This longitudinal population-based register study was conducted in Sweden, where women between the ages of 40 and 74 are invited to mammography screening every 18–24 months depending on age and regional capacity. All invitations are sent by post and offer a pre-booked appointment date and time, which does not need to be confirmed and can be rescheduled or cancelled. Since each health care region individually conducts and administers their screening, there are differences in intervals between screening appointments, the layout and content of the invitation letter, hours of operation, ways of cancelling or rescheduling appointments, reminders, etc. All but two (Stockholm and Östergötland) of the 21 health care regions charged a small out-of-pocket fee between 80 and 200 SEK (≈$9–23 USD) before the implementation of free screening in 2016.
A study period between 2014 and 2018 was chosen to study the change in screening attendance during the two-year period before and after removal of the out-of-pocket fee in 2016. Individual screening-related data were extracted for all women invited to the screening programme in 15 of 21 health care regions in Sweden. These regions all used the same company (Sectra AB) for their radiological information system (RIS) to administer and track invitations, attendance, and results throughout the entire study period, which enabled high-quality and consistency of data between regions. Of the six regions initially excluded, four (Jönköping, Kronoberg, Norrbotten, and Uppsala) used different radiological information systems for all or part of the study period, and two (Sörmland and Östergötland) did not grant us permission to extract data. Two of three programmes operating in the Stockholm Region granted permission. However, these were excluded from the final study sample, since they had already removed the out-of-pocket fee before the study period. This study was approved by the local ethics committee at Lund University (Nos. 2018/576 and 2018/965). Active informed consent as a requirement for data collection was waived.
The extracted data included the screening appointment date, age at the screening appointment, and attendance outcome (attended, cancelled, missed and unavailable), for each regional mammography programme separately, and were combined into one dataset. The unique personal identity number assigned to every resident in Sweden was used to merge screening data with information on individual-level sociodemographic characteristics obtained from population registers at Statistics Sweden (the Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA),16 the Total Population Register,17 and the Geodatabase). To secure anonymity, Statistics Sweden replaced this number with an arbitrary code before releasing the data to the research group. The most recent sociodemographic information was used for each screening appointment. Same-year sociodemographic data were linked to each screening appointment in 2014, 2015 and 2017; in 2018, same-year data were available only for home ownership and type of municipality, and data on income, education, and cohabitation from 2017 were used.
Initially, the dataset included a total of 4,582,477 appointments among 1,780,164 women in 15 regions (including Stockholm). The flow chart in Fig. 1 describes the different steps of exclusion, resulting in a final selection of 2,381,142 appointments among 1,350,654 women in 14 regions (Fig. 2). These 14 regions encompass about 59% of the women eligible for mammography screening in Sweden and about 81% of the women affected by the fee removal. The most recent screening appointment for each woman was selected, aged 40–75, within each time-period (2014-15 and 2017-18); excluding appointments during the transition year of 2016. Women who were 75 were included to allow for overflow from the age limit of 74 years due to administrative reasons, e.g., rescheduling. Appointments were excluded when personal identity numbers lacked a match, had duplicates, or were suspected to have been recycled according to data from Statistics Sweden. Furthermore, appointments with examination or cancellation codes that were not related to mammography screening were excluded. Duplicate appointments within the same programme (both identical and non-identical), at different locations and within the same year were excluded as well.
Outcome variable
The outcome variable in this study was mammography screening attendance (yes/no), irrespective of whether it was the original or a rescheduled appointment date, according to the most recent screening appointment for each woman during the periods 2014–2015 and 2017–2018. The rationale for studying two-year time-periods was to allow for a longer screening cycles which is common in several regions.
Sociodemographic and programme-related variables
Sociodemographic variables and categorizations are presented in Table 1 and include age group, cohabitation (in which only couples who have children together were categorized as cohabiting), level of education, income (individual share of equivalized disposable household income in SEK), main source of income, home ownership, country of birth, and type of municipality (based on categorization by the Swedish Association of Local Authorities and Regions).18 Programme-related variables included region, out-of-pocket fee in 2015, and year of screening appointment. Variables were categorized based on the way in which they were provided by Statistics Sweden, and by logically and conceptually combining categories without losing important differences in the attendance outcome, in order to minimize the number of categories. Missing values, which were excluded from the analyses, ranged from none (region, year and age) to 1.2% for education and 2.3% for home ownership in 2014-15, and were below 0.5% for all other variables (Table 1). All variables were analysed as categorical variables.
Table 1
Sociodemographic and other characteristics of the study sample at time of the most recent mammography screening appointment in Sweden (2014-15 and 2017-18).
| 2014–2015 N = 1,191,609 | 2017–2018 N = 1,189,533 | Standardized difference |
Characteristic | N | % | N | % | % |
Mean age (SD) | 56.12 | (10.06) | 56.37 | (10.10) | 7.87 |
Age group (years) | | | | | |
40–44 | 192,887 | 16.19 | 185,197 | 15.57 | 1.69 |
45–49 | 186,686 | 15.67 | 176,442 | 14.83 | 2.32 |
50–54 | 172,539 | 14.48 | 188,375 | 15.84 | 3.78 |
55–59 | 164,171 | 13.78 | 162,726 | 13.68 | 0.28 |
60–64 | 163,671 | 13.74 | 159,841 | 13.44 | 0.87 |
65–69 | 173,291 | 14.54 | 159,718 | 13.43 | 3.22 |
70–75 | 138,364 | 11.61 | 157,234 | 13.22 | 4.87 |
Cohabitation (living with partner) | | | | | |
Yes | 721,892 | 60.58 | 721,839 | 60.68 | 0.21 |
No | 464,111 | 38.95 | 464,613 | 39.06 | 0.23 |
Missing | 5606 | 0.47 | 3081 | 0.26 | 3.51 |
Level of education | | | | | |
Low (elementary school, ≤9 years) | 193,073 | 16.20 | 171,287 | 14.40 | 5.01 |
Intermediate (secondary school) | 550,549 | 46.20 | 542,545 | 45.61 | 1.19 |
High (post-secondary) | 433,252 | 36.36 | 462,339 | 38.87 | 5.18 |
Missing | 14,735 | 1.24 | 13,362 | 1.12 | 1.05 |
Income category | | | | | |
Lowest (decile 1) | 118,754 | 9.97 | 118,677 | 9.98 | 0.04 |
Low-medium (decile 2–4) | 355,513 | 29.83 | 355,832 | 29.91 | 0.17 |
Medium-high (decile 5–10) | 711,736 | 59.73 | 711,944 | 59.85 | 0.25 |
Missing | 5606 | 0.47 | 3080 | 0.26 | 3.51 |
Main source of income | | | | | |
Employment | 693,208 | 58.17 | 716,433 | 60.23 | 4.18 |
Retirement pension | 315,836 | 26.51 | 299,031 | 25.14 | 0.32 |
Student finance | 4574 | 0.38 | 4293 | 0.36 | 0.38 |
Care of a sick child or relative | 5777 | 0.48 | 6090 | 0.51 | 0.39 |
Social assistance and benefits: | | | | | |
Sickness benefit | 25,046 | 2.10 | 25,792 | 2.17 | 0.46 |
Sickness compensation | 74,548 | 6.26 | 67,550 | 5.68 | 1.19 |
Unemployment insurance/benefit | 7382 | 0.62 | 6298 | 0.53 | 2.44 |
Labour market program | 15,125 | 1.27 | 14,261 | 1.20 | 0.64 |
Financial assistance | 20,339 | 1.71 | 23,107 | 1.94 | 1.76 |
No income | 24,168 | 2.03 | 23,598 | 1.98 | 0.32 |
Missing | 5606 | 0.47 | 3080 | 0.26 | 3.51 |
Home ownership | | | | | |
Yes (house or apartment) | 883,642 | 74.16 | 882,378 | 74.18 | 1.63 |
No | 280,078 | 23.50 | 287,870 | 24.20 | 0.05 |
Missing | 27,889 | 2.34 | 19,285 | 1.62 | 5.16 |
Country of birth | | | | | |
Sweden | 1,001,904 | 84.08 | 979,249 | 82.32 | 4.70 |
Nordic country (except Sweden) | 73,001 | 6.13 | 78,051 | 6.56 | 1.79 |
Europe (except Sweden and Nordic countries) | 72,832 | 6.11 | 92,728 | 7.80 | 6.62 |
Other | 43,813 | 3.68 | 39,427 | 3.31 | 1.97 |
Missing | 59 | 0.00 | 78 | 0.01 | 0.21 |
Region | | | | | |
Blekinge | 32,735 | 2.75 | 33,110 | 2.78 | 0.22 |
Dalarna | 63,497 | 5.33 | 60,682 | 5.10 | 1.02 |
Gotland | 13,577 | 1.14 | 13,832 | 1.16 | 0.22 |
Gävleborg | 62,516 | 5.25 | 60,775 | 5.11 | 0.62 |
Halland | 64,814 | 5.44 | 65,874 | 5.54 | 0.43 |
Jämtland/Härjedalen | 25,501 | 2.14 | 26,699 | 2.24 | 0.71 |
Kalmar | 51,321 | 4.31 | 50,630 | 4.26 | 0.25 |
Skåne | 265,192 | 22.25 | 271,723 | 22.84 | 1.41 |
Värmland | 60,219 | 5.05 | 59,993 | 5.04 | 0.05 |
Västerbotten | 50,257 | 4.22 | 49,076 | 4.13 | 0.46 |
Västernorrland | 50,328 | 4.22 | 50,988 | 4.29 | 0.31 |
Västmanland | 53,995 | 4.53 | 47,125 | 3.96 | 2.83 |
Västra Götaland | 342,100 | 28.71 | 348,371 | 29.29 | 1.27 |
Örebro | 55,557 | 4.66 | 50,655 | 4.26 | 1.96 |
Type of municipality | | | | | |
Large cities (> 200,000) | 294,396 | 24.71 | 305,857 | 25.71 | 2.32 |
Mid-sized cities (50,000-200,000) | 455,161 | 38.20 | 451,125 | 37.92 | 0.56 |
Smaller cities, towns and rural areas | 436,446 | 36.63 | 429,897 | 36.14 | 1.01 |
Missing | 5606 | 0.47 | 2654 | 0.22 | 4.21 |
Out-of-pocket fee (2015) in SEK | | | | | |
80 | 55,557 | 4.66 | 50,655 | 4.26 | 1.96 |
100 | 393,421 | 33.02 | 399,001 | 33.54 | 1.12 |
120 | 297,927 | 25.00 | 304,833 | 25.63 | 1.44 |
150 | 204,140 | 17.13 | 204,243 | 17.17 | 0.10 |
200 | 240,564 | 20.19 | 230,801 | 19.40 | 1.97 |
Year of scheduled appointment | | | | | |
2014 | 542,148 | 45.50 | | | |
2015 | 649,461 | 54.50 | | | |
2017 | | | 542,602 | 45.61 | |
2018 | | | 646,931 | 54.39 | |
Statistical analysis
Standardized differences were calculated to examine change in the sociodemographic distribution of the study sample between time-periods; with a value greater than 10% considered potentially meaningful. This measure describes the difference between the groups divided by the pooled standard deviation. The percentage of screening appointments attended were calculated for each time-period, by region and sociodemographic factor, and reported with 95% confidence intervals (CIs). Change in attendance before and after removing the out-of-pocket fee was reported in percentage points with 95% CIs. We identified six large sociodemographic groups (n > 50,000) with attendance below 80% in 2014-15 and further examined change in attendance before and after the fee removal in each of these groups and combinations thereof. Since the same women can be included in both time periods, mixed logistic regression was used to account for the correlation of within individuals. Results are presented as odds ratios (ORs) and 95% CIs for mammography attendance in 2017–2018 vs. 2014-15. We calculated unadjusted estimates, as well as estimates adjusted for the potential confounding effect of various sociodemographic factors. Statistical software used for the analyses were SPSS, version 25, and R, version 4.0.