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

Abstract

Purpose: To investigate whether women and men diagnosed with depressive disorder were managed equally 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 in Uppsala county, Sweden (n = 19 448) were used to investigate associations between gender and issued sick-leave certificate, prescriptions of anti-depressants, anxiolytics, hypnotics and sedatives, and cognitive behavioral psychotherapy referrals, at different time points up till 180 days after diagnosis.

Results: At diagnosis date, 50.1% were prescribed antidepressants; 14.2% anxiolytics; 13.3% hypnotics or 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 (OR 1.16; 95% CI 1.09–1.24); anxiolytics (1.10; 95% CI 1.02–1.21), hypnotics and sedatives (OR 1.09; 95% CI 1.00–1.19) 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- and 6-month follow-up periods.

Conclusions: Men had somewhat higher odds of being prescribed psychoactive drugs and slightly lower odds of being sick-leave certified as compared to women at date when diagnosed with depression. The absolute differences were, however, small and the overall conclusion is that women and men with current diagnosed depressive episode/recurrent depressive disorder are generally managed likewise regarding sick leave and psychoactive treatment.

Keywords:

• Depression
• sick leave
• anxiolytics
• hypnotics and sedatives
• antidepressants

Introduction

Depression is a leading cause of burden of disease, associated with suffering and disability for the individual as well as with high costs for the society [1]. The estimated life-time risk of developing depression is estimated to be around 10% [2] but the rate varies across countries [3]. Depression commonly 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 concurrent use of other drug classes [7].

Both depression and other mental disorders, e.g. anxiety disorders, affect more women than men [5,8]. In Sweden, where this study was conducted, women are also more likely to be sickness absent due to mental diagnoses, such as depressive episode [9,10]. The life-time prevalence of a depression 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 primary healthcare during 2011 in Stockholm, Sweden, was 2.3 [13]. The reasons for the gender gap in the development and prevalence of depression are multifaceted and are believed to result from a complex interaction of biologic and psychological factors, social determinants, and factors associated with healthcare organization and practices [11,12,14,15].

The main treatment options for depression involve both psychotherapies and pharmaceuticals [16]. Regarding antidepressants, some studies suggest that women respond better to SSRIs (selective serotonin inhibitors) than men, although the results are not clear [17–20]. There are also inconsistent results for tricyclic antidepressants, where some studies have found no gender difference in treatment response [21,22], whereas others suggest that men respond better [17], however, a difference not considered clinically relevant [23]. There is no clear evidence that women and men respond differently to psychotherapy [24,25]. In summary, there is some research suggesting that men and women may respond differently to psychotropic drugs and psychotherapy, but the evidence is not convincing enough to translate into gender-differentiated treatment guidelines.

There are, nevertheless, indications of factual gender differences in the treatment of depression in clinical practice in Sweden. Data from the National Prescribed Drug Register show that women are dispensed almost twice as much antidepressants than men [26]. Such results do, however, not account 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 more likely than men to use pharmaceuticals with an abuse potential, also after adjusting for diagnosis, demographic factors, health status, and health insurance [28]. There are several other factors that may contribute to differences in prescription, dispensing, and drug utilization, such as gender differences for the preference of psychological versus pharmacological treatment [29].

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

The aim of this study was to explore potential gender differences in the management of patients with current diagnosed depression, regarding sick-leave certification, prescribed medication (antidepressants, anxiolytics, and hypnotic and sedatives), and referral to psychotherapy.

Materials and methods

Study design and sample

This study used register data from individuals diagnosed with depression during 2010–2015 in Uppsala County (about 340,000 inhabitants) and collects various data from each individual 12 months prior and until 180 days after case identification (date of diagnosis). Although this study has a longitudinal approach in the collection of the outcome measures, the analyses have a cross-sectional design.

Patients were identified using a database consisting of data (no full text) from the electronic medical files record system ‘Cosmic’. Labeled data, corresponding to the variables described below, were extracted using Microsoft SQL Server/SAP BusinessObjects BI product suite, concealing information about the identity of individuals also for the researcher performing the task. Once all data had been obtained, the accuracy of the data was validated against the Cosmic medical files for a few random cases, by a person not part of the research team and working under law of confidentiality. The validation procedure was performed to ascertain that the data obtained from the data base corresponded to the exact same information in the full text electronic medical files. The Cosmic system is used in both primary and secondary healthcare by virtually all healthcare providers in Uppsala County (at the time with the exception of one private general practitioner and ten private psychiatrists, whose patient visits are not represented in the data, covering maximum 5% of the patients). The sample is considered as to a high-degree represent healthcare provided within Uppsala County.

The sample

There were two inclusion criteria. The first was having received a depression diagnosis (IDC 10 F32/F33) during the study period. The F32/F33 code encompasses current depressive episode and recurrent depressive disorder, respectively, where additional code characters could further specify severity or subtype [35]. The second inclusion criterion was not having been diagnosed with an F32/F32 diagnosis within a 12-month period prior to case identification (wash-out period). Thus, a case was considered being a reasonably new depressive episode, but does not rule out having been diagnosed with a depression diagnosis prior the 12-month period. To avoid bias, an individual could only become a case once.

The total sample consisted of 20,227 individuals. People diagnosed with bipolar disorder (ICD-10 F31, n = 543); severe depression with psychotic symptoms (ICD 10 F32.3/F33.3, n = 215), or both (n = 22) at either the date of diagnosis or during the future 6 months were excluded because the management of these disorders are likely to substantially differ from other types of depressive disorders. After exclusion, the total sample consisted of 19,448 individuals.

Variables and management of data

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

Other information collected for each individual was the total number of healthcare visits 12 months before the date of diagnosis, and whether the visit at the time of diagnosis occurred in primary or secondary healthcare and whether it occurred at an in- or outpatient healthcare visit. Data at T₀ was also collected on assessments of depressive depth using MADRS score [36] and alcohol usage using AUDIT score [37], however, with low overall coverage for these variables.

Some individuals (n = 53) were at T₀ diagnosed with more than one F32 and/or F33 diagnoses, with or without sub classification. This was managed the following way: F32 and F33 diagnoses, without further sub classification, were together with F32.9 or F33.9 (‘unspecified’) subclassification recategorized as F32 and F33 ‘Unspecified’, respectively. If an individual had both an F32.X and a F33.X diagnosis at T₀ (n = 26), then the F33.X diagnosis was considered as the main. If an individual had two diagnoses (n = 27), both in either the F32 or the F33 domain, then the more severe or specified diagnosis were considered as the main.

Other variables assessed during follow-up periods were whether a patient was diagnosed with bipolar disorder or depression with psychotic symptoms (reasons for exclusion).

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

• Whether a sick-leave certificate had been issued at least once (y/n) in the designated outcome period. There was no data on grade or duration of sick leave.

• Whether one or more prescriptions of the following medications were issued at least once in the designated outcome 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 cognitive behavioral psychotherapy (CBT) within the rehabilitation-guarantee program (rehabiliteringsgarantin), which was an incentive program during 2008–2015 aiming to increase CBT treatment in patients with depressive and stress-related disorders in working age. Not all healthcaregivers were eligible for reimbursement from that incentive program, thus the outcome does not comprise all CBT 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 + T₁ + T₂).

Analysis

The main analyses investigated associations between gender and the outcomes of (1) whether a sick-leave certificate was issued; having received a prescription of (2) antidepressants, (3) anxiolytics, or (4) hypnotics or sedatives; (5) having received a CBT referral within the rehabilitation-guarantee program. Associations were tested at the four time perspectives using multiple logistic regressions presenting results as crude and adjusted odds ratios (OR) with 95% confidence intervals (CI). Outcomes 1 and 5 (whether a sick-leave certificate was issued and having been referred to CBT) were only assessed in individuals of working age (18–64 years), whereas the other outcomes were assessed in all adults (>18 years), total group (7–97 years) and according to predefined age groups, being <18 years, 18–29 years, 30–64 years, and ≥65 years, respectively. The reason for investigating associations in age subgroups is that they are believed to roughly correspond to life stages that are different.

The fully multiple regression models adjusted for age, number of healthcare visits previous year, having severe depression or not, having current psychiatric comorbidity (at T₀ for outcome at day 0, at T₀ + T₁ at outcome during days 1–89; at T₀ + T₁ + T₂ for outcome until day 180), and whether diagnosis at day 0 was set in primary or secondary healthcare setting. All tests were two-sided and a p-value of <0.05 was considered statistically significant. Since many tests were performed for different age groups at different follow-up times, there was a risk of false positive findings due to random reasons. Thus, exact p-values are not presented and subgroup estimates should be viewed in regards to overall trends. Stata version 14.2, TX, was used for all statistical analyses.

Ethics

The project was approved by the Ethical Review Board at Uppsala University (Dnr 2011/262) and the central Ethical Review Board approved the validation procedure (Dnr Ö 10-2012).

Results

The sample consisted of 19,448 individuals of whom 12,469 (64.1%) were women and 6979 (35.9%) were men. The average age of the study population was 41.3 (SD 19.9) years and 75% had a depressive diagnosis (F32 or F33) as their main diagnosis. Most of the study population (82.9%) were diagnosed with a F32 diagnosis and 17.1 with a F33 diagnosis. Most F32 and F33 diagnoses were ‘unspecified’, i.e. were not further sub-classified. Of all patients, 3.7% were diagnosed with a severe depressive episode and 15.0% were diagnosed with some kind of psychiatric comorbidity. Almost two thirds, 62.4%, were diagnosed in primary healthcare. The distribution of F32 and F33 diagnoses in the sample is presented in Table 1, and the distribution of study population characteristics assessed at the date of diagnosis is presented in Table 2.

Table 1. Type and severity of depressive episode diagnosis presented according to ICD-10 classification.

Diagnosis sub classification	Women	Men	Total
	n = 12,469	n = 6979	n = 19,448
F32 single episode
F32 unspecified	59.2 (7381)	57.5 (4013)	58.6 (11,394)
F32.0 mild	9.0 (1122)	9.4 (659)	9.2 (1781)
F32.1 moderate	11.2 (1397)	12.5 (872)	11.7 (2269)
F32.2 severe	2.5 (312)	3.2 (223)	2.8 (535)
F32.8 other specified	0.6 (70)	0.7 (46)	0.6 (116)
F33 recurrent
F33 unspecified	8.3 (1032)	7.6 (533)	8.0 (1565)
F33.0 mild	1.8 (223)	2.0 (136)	1.8 (359)
F33.1 moderate	4.3 (538)	4.1 (287)	4.2 (825)
F33.2 severe	1.0 (125)	1.0 (73)	1.0 (198)
F33.4 in remission	1.9 (232)	1.7 (118)	1.8 (350)
F33.8 Other specified	0.3 (37)	0.3 (19)	0.3 (56)

F32 and F33 diagnoses without further sub classification were together with F32.9 and F33.9 (‘unspecified’) categorized as F32 and F33 ‘Unspecified’, respectively.

Table 2. Distribution of characteristics in the study population, by gender and total, at the date of the incident diagnosis of depressive episode (F32/F33) in 2010–2015.

	Women	Men	Total
	n = 12,469	n = 6979	n = 19,448
Age, mean years (SD)	41.7 (20.3)	40.7 (19.4)	41. 3 (19.9)
Age groups, years
Younger 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 depression^a^¶ (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.

^a Depressive episode F32 or recurrent depressive disorder F33.

SD: standard deviation; MADRS-S = Montgomery-Åsberg Depression Rating Scale (range 0–54); AUDIT Alcohol Use Disorders Identification Test (range 0–40).

The proportion of patients, in total and in different age groups, being sick-leave certified, given a psychoactive drug prescription and a referral to CBT, at different time periods, is illustrated in Table 3. In those of working age, 16.6% (women 17.6%, men 14.9%) were sick-leave certified at the same date diagnosed. Among all, 50.1% (women 49.2%, men 51.8%) were prescribed antidepressants, 14.2% (women 13.9%, men 14.9%) received a prescription of anxiolytics, and 13.3% (women 12.9%, men 14.0%) received a prescription of hypnotics or sedatives at the date of diagnosis. The corresponding numbers for the total follow-up period (180 days) was 29.9 for receiving a sick-leave certificate, 66.6% for receiving a prescription of antidepressants, 22.8% for receiving a prescription of anxiolytics, and 23.7% for receiving a prescription of hypnotics and sedatives. There were generally small differences in proportions in regards to gender within a given age group, whereas there were larger differences in the outcomes between age groups (Table 3). Among all patients, 74.2% (women 74.0%, men 74.6%) were prescribed some type of psychoactive drug during the total follow-up period of 180 days (not shown in table).

Table 3. Proportions of patients having been sick-leave certified, prescribed antidepressants (N06A), anxiolytics (N05B), hypnotics and sedatives (H&S, N05C), and referred to Cognitive behavioral therapy (CBT) within the rehabilitation-guarantee program,^a respectively, by sex, age groups, and different time perspectives after being diagnosed with depressive episode (F32/F33, day 0) during 2010–2015.

		Day of diagnosis (Day = 0)			Days 1–89			Days 90–180			Days 0–180
		W	M	Tot	W	M	Tot	W	M	Tot	W	M	Tot
	Age groups	n = 12,469	n = 6979	n = 19,448	n = 12,469	n = 6979	n = 19,448	n = 12,469	n = 6979	n = 19,448	n = 12,469	n = 6979	n = 19,448
Sick-leave certificate	Total^b	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)
Sick-leave certificate	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)
Sick-leave certificate	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)
Antidep. 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)
Antidep. 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)
Antidep. N06A	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)
Antidep. N06A	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)
Antidep. N06A	>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)
Anxiolytics N05B	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)
Anxiolytics N05B	<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)
Anxiolytics N05B	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)
Anxiolytics N05B	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)
Anxiolytics N05B	>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	0	0	1	2.0	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	0	0	0	2.4	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	0	0	0	1.9	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)

Sick-leave certificate issued by physician, Antidep.: antidepressants; CBT: cognitive Behavioral therapy; H&S: Hypnotics and sedatives.

^a The rehabilitation-guarantee program was e reimbursement system for CBT referrals. It does not reflect the total number of CBT referrals.

^b Restricted to patients of working age; 18–64 years (n = 14,991).

Regression models

In the fully adjusted logistic regressions, men in working age, as compared to women, had an OR of 0.90 (95% CI 0.82–0.98) for sick-leave certification at the date of diagnosis. The corresponding numbers for receiving a prescription of antidepressants 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 prescription for hypnotics and sedatives: OR 1.09 (95% CI 1.00–1.19). 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 being sick-leave certified, and in the adult population an OR of 1.17 (95% CI 1.10–1.26) for having received a prescription of antidepressants; OR 1.03 (95% CI 0.96–1.11) for having received a prescription of anxiolytics and an OR of 1.15 (95% CI 1.07–1.24) for having received a prescription for hypnotics and sedatives.

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

Table 4. Bivariate and multivariable logistic regressions presenting odd ratios (OR) with 95% confidence intervals (CI) for men, as compared to women, to have received at least once a (1) sick-leave certificate, (2) prescription of antidepressants (N06A), anxiolytics (N05B), or hypnotics and sedatives (N05C) and (3) having been refereed to cognitive behavioral therapy (CBT)^a by age groups at different time periods after being diagnosed with depressive episode (F32/F33, day 0) during 2010–2015.

			Sick-leave certificate day 0		Sick-leave certificate days 1–89		Sick-leave certificate days 90–180		Sick-leave certificate days 0–180
Age group (years)	n (total 19447)	Gender (women ref)	OR crude (95% CI)	OR full model^b (95% CI)	OR crude (95% CI)	OR full model^b (95% CI)	OR crude (95% CI)	OR full model^b (95% CI)	OR crude (95% CI)	OR full model^b (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)
			Prescription of antidepressants (N06A) day 0		Prescription of antidepressants (N06A) days 1–89		Prescription of antidepressants (N06A) days 90–180		Prescription of antidepressants (N06A) days 1–180
Age group	n	Gender (women ref)	OR crude (95% CI)	OR full model (95% CI)	OR crude (95% CI)	OR full model (95% CI)	OR crude (95% CI)	OR full model (95% CI)	OR crude (95% CI)	OR full model (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)
			Prescription of anxiolytics N05B day 0		Prescription anxiolytics N05B days 1–89		Prescription anxiolytics N05B days 90–180		Prescription anxiolytics N05B days 1–180
Age group	n	Gender (women ref)	OR crude (95% CI)	OR full model (95% CI)	OR crude (95% CI)	OR full model (95% CI)	OR crude (95% CI)	OR full model (95% CI)	OR crude (95% CI)	OR full model (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)
			Prescription of hypnotics and sedatives (NO5C) day 0		Prescription of hypnotics and sedatives (NO5C) days 1–89		Prescription of hypnotics and sedatives (NO5C) days 90–180		Prescription of hypnotics and sedatives (NO5C) days 1–180
Age group	n	Gender (women ref)	OR crude (95% CI)	OR full model (95% CI)	OR crude (95% CI)	OR full model (95% CI)	OR Crude (95% CI)	OR full model (95% CI)	OR crude (95% CI)	OR full model (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)
			CBT referral day 0		CBT referral days 1–89		CBT referral days 90–180		CBT referral days 1–180
Age group	n	Gender (women ref)	OR crude (95% CI)	OR full model (95% CI)	OR crude (95% CI)	OR full model (95% CI)	OR crude (95% CI)	OR full model (95% CI)	OR crude (95% CI)	OR full model (95% CI)
18–64	14 991	Men	na	na	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	na	na	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	na	0.98	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 Referrals with the rehabilitation-guarantee program was a reimbursement system for CBT referrals. It does not reflect the total number of CBT referrals.

^b Full multivariate model adjusted for age, number of healthcare visits previous year, having severe depression or not, other current psychiatric comorbidity, and whether the visit when diagnosed was in a primary or secondary healthcare setting.

Some results of covariates included in the adjusted models are worth noting. Having a severe depression sub classification and having other psychiatric comorbidity were factors separately associated with having higher odds of being sick-leave certified at the date of diagnosis; OR 1.40 (95% CI 1.10–1.79) and OR 1.40 (95% CI 1.25–1.58), respectively. These findings remained and were more pronounced when investigating odds of sick-leave certification within the first 180 days (not shown).

Discussion

The aim of this study was to explore gender differences in the management of depression in both primary and secondary healthcare. The main result showed no large gender differences in sick-leave certification at the date of diagnosis nor during the following 6 months. The regression analyses suggest that men had somewhat lesser risk of being sick-leave certified than women, however, the difference was small and not consistent over follow-up time and different age groups. Thus, the results imply that women and men largely were treated likewise in terms of being sick-leave certified when diagnosed with depression.

The same seems to be true also for psychoactive drug prescriptions, where there were small absolute differences in issued prescriptions for antidepressants, anxiolytics, and hypnotics and sedatives.

The outcomes in this study were assessed for three different time perspectives, the date of diagnosis, and during the subsequent 3 and 6 months, respectively. At the date of diagnosis, it is reasonable to assume that outcomes are direct consequences of the health situation resulting in the depressive disorder diagnosis, most likely carried out by the same physician. This is not necessarily the case for the longer time periods. The reasons for including longer periods was that consequences of having depression (such as receiving treatments or a sick-leave certificate) may occur after the day when the diagnosis is set. There is, furthermore, a complexity in healthcare seeking behavior and healthcare utilization that, beyond the clinical condition and its progression, will affect how the condition is managed. Factors such as local organization of healthcare, availability of resources, referral routines, and the physician’s knowledge as well as the patient’s own preferences, may affect where and by which physician a diagnosis is set and whom later will treat and care for the patient. In Sweden, it is common that patients with newly developed and uncomplicated depressive episodes are managed within primary healthcare, whereas patients with complex, recurrent, or severe conditions more commonly are referred to specialists in psychiatry. Depending on the severity of the condition, a psychiatrist may then refer the patient back, with a diagnosis and treatment recommendations, or the psychiatrist might ‘keep’ the patient if considered warranted. A physician might, further, want to see how a condition develops before initiating treatment or issuing a sick-leave certificate, and a patient might want to consider a treatment option some time before accepting it. We, thus, believe it was relevant to assess the studied outcomes also over time. The potential drawback of this approach is that we cannot be sure that the depressive disorder directly contributes to a specific outcome that occurs during a period of time. This mainly concerns the sick-leave outcome in the analyses, since it is possible to be sick-leave certified for other diagnoses than depression. If women and men are as likely to be sickness certified or other diagnoses during follow-up, this will not affect our main analysis, which assesses gender differences.

Less than two thirds (62.4%) of the population were diagnosed in primary healthcare. This seems to be a low figure considering that most healthcare is delivered by general practitioners, and it might reflect referral routines as well primary healthcare’s known low sensitivity to identify clinically depressed patients [38]. Another reason, contributing to where patients seek care, is that people in Uppsala have the possibility to seek secondary healthcare directly, by self-referrals. Such referrals are considered by medical specialists and the individual is then either welcomed or directed to an adequate care level.

The strengths of this study include the high coverage community sampling method with no attrition, covering almost all healthcare given. It is further a strength that it was possible to control for comorbidity and number of healthcare visits, since these factors are likely to be associated with the outcomes. The main limitations of the study include the many potential factors that might affect the management of depression and that this study has not been able to control for. The covariates used in this study were limited to those accessible within the search terms of the database. The denominator of all outcome proportions are all patients, not the patients at risk of the outcome. It was not possible to determine if the patients already were on sick leave or treated with any psycho-active drug at the time of outcome measurement. Nor was it possible to control whether participants moved out of the county or sought healthcare elsewhere during follow-up. For these reasons, proportions and rates of the outcomes are believed to be somewhat underestimated. We tried to alleviate these problems by using a wash-out period and by the adjustment of accessible covariates. The washout period for having a F32/F33 diagnosis was 12 months, but since an individual only could be included once this means that some individuals had washout periods longer than 12 months. We tested gender differences in management for depression for five different outcomes, but also performed many subgroups analyses, which increases the risk of false positive findings. Thus, we suggest the results of the regression analyses to be viewed in terms of over all trends.

The adjusted analyses controlled for having severe depression. It would have been desirable to adjust for depression severity using MADRS-S estimates, however, this measure was only assessed in 10.9% of the cases; considered too few to be included in the fully adjusted regression models, reducing the overall study sample. The averages MADRS-scores assessed, nevertheless, did not differ significantly between women and men (Table 1).

The fact that a higher proportion of men in all ages received prescriptions in all three categories of psychoactive drugs in this study is notable. It is known that women in general are prescribed more psychoactive drugs than men. The Swedish Board of Health and Welfare provides statistics of all dispensed prescribed drugs and it is possible to investigate gender differences in this database [39]. When using these statistics: antidepressants, anxiolytics, and hypnotics and sedatives (as daily day doses/1000 inhabitants) and restricting data to the years of this study (2010–2015), the average 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 psychoactive drugs. However, according to the results in this study, this is not because women are more likely to receive prescriptions at the time of diagnosis or in the following months. Instead, the reasons seem to be that more women than men are diagnosed with depression, a finding that was also apparent in the present study where almost two thirds of the cases in the study population were women. The findings in this study, thus, do not support the idea that women and men are treated differently regarding sick-leave certification or medication. On the contrary, although there were small differences, the main pattern regarding sick leave and treatment seemed to be gender neutral.

Conclusion

In conclusion, there were small differences in proportions of men and women who were sick-leave certified or prescribed antidepressants, anxiolytics, or hypnotics and sedatives at the date diagnosed with depression as well as during the following 6 months. Men had, compared to women, somewhat lower odds of being sick-leave certified and somewhat higher odds of receiving prescriptions of psychoactive drugs the same day they were diagnosed; however, the overall conclusion is that women and men diagnosed with depression generally seems to be treated in equal ways regarding the here studied aspects.

Acknowledgements

The authors are much grateful to Mats Norman and Mats Byström for help with the planning and execution of the extraction of data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

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

Notes on contributors

Per Lytsy

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

Johan Hallqvist

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

Kristina Alexanderson

Kristina Alexanderson, PhD, professor of social insurance, has >300 original international peer-reviewed publications. Her research focus is on health and sickness absence, in general and regarding specific diagnoses and life situations. She uses both epidemiological and qualitative analytical methods and has established large population-based research databases. Extensive international collaborations.

Annika Åhs

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