Risk factors for hospitalizations associated with depression among women during the years around a birth: a retrospective cohort study

Main Article Content

Jennifer Christine Fairthorne
Tim F Oberlander
Rollin Brant
Gillian E Hanley

Abstract

Introduction
Socio-economic status (SES) is an important determinant of health and low SES is associated with higher rates of prenatal and post-partum depression while prenatal and post-partum depression are associated with sub-optimal maternal and infant health. Furthermore, increased negative effects of post-partum depression have been reported in children from low SES backgrounds.


Objectives
To assess whether socio-economic status (SES) was related to the risk of a medical or psychiatric hospitalization associated with depression (HAWD) and the risk of a HAWD by anti-depressant (AD) use during the years around a birth


Methods
This retrospective cohort study used linked birth, hospitalization, prescription and tax-file records of the study cohort. We linked registry data of 243,933 women delivering 348,273 live infants in British Columbia (1999-2009). The outcomes of interest were a HAWD and a HAWD and the associated patient anti-depressant (AD) use. Ranked area-based measures of equivalised, family disposable income were used to create income deciles (Decile-1 low), our proxy for SES. Mothers from Decile-6 were the comparator group. Anti-depressant use was defined as having a prenatal prescription for a serotonin reuptake inhibitor or other AD and the years around a birth were the period beginning 12 months before conception and ending 12 months after the birth. We analysed by pregnancy using mixed effects logistic regression whilst adjusting for maternal age and parity.


Results
Compared to middle-income mothers from Decile-6, (Decile-1, Decile-2) mothers from low income neighbourhoods had increased odds of HAWDs [aOR=1.77(CI: 1.43, 2.19); aOR=1.56(CI: 1.26, 1.94)]. Mothers from low income areas with depression and no AD use had even higher odds of HAWDs [aOR=1.83(CI: 1.33, 2.20); aOR=1.71(CI: 1.33, 2.20)].


Conclusions
Results provide preliminary evidence that barriers to treating depression with ADs in mothers from low income areas during the years around a birth might contribute to their increased risk of a hospitalization associated with non-pharmacologically treated depression. Further research is implicated to further elucidate the origins of this increased risk.


Keywords
Mothers; Socio-economic status; Antidepressive agents; Pregnancy; Post-partum period; Parturition; Prescriptions; Hospitalization.


Disclaimer
All inferences, opinions, and conclusions drawn in this manuscript are those of the authors and do not reflect the opinions or policies of the Data Stewards of Population Data BC.

Introduction

Socio-economic status (SES) remains one of the most important determinants of health and well-being across the life course (1) and is associated with many health outcomes (2, 3). Yet SES has no recognised definition or gold standard for measurement (3, 4). Some measures of SES, such as the Hollingshead Index of Social Status are structured and are comprised of combinations of material dimensions including income, residential location, occupation and education (5). Government bodies are often reluctant to release individual income data. Instead, researchers must use an area-based proxy for family income. In Canada, area-based measures of equivalised family disposable income have been found suitable for comparing differences in income of more than four deciles (6). Statistics Canada provide a ranking of ten levels of these data by post-code areas of 400 to 700 residences. In keeping with Rossi and Gilmartin’s (7) criteria for a valid and useful social index, the aforementioned equivalised income data are conceptually based (8), valid (9), reliable (9), accessible (10) and complete (8) and are commonly used in health research (9).

In North America, rates of prenatal and post-partum depression were estimated at 12% to 18% (11, 12). In studies that used different proxies for SES (such as education, income, housing conditions and employment), women of lower SES had higher rates of prenatal (13, 14) and post-partum depression (12, 14-16). Despite these statistics, a study of overall health service utilization in 1,000 women in Ontario, Canada (17) found no difference in use between the socially advantaged and disadvantaged during a four week post-partum period. On the other hand, Scottish women from the most deprived quintile had the highest proportion of psychiatric hospitalizations during pre-pregnancy, pregnancy and over a two-year post-partum period (18). Prenatal depression is associated with negative maternal health behaviours such as increased use of non-prescription drugs (19, 20) and negative neonatal and child health outcomes including premature birth (15), lower birth weight (15) and an increased risk of later mental health issues in the child (21, 22). Post-partum depression is associated with poorer developmental trajectories of the child (23) such as poorer language (24) and overall cognitive development (25).

Increased associations of the negative effects of post-partum depression in children from low SES backgrounds have been reported (22, 26, 27). For example, the results of a French cohort study (26) used the responses of nearly 2,000 mothers and indicated that children with mothers of low income with post-partum depression had a significantly higher risk of developing intense, negative, emotional reactions. It is difficult to separate the effects of prenatal anti-depressant (AD) use and maternal depression on the fetus (28) and it is now considered that some previous studies investigating the effect of prenatal AD use may have been confounded by co-existing depression (28). In 2007, a large population-based study (29) accessed registry data of more than one and a half million pregnancies and used innovative statistical methods to allow for possible confounding. While AD use in the first trimester was associated with a slight increase in preterm births, previously documented associations with infants who were small for gestational age (30) and attention-deficit/hyperactivity disorder (31) were not present. This emphasizes that early diagnosis and treatment of depression in women of child-bearing age are important and risks associated with prenatal AD use may be less than previously ascertained. Furthermore, for each patient, the risk of not treating a depressive illness needs to be considered in relation to the risk associated with prenatal treatment with an AD (32).

While relatively little is known regarding the relationship between SES and AD use in the year before conception, during pregnancy and the post-partum, two small studies have reported lower rates of AD use among depressed women of lower SES, as indicated by the proxies of sub-optimal home environment (33) and lower educational attainment (34). If women of lower SES are less likely to use ADs and are less able to access alternative forms of therapy such as cognitive behavioural therapy or counselling (35, 36) due to cost or other issues, we might expect a relationship between lack of access to pharmacotherapy and hospitalizations (medical or psychiatric) associated with depression (HAWDs). This is further supported by several studies, which indicate that a combination of pharmacotherapy with cognitive behavioural therapy or interpersonal therapy results in the best outcomes for women with post-partum depression (37, 38). As a result of the Canada Health Act (39), Canada has a one-tiered health-care system where hospitalizations are free but prescription drugs are not. This suggests that one of the driving factors behind this phenomenon might be the cost of ADs. As a result, provinces have independently developed prescription drug insurance programs and all include some patient co-payment. In British Columbia (BC), the program is entitled Fair PharmaCare and is an income-based program where co-payment increases with rising household income. However, even for those of the lowest household incomes, there is a 30% co-payment rate up to a maximum of 2% of gross household income (40). Further, there is a large body of research indicating that even small co-payments represent access barriers among vulnerable populations (41-44).

In summary, area-based income data provide useful approximations for comparing major differences in income. There are higher rates of prenatal and post-partum depression in women of low SES though this may not be reflected by health service use. Negative associations of maternal depression on the fetus and subsequent child, along with adverse interactions of post-partum depression with low SES and, to a lesser extent, negative effects of prenatal AD use on the resultant child have been implicated. There is also evidence that co-payments associated with ADs provide a barrier to access for those of low SES. Finally, reports of health care use in Canadian women by SES around the time of a birth are scant and inconsistent and we found no studies examining depression and AD use over SES during the years around a birth. Therefore, by using linked registry data, our aim was to explore whether mothers of low SES are at an increased risk of hospitalizations associated with depression and whether this risk is exacerbated in those untreated with an AD.

Methods

To estimate maternal SES, we used income band data derived from tax-file records for 2002 and 2006 (58). For each post code (comprising 400-700 residences), equivalised family disposable incomes (allowing for the number and age of people within the household) had been calculated, averaged and ranked into ten incremental bands where Decile-1 represented the most disadvantaged mothers. For births after June 30, 2004, we used 2006 data for assessment and otherwise, 2002 data.

By linking registry data during time periods associated with a birth and compared to women from Decile-6, over SES, we aimed to report the odds of:

  1. At least one HAWD
  2. At least two HAWDs
  3. At least one HAWD with a primary diagnosis associated with depression
  4. At least one HAWD by AD use

Finally, we aimed to identify linear or quadratic trends over increasing SES for each of the previous outcomes.

Given that women of lower SES have higher rates of prenatal (13, 14) and post-partum depression (12, 14, 16), depression generally (45) and poorer overall health (15), we hypothesized that women of lower SES would have highest odds of each outcome during periods associated with a birth. For most outcomes associated with poorer health, there are negative linear trends over increasing SES (46-48). Therefore, we hypothesized that each of our outcomes would also have a negative linear trend over increasing SES. Due to the lack of available evidence, we were less sure of the relationship of at least one HAWD and AD use over SES. However, since women of lowest SES are less likely to access ADs (33, 34), and more likely to experience prenatal and post-natal depression, and depression generally (13, 14, 16), we hypothesized that they would be more likely to experience a HAWD with no AD use.

After obtaining approval from the Behavioural Research Ethics Board (49) and all relevant data stewards (10), Population Data BC supplied complete, accurate and valid datasets (50-52) from Perinatal Services (53), the Insurance Registry (54), PharmaNet (55) and the Hospital Registry (56) along with Income band data (57) prepared by Statistics Canada.

Cohort Definition

Our cohort comprised women who gave birth to a live singleton in BC from 1999-2009 inclusive. This included about 95% of all women who lived in BC before, during and after their pregnancies. Women indigenous to Canada or from the Military were excluded since their health insurance was from a different source. All residents of BC are registered for health insurance. Therefore, to ensure that study mothers lived in BC for the majority of the study period, we required that mothers be registered for health insurance for at least 275 days in the calendar year before the infant’s birth, the birth year and the following calendar year.

Time periods

For each pregnancy, we examined hospitalizations within one or more of the periods: pre-pregnancy (the 12 months before conception), pregnancy, extended post-partum (the 12 months after the birth), and the combined period (the combination of the previous three periods). The date of admission was used to allocate a hospitalization to a time period. We included the combined period in order to utilize the increased power resulting from the greater number of hospitalizations during this longer period. We reasoned that some associations of SES, might be identified during the combined period that were not apparent in the shorter, contributing periods, particularly for less frequently occurring outcomes.

Protecting respondent privacy

Statistics Canada ensures respondent privacy and confidentiality during the linkage process and subsequent use of linked files. Only employees directly involved in the linkage process have access to the unique identifying information required for linkage (such as names and birthdates) but have no access to the analysis variables. Once the data linkage process is complete, the resulting linked keys are used to create a linked file without identifying information and only the de-identified file is accessed by analysts for research purposes including validation and statistical analyses. The application for the record linkage was reviewed and approved by the Executive Management Board at Statistics Canada under the Statistics Canada Policy on Record Linkage (see http://www.statcan.gc.ca/eng/record/policy4-1 .)

Outcome measures

Each hospitalization had provision for listing a maximum of 25 ICD-9 diagnostic codes, which were provided by the attending clinician. We defined a HAWD as a hospitalization with a listed ICD-9 code of 296.2, 296.3, 296.9, 298.0, 300.4, 309.1 or 311 or an ICD-10 code of F32, F33, F34.1, F34.9, F38, F39 or F43.21. A HAWD with a primary diagnosis associated with depression was defined as one where the first listed ICD-9 or ICD-10 code was associated with depression (as described previously). This is because the first listed diagnostic code in the hospitalization data represents the diagnosis that was primarily responsible for the hospital admission. Our principal outcome measure was at least one HAWD over each of the four defined time periods. Due to smaller numbers of women having at least two HAWDs or at least one HAWD with a primary diagnosis associated with depression, we calculated the odds of at least two HAWDs and at least one HAWD with a primary diagnosis associated with depression only over the combined period. Having at least two HAWDs provided an indication that the depression was recurrent and having at least one HAWD with a primary diagnosis associated with depression indicated that severity of the depression was likely to be more severe than other HAWDs.

Other covariates and exposures

Since maternal age (59) and parity (60) are related to the risk of depression, we included each trait at the infant’s birth-date as explanatory variables. The variable - Maternal age had four categories: Less than 20 years , 20-29 years , 30-39 years and 40 years or older . Parity had two levels: Nulliparous and Multiparous . A variable provided by the Perinatal Data Registry was Final gestational age (in complete weeks) which had been calculated using an algorithm incorporating the last menstrual period, first ultrasound, newborn examination and maternal charts (61). We subtracted Final gestational age (in days) from the infant’s date of birth to provide the estimated date of conception.

We defined an AD as either a selective serotonin reuptake inhibitor (SSRI), a serotonin and norepinephrine re-uptake inhibitor (SNRI) or other anti-depressant (OAD) ( Supplementary table 1 ). For each member of the cohort, AD use was defined as having filled at least one prescription for an SSRI, SNRI or OAD during the combined period.

Analyses

For each outcome, mothers with no HAWD formed the reference group and mothers from Decile-6 were the comparator group. We chose a middle decile (rather than Decile-1 or Decile-10) as the comparator since we wanted to compare the odds ratios (ORs) of mothers from the lowest income areas (Decile-1) and mothers from the highest income areas (Decile-10). Within the eleven year study period, mothers had varying numbers of pregnancies. To allow for the resulting clusters, we used Mixed Effects Logistic Regression with the variable, maternal identity number specifying the random effects and the variable, pregnancy identity number specifying the fixed effects. The type or number of HAWDs was the dependent variable and we assessed odds by pregnancy and SES decile. For each outcome over a time period, we used a separate regression model. All unadjusted odds ratios (uORs) and adjusted odds ratios (aORs) are tabulated but we report only significant aORs in the text. In the analyses associated only with HAWDs, we made comparisons over six outcomes (at least one HAWD over each of four time periods, at least two HAWDs over the combined period and a HAWD associated with a primary diagnosis over the combined period) and over nine SES deciles. Hence, using Bonferroni’s approximation for multiple comparisons (62), we divided the usual significance level (0.05) by 54 (6 x 9) to produce a new significance level of 0.001. In our investigation of AD use and HAWDs, we made comparisons over the two outcomes of at least one HAWD with AD use and at least one HAWD without AD use and nine deciles resulting in 18 comparisons. Here, the application of Bonferroni’s approximation produced an adjusted significance level of 0.003 (0.05/18). When investigating the presence of a linear or quadratic trend, we again used Mixed Effects Logistic Regression , but, we reverted to the traditional level of significance (0.05) since no comparisons were being made. We compared proportions using a two sample test of proportions which used the prtesti command (63). STATA14 was used for all analyses.

Results

There were 243,933 women with 348,273 pregnancies resulting in live births in BC between January 1, 1999 and December 31, 2009. Mothers from the lowest income areas (Decile-1) were more likely to be of younger age (less than 20 years) compared to mothers from Decile-10 (p-value=0.01). Conversely, mothers from Decile-10 were more likely to be of mature age (40 years or more) than mothers from Decile-1 (p-value=0.001). The proportion of pregnancies associated with each SES decile varied significantly with the largest number, 38,796 in Decile-3 (11.1%) and the smallest, 26,583 (7.6%) in Decile-10 (p-value< 0.005) [ Table 1 ]. Of the pregnancies in our cohort, 383 (0.11%) were subject to at least one HAWD during pre-pregnancy, 293 (0.08%) to a HAWD during pregnancy, 1,551 (0.45%) to a HAWD during the extended post-partum and 2,044 (0.59%) to a HAWD during the combined period. Of the pregnancies associated with a HAWD during the combined period, 433 (21.2%) were associated with AD use and 1,611 (78.8%) were not ( Table 2 ).

SES decile Age in years Parity Total
<20 20-29 30-39 ≥40 0 ≥1 Missing
1 1,541 16,769 16,189 1,252 16,506 19,243 * 35,751
(4.30%) (46.90%) (45.30%) (3.50%) (46.20%) (53.80%) (100%)
2 1,195 17,615 18,430 1,262 17,375 21,123 * 38,502
(3.10%) (45.80%) (47.90%) (3.30%) (45.10%) (54.90%) (100%)
3 1,111 17,412 18,926 1,347 17,506 21,123 * 38,796
(2.90%) (44.90%) (48.80%) (3.50%) (45.10%) (54.90%) (100%)
4 970 15,891 18,952 1,259 16,623 20,446 * 37,072
(2.60%) (42.90%) (51.10%) (3.40%) (44.80%) (55.20%) (100%)
5 895 14,921 19,290 1,400 16,146 20,358 * 36,506
(2.50%) (40.90%) (52.80%) (3.80%) (44.20%) (55.80%) (100%)
6 851 13,947 19,415 1,420 15,844 19,787 * 35,633
(2.40%) (39.10%) (54.50%) (4.00%) (44.50%) (55.50%) (100%)
7 721 12,754 19,246 1,437 15,180 18,977 * 34,158
(2.10%) (37.30%) (56.30%) (4.20%) (44.40%) (55.50%) (100%)
8 666 12,216 19,389 1,428 14,677 19,019 * 33,699
(2.00%) (36.30%) (57.50%) (4.20%) (43.60%) (56.40%) (100%)
9 646 10,779 18,580 1,568 13,693 17,878 * 31,573
(2.10%) (34.10%) (58.90%) (5.00%) (43.40%) (56.60%) (100%)
10 449 8,036 16,441 1,657 11,541 15,042 * 26,583
(1.70%) (30.20%) (61.90%) (6.20%) (43.40%) (56.60%) (100%)
Total 9,045 140,340 184,858 14,030 155,091 193,125 20 348,273
(2.60%) (40.30%) (53.10%) (4.00%) (44.50%) (55.50%) (0.0%) (100%)
Table 1: Maternal traits by socio-economic status. SES, socio-economic status; *, numbers are suppressed in all cells where N<5 due to conditions imposed by the data custodians. Note: Maternal age and parity were calculated at the time of the birth
Socio-economic status decile
1 2 3 4 5 6 7 8 9 10 Total
At least one HAWD during pre-pregnancy
57 51 46 39 48 33 25 33 26 25 383
0.16% 0.13% 0.12% 0.11% 0.13% 0.09% 0.07% 0.10% 0.08% 0.09% 0.11%
At least one HAWD during pregnancy
39 41 38 27 34 29 17 26 23 19 293
0.11% 0.11% 10.00% 0.07% 0.09% 0.08% 0.05% 0.08% 0.07% 0.07% 0.08%
At least one HAWD during extended post-partum
205 222 145 183 154 137 133 133 135 101 1551
0.57% 0.58% 0.37% 0.49% 0.42% 0.38% 0.39% 0.39% 0.43% 0.38% 0.45%
At least one HAWD during combined period
284 273 211 240 207 171 178 176 169 135 2,044
0.79% 0.71% 0.54% 0.65% 0.57% 0.48% 0.52% 0.52% 1% 0.51% 0.59%
At least two HAWDs during combined period
47 40 25 30 23 16 13 20 18 7 239
0.13% 0.10% 0.06% 0.08% 0.06% 0.04% 0.04% 0.06% 0.06% 0.03% 0.07%
At least one HAWD with primary diagnosis associated with depression during combined period
44 46 43 49 39 29 29 28 28 24 359
0.12% 0.12% 0.11% 0.13% 0.11% 0.08% 0.08% 0.08% 0% 0.09% 0.10%
At least one HAWD with anti-depressant use during combined period
67 55 39 41 29 45 43 40 39 35 433
0.19% 0.14% 0.10% 0.11% 0.08% 0.13% 0.13% 0.12% 0.12% 0.13% 0.12%
At least one HAWD with no anti-depressant use during combined period
217 218 172 199 178 126 135 136 130 100 1,611
0.61% 0.57% 0.44% 0.54% 0.49% 0.36% 0.40% 0.40% 0.41% 0.38% 0.46%
Table 2: Numbers and percentages of hospitalizations (medical or psychiatric) associated with a HAWD by SES. HAWD, hospitalization (medical or psychiatric) associated with depression.

Hospitalizations (medical or psychiatric) associated with depression

During the extended post-partum, compared to mothers from Decile-6, mothers from Decile-1 and Decile-2 had more than 60% increased odds of at least one HAWD [aOR=1.62 (CI: 1.28, 2.07); aOR=1.63 (CI: 1.28, 2.07) p-value <0.001]. During the combined period, compared to mothers from Decile-6, mothers from Decile-1 and Decile-2 had significantly increased odds of a HAWD [1.77 (CI: 1.43, 2.19); aOR=1.56 (CI: 1.26, 1.94) p-values<0.001] ( Table 3 ). During the combined period, compared to mothers from Decile-6, mothers from Decile-1 were nearly three times more likely to have at least two HAWDs [aOR=2.85 (CI: 1.62, 5.03) p-value<0.001]. Further, the odds reduced over increasing SES with a linear trend (p-value<0.001) [ Figure 1 , Table 4 ]. There were significant negative linear trends over increasing SES for having a HAWD over all six outcomes with p-values ranging from <0.001 to 0.013 ( Supplementary table 2 ).

Figure 1: Odds of at least two hospitalizations associated with depression over the combined period according to socio-economic status. HAWD, Medical or psychiatric hospitalization associated with depression; SES, socio-economic status. Note: Unadjusted odds have been graphed.

At least one HAWD
SES Model Ref (0) Pre-pregnancy Pregnancy Extended post-partum Combined period
1 U 1.72(1.12, 2.65) 1.34(0.83, 2.17) 1.56(1.22, 1.99) 1.74(1.41, 2.15)
A 1.65(1.07, 2.53) 1.30(0.80, 2.11) 1.62(1.28, 2.07) 1.77(1.43, 2.19)
2 U 1.43(0.92, 2.22) 1.31(0.81, 2.11) 1.58(1.24, 2.00) 1.55(1.25, 1.91)
A 1.38(0.89, 215) 1.28(0.79, 2.06) 1.63(1.28, 2.07) 1.56(1.26, 1.94)
3 U 1.28(0.82, 2.00) 1.20(0.74, 1.95) 0.97(0.75, 1.25) 1.15(0.92, 1.43)
A 1.25(0.80, 1.95) 1.18(0.73, 1.92) 0.99(0.76, 1.29) 1.16(0.92, 1.45)
4 U 1.14(0.71, 1.81) 0.89(0.53, 1.51) 1.32(1.03, 1.68) 1.39(1.12, 1.72)
A 1.12(0.70, 1.78) 0.88(0.52, 1.49) 1.34(1.05, 1.73) 1.40(1.13, 1.74)
5 U 1.42(0.91, 2.21) 1.14(0.70, 1.88) 1.11(0.86, 1.43) 1.20(0.96, 1.50)
A 1.41(0.90, 2.19) 1.14(0.69, 1.87) 1.13(0.87, 1.46) 1.22(0.97, 1.52)
6 Comparator
7 U 0.79(0.47, 1.33) 0.61(0.34, 1.11) 1.04(0.80, 1.35) 1.10(0.87, 1.38)
A 0.80(0.47, 1.34) 0.62(0.34, 1.12) 1.04(0.80, 1.36) 1.11(0.88, 1.40)
8 U 1.06(0.65, 1.71) 0.95(0.56, 1.61) 1.03(0.79, 1.34) 1.10(0.88, 1.39)
A 1.07(0.66, 1.74) 0.96(0.56, 1.62) 1.03(0.79, 1.35) 1.11(0.88, 1.41)
9 U 0.89(0.53, 1.49) 0.90(0.52, 1.55) 1.12(0.86, 1.46) 1.13(0.89, 1.42)
A 0.91(0.54, 1.52) 0.91(0.53, 1.57) 1.11(0.85, 1.46) 1.14(0.90, 1.44)
10 U 1.02(0.60, 1.71) 0.88(0.49, 1.57) 0.99(0.75, 1.32) 1.07(0.83, 1.37)
A 1.06(0.63, 1.79) 0.91(0.51, 1.62) 0.97(0.73, 1.29) 1.08(0.84, 1.39)
Table 3: Odds (with 95% confidence intervals) of anti-depressant use and hospitalizations (medical or psychiatric) associated with depression over SES. HAWD, hospitalization (medical or psychiatric) associated with depression; SES, socio-economic status decile; OR, odds ratio; Ref, Reference group; 0, No HAWD from the corresponding SES group over the same time period; primary diagnosis depression, primary diagnosis associated with depression; U, unadjusted OR; A, adjusted OR;. Note 1: Adjusted model allows for maternal age and parity at the birth of the child. Note 2: Level of significance is 0.001 and significant ORs are highlighted.
SES Model Reference group (0 HAWDs) ≥2 HAWDs Combined period ≥1 HAWD (primary diagnosis depression) Combined period
1 U 2.97(1.67, 5.18) 1.49(0.91, 2.43)
A 2.85(1.62, 5.03) 1.32(0.81, 2.15)
2 U 2.32(1.30, 4.14) 1.47(0.91, 2.39)
A 2.27(1.27, 4.05) 1.36(0.84, 2.20)
3 U 1.44(0.77, 2.69) 1.36(0.83, 2.22)
A 1.41(0.75, 2.64) 1.27(0.78, 2.07)
4 U 1.81(0.98, 3.31) 1.64(1.02, 2.65)
A 1.78(0.97, 3.27) 1.57(0.97, 2.53)
5 U 1.40(0.74, 2.66) 1.32(0.80, 2.18)
A 1.40(0.74, 2.64) 1.29(0.78, 2.12)
6 Comparator
7 U 0.85(0.41, 1.76) 1.05(0.62, 1.79)
A 0.85(0.41, 1.78) 1.08(0.63, 1.84)
8 U 1.32(0.69, 2.55) 1.02(0.60, 1.75)
A 1.33(0.69, 2.58) 1.05(0.62, 1.80)
9 U 1.27(0.65, 2.49) 1.08(0.63, 1.84)
A 1.29(0.66, 2.53) 1.14(0.67, 1.95)
10 U 0.59(0.24, 1.43) 1.11(0.63, 1.94)
A 0.61(0.25, 1.47) 1.25(0.71, 2.19)
Table 4: Odds ratios of at least two HAWDs and at least one HAWD associated with a primary diagnosis of depression. HAWD, hospitalization (medical or psychiatric) associated with depression; SES, socio-economic status decile; OR, odds ratio; 0, No HAWD from the corresponding SES group over the same time period; primary diagnosis depression, primary diagnosis associated with depression; U, unadjusted OR; A, adjusted OR;. Note 1: Adjusted model allows for maternal age and parity at the birth of the child. Note 2: Level of significance is 0.001 and significant ORs are highlighted.

Anti-depressant use and hospitalizations (medical or psychiatric) associated with depression

During the combined period and compared to mothers from Decile-6, mothers from Decile-1 and Decile-2 were more likely to have at least one HAWD with no AD use [aOR=1.83 (CI: 1.33, 2.20); aOR=1.71 (CI: 1.33, 2.20) p-values<0.0005)]. Likewise, mothers from Decile-4 were more likely to have at least one HAWD with no AD use over the combined period [aOR=1.60(CI: 1.24, 2.06) p-value<0.0005)] See Table 5 . In mothers with at least one HAWD and no AD use, there was a negative trend (p-value=0.003) over increasing SES. In mothers with at least one HAWD and AD use, there was a negative trend (p-value=0.001) over increasing SES [ Supplementary table 2 ].

SES Model No HAWD & no AD use At least one HAWD with no AD use At least one HAWD with AD use
1 U 1.84(1.43, 2.36) 1.60(1.01, 2.52)
A 1.83(1.33, 2.20) 1.68(1.06, 2.65)
2 U 1.71(1.33, 2.19) 1.16(0.72, 1.86)
A 1.71(1.33, 2.20) 1.20(0.75, 1.93)
3 U 1.28(0.99, 1.66) 0.75(0.45, 1.25)
A 1.28(0.98, 1.66) 0.78(0.47, 1.30)
4 U 1.59(1.24, 2.05) 0.86(0.52, 1.42)
A 1.60(1.24, 2.06) 0.89(0.54, 1.47)
5 U 1.44(1.11, 1.86) 0.58(0.33, 1.00)
A 1.44(1.11, 1.87) 0.59(0.34, 1.03)
6 Comparator
7 U 1.14(0.87, 1.50) 1.00(0.61, 1.66)
A 1.15(0.87, 1.51) 1.03(0.62, 1.69)
8 U 1.17(0.89, 1.54) 0.93(0.56, 1.55)
A 1.18(0.90, 1.55) 0.95(0.57, 1.57)
9 U 1.19(0.91, 1.57) 0.95(0.57, 1.59)
A 1.20(0.91, 1.58) 0.96(0.58, 1.60)
10 U 1.07(0.80, 1.44) 1.06(0.63, 1.80)
A 1.08(0.81, 1.46) 1.05(0.62, 1.79)
Table 5: Odds (with 95% confidence intervals) of hospitalizations (medical or psychiatric) associated with depression by anti-depressant use over SES. HAWD, hospitalization (medical or psychiatric) associated with depression; primary diagnosis depression, primary diagnosis associated with depression; SES, socio-economic status decile; 0, No HAWD from the corresponding SES group over the same time period; U, unadjusted odds ratio; A, adjusted odds ratio. Note 1: Adjusted model allows for maternal age and parity at the birth of the child. Note 2: Level of significance is 0.001 and significant ORs are highlighted.

Discussion

In this retrospective cohort study, the mothers from the lowest income areas (Decile-1 and Decile-2) had highest odds of at least one HAWD during the extended post-partum and the combined period and at least two HAWDS during the combined period. When AD use was included in the analysis, it was shown that mothers from the lowest income areas had significantly heightened odds of a HAWD with no AD use, and that the risk reduced linearly over increasing SES.

Hospitalizations (medical or psychiatric) associated with depression

As expected, mothers from low income areas had increased likelihood of all outcomes (at least one HAWD over each of the time periods, at least two HAWDs and at least one HAWD with a primary diagnosis of depression over the combined period). In particular, mothers of Decile-1 had nearly three times the risk of multiple HAWDs over the combined period, and significantly increased risks of a HAWD during the extended post-partum period. Combined periods and all outcomes were associated with a significant negative trend over increasing SES. Such a negative trend has not previously been reported. Our results reflect the association of lower SES and increased rates of depression associated with prenatal and post-partum periods previously cited (12-14, 16). Our findings also likely reflect Canada’s one tier system with hospitalizations at no patient cost and provide evidence of equity of access to hospitalizations associated with depression, regardless of SES, in BC. Our results differ from the from the Ontario study (17) which found no difference in health-care use between socially disadvantaged and advantaged women. This might be accounted for by their use of only urban data, compared to our provincial use. Further, the Ontario study used different measures of health-care use such as GP visits rather than hospitalizations.

Anti-depressant use and hospitalizations (medical or psychiatric) associated with depression

We found no previous studies which compared the risk of a HAWD in women according to AD use in relation to SES. During the combined period, mothers from low income areas (Decile-1 and Decile-2) with no AD use were significantly more likely to be hospitalized than mothers from Decile-6. Others have reported a relationship between prenatal maternal depressive disorder with no pharmacological treatment and low SES (33, 34). However, the larger size of our study enabled us to identify that the risk of pharmacologically untreated depression and a HAWD reduced significantly with increasing SES ( Supplementary table 2 ). The universal free access to hospitalizations combined with the necessary co-payments for ADs could explain why the risk of a hospitalization with pharmacologically untreated depression is highest in mothers from the lowest income areas and reduces with increasing SES.

Strengths and limitations

Our linkage of registry data enabled us to access records of about 240,000 women increasing the power to assess smaller differences than other studies in the area. No previous studies have compared the risk of a hospitalization in women with pharmacologically treated and untreated depression according to SES. Moreover, no previous studies have established an SES gradient for hospitalizations associated with depression according to whether the depression was pharmacologically treated. Our proxies for depression (HAWDs) and AD use did not rely on recall but were objectively extracted from administrative data.

A limitation was that our measure of SES, equivalised family income by postcode, is a less precise measure of SES than individual income level and did not include education or occupation. In addition, SES information was generally not available for the year of birth of the child. In some instances, where a woman’s income changed between the birth year and either 2002 or 2006, this may have resulted in less precise SES measures. Also, we were unable to adjust for ethnicity or immigration and there is evidence that some demographic groups have a degree of protection from the effects of low SES on depression (64). Having a partner is also a protective factor for depression (65) and we were unable to adjust for marital status.

Conclusion

During the years around a birth, mothers from low income areas (Decile-1 and Decile-2) had higher odds of HAWDs and the risk reduced linearly over increasing SES. Pharmacologically untreated depression further increased the risk of a HAWD. Our results provide preliminary evidence that barriers to accessing ADs for mothers of lower SES might contribute to their increased risk of pharmacologically untreated depression. Implications for further research include quantitative and qualitative studies investigating women of low SES during the years around a birth who have experienced a HAWD whilst untreated with ADs. This might enable the identification of the driving factor(s) of this phenomenon.

Anti-depressant Generic names
Selective serotonin re-uptake inhibitors Fluoxetine, citalopram, paroxetine, sertraline, fluvoxamine, escitalopram, duloxetine HCL
Serotonin and norepinephrine re-uptake inhibitors Venlafaxine HCL, duloxetine HCL, desvenlafaxine succinate
Other anti-depressants Amitryptaline, amoxapine, clomipramine, desipramine, doxepin, imipramine, nortriptyline, protriptyline, trimipramine, phenelzine, hydroxytryptophan, tranylcypromine
Supplementary table 1: Serotonin re-uptake inhibitors and other anti-depressants. HCL, hydrochloride.
Time period Outcome Sign and p-value
Pre-pregnancy ≥1 HAWD Negative, < 0.0005
Pregnancy ≥1 HAWD Negative, 0.007
Extended post-partum ≥1 HAWD Negative, < 0.0005
Combined period ≥1 HAWD Negative, < 0.0005
Combined period ≥2 HAWDs Negative, < 0.0005
Combined period ≥1 HAWD with primary diagnosis associated with depression Negative, 0.013
Combined period ≥1 HAWD & no AD use Negative, 0.003
Combined period ≥1 HAWD & AD use Negative, 0.001
Supplementary table 2: Linear trends of outcome measures over increasing SES. ≥, at least; HAWD, hospitalization (medical or psychiatric) associated with depression; AD, anti-depressant (defined in Supplementary table 1 ). Note 1: Level of significance is 0.05. Note 2: There were no quadratic trends.

Statement on conflicts of interest

The authors declared no conflicts of interest

Abbreviations

SES socio-economic status
AD anti-depressant
CI confidence interval
HAWD medical or psychiatric hospitalizations associated with depression
BC British Columbia
SSRI selective serotonin reuptake inhibitor
SNRI serotonin and norepinephrine re-uptake inhibitor
OAD other anti-depressant
OR odds ratio
uOR unadjusted odds ratio
aOR adjusted odds ratio

Funding Statement

Dr Fairthorne was supported by a post-doctoral fellowship from the Department of Paediatrics, University of British Columbia. Dr. Hanley was supported by a Canadian Institutes of Health Research New Investigator Award from the Drug Safety and Effectiveness Network. The sponsors had no role in the production of this research.

References

  1. Braveman PA, Egerter S, Williams DR. The social determinants of health: coming of age 2011 [2017 Dec 12]. 381-98]. Available from: . 10.1146/annurev-publhealth-031210-101218

    https://doi.org/10.1146/annurev-publhealth-031210-101218
  2. Berkman L, Macintyre S. The measurement of social class in health studies: old measures and new formulations. 1997. In: Social inequalities and Cancer [Internet]. Lyon, France: IARC Scientific Publications. Available from: http://www.iarc.fr/en/publications/pdfs-online/epi/sp138/sp138-chap4.pdf

  3. Oakes JM, Rossi PH. The measurement of SES in health research: current practice and steps toward a new approach. Social Science and Medicine [Internet]. 2003; 56(4):[769-84 pp.]. Available from: http://psych415.class.uic.edu/Readings/Oakes,%20SES%20measurement,%20SocSciMed,%202003.pdf . 10.1016/S0277-9536(02)00073-4

    https://doi.org/10.1016/S0277-9536(02)00073-4
  4. Grotto I, Huerta M, Sharabi Y. Hypertension and socioeconomic status. Current Opinion in Cardiology [Internet]. 2008; 23(4):[335-9 pp.]. Available from: http://journals.lww.com/co-cardiology/Abstract/2008/07000/Hypertension_and_socioeconomic_status.10.aspx . 10.1097/HCO.0b013e3283021c70

    https://doi.org/10.1097/HCO.0b013e3283021c70
  5. Hollingshead AB. Four factor index of social status. 1975. Available from: https://s3.amazonaws.com/academia.edu.documents/30754699/yjs_fall_2011.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1514105218&Signature=uq%2FIMQ5pB9QC3KI8t8fnxmwqINw%3D&response-content-disposition=inline%3B%20filename%3DAugust_B._Hollingshead_s_Four_Factor_Ind.pdf#page=21.

  6. Hanley GE, Morgan S. On the validity of area-based income measures to proxy household income. BMC Health Services Research [Internet]. 2008; 8(1):[79 p.]. Available from: . 10.1186/1472-6963-8-79

    https://doi.org/10.1186/1472-6963-8-79
  7. Rossi RJ, Gilmartin K. The handbook of social indicators: sources, characteristics, and analysis 1980. Available from: https://library.villanova.edu/Find/Record/7622 .

  8. Population Data BC. Income band data 2017. Available from: https://www.popdata.bc.ca/data/internal/demographic/incomeband .

  9. Roos NP, Mustard CA. Variation in health and health care use by socioeconomic status in Winnipeg, Canada: Does the system work well/Yes and no. The Milbank Quarterly [Internet]. 1997; 75. Available from: . 10.1111/1468-0009.00045

    https://doi.org/10.1111/1468-0009.00045
  10. Population Data BC. Data available 2017 [updated 2017 June 23]. Available from: https://www.popdata.bc.ca/data .

  11. Banti S, Mauri M, Oppo A, Borri C, Rambelli C, Ramacciotti D, et al. From the third month of pregnancy to one year postpartum: prevalence, incidence, recurrence, and new onset of depression. Results from the Perinatal Depression–Research & Screening Unit study. Comprehensive Psychiatry [Internet]. 2011; 52(4):[343-51 pp.]. Available from: . 10.1016/j.comppsych.2010.08.003

    https://doi.org/10.1016/j.comppsych.2010.08.003
  12. Lanes A, Kuk JL, Tamim H. Prevalence and characteristics of postpartum depression symptomatology among Canadian women: a cross-sectional study. BMC Public Health [Internet]. 2011; 11(1):[1 p.]. Available from: . 10.1186/1471-2458-11-302

    https://doi.org/10.1186/1471-2458-11-302
  13. Field T, Diego M, Hernandez-Reif M, Schanberg S, Kuhn C, Yando R, et al. Prenatal depression effects on the foetus and neonate in different ethnic and socio-economic status groups. Journal of Reproductive and Infant Psychology [Internet]. 2002; 20(3):[149-57 pp.]. Available from: https://www.andrews.edu/~rbailey/Chapter%20five/infant/7287850.pdf . 10.1080/026468302760270809

    https://doi.org/10.1080/026468302760270809
  14. Hein A, Rauh C, Engel A, Häberle L, Dammer U, Voigt F, et al. Socioeconomic status and depression during and after pregnancy in the Franconian Maternal Health Evaluation Studies (FRAMES). Archives of Gynecology and Obstetrics [Internet]. 2014; 289(4):[755-63 pp.]. Available from: . 10.1007/s00404-013-3046-y

    https://doi.org/10.1007/s00404-013-3046-y
  15. Beck CT. Predictors of postpartum depression: an update. Nursing Research [Internet]. 2001; 50(5):[275-85 pp.]. Available from: https://pdfs.semanticscholar.org/5f1d/4110299a6342ab8d1c21130d0635f976d66f.pdf . 10.1097/00006199-200109000-00004

    https://doi.org/10.1097/00006199-200109000-00004
  16. Nagy E, Molnar P, Pal A, Orvos H. Prevalence rates and socioeconomic characteristics of post-partum depression in Hungary. Psychiatry Research [Internet]. 2011 1/30/; 185(1–2):[113-20 pp.]. Available from: . 10.1016/j.psychres.2010.05.005

    https://doi.org/10.1016/j.psychres.2010.05.005
  17. Kurtz Landy C, Sword W, Ciliska D. Urban women’s socioeconomic status, health service needs and utilization in the four weeks after postpartum hospital discharge: findings of a Canadian cross-sectional survey. BMC Health Services Research [Internet]. 2008; 8(1):[203 p.]. Available from: . 10.1186/1472-6963-8-203

    https://doi.org/10.1186/1472-6963-8-203
  18. Martin JL, McLean G, Cantwell R, Smith DJ. Admission to psychiatric hospital in the early and late postpartum periods: Scottish National Linkage Study. BMJ Open [Internet]. 2016; 6(1):[1-9 pp.]. Available from: . 10.1136/bmjopen-2015-008758

    https://doi.org/10.1136/bmjopen-2015-008758
  19. Lancaster CA, Gold KJ, Flynn HA, Yoo H, Marcus SM, Davis MM. Risk factors for depressive symptoms during pregnancy: a systematic review. American Journal of Obstetrics and Gynecology [Internet]. 2010; 202(1):[5-14 pp.]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2919747/ . 10.1016/j.ajog.2009.09.007

    https://doi.org/10.1016/j.ajog.2009.09.007
  20. Stewart DE. Perinatal depression. General Hospital Psychiatry [Internet]. 2006; 28(1):[1-2 pp.]. Available from: . 10.1016/j.genhosppsych.2005.09.001

    https://doi.org/10.1016/j.genhosppsych.2005.09.001
  21. Murray L, Arteche A, Fearon P, Halligan S, Goodyer I, Cooper P. Maternal postnatal depression and the development of depression in offspring up to 16 years of age. Journal of the American Academy of Child and Adolescent Psychiatry [Internet]. 2011; 50(5):[460-70 pp.]. Available from: . 10.1016/j.jaac.2011.02.001

    https://doi.org/10.1016/j.jaac.2011.02.001
  22. Arroyo-Borrell E, Renart G, Saurina C, Saez M. Influence maternal background has on children’s mental health. International Journal for Equity in Health [Internet]. 2017; 16(1):[63 p.]. Available from: . 10.1186/s12939-017-0559-1

    https://doi.org/10.1186/s12939-017-0559-1
  23. Chalmers B, Dzakpasu S, Heaman M, Kaczorowski J. The Canadian maternity experiences survey: an overview of findings. Journal of Obstetrics and Gynaecology Canada [Internet]. 2008; 30(3):[217-28 pp.]. Available from: http://www.jogc.com/article/S1701-2163(16)32758-X/pdf . 10.1016/S1701-2163(16)32758-X

    https://doi.org/10.1016/S1701-2163(16)32758-X
  24. Quevedo L, Silva R, Godoy R, Jansen K, Matos M, Tavares Pinheiro K, et al. The impact of maternal post‐partum depression on the language development of children at 12 months. Child: Care, Health and Development [Internet]. 2012; 38(3):[420-4 pp.]. Available from: . 10.1111/j.1365-2214.2011.01251.x

    https://doi.org/10.1111/j.1365-2214.2011.01251.x
  25. Poobalan AS, Aucott LS, Ross L, Smith W, Helms PJ, Williams J. Effects of treating postnatal depression on mother–infant interaction and child development. British Journal of Psychiatry [Internet]. 2007; 191(5):[378-86 pp.]. Available from: . 10.1192/bjp.bp.106.032789

    https://doi.org/10.1192/bjp.bp.106.032789
  26. Melchior M, Chastang J-F, de Lauzon B, Galéra C, Saurel-Cubizolles M-J, Larroque B, et al. Maternal depression, socioeconomic position, and temperament in early childhood: the EDEN mother–child cohort. Journal of Affective Disorders. 2012;137(1):165-9. 10.1016/j.jad.2011.09.018

    https://doi.org/10.1016/j.jad.2011.09.018
  27. Postpartum Implementation Guidelines for Healthy Babies Healthy Children Program. [ http://www.health.gov.on.ca/english/providers/pub/child/hbabies/postpartum.html ].

  28. Oberlander T, Zwaigenbaum L. Disentangling maternal depression and antidepressant use during pregnancy as risks for autism in children. Journal of the American Medical Association [Internet]. 2017; 317(15):[1533-4 pp.]. Available from: . 10.1001/jama.2017.3414

    https://doi.org/10.1001/jama.2017.3414
  29. Sujan A, Rickert M, Öberg A, Quinn P, Hernández-Díaz S, Almqvist C, et al. Associations of maternal antidepressant use during the first trimester of pregnancy with preterm birth, small for gestational age, autism spectrum disorder, and attention-deficit/hyperactivity disorder in offspring. Journal of the American Medical Association [Internet]. 2017. Available from: . 10.1001/jama.2017.3413

    https://doi.org/10.1001/jama.2017.3413
  30. Oberlander TF, Warburton W, Misri S, Aghajanian J, Hertzman C. Neonatal outcomes after prenatal exposure to selective serotonin reuptake inhibitor antidepressants and maternal depression using population-based linked health data. Archives of General Psychiatry [Internet]. 2006; 63(8):[898-906 pp.]. Available from: . 10.1001/archpsyc.63.8.898

    https://doi.org/10.1001/archpsyc.63.8.898
  31. Figueroa R. Use of antidepressants during pregnancy and risk of attention-deficit/hyperactivity disorder in the offspring. Journal of Developmental and Behavioral Pediatrics [Internet]. 2010; 31(8):[641-8 pp.]. Available from: . 10.1097/dbp.0b013e3181e5ac93

    https://doi.org/10.1097/dbp.0b013e3181e5ac93
  32. Ryan D, Milis L, Misri N. Depression during pregnancy. Canadian Family Physician [Internet]. 2005; 51(8):[1087-93 pp.]. Available from: http://www.cfp.ca/content/cfp/51/8/1087.full.pdf .

  33. Eriksen HLF, Kesmodel US, Pedersen LH, Mortensen EL. No association between prenatal exposure to psychotropics and intelligence at age five. Acta Obstetricia et Gynecologica Scandinavica [Internet]. 2015; 94(5):[501-7 pp.]. Available from: . 10.1111/aogs.12611

    https://doi.org/10.1111/aogs.12611
  34. Santucci AK, Singer LT, Wisniewski SR, Luther JF, Eng HF, Dills JL, et al. Impact of prenatal exposure to serotonin reuptake inhibitors or maternal major depressive disorder on infant developmental outcomes. Journal of Clinical Psychiatry [Internet]. 2014; 75(10):[1088-95 pp.]. Available from: https://www.scholars.northwestern.edu/en/publications/impact-of-prenatal-exposure-to-serotonin-reuptake-inhibitors-or-m . 10.4088/JCP.13m08902

    https://doi.org/10.4088/JCP.13m08902
  35. Chabrol H, Teissedre F, Saint-Jean M, Teisseyre N, Roge B, Mullet E. Prevention and treatment of postpartum depression: a controlled randomized study on women at risk. Psychological Medicine [Internet]. 2002; 32(6):[1039-47 pp.]. Available from: . 10.1017/S0033291702006062

    https://doi.org/10.1017/S0033291702006062
  36. Cooper PJ, Murray L, Wilson A, Romaniuk H. Controlled trial of the short-and long-term effect of psychological treatment of post-partum depression. The British Journal of Psychiatry [Internet]. 2003; 182(5):[412-9 pp.]. Available from: . 10.1192/bjp.182.5.412

    https://doi.org/10.1192/bjp.182.5.412
  37. Appleby L, Warner R, Whitton A, Faragher B. A controlled study of fluoxetine and cognitive-behavioural counselling in the treatment of postnatal depression. British Medical Journal [Internet]. 1997; 314(7085):[932 p.]. Available from: . 10.1136/bmj.314.7085.932

    https://doi.org/10.1136/bmj.314.7085.932
  38. Schramm E, Schneider D, Zobel I, van Calker D, Dykierek P, Kech S, et al. Efficacy of interpersonal psychotherapy plus pharmacotherapy in chronically depressed inpatients. Journal of Affective Disorders [Internet]. 2008; 109(1):[65-73 pp.]. Available from: . 10.1016/j.jad.2007.10.013

    https://doi.org/10.1016/j.jad.2007.10.013
  39. Canada Health Act. [ http://laws.justice.gc.ca/en/C-6/ ].

  40. Ministry of Health Services. Fair PharmaCare Assistance Levels – Regular 2009 [2017 Dec 11]. Available from: https://www2.gov.bc.ca/assets/gov/health/health-drug-coverage/pharmacare/income_bands_fair_pcare_regular.pdf .

  41. Soumerai SB, Avorn J, Ross-Degnan D, Gortmaker S. Payment restrictions for prescription drugs under Medicaid. New England Journal of Medicine [Internet]. 1987; 317(9):[550-6 pp.]. Available from: . 10.1056/NEJM198708273170906

    https://doi.org/10.1056/NEJM198708273170906
  42. Soumerai SB, McLaughlin TJ, Ross-Degnan D, Casteris CS, Bollini P. Effects of limiting Medicaid drug-reimbursement benefits on the use of psychotropic agents and acute mental health services by patients with schizophrenia. New England Journal of Medicine [Internet]. 1994; 331(10):[650-5 pp.]. Available from: . 10.1056/nejm199409083311006

    https://doi.org/10.1056/nejm199409083311006
  43. Tamblyn R, Laprise R, Hanley JA, Abrahamowicz M, Scott S, Mayo N, et al. Adverse events associated with prescription drug cost-sharing among poor and elderly persons. Journal of the American Medical Association [Internet]. 2001; 285(4):[421-9 pp.]. Available from: . 10.1001/jama.285.4.421

    https://doi.org/10.1001/jama.285.4.421
  44. Goldman DP, Joyce GF, Zheng Y. Prescription drug cost sharing: associations with medication and medical utilization and spending and health. Journal of the American medical Association [Internet]. 2007; 298(1):[61-9 pp.]. Available from: . 10.1001/jama.298.1.61

    https://doi.org/10.1001/jama.298.1.61
  45. Kosidou K, Dalman C, Lundberg M, Hallqvist J, Isacsson G, Magnusson C. Socioeconomic status and risk of psychological distress and depression in the Stockholm Public Health Cohort: a population-based study. Journal of Affective Disorders [Internet]. 2011; 134(1):[160-7 pp.]. Available from: . 10.1016/j.jad.2011.05.024

    https://doi.org/10.1016/j.jad.2011.05.024
  46. Adler NE, Boyce T, Chesney MA, Cohen S, Folkman S, Kahn RL, et al. Socioeconomic status and health: the challenge of the gradient. American Psychologist [Internet]. 1994; 49(1):[15-24 pp.]. Available from: http://psycnet.apa.org/record/1994-29613-001 .

  47. Fairthorne J, Hanley GE, Oberlander TF. The risk of teenage motherhood decreases in a dose-response fashion with increasing SES: a population-based study from British Columbia, Canada. J Women’s Health Gyn [Internet]. 2017; 4:[1-6 pp.]. Available from: http://www.jscholaronline.org/articles/JWHG/The-Risk-of-Teenage-Motherhood-Decreases.pdf . 10.17303/jwhg.2017.4.104

    https://doi.org/10.17303/jwhg.2017.4.104
  48. Fairthorne J, Hanley GE, Oberlander TF. Depressed women of low socioeconomic status have high numbers of physician visits in the year before pregnancy: implications for care. Journal of Clinical Medicine Research. 2018;10(6):516-22. 10.14740/jocmr3377w

    https://doi.org/10.14740/jocmr3377w
  49. University of British Columbia. Behavioural Research Ethics 2016 [2017 Sept 15]. Available from: https://ethics.research.ubc.ca/behavioural-research-ethics .

  50. Frosst G, Hutcheon J, Joseph K, Kinniburgh B, Johnson C, Lee L. Validating the British Columbia Perinatal Data Registry: a chart re-abstraction study. BMC Pregnancy and Childbirth [Internet]. 2015; 15(1):[1 p.]. Available from: . 10.1186/s12884-015-0563-7

    https://doi.org/10.1186/s12884-015-0563-7
  51. Hu W. Diagnostic codes in MSP claim data1996. Available from: http://secure.cihi.ca/cihiweb/dispPage.jsp?cw_page=GR_27_E&cw_topic=27 .

  52. Juurlink D, Preyra C, Croxford R, Chong A, Austin P, Tu J, et al. Canadian Institute for Health Information Discharge Abstract Database: a validation study. Institute for Clinical Evaluative Sciences Investigative Report [Internet]. 2006. Available from: file:///C:/Users/jfairthorne/Downloads/Full%20report.pdf .

  53. Perinatal Services BC. British Columbia Perinatal Data Registry: Population Data BC; Data Extract 2015. Available from: http://www.perinatalservicesbc.ca/health-professionals/data-surveillance/perinatal-data-registry .

  54. BC Ministry of Health. Consolidation File 2011 (MSP Registration & Premium Billing): Data Extract MOH: Population Data BC; 2013. Available from: http://www.popdata.bc.ca/data .

  55. Government of British Columbia. PharmaNet [2017 July 19]. Available from: http://www2.gov.bc.ca/gov/content/health/health-drug-coverage/pharmacare-for-bc-residents/pharmanet .

  56. Canadian Institute for Health Information. Discharge abstract database (hospital separations). Vancouver, 2015. Available from: http://www.popdata.bc.ca/data .

  57. Statistics Canada. Small Area and Administrative Data Division: Income band data2007. Available from: https://www.popdata.bc.ca/data/internal/demographic/incomeband

  58. Population Data BC. Income band data: extracted from Census geocodes collection 2015-04-10 Vancouver, BC [2016 July 25]. Available from: https://www.popdata.bc.ca/data/internal/demographic/incomeband .

  59. Kessler RC, Mickelson KD, Walters EE, Zhao S, Hamilton L. Age and depression in the MIDUS survey. In: Ryff C, Kessler RC, editors. How healthy are we? A national study of well-being at mid-life. Chicago: University of Chicago Press; 2005.

  60. Kahn RS, Certain L, Whitaker RC. A re-examination of smoking before, during, and after pregnancy. American Journal of Public Health. 2002;92(11):1801-8. 10.2105/AJPH.92.11.1801

  61. BC Perinatal Data Registry. British Columbia Perinatal Data Registry Reference Manual Version 6.01 – Addendum2014. Available from: http://www.perinatalservicesbc.ca/Documents/Data-Surveillance/PDR/Resources-Coders/PDR_ReferenceManual_2014.pdf .

  62. Napierala MA. What is the Bonferroni correction? American Academy of Orthopedic Surgeons Now [Internet]. 2012; 6(4):[40 p.]. Available from: http://www.aaos.org/aaosnow/?ssopc=1 .

  63. Stata Corporation. STATA 14 statistical software College Station, TX2015. Available from: https://www.stata.com/stata14/ .

  64. Kramer MS, Séguin L, Lydon J, Goulet L. Socio-economic disparities in pregnancy outcome: Why do the poor fare so poorly? Paediatric and Perinatal Epidemiology [Internet]. 2000; 14(3):[194-210 pp.]. Available from: . 10.1046/j.1365-3016.2000.00266.x

    https://doi.org/10.1046/j.1365-3016.2000.00266.x
  65. Scott KM, Wells JE, Angermeyer M, Brugha TS, Bromet E, Demyttenaere K, et al. Gender and the relationship between marital status and first onset of mood, anxiety and substance use disorders. Psychological Medicine [Internet]. 2010; 40(9):[1495-505 pp.]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2891411/ . 10.1017/S0033291709991942

    https://doi.org/10.1017/S0033291709991942

Article Details

How to Cite
Fairthorne, J. C., Oberlander, T. F., Brant, R. and Hanley, G. E. (2021) “Risk factors for hospitalizations associated with depression among women during the years around a birth: a retrospective cohort study”, International Journal of Population Data Science, 4(1). doi: 10.23889/ijpds.v4i1.453.

Author Biographies

Jennifer Christine Fairthorne, British Columbia Children’s Hospital Research Institute, 938 West 28th Avenue, Vancouver, BC Canada V5Z 4H4.

1.British Columbia Children’s Hospital Research Institute, 938 West 28th Avenue, Vancouver, BC Canada V5Z 4H4. 

2. Telethon Kids Institute, Perth Children's Hospital, Nedlands WA 6009

 

Rollin Brant, British Columbia Children’s Hospital Research Institute, 938 West 28th Avenue, Vancouver, BC Canada V5Z 4H4.

Department of Statistics, Faculty of Science, 3182 Earth Sciences Building, 2207 Main Mall, VancouverBC Canada V6T 1Z4

Gillian E Hanley, Department of Obstetrics and Gynaecology, University of British Columbia, Faculty of Medicine, 1125 Howe Street, Vancouver, BC Canada V6Z 2K8

British Columbia Children’s Hospital Research Institute, 938 West 28th Avenue, Vancouver, BC Canada V5Z 4H4.