Sickness absence and disability pension among women with breast cancer: a population-based cohort study with a predictive model

Women’s return to work after diagnosis of breast cancer (BC) are becoming more prevalent. However, register-based national investigation on sickness absence (SA) and disability pension (DP) in BC women is lacking. The aim of the study was to explore SA and DP before and after a first BC diagnosis and the possibility to predict new cancer-related SA by using disease-related and sociodemographic factors. Methods: A longitudinal register study of the 3536 women in Sweden aged 19-64 with a first BC diagnosis in 2010 was conducted by linkage of multiple national registers. Particularly, information on SA and DP was obtained from the National Social Insurance Agency’s database. Descriptive statistics on SA and DP two years before and three years after the BC diagnosis were performed. The risk of having a new SA spell due to BC or BC-related diagnoses was modeled using logistic regression. Results: The proportion of women with SA increased during the year following the BC diagnosis date and declined over the next two years to proportions before diagnosis. At the time of BC diagnosis, half of the women began a new SA spell >14 days with cancer, cancer-related, or mental diagnosis. Disease-related and sociodemographic factors including occupational sector, living area, age, cancer stage, educational level, and number of previous SA days showed statistical significance (p<0.05) in predicting a new SA around BC diagnosis. By using these factors, it was possible to correctly predict 67% of the new SA spell. Conclusions: SA among women with BC was elevated mainly in the first year after diagnosis. New SA following BC diagnosis can accurately be predicted.


Introduction
Breast cancer (BC) is a major health problem with 1.67 million new cases worldwide annually. [1] Due to early detection and better treatments, mortality has decreased, hence more knowledge is needed on potential adverse long-term social consequences of BC for the growing number of survivors. [2][3][4] About half of the women diagnosed with BC are of working age,[5, 6] thus, BC might imply sickness absence (SA) or even disability pension (DP) for many of them due to effects of BC and/or BC treatments. Studies indicate that many women with BC value paid work highly and want to continue working after diagnosis or return to work (RTW) as soon as possible [7][8][9][10], and more knowledge is needed on patterns of SA and DP in order to get the knowledge base for interventions to facilitate part-or full-time (return to) work.
[35] Thus, the healthy-selection effects on outcomes in Sweden are smaller which is an advantage when aiming at gaining knowledge on associations of BC with future SA/DP.
Most previous studies are hampered by short follow-ups, selected study populations, high drop-out, no information on DP or on pre-diagnosis SA/DP, or only have self-reported SA/DP. [20] Although elevated levels of post-diagnostic anxiety and depression are reported, [36,37] detailed analyses of SA and DP due to mental diagnoses are seldom conducted. Thus, knowledge is limited on pre-and post-diagnosis diagnosis-specific SA and DP in women with BC, in nationwide population-based studies; knowledge that is needed to better understand the situation for women diagnosed with BC, as a basis to identify potential risk factors for SA/DP as a basis for preventive measures. Moreover, in healthcare, among employers, insurance organizations, and patients, information regarding possible future SA following a BC diagnosis is asked for in order to take prevention measures and facilitate work accommodations.
[38] Using information on disease-related and sociodemographic factors is one way to gain more such basic knowledge.
[39] To the best of our knowledge, this is the first study to test a model for prediction of SA following a first BC diagnosis.
The aims were: (a) to explore the annual prevalence of SA and DP due to cancer, other somatic, and/or mental diagnoses during the two years before and the three years following a BC diagnosis, and (b) to predict risk of a new SA spell following a BC diagnosis.

Methods
A population-based longitudinal cohort study was performed.
We included all the 3536 women in Sweden, aged 19-64 who were diagnosed with a first Data were linked at individual level using the ten-digit personal identity numbers assigned to all residents in Sweden.

SA and DP benefits in Sweden
All people in Sweden ≥16 years, with an income from work or unemployment benefit, with reduced work capacity due to disease or injury can be granted SA benefit from the Social Insurance Agency.
[42] The employers usually provide reimbursement for the first 14 days of a SA spell, which is why we do not have information on most SA spells ≤14 days. From day 8, a medical certificate issued by the treating physician is required. All residents aged 19-64 can be granted with DP if having long-term or permanent work incapacity due to disease or injury. SA and DP can be granted for full-time (100%) or part-time (25, 50, or 75%) of ordinary working hours. SA benefits cover 80% and DP 64% of lost income, up to a certain level.

Measures
We investigated two types of outcomes; DP and SA (for spells >14 days). SA and DP days were transformed into net days; e.g., 2 days on half-time SA or DP was counted as one net day. SA and DP diagnoses were coded by the certifying physician who assessed the patient's condition and work capacity. Diagnoses were for some analyses classified into four categories: 1): BC (ICD10: C50), BC-related diagnoses (Z80, Z85, N61-N63), and other cancer (C00-D48), 2): mental diagnoses (F00-F99, Z73), 3): other diagnoses (all remaining ICD codes), and 4): missing information. The outcome in the predictive model was defined as starting a new SA spell >14 days due to one of the following SA diagnoses (C00-D48, Z80, Z85, N61-N63, F00-F99, or Z73) during the time-window of 14 days before to 29 days after the BC diagnosis. This time window was based on the frequencies of start of new SA spells in the full cohort, in relation to diagnosis date (T 0 ). For some women there was a delay before the diagnosis was included in the Cancer Register (even if the women were informed) and for others, treatment did not start until weeks later. The reason for including "diagnoses related to BC" and "other cancer diagnoses" in the predictive model was that sometimes a broader category of cancer diagnoses is given in the medical certificate.
[43] Mental diagnoses were also included in the predictive model as that a cancer diagnosis might lead to anxiety or depression. [44,45] The included sociodemographic, disease-related, and comorbidity covariates (listed in respectively. When T, N, or M information was missing in one or two of the categories or classified as 'X' (assessment not possible), the value was set to 0. If more than one tumour was registered, with different diagnosis dates in 2010, the most advanced tumour was selected. The main ICD-10 diagnoses for healthcare were coded by the treating physicians. Healthcare due to uncomplicated delivery (O80) or not related to morbidity (e.g., screening) was excluded.

Statistical analyses
The mean number of SA and of DP net days/year, respectively, were calculated for all women, using the BC diagnosis date (T 0 ) as reference, for the two years before T 0 and three years after T 0 (Y -2 to Y + 3 ). This was done for all SA and DP as well as for the four SA/DP diagnostic categories mentioned above. The annual numbers and proportions of women with SA/DP due to the different diagnoses were also calculated. The denominator used in these calculations varied somewhat over the years due to the exclusion of women (turning 65 years, emigration, or death).
In the predictive model regarding risk of new SA related to time of diagnosis, 2954 women were included. For those analyses we excluded the 521 women (14.7%) already on SA or on DP for full-time or nearly full-time (75-100%) at T 0 . Additionally, 61 women were excluded due to lack of covariate information, or because of extreme values on some of the continuous variables, e.g., number of healthcare visits or inpatient days. The five variables that were modelled using splines were: age and number of previous: SA days, DP days, outpatient healthcare visits, and inpatient days, respectively, in the two pre-diagnostic years. An optimal threshold c was selected, such that predicting SA=1 whenever the fitted probability was above c, minimized the sum of false positive (FP) and false negative (FN) and maximized the proportion of correctly classified observations. Also, the receiver operating characteristic (ROC) was calculated.

Results
Sociodemographic and diagnostic covariates are presented in Table 1 for the entire cohort (N=3536) as well as for the group included in the modelling (n=2954). About 40% of the women in both groups were ≥56 years. The compositions of women regarding distribution of characteristics in the two groups were fairly similar, except for no DP days during the two pre-diagnostic years: 82% among all vs. 94% in the model, as expected due to the inclusion criteria in the modelling group. The majority had the earliest stages of BC. In the two pre-diagnosis years (Y-1 and Y-2), the majority had no SA days (81% vs. 83%), about half (56% vs. 53%) had at least one visit in specialized outpatient healthcare while few (12% vs. 8%) had at least one inpatient day. At BC diagnosis, 11.3% of the women were already on SA and 17.5% on DP.

Proportions of women with SA and/or DP
During the year after the BC diagnosis date (Y+1), 28% of the women had no SA >14 days ( During Y+3, the corresponding proportions were 25% and 12%, respectively. The proportions with SA due to mental diagnoses did not vary much between the studied years (3-4%). For SA due to other and missing diagnoses, the corresponding proportions were 8-11% all years, except for Y+1 when it was 6%. The proportion of women with DP ranged from 15 to 18% during all the five studied years. During Y+1, 15% of the women had neither SA nor DP. Before BC diagnosis, i.e., during Y-2 and Y-1, that proportion was 73%.

Mean SA and DP days/year
During Y+1, the mean number of SA days was 121.3. This was significantly higher than the numbers before diagnosis; (6.7 in Y-1 and 9.0 in Y-2) ( Figure 1). During Y+1, 108.8 of the SA days were due to cancer. That number was lower already in Y+2, i.e., 26.6 days. In Y+3, it was 14.0 days. Mean number of DP days/year was about 50 before BC diagnosis.
Due to older women already on DP in the year before T0 becoming 65 years of age, that number decreased to about 40 days/year in Y+2 and Y+3.

New SA spell
For the 3015 women who did not have an ongoing SA nor DP of the extent of 75-100% at the time of BC diagnoses (T0) Table 3 shows the numbers and percentages of women who had a first new SA spell in relation to T0. The same is shown for three specific SA diagnostic groups, i.e., cancer, mental diagnoses, and the other diagnoses (including missing), respectively. Of these women, 51% had a first new SA spell in relation to T0, that is, the period that was studied in the predictive modelling. Of these SA spells, 95% were due to cancer. In the following 30-day period, i.e., from 30 to 59 days after T0, another 20% had a first SA spell, of which 96% were with cancer. A little less than one fifth of the women (18%) had no new SA spells from T0 until end of follow-up three years later. Nine women were granted DP during the period. That is, about 80% of those at risk of a new SA spell following BC diagnosis, had such a spell in the first year (Y+1), and the majority of them (70%) in the first three months.

Predictive model
Out of the variables (see Table 1

Strengths and limitations
Data from a total population provided us with a rare opportunity to study SA and DP in connection with BC in all women of working age. Other strengths are the longitudinal cohort design, that all women fulfilling the inclusion criteria of a first BC diagnosis in a whole country could be included, not a sample; also that extensive microdata on morbidity and sociodemographic variables from several high-quality registers could be linked at individual level;[53-55] and that data were not self-reported, avoiding recall bias. The large cohort also allowed sub-group analyses of specific SA/DP diagnoses, not only the BC diagnosis. The latter circumstance makes the results and model more useful in practice.
High female employment frequency, complete coverage of the public SA/DP insurances, and no dropouts make the internal validity of the study very strong. A further strength is that we were able to exclude women no longer at risk of SA/DP due to death, turning 65, or emigration during follow-up. Findings can be generalized to women with BC in countries with comparable employment frequencies and coverage of SA/DP benefits.

Ethics approval
The project was approved by the Regional Ethical Review Board of Stockholm, Sweden and was conducted in accordance with the Declaration of Helsinki.

Consent for publication
Not applicable

Availability of data and materials
The data used in this study is administered by the Division of Insurance Medicine, regarding the data.

Competing interests
The authors declare that they have no competing interests.

Funding
This work was financially supported by the Swedish Research Council for Health, Working Life and Welfare and the Swedish Social Insurance Agency.

Authors' contributions
Contribution statement: KA, PK, EM-R, and EF were responsible for the study concept and design. KA obtained the research funding. PK, PF conducted the data management and analysis. PK drafted the manuscript. All authors were involved in result interpretation and manuscript revision. LC finalized the results and manuscript, and was responsible for manuscript submission. All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

21.
Fantoni SQ, Peugniez C, Duhamel A, Skrzypczak J, Frimat P, Leroyer A: Factors  Tables   Table 1. Characteristics of the study cohort and the sub-cohort for modelling. Included in the table were all women in Sweden <65 years with a first breast cancer diagnosis in 2010 (N=3536), during the two years before and three years after the breast cancer diagnosis date, presented for all SA/DP as well as by three categories of SA/DP diagnoses. Also, the number of women not included in the respective year are presented by reason for not being included.

Journal of Occupational Rehabilitation
1 The first 14 days of SA spells are excluded.
5 Women who turned 65, died, or emigrated were included up to and including the year of the event. The included individuals were 3015 women <65 years related to date of a first breast cancer diagnosis in 2010 (T0), during the following three years; all SA and diagnosisspecific SA (cancer, mental, or others).