedish setting, Nordic Journal of Psychiatry, 73:7, 441-450, DOI:10.1080/08039488.2019.1649723

To link to this article: https://doi.org/10.1080/08039488.2019.1649723

© 2019 The Author(s). Published by InformaUK Limited, trading as Taylor & FrancisGroup.

Published online: 12 Aug 2019.

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ARTICLE

Gender differences in healthcare management of depression: aspects of sickleave and treatment with psychoactive drugs in a Swedish setting

Per Lytsya,b , Johan Hallqvistb, Kristina Alexandersona and Annika Åhsb

aDepartment of Clinical Neuroscience, Division of Insurance Medicine, Karolinska Institutet, Stockholm, Sweden; bDepartment of PublicHealth and Caring Sciences, University of Uppsala, Uppsala, Sweden

ABSTRACTPurpose: To investigate whether women and men diagnosed with depressive disorder were managedequally in terms of being sick-leave certified and being prescribed psychoactive drugs.Materials and methods: Data from all patients diagnosed with depression during 2010–2015 inUppsala county, Sweden (n¼ 19 448) were used to investigate associations between gender andissued sick-leave certificate, prescriptions of anti-depressants, anxiolytics, hypnotics and sedatives, andcognitive behavioral psychotherapy referrals, at different time points up till 180days after diagnosis.Results: At diagnosis date, 50.1% were prescribed antidepressants; 14.2% anxiolytics; 13.3% hypnoticsor sedatives. Corresponding proportion regarding issue of sick-leave certificate among working aged(18–64 years) was 16.6%. Men had higher odds than women of being prescribed antidepressants (OR1.16; 95% CI 1.09–1.24); anxiolytics (1.10; 95% CI 1.02–1.21), hypnotics and sedatives (OR 1.09; 95% CI1.00–1.19) and lower odds (among those aged 18–64 years) of being sick-leave certified (OR 0.90; 95%CI 0.82-0.98) in adjusted regression models. There were subtle changes in ORs for outcomes at 3- and6-month follow-up periods.Conclusions: Men had somewhat higher odds of being prescribed psychoactive drugs and slightlylower odds of being sick-leave certified as compared to women at date when diagnosed with depres-sion. The absolute differences were, however, small and the overall conclusion is that women andmen with current diagnosed depressive episode/recurrent depressive disorder are generally managedlikewise regarding sick leave and psychoactive treatment.

ARTICLE HISTORYReceived 21 January 2019Revised 24 June 2019Accepted 23 July 2019

KEYWORDSDepression; sick leave;anxiolytics; hypnotics andsedatives; antidepressants

Introduction

Depression is a leading cause of burden of disease, associ-ated with suffering and disability for the individual as well aswith high costs for the society [1]. The estimated life-timerisk of developing depression is estimated to be around 10%[2] but the rate varies across countries [3]. Depression com-monly occurs together with different kinds of comorbidity,such as anxiety, substance abuse, and also somatic disease,adding to the total burden of disease [4–6] and concurrentuse of other drug classes [7].

Both depression and other mental disorders, e.g. anxietydisorders, affect more women than men [5,8]. In Sweden,where this study was conducted, women are also more likelyto be sickness absent due to mental diagnoses, such asdepressive episode [9,10]. The life-time prevalence of adepression is almost twice as high in women than in men[11,12], and the prevalence gender ratio for depression,based on data from all healthcare appointments in primaryhealthcare during 2011 in Stockholm, Sweden, was 2.3 [13].The reasons for the gender gap in the development andprevalence of depression are multifaceted and are believed

to result from a complex interaction of biologic and psycho-logical factors, social determinants, and factors associatedwith healthcare organization and practices [11,12,14,15].

The main treatment options for depression involve bothpsychotherapies and pharmaceuticals [16]. Regarding antide-pressants, some studies suggest that women respond betterto SSRIs (selective serotonin inhibitors) than men, althoughthe results are not clear [17–20]. There are also inconsistentresults for tricyclic antidepressants, where some studies havefound no gender difference in treatment response [21,22],whereas others suggest that men respond better [17], how-ever, a difference not considered clinically relevant [23].There is no clear evidence that women and men respond dif-ferently to psychotherapy [24,25]. In summary, there is someresearch suggesting that men and women may respond dif-ferently to psychotropic drugs and psychotherapy, but theevidence is not convincing enough to translate into gender-differentiated treatment guidelines.

There are, nevertheless, indications of factual gender differ-ences in the treatment of depression in clinical practice inSweden. Data from the National Prescribed Drug Register showthat women are dispensed almost twice as much

CONTACT Per Lytsy [email protected] Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm SE-17177,Sweden� 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon inany way.

NORDIC JOURNAL OF PSYCHIATRY2019, VOL. 73, NO. 7, 441–450https://doi.org/10.1080/08039488.2019.1649723

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antidepressants than men [26]. Such results do, however, notaccount for the gender differences in prevalence of depression.

It is also known that women have more healthcare visits[27], which possibly contributes to higher depression rates.More women use psychotropic drugs, and women are morelikely than men to use pharmaceuticals with an abusepotential, also after adjusting for diagnosis, demographicfactors, health status, and health insurance [28]. There areseveral other factors that may contribute to differences inprescription, dispensing, and drug utilization, such as gen-der differences for the preference of psychological versuspharmacological treatment [29].

Since the 1980s, sickness absence due to mental diagno-ses has been one of the most common reason for sick leavein Sweden as well as in many other countries [30–32]. Suchdata also show a clear gender gap, with about twice as highsick-leave rates for women as compared to men [33,34].

The aim of this study was to explore potential genderdifferences in the management of patients with currentdiagnosed depression, regarding sick-leave certification, pre-scribed medication (antidepressants, anxiolytics, and hyp-notic and sedatives), and referral to psychotherapy.

Materials and methods

Study design and sample

This study used register data from individuals diagnosedwith depression during 2010–2015 in Uppsala County (about340,000 inhabitants) and collects various data from each indi-vidual 12months prior and until 180 days after case identifi-cation (date of diagnosis). Although this study has alongitudinal approach in the collection of the outcomemeasures, the analyses have a cross-sectional design.

Patients were identified using a database consisting ofdata (no full text) from the electronic medical files record sys-tem ‘Cosmic’. Labeled data, corresponding to the variablesdescribed below, were extracted using Microsoft SQL Server/SAP BusinessObjects BI product suite, concealing informationabout the identity of individuals also for the researcher per-forming the task. Once all data had been obtained, the accur-acy of the data was validated against the Cosmic medical filesfor a few random cases, by a person not part of the researchteam and working under law of confidentiality. The validationprocedure was performed to ascertain that the data obtainedfrom the data base corresponded to the exact same informa-tion in the full text electronic medical files. The Cosmic sys-tem is used in both primary and secondary healthcare byvirtually all healthcare providers in Uppsala County (at thetime with the exception of one private general practitionerand ten private psychiatrists, whose patient visits are not rep-resented in the data, covering maximum 5% of the patients).The sample is considered as to a high-degree representhealthcare provided within Uppsala County.

The sample

There were two inclusion criteria. The first was havingreceived a depression diagnosis (IDC 10 F32/F33) during the

study period. The F32/F33 code encompasses current depres-sive episode and recurrent depressive disorder, respectively,where additional code characters could further specify sever-ity or subtype [35]. The second inclusion criterion was nothaving been diagnosed with an F32/F32 diagnosis within a12-month period prior to case identification (wash-outperiod). Thus, a case was considered being a reasonably newdepressive episode, but does not rule out having been diag-nosed with a depression diagnosis prior the 12-monthperiod. To avoid bias, an individual could only become acase once.

The total sample consisted of 20,227 individuals. Peoplediagnosed with bipolar disorder (ICD-10 F31, n¼ 543); severedepression with psychotic symptoms (ICD 10 F32.3/F33.3,n¼ 215), or both (n¼ 22) at either the date of diagnosis orduring the future 6 months were excluded because the man-agement of these disorders are likely to substantially differfrom other types of depressive disorders. After exclusion, thetotal sample consisted of 19,448 individuals.

Variables and management of data

Baseline data assessed at the date of diagnosis (T0), includedinformation about gender, age, and subtype of F32/F33-diag-nosis as well as whether the depression diagnosis was set asmain diagnosis or not. Other information collected at T0 waswhether any of the following comorbidity diagnoses (includ-ing subtypes) was registered the same day: anxiety disorders(F41); obsessive-compulsive disorders (F42); reaction tosevere stress and adjustment disorders (F43); or any mentaland behavioral disorders due to use of alcohol (F10).Individuals with one or more concurrent F41, F42, or F43diagnoses were considered having psychiatric comorbidity.Psychiatric comorbidity was also measured during follow-ups, to be controlled for in the regression models. Dataabout number of visits to physicians and other healthcareprofessionals during follow-up was also collected. It was notpossible to collect data about sick leave and treatment statusat a time point just before case identification.

Other information collected for each individual was thetotal number of healthcare visits 12months before the dateof diagnosis, and whether the visit at the time of diagnosisoccurred in primary or secondary healthcare and whether itoccurred at an in- or outpatient healthcare visit. Data at T0was also collected on assessments of depressive depth usingMADRS score [36] and alcohol usage using AUDIT score [37],however, with low overall coverage for these variables.

Some individuals (n¼ 53) were at T0 diagnosed with morethan one F32 and/or F33 diagnoses, with or without subclassification. This was managed the following way: F32 andF33 diagnoses, without further sub classification, weretogether with F32.9 or F33.9 (‘unspecified’) subclassificationrecategorized as F32 and F33 ‘Unspecified’, respectively. If anindividual had both an F32.X and a F33.X diagnosis at T0(n¼ 26), then the F33.X diagnosis was considered as themain. If an individual had two diagnoses (n¼ 27), both ineither the F32 or the F33 domain, then the more severe orspecified diagnosis were considered as the main.

442 P. LYTSY ET AL.

Other variables assessed during follow-up periods werewhether a patient was diagnosed with bipolar disorder ordepression with psychotic symptoms (reasons for exclusion).

The main independent variable was gender which wasinvestigated in regards to the following outcomes for differ-ent time periods until 180 days after diagnosis of depression.

� Whether a sick-leave certificate had been issued at leastonce (y/n) in the designated outcome period. There wasno data on grade or duration of sick leave.

� Whether one or more prescriptions of the following medi-cations were issued at least once in the designated out-come period:� antidepressants (ATC class N06A, yes/no)� anxiolytics (ATC class N05B, yes/no)� hypnotics and sedatives (ATC N05C, yes/no)

� Whether the individual had been refereed to cognitivebehavioral psychotherapy (CBT) within the rehabilitation-guarantee program (rehabiliteringsgarantin), which wasan incentive program during 2008–2015 aiming toincrease CBT treatment in patients with depressive andstress-related disorders in working age. Not all healthcare-givers were eligible for reimbursement from that incen-tive program, thus the outcome does not comprise allCBT therapy given in the county.

Outcomes were measured during four different periods:the day of diagnosis (T0) and days 1–89 (T1), days 90–180(T2), and the first 6 months, days 0–180 (T0 þ T1 þ T2).

Analysis

The main analyses investigated associations between genderand the outcomes of (1) whether a sick-leave certificate wasissued; having received a prescription of (2) antidepressants,(3) anxiolytics, or (4) hypnotics or sedatives; (5) havingreceived a CBT referral within the rehabilitation-guaranteeprogram. Associations were tested at the four time perspec-tives using multiple logistic regressions presenting results ascrude and adjusted odds ratios (OR) with 95% confidenceintervals (CI). Outcomes 1 and 5 (whether a sick-leave certifi-cate was issued and having been referred to CBT) were onlyassessed in individuals of working age (18–64 years), whereasthe other outcomes were assessed in all adults (>18 years),total group (7–97 years) and according to predefined agegroups, being

was 29.9 for receiving a sick-leave certificate, 66.6% forreceiving a prescription of antidepressants, 22.8% for receiv-ing a prescription of anxiolytics, and 23.7% for receiving aprescription of hypnotics and sedatives. There were generallysmall differences in proportions in regards to gender withina given age group, whereas there were larger differences inthe outcomes between age groups (Table 3). Among allpatients, 74.2% (women 74.0%, men 74.6%) were prescribedsome type of psychoactive drug during the total follow-upperiod of 180 days (not shown in table).

Regression models

In the fully adjusted logistic regressions, men in working age,as compared to women, had an OR of 0.90 (95% CI0.82–0.98) for sick-leave certification at the date of diagnosis.The corresponding numbers for receiving a prescription ofantidepressants in the adult age population was OR 1.16(95% CI 1.09–1.24); for receiving a prescription of anxiolytics:OR 1.10 (95% CI 1.02–1.21), and for having received a pre-scription for hypnotics and sedatives: OR 1.09 (95% CI1.00–1.19). The corresponding numbers for the total follow-up period (180 days) was an OR of 0.94 (95% CI 0.87–1.01)for men as compared to women in working age for beingsick-leave certified, and in the adult population an OR of1.17 (95% CI 1.10–1.26) for having received a prescription ofantidepressants; OR 1.03 (95% CI 0.96–1.11) for havingreceived a prescription of anxiolytics and an OR of 1.15 (95%CI 1.07–1.24) for having received a prescription for hypnoticsand sedatives.

None of the patients were referred to CBT at the date ofdiagnosis, but during follow-up there were no gender differ-ences in those qualifying for such referrals (Table 4).

Some results of covariates included in the adjusted mod-els are worth noting. Having a severe depression sub classifi-cation and having other psychiatric comorbidity were factorsseparately associated with having higher odds of being sick-leave certified at the date of diagnosis; OR 1.40 (95% CI1.10–1.79) and OR 1.40 (95% CI 1.25–1.58), respectively.These findings remained and were more pronounced wheninvestigating odds of sick-leave certification within the first180 days (not shown).

Discussion

The aim of this study was to explore gender differences inthe management of depression in both primary and second-ary healthcare. The main result showed no large gender dif-ferences in sick-leave certification at the date of diagnosisnor during the following 6 months. The regression analysessuggest that men had somewhat lesser risk of being sick-leave certified than women, however, the difference wassmall and not consistent over follow-up time and differentage groups. Thus, the results imply that women and menlargely were treated likewise in terms of being sick-leave cer-tified when diagnosed with depression.

The same seems to be true also for psychoactive drugprescriptions, where there were small absolute differences inissued prescriptions for antidepressants, anxiolytics, and hyp-notics and sedatives.

The outcomes in this study were assessed for three differ-ent time perspectives, the date of diagnosis, and during thesubsequent 3 and 6 months, respectively. At the date ofdiagnosis, it is reasonable to assume that outcomes are dir-ect consequences of the health situation resulting in thedepressive disorder diagnosis, most likely carried out by thesame physician. This is not necessarily the case for the longertime periods. The reasons for including longer periods wasthat consequences of having depression (such as receivingtreatments or a sick-leave certificate) may occur after the daywhen the diagnosis is set. There is, furthermore, a complexityin healthcare seeking behavior and healthcare utilizationthat, beyond the clinical condition and its progression, willaffect how the condition is managed. Factors such as localorganization of healthcare, availability of resources, referralroutines, and the physician’s knowledge as well as thepatient’s own preferences, may affect where and by whichphysician a diagnosis is set and whom later will treat andcare for the patient. In Sweden, it is common that patientswith newly developed and uncomplicated depressive epi-sodes are managed within primary healthcare, whereaspatients with complex, recurrent, or severe conditions morecommonly are referred to specialists in psychiatry.Depending on the severity of the condition, a psychiatristmay then refer the patient back, with a diagnosis and treat-ment recommendations, or the psychiatrist might ‘keep’ thepatient if considered warranted. A physician might, further,want to see how a condition develops before initiating

Table 2. Distribution of characteristics in the study population, by gender andtotal, at the date of the incident diagnosis of depressive episode (F32/F33) in2010–2015.

Women Men Totaln¼ 12,469 n¼ 6979 n¼ 19,448

Age, mean years (SD) 41.7 (20.3) 40.7 (19.4) 41. 3 (19.9)Age groups, yearsYounger than 18 8.1 (1014) 7.2 (506) 7.8 (1520)18–29 27.5 (3429) 30.0 (2087) 28.4 (5516)30–64 48.5 (6049) 49.1 (3426) 48.7 (9475)65 and older 15.9 (1977) 13.8 (960) 15.1 (2937)

Depression-a as main diagnosis 75.3 (9388) 74.4 (5191) 75.0 (14,579)Severe episode of depressiona¶

(F32.2/F33.2)3.5 (437) 4.2 (296) 3.7 (722)

Anxiety diagnosis (F41)disorders

10.5 (1312) 9.3 (649) 10.0 (1962)

OCD diagnosis (F42) 1.2 (143) 1.2 (81) 1.2 (224)Stress diagnoses (F43) 4.9 (610) 4.3 (299) 4.7 (909)Psychiatric comorbidity

(F41, F42 or F43)15.5 (1932) 14.1 (984) 15.0 (2916)

Alcohol diagnosis (F10) 0.6 (69) 2.0 (138) 1.1 (207)MADRS-S (mean, n) 23.9 (1311) 24.1 (810) 24.0 (2121)AUDIT (mean, n) 15.5 (313) 18.0 (221) 16.5 (534)Number of healthcare visits last

year (SD)4.4 (6.4) 2.9 (6.1) 3.9 (6.3)

Primary healthcare diagnosis 65.5 (8172) 56.7 (3955) 62.4 (12,127)Secondary healthcare diagnoses

(i.e. psychiatry)34.5 (4297) 43.3 (3024) 37.6 (7321)

Inpatient healthcare 2.8 (346) 4.7 (325) 3.4 (671)Outpatient healthcare 97.2 (12,123) 95.3 (6654) 96.6 (18,777)

Percentages and n for subgroups in brackets, if not otherwise stated.aDepressive episode F32 or recurrent depressive disorder F33.SD: standard deviation; MADRS-S¼Montgomery-Åsberg Depression RatingScale (range 0–54); AUDIT Alcohol Use Disorders Identification Test(range 0–40).

444 P. LYTSY ET AL.

Table3.

Prop

ortio

nsof

patientshaving

been

sick-leavecertified,p

rescrib

edantid

epressants

(N06A),anxiolytics(N05B),h

ypno

ticsandsedatives

(H&S,N05C),and

referred

toCo

gnitive

behavioral

therapy(CBT)with

inthe

rehabilitation-gu

aranteeprog

ram,arespectively,by

sex,agegrou

ps,and

diffe

rent

timeperspectives

afterbeingdiagno

sedwith

depressive

episod

e(F32/F33,d

ay0)

durin

g2010–2015.

Day

ofdiagno

sis(Day

¼0)

Days1–89

Days90–180

Days0–180

WM

Tot

WM

Tot

WM

Tot

WM

Tot

Agegrou

psn¼12,469

n¼6979

n¼19,448

n¼12,469

n¼6979

n¼19,448

n¼12,469

n¼6979

n¼19,448

n¼12,469

n¼6979

n¼19,448

Sick-leavecertificate

Totalb

17.6

14.9

16.6

15.8

14.8

15.4

11.6

10.0

11.0

31.0

27.8

29.9

(1668)

(821)

(2489)

(1499)

(813)

(2312)

(1099)

(555)

(1654)

(2947)

(1530)

(4477)


18–29

11.4

10.5

11.0

11.7

11.6

11.6

8.0

8.2

8.0

22.6

22.5

22.6

(392)

(220)

(612)

(401)

(242)

(643)

(273)

(170)

(443)

(774)

(470)

(1244)


30–64

21.1

17.5

19.8

18.2

16.7

17.6

13.7

11.2

12.8

35.9

30.9

34.1

(1276)

(601)

(1877)

(1098)

(571)

(1669)

(826)

(385)

(1211)

(2173)

(1060)

(3233)

Antid

ep.

N06A

Total

49.2

51.8

50.1

38.2

39.6

38.7

26.6

24.1

25.7

65.9

68.0

66.6

(6138)

(3612)

(9750)

(4770)

(2763)

(7533)

(3310)

(1685)

(4995)

(8213)

(4743)

(12956)

Antid

ep.

N06A

<18

22.3

25.7

23.4

(356)

43.6

41.9

43.0

31.8

29.2

31.0

53.2

53.8

53.4

(226)

(130)

(442)

(212)

(654)

(323)

(148)

(471)

(540)

(272)

(812)

Antid

ep.N

06A

18–29

48.8

51.4

49.8

42.8

41.3

42.2

29.0

26.1

27.9

66.4

68.8

67.3

(1678)

(1072)

(2750)

(1466)

(863)

(2329)

(1631)

(545)

(1538)

(2275)

(1435)

(3710)

Antid

ep.N

06A

30–64

52.7

54.9

53.5

38.1

40.1

38.8

27.0

23.4

25.7

68.4

70.3

69.1

(3189)

(1883)

(5072)

(2306)

(1374)

(3680)

(1631)

(803)

(2434)

(4138)

(2404)

(6546)

Antid

ep.N

06A

>65

52.8

51.8

53.5

28.1

32.7

29.6

18.4

19.7

18.8

63.7

65.4

64.3

(1045)

(527)

(1572)

(556)

(314)

(870)

(363)

(189)

(552)

(1260)

(628)

(1888)

AnxiolyticsN05B

Total

13.9

14.9

14.2

13.2

12.4

12.9

8.3

7.4

8.0

22.8

22.7

22.8

(1730)

(1037)

(2767)

(1646)

(868)

(2514)

(1033)

(520)

(1553)

(2848)

(1584)

(4432)

AnxiolyticsN05B

<18

5.6

3.4

4.9

9.4

6.1

8.3

5.9

3.4

5.1

13.2

8.7

11.7

(57)

(17)

(74)

(95)

(31)

(126)

(60)

(17)

(77)

(134)

(44)

(178)

AnxiolyticsN05B

18–29

15.3

15.1

15.2

14.4

11.7

13.4

7.7

5.9

7.0

25.6

23.0

24.6

(524)

(316)

(840)

(495)

(244)

(739)

(265)

(123)

(388)

(877)

(479)

(1356)

AnxiolyticsN05B

30–64

15.3

17.1

15.9

13.1

13.8

13.3

8.4

8.7

8.5

23.6

25.2

24.2

(923)

(586)

(1509)

(791)

(477)

(1262)

(597)

(298)

(805)

(1429)

(865)

(2294)

AnxiolyticsN05B

>65

11.4

12.3

11.7

13.4

12.7

13.2

10.2

8.5

9.6

20.6

20.4

20.6

(226)

(118)

(344)

(265)

(122)

(387)

(201)

(82)

(283)

(408)

(196)

(604)

H&S

NO5C

Total

12.9

14.0

13.3

15.1

16.7

15.7

11.6

11.3

11.5

23.0

25.0

23.7

(1610)

(976)

(2586)

(1886)

(1165)

(3051)

(1442)

(787)

(2229)

(2867)

(1741)

(4608)

H&S

NO5C

<18

9.6

8.1

9.1

18.0

16.0

17.3

12.4

10.0

11.6

22.9

20.0

21.9

(97)

(41)

(138)

(182)

(81)

(263)

(126)

(51)

(177)

(232)

(101)

(333)

H&S

NO5C

18–29

9.7

11.8

10.5

12.4

13.9

13.0

8.6

9.3

8.9

18.3

21.0

19.3

(333)

(246)

(579)

(426)

(289)

(715)

(295)

(195)

(490)

(628)

(439)

(1067)

H&S

NO5C

30–64

15.0

16.5

15.5

15.7

18.4

16.6

11.9

12.0

12.0

24.7

28.2

26.0

(907)

(565)

(1472)

(949)

(629)

(1578)

(721)

(413)

(1134)

(1496)

(968)

(2464)

H&S

NO5C

>65

13.8

12.9

13.5

16.6

17.3

16.8

15.2

11.3

14.6

25.8

24.3

25.3

(273)

(124)

(397)

(329)

(166)

(495)

(300)

(128)

(428)

(511)

(233)

(744)

CBT

Total

00

12.0

1.8

2.0

1.4

1.4

1.4

3.4

3.2

3.3

(195)

(103)

(298)

(135)

(76)

(211)

(324)

(178)

(502)

CBT

18–29

00

02.4

1.9

2.2

2.1

1.5

1.8

4.4

3.4

4.0

(82)

(40)

(122)

(71)

(31)

(102)

(149)

(70)

(219)

CBT

30–64

00

01.9

1.8

1.9

1.1

1.3

1.2

2.9

3.2

3.0

(113)

(63)

(176)

(64)

(45)

(109)

(175)

(108)

(283)


issued

byph

ysician,

Antid

ep.:antid

epressants;C

BT:cog

nitiveBehavioral

therapy;H&S:Hypno

ticsandsedatives.

a The

rehabilitation-gu

aranteeprog

ram

was

ereimbu

rsem

entsystem

forCB

Treferrals.Itdo

esno

treflect

thetotaln

umberof

CBTreferrals.

bRestrictedto

patientsof

working

age;18–64years(n¼14,991).

NORDIC JOURNAL OF PSYCHIATRY 445

Table4.

Bivariate

andmultivariablelogisticregression

spresentin

god

dratio

s(OR)

with

95%

confidence

intervals(CI)formen,as

comparedto

wom

en,tohave

received

atleaston

cea(1)sick-leavecertificate,(2)

pre-

scrip

tionof

antid

epressants

(N06A),anxiolytics(N05B),or

hypn

oticsandsedatives

(N05C)

and(3)having

been

refereed

tocogn

itive

behavioral

therapy(CBT)aby

agegrou

psat

diffe

rent

timeperio

dsafterbeingdiag-

nosedwith

depressive

episod

e(F32/F33,d

ay0)

durin

g2010–2015.


day0


days

1–89


days

90–180


days

0–180

Agegrou

p(years)

n(total

19447)

Gender

(wom

enref)

ORcrud

e(95%

CI)

ORfullmod

elb

(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

elb

(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

elb

(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

elb

(95%

CI)

18–64

14,991

Men

0.82

0.90

0.92

1.00

0.85

0.94

0.85

0.94

(0.75–0.90)

(0.82–0.98)

(0.84–1.01)

(0.91–1.10)

(0.77–0.95)

(0.84–1.05)

(0.79–0.92)

(0.87–1.01)

18–29

5516

Men

0.91

1.04

0.99

1.10

1.03

1.21

1.00

1.13

(0.77–1.09)

(0.87–1.25)

(0.84–1.17)

(0.93–1.32)

(0.84–1.25)

(0.98–1.48)

(0.88–1.14)

(0.99–1.30)

30–64

9475

Men

0.80

0.85

0.90

0.95

0.80

0.86

0.79

0.86

(0.71–0.89)

(0.76–0.95)

(0.81–1.01)

(0.86–1.08)

(0.70–0.91)

(0.75–0.98)

(0.73–0.87)

(0.79–0.95)

Prescriptio

nof

antid

epressants

(N06A)

day0

Prescriptio

nof

antid

epressants

(N06A)

days

1–89

Prescriptio

nof

antid

epressants

(N06A)

days

90–180

Prescriptio

nof

antid

epressants

(N06A)

days

1–180

Agegrou

pn

Gender

(wom

enref)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

7–97

19447

Men

1.11

1.19

1.06

1.09

0.88

0.90

1.10

1.18

(1.04–1.17)

(1.12–1.26)

(1.00–1.12)

(1.03–1.16)

(0.82–0.94)

(0.84–0.97)

(1.03–1.17)

(1.10–1.25)

18–97

17928

Men

1.09

1.16

1.07

1.10

0.88

0.90

1.10

1.17

(1.03–1.16)

(1.09–1.24)

(1.01–1.14)

(1.03–1.18)

(0.82–0.95)

(0.84–0.97)

(1.03–1.18)

(1.10–1.26)

<18

1529

Men

1.21

1.30

0.94

1.00

0.88

0.97

1.02

1.13

(0.94–1.54)

(1.01–1.68)

(0.75–1.16)

(0.80–1.25)

(0.70–1.12)

(0.76–1.23)

(0.82–1.26)

(0.91–1.42)

18–29

5516

Men

1.10

1.13

0.94

1.01

0.87

0.93

1.12

1.19

(0.99–1.23)

(1.01–1.26)

(0.84–1.05)

(0.90–1.13)

(0.77–0.98)

(0.81–1.05)

(0.99–1.25)

(1.05–1.34)

30–64

9475

Men

1.09

1.17

1.09

1.13

0.83

0.86

1.09

1.16

(1.01–1.19)

(1.07–1.28)

(1.00–1.18)

(1.04-1.23)

(0.75–0.91)

(0.78–0.95)

(1.00–1.20)

(1.06–1.28)

>65

2937

Men

1.09

1.14

1.24

1.23

1.09

1.09

1.08

1.12

(0.93–1.27)

(0.97–1.34)

(1.05–1.47)

(1.03–1.45)

(0.89–1.32)

(0.89–1.33)

(0.92–1.27)

(0.94–1.33)

Prescriptio

nof

anxiolytics

N05Bday0

Prescriptio

nanxiolytics

N05Bdays

1–89

Prescriptio

nanxiolytics

N05Bdays

90–180

Prescriptio

nanxiolytics

N05Bdays

1–180

Agegrou

pn

Gender

(wom

enref)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

7–97

19,447

Men

1.08

1.11

0.93

0.98

0.89

0.95

0.99

1.04

(1.00–1.18)

(1.03–1.21)

(0.86–1.02)

(0.89–1.07)

(0.80–0.99)

(0.85–1.06)

(0.92–1.06)

(0.97–1.12)

18–97

17,928

Men

1.09

1.10

0.94

0.98

0.91

0.96

1.01

1.03

(1.00–1.19)

(1.02–1.21)

(0.87–1.04)

(0.89–1.07)

(0.81–1.02)

(0.86–1.08)

(0.94–1.08)

(0.96–1.11)

<18

1529

Men

0.58

0.61

0.63

0.68

0.55

0.59

0.63

0.71

(0.34–1.01)

(0.35–1.07)

(0.41–0.96)

(0.45–1.05)

(0.32–0.96)

(0.33–1.03)

(0.44–0.90)

(0.49–1.03)

18–29

5516

Men

0.99

1.02

0.78

0.84

0.75

0.83

0.86

0.92

(0.85–1.15)

(0.87–1.20)

(0.67–0.92)

(0.71–0.99)

(0.60–0.93)

(0.66–1.04)

(0.76–0.98)

(0.80–1.04)

30–64

9475

Men

1.15

1.14

1.06

1.07

1.04

1.04

1.09

1.10

(1.02–1.28)

(1.02–1.28)

(0.94–1.20)

(0.94–1.21)

(0.90–1.21)

(0.89–1.22)

(0.99–1.20)

(1.00–1.22)

>65

2937

Men

1.09

1.07

0.94

0.91

0.83

0.82

0.99

0.96

(0.86–1.38)

(0.84–1.36)

(0.75–1.18)

(0.71–1.15)

(0.63–1.08)

(0.63–1.10)

(0.82–1.19)

(0.79–1.18)

(continued)

446 P. LYTSY ET AL.

Prescriptio

nof

hypn

oticsand

sedatives

(NO5C)day0

Prescriptio

nof

hypn

oticsand

sedatives

(NO5C)days

1–89

Prescriptio

nof

hypn

oticsand

sedatives

(NO5C)days

90–180

Prescriptio

nof

hypn

oticsand

sedatives

(NO5C)days

1–180

Agegrou

pn

Gender

(wom

enref)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORCrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

7–97

19,447

men

1.10

1.09

1.12

1.15

0.97

1.01

1.11

1.13

(1.01–1.19)

(1.00–1.19)

(1.04–1.22)

(1.06–1.24)

(0.89–1.07)

(0.92–1.11)

(1.03–1.19)

(1.05–1.21)

18–97

17,927

men

1.10

1.09

1.15

1.18

0.99

1.03

1.14

1.15

(1.02–1.21)

(1.00–1.19)

(1.06–1.25)

(1.08–1.28)

(0.90–1.09)

(0.94–1.14)

(1.06–1.22)

(1.07–1.24)

<18

1529

men

0.83

0.91

0.87

0.93

0.79

0.84

0.84

0.92

(0.57–1.22)

(0.62–1.35)

(0.65–1.16)

(0.69–1.25)

(0.56–1.11)

(0.58–1.19)

(0.65–1.09)

(0.70–1.21)

18–29

5516

men

1.24

1.24

1.13

1.18

1.09

1.19

1.19

1.16

(1.04–1.47)

(1.04–1.49)

(0.97–1.33)

(1.01–1.40)

(0.91–1.32)

(0.98–1.45)

(1.04–1.36)

(1.06–1.28)

30–64

9475

men

1.12

1.06

1.21

1.19

1.01

1.00

1.20

1.16

(1.00–1.26)

(0.95–1.20)

(1.08–1.35)

(1.06–1.34)

(0.89–1.15)

(0.88–1.14)

(1.09–1.32)

(1.06–1.28)

>65

2937

men

0.93

0.90

1.05

1.03

0.86

0.87

0.92

0.90

(0.74–1.16)

(0.71–1.13)

(0.85–1.29)

(0.84–1.27)

(0.69–1.07)

(0.70–1.10)

(0.77–1.10)

(0.75–1.08)

CBTreferral

day0

CBTreferral

days

1–89

CBTreferral

days

90–180

CBTreferral

days

1–180

Agegrou

pn

Gender

(wom

enref)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

ORcrud

e(95%

CI)

ORfullmod

el(95%

CI)

18–64

14991

Men

nana

0.91

1.00

0.98

1.00

0.94

1.00

(0.72–1.15)

(0.78–1.28)

(0.73–1.28)

(0.75–1.33)

(0.78–1.13)

(0.83–1.21)

18–29

5516

Men

nana

0.80

0.84

0.71

0.72

0.76

0.78

(0.54–1.17)

(0.57–1.24)

(0.47–1.09)

(0.46–1.10)

(0.57–1.02)

(0.59–1.06)

30–64

9475

Men

Na

na0.98

1.11

1.24

1.27

1.09

1.18

(0.72–1.34)

(0.81–1.52)

(0.85–1.83)

(0.86–1.88)

(0.86–1.39)

(0.92–1.52)

a Referralswith

therehabilitation-gu

aranteeprog

ram

was

areimbu

rsem

entsystem

forCB

Treferrals.Itdo

esno

treflect

thetotaln

umberof

CBTreferrals.

bFullmultivariate

mod

eladjusted

forage,

numberof

healthcare

visits

previous

year,having

severe

depression

orno

t,othercurrentpsychiatric

comorbidity,andwhether

thevisitwhendiagno

sedwas

inaprimaryor

second

aryhealthcare

setting.


treatment or issuing a sick-leave certificate, and a patientmight want to consider a treatment option some time beforeaccepting it. We, thus, believe it was relevant to assess thestudied outcomes also over time. The potential drawback ofthis approach is that we cannot be sure that the depressivedisorder directly contributes to a specific outcome thatoccurs during a period of time. This mainly concerns thesick-leave outcome in the analyses, since it is possible to besick-leave certified for other diagnoses than depression. Ifwomen and men are as likely to be sickness certified orother diagnoses during follow-up, this will not affect ourmain analysis, which assesses gender differences.

Less than two thirds (62.4%) of the population were diag-nosed in primary healthcare. This seems to be a low figureconsidering that most healthcare is delivered by generalpractitioners, and it might reflect referral routines as well pri-mary healthcare’s known low sensitivity to identify clinicallydepressed patients [38]. Another reason, contributing towhere patients seek care, is that people in Uppsala have thepossibility to seek secondary healthcare directly, by self-refer-rals. Such referrals are considered by medical specialists andthe individual is then either welcomed or directed to anadequate care level.

The strengths of this study include the high coveragecommunity sampling method with no attrition, coveringalmost all healthcare given. It is further a strength that it waspossible to control for comorbidity and number of healthcarevisits, since these factors are likely to be associated with theoutcomes. The main limitations of the study include themany potential factors that might affect the management ofdepression and that this study has not been able to controlfor. The covariates used in this study were limited to thoseaccessible within the search terms of the database. Thedenominator of all outcome proportions are all patients, notthe patients at risk of the outcome. It was not possible todetermine if the patients already were on sick leave ortreated with any psycho-active drug at the time of outcomemeasurement. Nor was it possible to control whether partici-pants moved out of the county or sought healthcare else-where during follow-up. For these reasons, proportions andrates of the outcomes are believed to be somewhat underes-timated. We tried to alleviate these problems by using awash-out period and by the adjustment of accessible covari-ates. The washout period for having a F32/F33 diagnosis was12months, but since an individual only could be includedonce this means that some individuals had washout periodslonger than 12months. We tested gender differences in man-agement for depression for five different outcomes, but alsoperformed many subgroups analyses, which increases therisk of false positive findings. Thus, we suggest the results ofthe regression analyses to be viewed in terms of over alltrends.

The adjusted analyses controlled for having severedepression. It would have been desirable to adjust fordepression severity using MADRS-S estimates, however, thismeasure was only assessed in 10.9% of the cases; consideredtoo few to be included in the fully adjusted regressionmodels, reducing the overall study sample. The averages

MADRS-scores assessed, nevertheless, did not differ signifi-cantly between women and men (Table 1).

The fact that a higher proportion of men in all agesreceived prescriptions in all three categories of psychoactivedrugs in this study is notable. It is known that women ingeneral are prescribed more psychoactive drugs than men.The Swedish Board of Health and Welfare provides statisticsof all dispensed prescribed drugs and it is possible to investi-gate gender differences in this database [39]. When usingthese statistics: antidepressants, anxiolytics, and hypnoticsand sedatives (as daily day doses/1000 inhabitants) andrestricting data to the years of this study (2010–2015), theaverage women-to-men ratio was 1.86 for antidepressants,1.39 for anxiolytics, and 1.69 for hypnotics and sedatives.These figures indicate that women consume more psycho-active drugs. However, according to the results in this study,this is not because women are more likely to receive pre-scriptions at the time of diagnosis or in the followingmonths. Instead, the reasons seem to be that more womenthan men are diagnosed with depression, a finding that wasalso apparent in the present study where almost two thirdsof the cases in the study population were women. The find-ings in this study, thus, do not support the idea that womenand men are treated differently regarding sick-leave certifica-tion or medication. On the contrary, although there weresmall differences, the main pattern regarding sick leave andtreatment seemed to be gender neutral.

Conclusion

In conclusion, there were small differences in proportions ofmen and women who were sick-leave certified or prescribedantidepressants, anxiolytics, or hypnotics and sedatives atthe date diagnosed with depression as well as during the fol-lowing 6months. Men had, compared to women, somewhatlower odds of being sick-leave certified and somewhathigher odds of receiving prescriptions of psychoactive drugsthe same day they were diagnosed; however, the overallconclusion is that women and men diagnosed with depres-sion generally seems to be treated in equal ways regardingthe here studied aspects.

Acknowledgements

The authors are much grateful to Mats Norman and Mats Bystr€om forhelp with the planning and execution of the extraction of data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by research grants from the Uppsala AcademicHospital (no grant number, for the first author).

448 P. LYTSY ET AL.

Notes on contributors

Per Lytsy, MD PhD is specialised in social medicine. He does research atthe Department of Clinical Neuroscience, Karolinska Institutet and holdsa position the Swedish Agency for Health Technology Assessment andAssessment of Social Services (SBU). His research interests involveresearch methodology, mental and public health and insurancemedicine.

Johan Hallqvist, MD PhD, senior professor in preventive medicine andformer head of Department of Public Health and Caring Sciences atUppsala University. His research interest involves research methodology,social epidemiology and health policy.

Kristina Alexanderson, PhD, professor of social insurance, has >300 ori-ginal international peer-reviewed publications. Her research focus is onhealth and sickness absence, in general and regarding specific diagnosesand life situations. She uses both epidemiological and qualitative analyt-ical methods and has established large population-based research data-bases. Extensive international collaborations.

Annika Åhs, PhD, is a clinical psychologist and researcher. The mainfields of her research interest are predictors of health and health careutilization in relation to employment status. She works as a clinicalpsychologist in psychiatry.

ORCID

Per Lytsy http://orcid.org/0000-0003-1949-6299

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450 P. LYTSY ET AL.

http://www.socialstyrelsen.se/statistik/statistikdatabas/lakemedelhttp://www.socialstyrelsen.se/statistik/statistikdatabas/lakemedel

AbstractIntroductionMaterials and methodsStudy design and sampleThe sampleVariables and management of dataAnalysisEthics

ResultsRegression models

DiscussionConclusionAcknowledgementsDisclosure statementNotes on contributorsReferences

gender differences in healthcare management of depression...

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