Skip to main content
PLOS One logoLink to PLOS One
. 2020 Nov 6;15(11):e0241911. doi: 10.1371/journal.pone.0241911

Changes in data management contribute to temporal variation in gestational duration distribution in the Swedish Medical Birth Registry

Dominika Modzelewska 1,*, Pol Sole-Navais 1, Anna Sandstrom 2,3,4, Ge Zhang 5,6, Louis J Muglia 5,6,7, Christopher Flatley 8, Staffan Nilsson 9, Bo Jacobsson 1,8,10
Editor: Frank T Spradley11
PMCID: PMC7647076  PMID: 33156833

Abstract

Multiple factors contribute to gestational duration variability. Understanding the sources of variability allows to design better association studies and assess public health measures. Here, we aimed to assess geographical and temporal changes in the determination of gestational duration and its reporting in Sweden between 1973 and 2012. Singleton live births between 1973 and 2012 were retrieved from the Swedish Medical Birth Register. Gestational duration trends in percentiles and rates of pre- and post-term deliveries were analyzed by plotting the values over time. Temporal changes in gestational duration based on ultrasound and last menstrual period (LMP) estimation methods were compared. Intervals between LMP date and LMP-based due date were analyzed to assess changes in expected gestational duration. In total, 3 940 577 pregnancies were included. From 1973 until 1985, the median of gestational duration estimated based on LMP or ultrasound decreased from 283 to 278 days, and remained stable until 2012. The distribution was relatively stable when ultrasound-based estimates were used. Until the mid-1990s, there was a higher incidence than expected of births occurring on every seventh gestational day from day 157 onward. On an average, these gestational durations were reported 1.8 times more often than adjacent durations. Until 1989, the most common expected gestational duration was 280 days, and thereafter, it was 279 days. The expected gestational duration varied from 279 to 281 days across different Swedish counties. During leap years, the expected gestational duration was one day longer. Consequently, leap years were also associated with significantly higher preterm and lower post-term delivery rates than non-leap years. Changes in data handling and obstetrical practices over the years contribute to gestational duration variation. The resulting increase in variability might reduce precision in association studies and hamper the assessment of public health measures aimed to improve pregnancy outcomes.

Introduction

Gestational age at birth is one of the most important factors in predicting pregnancy and neonatal outcomes [1, 2], but the biological mechanism that initiates parturition remains unclear. Relatively large estimates of heritability, i.e., up to 31% for gestational duration and 36% for preterm delivery (PTD), suggest the importance of genetic factors [37]. However, it has been difficult to find specific genetic variants associated with gestational duration. The largest genome-wide association study on gestational duration performed to date (n = 43 968) discovered six loci, but these account for only 1% of population variance [8]. Larger sample sizes are required to detect smaller effects that are spread across hundreds of genetic variants. However, aspects other than sample size may account for the difficulty in discovering and replicating genetic associations.

Gestational duration estimation methods, reporting protocols, and obstetrical practices have changed globally over time, contributing to gestational duration variability. Furthermore, these changes occur at different times in different countries, and even within countries [912]. In the USA, an increase in the PTD rate has been reported in relation to an increase in the proportion of multiple gestations [13]; this could be due to the increased use of assisted reproductive technologies [12]. In the UK, differences in gestational age reporting (i.e. rounding gestational age to the closest week) has hampered the PTD rate comparisons between different hospitals and regions [13]. In Sweden, changes in PTD rates over time and across the country have been reported [10, 11, 14, 15]. In the first years of 1980s, the PTD rate increased with respect to a shift in the estimation method, which is from using the last menstrual period (LMP) to ultrasound-based dating [10, 16]. Since 1985 to 2001, PTD rates dropped owing to a decrease in the prevalence of deliveries between 34 and 37 completed weeks of gestation [10]. Morken et al. evaluated whether the changes in gestational duration distribution were affected by changes in the prevalence of PTD risk factors, such as maternal age, smoking, and primiparity, but no such association was found [10].

In this paper, we aim to assess the changes in determination of gestational duration and the impact that data recording practices have on accuracy. We also investigate evolving obstetric practices and their relationship with gestational duration in Sweden between 1973 and 2012.

Materials and methods

Sample

This study is based on the national Swedish Medical Birth Register (MBR) from 1973 to 2012. The MBR contains information on approximately 99% of births occurring in Sweden, compiling reproductive history, complications during pregnancy, delivery, neonatal period, as well as demographic information [17]. Regarding gestational duration, MBR contains LMP date, due date based on LMP date and clinical investigation, and due date based on ultrasound scan. The MBR also provides the “best estimate” for gestational duration which is based on both availability of data related to gestational duration and the current consensus on higher accuracy of ultrasound, compared to LMP-based estimates.

The study sample was restricted to live singleton births with available gestational duration estimates in the range of 154–301 days. In line with the aim of the study, the cohort was further restricted to pregnancies with available information regarding gestational duration estimation method (LMP or ultrasound) and onset of delivery (spontaneous, cesarean section, or induction).

Variable definitions

Analyses were based on the best gestational duration estimate listed in the MBR. The best estimate includes gestational duration retrieved from a combination of various sources and is ordered on the basis of reliability of the estimate made from such combinations [18]. In this study, the best estimates, based either on LMP (categories two, four, eight, nine, or ten of the MBR’s best gestational duration estimate) or ultrasound (categories one, five, six, or seven of the MBR’s best gestational duration estimate) dating, were used to define the duration of gestation (LMP- or ultrasound, respectively), as per the hierarchical set of rules laid out in the Swedish MBR [18].

Onset of labor was categorized as spontaneous or iatrogenic (induction or cesarean section). If onset of labor was recorded as “spontaneous” in a check-box, or had the International Classification of Diseases version 10th (ICD-10) codes, namely, O42 (pre-labor rupture of membranes), O75.6 (delayed delivery after spontaneous or unspecified rupture of membranes), O60.1 (preterm spontaneous labor with preterm delivery), or O60.2 (preterm spontaneous labor with term delivery) in the MBR, onset of labor was defined as spontaneous. Onset of labor was defined as iatrogenic if it was recorded in check-boxes as “induced,” “started with a cesarean section,” “planned cesarean section,” or had the ICD-10 code O61.0 (failed medical induction of labor) in the MBR.

Statistical analyses

To detect possible changes in the estimation or data reporting methods, temporal changes in gestational duration distribution were explored graphically. Gestational duration trends in percentiles, rates of PTD (< 259 gestational days) and post-term delivery (> 294 gestational days), and expected gestational duration were studied by plotting the values over time. To assess changes in expected gestational duration, we analyzed changes in the interval between LMP date and predicted LMP-based due date temporally and across Swedish counties. For each pregnancy, an interval was calculated by subtracting the LMP date from the LMP-based due date. This definition of expected gestational duration allowed us to not only assess changes in expected duration, but also observe whether mistakes were introduced when estimating the due date. Chi-square test of independence was used to analyze significance of temporal changes in PTD and post-term delivery rates, and between leap and non-leap years. Histograms were used to visualize unexpected frequencies of gestational duration. The occurrence of gestational durations of unexpectedly high frequency was determined by estimating the ratio of observed to expected gestational duration frequency. All analyses were performed with R software, version 3.5.1.

The study was approved by Regional Ethic Committee of the Western Health Care Region in Sweden (Dnr. 576–13). MBR is a national population-wide database; therefore, no informed consent is required. Individual-level data are anonymous. Personal identification numbers are kept and known only to the National Board of Health and Welfare.

Results

Gestational duration distribution

The study sample consisted of 3 940 577 pregnancies that met the inclusion criteria. During the study period, we observed several changes in gestational duration distribution. From the start of the study period until 1985, the median gestational duration decreased from 283 to 278 days, and then it plateaued until the end of the study period (Fig 1).

Fig 1. Pregnancy duration distribution, Swedish Medical Birth Register, 1973–2012.

Fig 1

Gestational duration percentiles: 0.1th, 0.5th, 1st, 2.5th, 5th, 10th, 30th, 50th, 70th, and 90th; percentiles are marked by solid lines. Dotted line represents mean gestational duration. The left-hand y-axis indicates pregnancy duration in days and the right-hand y-axis indicates percentiles. Vertical black dashed line indicates leap years. Sample size: n = 3 940 577.

The drop in median gestational duration occurred with a simultaneous increase in PTD and decrease in post-term delivery rates (Fig 2). The change in PTD and post-term delivery rates correlated with the introduction of ultrasound-based estimation of gestational duration in 1982. Before 1980, the PTD rate was at its lowest, and then it gradually increased to a peak of 5.6% in 1984 (Fig 2). A steady decline followed thereafter with the incidence stabilizing at 4.8% after the 1990s (chi-square test, p < 0.01). From 1973 to 1984, post-term birth incidence dropped substantially from a high of 12.3% to 5.5% (chi-square test, p < 0.01). In the later years, post-term delivery rate fluctuated, peaking at 6% from 2001 to 2003, before declining to 4.9% in 2012.

Fig 2. Incidence of preterm and post-term deliveries, Swedish Medical Birth Register, 1973–2012.

Fig 2

Percentages of preterm (triangle) and post-term (circle) deliveries registered from 1973 to 2012. Red dots and vertical black dashed line marks leap years. Preterm and post-term deliveries defined as pregnancies lasting < 259 days and > 294 days, respectively. Sample size: n = 3 940 577.

Until the mid-1990s, gestational duration was commonly reported in weeks instead of days. Conversion to day-units involved multiplication of gestation duration in weeks by seven and addition of three days. This resulted in a higher incidence of births than expected, occurring on every seventh gestational day from day 157 onwards (Fig 3A). On average, these gestational duration values were reported 1.8 times more often than for the neighboring durations (Fig 3B). In 1982, the frequency of every seventh day (from day 157 onwards) was the highest (2.7 times more than expected).

Fig 3. Gestational duration distribution, Swedish Medical Birth Register, 1973–2012.

Fig 3

A) Gestational duration distribution. Gestational durations that occurred at higher frequency than expected (peaks) are marked by solid black lines. Sample was reduced to pregnancies registered from 1973 to 1982, that is, to the period in which peaks were most common. Adjacent gestational durations to peaks are marked by black dashed lines. B) Average ratio of peaks’ frequency to the mean of adjacent gestational duration frequency from 1973 to 2012. Sample size: n = 3 940 577.

Gestational duration distribution in deliveries with spontaneous onset

To understand whether obstetric care changes had an impact on gestational duration variability, a restricted analysis of deliveries with spontaneous-onset stratified by gestational duration estimation method was conducted. Data enabling the retrieval of spontaneous-onset deliveries were only available from 1990 to 2012 period. In the distribution of ultrasound-based gestational duration, small changes were observed within the 0.1th percentile (Fig 4). LMP-based gestational duration distribution underwent a gradual left-shift within the 90th percentile, and in the lower 1st, 0.5th and 0.1th percentiles (Fig 4).

Fig 4. Distribution of gestational duration in women with spontaneous-onset deliveries, Swedish Medical Birth Register, 1990–2012.

Fig 4

Temporal changes in the gestational duration percentiles: 0.1th, 0.5th, 1st, 5th, 90th, with regard to estimation method, LMP-based (dashed line) or ultrasound-based (solid line); percentiles are respectively marked with different colors. The left-hand y-axis indicates gestational duration in days and the right-hand y-axis indicates percentiles. Sample sizes: pregnancies with LMP-based duration estimate: n = 272 416; pregnancies with ultrasound-based duration estimate: n = 1 551 133.

Gestational duration distribution in iatrogenic-onset deliveries

Due to limitations in data availability, the distribution of gestational duration in deliveries with cesarean section or induction onsets could be studied only from 1990 to 2012 and 1999 to 2012, respectively. There was an increase in the proportion of planned cesarean sections from 4.6% in 1990 to 8.5% in 2012, and in the proportion of inductions from 9.3% in 1999 to 14.3% in 2012 (Fig 5A). The gestational duration distribution remained relatively constant over the years in pregnancies with planned cesarean section or with induced labor (Fig 5B).

Fig 5. Prevalence of iatrogenic onset deliveries, Swedish Medical Birth Register, 1990–2012.

Fig 5

A) Percentage of iatrogenic-onset delivery with regard to the type of medical intervention, i.e. cesarean section (solid line) or induction (dashed line). B) Gestational duration by percentiles (0.1th, 0.5th, 1st, 5th, and 90th percentiles, respectively marked by different colors in the cesarean section [solid line] and labor induction [dashed line] cohorts). Due to limited data availability, the cesarean section and induced labor cohorts were restricted between 1990 and 2012 and 1999 and 2012; n = 155 647 and 157 191, respectively.

Changes in expected gestational duration and due date estimation

Until 1989, the most common expected gestational duration was 280 days. Thereafter, it was more common to add 279 days to the LMP date to estimate the due date (Fig 6). Until 2008, the expected gestational duration was often one day longer in leap years compared to other years (Fig 6). This extra day was due to the manual calendar-based calculation of the due date, which ignored the leap day of February 29. Overestimation of the due date led to an underestimation of gestational duration, and consequently, to an increase in PTD and decrease in post-term delivery rate. We observed peaks in PTD rates in the leap years from 1984 to 2004 (chi-square test, p < 0.01) (Fig 2). Post-term delivery rates decreased in the leap years from 1984 to 1992 and in 2000 (chi-square test, p < 0.01) (Fig 2).

Fig 6. Interval (days) between the LMP and due date for babies born from 1983 to 2012, Swedish Medical Birth Register, 1983–2012.

Fig 6

Most common intervals in days (from 279 to 282 days), between LMP and LMP-based due date for a baby born in a given month during 1983 to 2012. Sample size: n = 2 480 726.

Before 2002, extra variation in expected gestation duration was noticed in pregnancies with deliveries in the December–February period (i.e. women with LMP in the March–May range). Women who gave birth in those months often had longer than expected gestation durations. This was because of the procedure used to estimate due date, i.e. adding 9 months 7 days to the LMP date and not 279 or 280 days. For example, if the mother’s LMP date was 1 March 1985, addition of 9 months and 7 days would make her due date 8 December 1985. Therefore, the interval between due date and LMP date was longer at 282 days. Additionally, the expected gestation duration varied from 279 to 281 days across different Swedish counties (S1 Table).

Discussion

In this study, we analyzed changes in gestational distribution, proportion of iatrogenic deliveries and changes made to the estimation of gestational duration over time. We observed unequivocal patterns in the variation of gestational duration for specific years or across the whole study period. Changes in date estimation procedures and clinical management over time underlie these patterns. In this paper, we observed several technical factors contributing to gestational duration variability. These are related to temporal and spatial variability in expected gestational duration, changes in the selection of measurement units (weeks/days) for estimation, changes in the reporting of due dates in medical records, or a lack of accounting for an extra day in leap years when estimating due dates. These differences may not have a strong impact in clinical practice but might hamper the assessment of public health measures or might affect association studies; for example, reporting gestational duration in various unit-measures (days or weeks) might decrease the correlation between the relatives, in consequence, that might affect heritability estimates.

The digitization of medical record data entry had a substantial impact on the variability of gestational duration, providing estimates that are more accurate. Electronic systems have helped reduce variation in the definition of expected gestational duration. We show that before the 1990s, the most common expected gestational duration was 280 days and thereafter, it was 279 days. However, there was never a universal definition of the expected duration of gestation and it varied temporally and geographically across Sweden. The lack of consensus on expected gestation duration was already reported in the 1990s [19]. According to a previous study, the incidence of post-term deliveries decreased by 1.7% when expected gestational duration was assumed to be 282 instead of 280 days [20]. Currently, there are three different medical record systems in Sweden; the most common, used in 17 regions, is Obstetrix®, and the others are Partus® and Cosmic® [21]. The systems differ when it comes to expected gestational duration, which is 279 days in Obstetrix® and Partus® and 280 days in Cosmic® [21].

Until around 1995, we noted a regularly high birth frequency on every seventh gestational day from day 157 onwards, suggesting that until the mid-1990s, hospitals commonly recorded gestational duration in weeks instead of days. At the registry level, this information was converted into day-units by multiplying gestation duration by seven and adding three days. Such a formula was possibly used to represent the week by its middle day to provide the most optimal estimate. In 1982, the difference between the observed and expected frequency of gestational duration values was the largest. We speculate that this arose from the digitization of medical data transfer from hospitals to the MBR.

Several changes in the approach used to estimate gestational duration can be detected by analyzing the patterns of expected gestational duration, defined as the interval between LMP date and predicted due date. First, we observed that before 1990, expected gestational duration varied over the years. Women with LMP in the March–May period happened to have a longer interval to due date than women with their LMP in other months. Due date was estimated by adding the interval in month units (9 months and 7 days) to the LMP date and not days [22]. Such a method involved omitting the differences in month lengths (9 months might cover 273–276 days). This study suggests that this approach was changed around 1987, when days, instead of months, were added to the LMP to estimate due date. Second, we also observed that the expected gestational duration was longer in gestations that included the end of February during leap years. This phenomenon was due to manual estimation of gestational duration based on a widely used calendar, which does not mark an additional day in February during leap years. Such omissions resulted in an underestimation of gestational duration, and consequently, we observed a significant increase in PTD and a decrease in post-term delivery rates during leap years.

From 1973 to 1985, there was a substantial drop in the median gestational duration (from 282 to 278 days) and post-term delivery rate (from 12% to 6%). The decreases correlated with the introduction of ultrasound-based estimation from 1982 to 1985. Studies have shown that LMP-based estimation yields a right-skewed distribution, leading to a general overestimation of gestational duration [23, 24]. Since the 1990s, when ultrasound became the most common method for estimation of gestational duration, the average rates of PTD and post-term delivery have remained stable. While ultrasound-based estimation did contribute to extra variability during the implementation period until 1992, relatively stable gestational duration has been observed since then, which proves consistency in the estimation. Until 2012, the number of post-term deliveries gradually decreased when gestational duration estimate was based on LMP date. There was also a gradual decrease in the number of post-term gestations and an increase in the number of very early (< 220 days) PTD throughout the whole study period. The findings of this study, thus, support the hypothesis that ultrasound-based estimation is more accurate than LMP-based estimation. We also show that restricting the sample to births, occurring after 1992, is desirable regarding gestational duration when using MBR data. This will avoid variability due to the simultaneous use of both estimation methods during the period when ultrasound was being introduced.

Additional variation in gestational duration might be introduced when it is stratified by mode of onset of delivery. Over the years, the accuracy of mode of onset of delivery has been improved in the MBR. In 1994, indications of whether cesarean section was performed before or after spontaneous onset of labor were introduced in the register [18]. Therefore, different definitions and coding of iatrogenic deliveries might produce different gestational duration distributions. Furthermore, changes in the definition of stillbirth also contributed to the increase in reported occurrences of PTD. In this paper, we observed an increase in the proportion of deliveries with cesarean section between 1990 and 2012 and increase in the proportion of induced onsets of delivery between 1999 and 2012.

The possibility of studying changes in pregnancy phenotype distributions and the effects of environmental factors is one of the biggest advantages of the MBR, or that of any other register-based study. Over the years, the MBR has been adjusted to improve its quality and content. In Sweden, medical diagnosis codes were switched from the ICD-8 to ICD-9 and subsequently, to ICD-10. Due to differences in the coding systems and compliance to register the codes, the quality of variables defined by ICD codes might differ. However, missing data on ICD codes, changes in reporting, and data handling from the MBR are obstacles in identifying the sources of population variability in gestational duration. This study shows that population variability of gestational duration is largely affected by changes in obstetrical practice, estimation methods, and registration methods. Better quality information will increase the accuracy of estimates obtained from both genetic and epidemiological studies with a consequent increase in statistical precision and power.

Conclusions

The changes in data handling and obstetrical practices over the years largely contribute to the distribution of gestational duration. Digitization of medical record data entry substantially reduced the gestational duration variation due to data management. Increased variability in gestational duration might reduce precision in association studies and hamper the assessment of public health measures. We propose that future studies on gestational duration adjust the study sample based on the findings outlined in this study; for example, accordingly to the scientific question, restrict the sample to the specific periods of time or Swedish counties.

Supporting information

S1 Table. Variations in the expected gestational duration among Swedish counties, Swedish Medical Birth Register, 1983–2012.

The table shows the year in which there was an observed change in the expected gestational duration. The first column shows the year in which there was a drop from the initial expected duration of 281 to 280 days. The second column shows the year in which there was a drop in the expected duration of 280 to 279 days. Sample size: n = 3 940 577. Analyses were limited to available counties. The counties where a change in the expected gestational duration was not observed during 1983–2012 (such as Uppsala and Kronoberg) are not included.

(DOCX)

Data Availability

The Swedish Medical Birth Registry is a national dataset; therefore, it is considered as public property. However, access to the data is given only to the researches with permission from a Swedish regional ethical review board and after approval of the research plan by the data manager. The data request may be sent to The Swedish National Board of Health and Welfare (https://www.socialstyrelsen.se/).

Funding Statement

BJ: 1. Swedish government grants to researchers in the public health sector, grants no.: ALFGBG-717501, ALFGBG-507701, ALFGBG-426411, URL: https://www.vr.se 2. The Swedish Research Council, grants no. 2015-02559, URL: https://www.vr.se, 3. The March of Dimes Foundation, grant no.: 21-FY16-121, URL: https://www.marchofdimes.org 4. The Burroughs Wellcome Fund Preterm Birth Research Grant, grants no.: 10172896, URL: https://www.bwfund.org LM: 1. The March of Dimes Prematurity Research Center Ohio Collaborative, URL: https://www.marchofdimes.org The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Goldenberg RL. Factors influencing perinatal outcomes. Ann N Y Acad Sci 2004; 1038: 227–234. 10.1196/annals.1315.032 [DOI] [PubMed] [Google Scholar]
  • 2.Hack M, Fanaroff AA. Outcomes of children of extremely low birthweight and gestational age in the 1990’s. Early Hum Dev 1999; 53: 193–218. 10.1016/s0378-3782(98)00052-8 [DOI] [PubMed] [Google Scholar]
  • 3.Clausson B, Lichtenstein P, Cnattingius S. Genetic influence on birthweight and gestational length determined by studies in offspring of twins. BJOG: An International Journal of Obstetrics & Gynaecology 2000; 107: 375–381. 10.1111/j.1471-0528.2000.tb13234.x [DOI] [PubMed] [Google Scholar]
  • 4.Wu W, Witherspoon DJ, Fraser A, et al. The heritability of gestational age in a two-million member cohort: implications for spontaneous preterm birth. Hum Genet 2015; 134: 803–808. 10.1007/s00439-015-1558-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.York TP, Eaves LJ, Lichtenstein P, et al. Fetal and Maternal Genes’ Influence on Gestational Age in a Quantitative Genetic Analysis of 244,000 Swedish Births. Am J Epidemiol 2013; 178: 543–550. 10.1093/aje/kwt005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Svensson AC, Sandin S, Cnattingius S, et al. Maternal effects for preterm birth: a genetic epidemiologic study of 630,000 families. Am J Epidemiol 2009; 170: 1365–1372. 10.1093/aje/kwp328 [DOI] [PubMed] [Google Scholar]
  • 7.Lunde A, Melve KK, Gjessing HK, et al. Genetic and Environmental Influences on Birth Weight, Birth Length, Head Circumference, and Gestational Age by Use of Population-based Parent-Offspring Data. American Journal of Epidemiology 2007; 165: 734–741. 10.1093/aje/kwk107 [DOI] [PubMed] [Google Scholar]
  • 8.Zhang G, Feenstra B, Bacelis J, et al. Genetic Associations with Gestational Duration and Spontaneous Preterm Birth. N Engl J Med 2017; 377: 1156–1167. 10.1056/NEJMoa1612665 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Nassar N, Schiff M, Roberts CL. Trends in the Distribution of Gestational Age and Contribution of Planned Births in New South Wales, Australia. PLoS One; 8 10.1371/journal.pone.0056238 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Morken N-H, Källen K, Hagberg H, et al. Preterm birth in Sweden 1973–2001: rate, subgroups, and effect of changing patterns in multiple births, maternal age, and smoking. Acta Obstet Gynecol Scand 2005; 84: 558–565. [DOI] [PubMed] [Google Scholar]
  • 11.Murray SR, Juodakis J, Bacelis J, et al. Geographical differences in preterm delivery rates in Sweden: A population-based cohort study. Acta Obstetricia et Gynecologica Scandinavica 2019; 98: 106–116. 10.1111/aogs.13455 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Purisch SE, Gyamfi-Bannerman C. Epidemiology of preterm birth. Semin Perinatol 2017; 41: 387–391. 10.1053/j.semperi.2017.07.009 [DOI] [PubMed] [Google Scholar]
  • 13.Balchin I, Whittaker JC, Steer PJ, et al. Are reported preterm birth rates reliable? An analysis of interhospital differences in the calculation of the weeks of gestation at delivery and preterm birth rate. BJOG: An International Journal of Obstetrics & Gynaecology 2004; 111: 160–163. 10.1046/j.1471-0528.2003.00026.x [DOI] [PubMed] [Google Scholar]
  • 14.Morken N-H, Vogel I, Kallen K, et al. Reference population for international comparisons and time trend surveillance of preterm delivery proportions in three countries. BMC Womens Health 2008; 8: 16 10.1186/1472-6874-8-16 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Ferrero DM, Larson J, Jacobsson B, et al. Cross-Country Individual Participant Analysis of 4.1 Million Singleton Births in 5 Countries with Very High Human Development Index Confirms Known Associations but Provides No Biologic Explanation for 2/3 of All Preterm Births. PLoS One 2016; 11: e0162506 10.1371/journal.pone.0162506 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Yang H, Kramer MS, Platt RW, et al. How does early ultrasound scan estimation of gestational age lead to higher rates of preterm birth? American Journal of Obstetrics and Gynecology 2002; 186: 433–437. 10.1067/mob.2002.120487 [DOI] [PubMed] [Google Scholar]
  • 17.Cnattingius S, Ericson A, Gunnarskog J, et al. A quality study of a medical birth registry. Scand J Soc Med 1990; 18: 143–148. 10.1177/140349489001800209 [DOI] [PubMed] [Google Scholar]
  • 18.Källén B, Källén K. The Swedish Medical Birth Register—A Summary of Content and Quality. Socialstyrelsen, 2003 [Google Scholar]
  • 19.Bergsjø P, Denman DW, Hoffman HJ, et al. Duration Of Human Singleton Pregnancy: A Population‐based Study. Acta Obstetricia et Gynecologica Scandinavica 1990; 69: 197–207. [DOI] [PubMed] [Google Scholar]
  • 20.Persson P-H. Ultrasound dating of pregnancy—still controversial? Ultrasound in Obstetrics & Gynecology 1999; 14: 9–11. [DOI] [PubMed] [Google Scholar]
  • 21.Stephansson O. 2015 annual report.
  • 22.Nguyen TH, Larsen T, Engholm G, et al. Evaluation of ultrasound-estimated date of delivery in 17 450 spontaneous singleton births: do we need to modify Naegele’s rule? Ultrasound in Obstetrics & Gynecology 1999; 14: 23–28. 10.1046/j.1469-0705.1999.14010023.x [DOI] [PubMed] [Google Scholar]
  • 23.Gardosi J, Vanner T, Francis A. Gestational age and induction of labour for prolonged pregnancy. Br J Obstet Gynaecol 1997; 104: 792–797. 10.1111/j.1471-0528.1997.tb12022.x [DOI] [PubMed] [Google Scholar]
  • 24.Tunón K, Eik‐Nes SH, Grøttum P. Fetal outcome when the ultrasound estimate of the day of delivery is more than 14 days later than the last menstrual period estimate. Ultrasound in Obstetrics & Gynecology 1999; 14: 17–22. [DOI] [PubMed] [Google Scholar]

Decision Letter 0

Frank T Spradley

29 Sep 2020

PONE-D-20-26656

Changes in data management contribute to temporal variation in gestational duration distribution in the Swedish Medical Birth Registry

PLOS ONE

Dear Dr. Modzelewska,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

There are a few minor comments from the reviewer that must be addressed.

Please submit your revised manuscript by Nov 13 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Frank T. Spradley

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Very important study showing that population variability of gestational duration is largely affected by changes in obstetrical practice, estimation methods, and registration methods. It shows eloquently the importance of increasing granularity of gestational age to days, which in Sweden is available since last century, using a combination (LMP, US) of data of the birth registry. This could be a great inspiration for other countries. Only when you overcome the binary term/preterm you can see more granular.

It also provides clever alternatives for visualizing data, the last graphic with the variation of GS in days, including leap years is one them.

The idea of having a reference for "expected gestational duration" and monitoring its trends in term period sounds very obvious but very under-explored.

Some small suggestions:

- When referring to "gestation duration mode" it would be best described by gestational duration estimates local cultures, as the author further explains?

- The effect of obstetric interventions could be a little more explored.

- A hypothesis: are ultrasound measures so uniform because there was a recalibration of growth/gestational age curves during the period?

Finally, reading it from São Paulo, Brazil, where the mean GA is 273, and for cesareans in the private sector is 268, it is great to see that in some regions in Sweden it is still 282, but at least 278. Adding granularity to GA is so feasible, so explanatory that should be widely available data. This is why I just approve the paper and I am eager to see it published.

Availability of data: under conditions.

English: very good for my non-native eyes.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Carmen Simone Grilo Diniz

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Nov 6;15(11):e0241911. doi: 10.1371/journal.pone.0241911.r002

Author response to Decision Letter 0


18 Oct 2020

Dear Editor Dr. Frank T. Spradley,

Thank you for considering our paper “Changes in data management contribute to temporal variation in gestational duration distribution in the Swedish Medical Birth Registry” for publication in PLOS ONE. We want to thank the reviewer for their comments on our manuscript. The detailed response to all the comments follows below.

Yours sincerely,

on behalf of all authors,

Dominika Modzelewska

Sahlgrenska Academy, University of Gothenburg

Institute of Clinical Sciences

Dept of Obstetrics and Gynecology

SE-405 30 Gothenburg, Sweden

e-mail: dominika.modzelewska@gu.se

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response: We are grateful for the reviewer’s valuable comments and thank the Editor for the chance to improve our paper accordingly. The following changes were introduced to the manuscript: 1) all the headings of the major sections were adjusted, 2) all the headings of the sub-sections were adjusted accordingly, 3) whole manuscript was double-spaced, 4) references were cited before the punctuation sign, 5) short title was removed from the title page, 6) titles were removed from the author list.

2. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

Response: The following explanation was introduced to the cover letter: “The Swedish Medical Birth Registry is a national dataset; therefore, it is considered as public property. However, access to the data is given only to the researches with permission from a Swedish regional ethical review board and after approval of the research plan by the data manager. The data request may be sent to The Swedish National Board of Health and Welfare (https://www.socialstyrelsen.se/)”.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Reviewers' comments:

Reviewer's Responses to Questions

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

3. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

5. Review Comments to the Author

Reviewer #1 comment #1: Very important study showing that population variability of gestational duration is largely affected by changes in obstetrical practice, estimation methods, and registration methods. It shows eloquently the importance of increasing granularity of gestational age to days, which in Sweden is available since last century, using a combination (LMP, US) of data of the birth registry. This could be a great inspiration for other countries. Only when you overcome the binary term/preterm you can see more granular.

Response: We thank the reviewer for her very supportive feedback. We also agree with and support the “granular approach” towards gestational duration. Working with binary (preterm, term) loses information and hides the stories behind the data. This is particularly the case in obstetrics where the risk curve is often parabolic in nature.

Reviewer’s comment #2: It also provides clever alternatives for visualizing data, the last graphic with the variation of GS in days, including leap years is one them.

Response: We thank the reviewer for her feedback. Communication is very important for us, and we find graphics convey a lot of information easily. We work towards better graphical data exploration and visualization. Every comment and suggestion on how to improve are very welcome.

Reviewer’s comment #3: The idea of having a reference for "expected gestational duration" and monitoring its trends in term period sounds very obvious but very under-explored.

Response: We thank the reviewer for sharing that observation. We also agree that sometimes very obvious things are left unexplored. That is why our work (research) never finishes.

Reviewer’s comment #4: Some small suggestions: When referring to "gestation duration mode" it would be best described by gestational duration estimates local cultures, as the author further explains?

Response: We understand that the reviewer is referring to Figure 3, of which, the caption includes the phrase “gestational duration mode”. The word “mode” refers to the statistical measure, the value that occurs the most frequently in the dataset. However, in gestational duration distribution we observed multiple gestational durations that occur more frequently than the neighbouring values. Such observation we called “local modes”. The phrase “gestational duration mode” refers to that distributional characteristic.

In order to reduce the ambiguity and felicitate the understanding, we rephrased “gestational duration mode” and “local modes” to “gestational duration distribution” and “peaks”, respectively. Figure’s caption and its detailed explanation were changed: “Fig 3. Gestational duration distribution, Swedish Medical Birth Register, 1973–2012. A) Gestational duration distribution. Gestational durations that occurred at higher frequency than expected (peaks) are marked by solid black lines. Sample was reduced to pregnancies registered from 1973 to 1982, that is, to the period in which peaks were most common. Adjacent gestational durations to peaks are marked by black dashed lines. B) Average ratio of peaks’ frequency to the mean of adjacent gestational duration frequency from 1973 to 2012. Sample size: n = 3 940 577.”, lines 192-198, page 9.

Reviewer’s comment #5: The effect of obstetric interventions could be a little more explored.

Response: We thank the reviewer for inspiring further exploration of the topic. However, the aim of this paper was slightly different. In this work, we wanted to show that there are many different types of variables affecting the observations of gestational duration. The multifactorial nature of gestational duration is an obvious and often stated fact. However, we have noticed that there is a tendency in restricting the focus to biological factors. In this paper, we wanted to show that other, technical factors contribute to the observed gestational duration variability as well. Therefore, we did not intend to explore and detect all the factors contributing to the variability. We wanted to present and remind about the variability in the subgroups of the factors that affect the observations and contribute to the gestational duration variability.

Our research group explores genetic and environmental contributions to gestational duration. In our work, we see that it is very crucial to keep in mind that observed variability in gestational duration is a result of different subgroups of factors. Considering only one group of factors may lead to possible problems of interpretation. Estimates of the association studies might contain the effects of all variables. Emphasizing the complexity of the problem will inspire even more scrupulous study designs and careful interpretation of the obtained estimates.

Reviewer’s comment #6: A hypothesis: are ultrasound measures so uniform because there was a recalibration of growth/gestational age curves during the period?

Response: Over the years, gestational duration estimation approach based on the ultrasound examination has been adjusted once and that was from a report published in 2010 (1). Before 2010, the most common reference used was from Person et al 1986 (2). In 2010, Swedish Association for Obstetricians and Gynaecologists released the guidelines for pregnancy dating. In general, the adjustments relate to the timing of the ultrasound examination (first, second trimester), biometric parameters (crown–rump length, biparietal diameter), fetal sex.

Reviewer’s comment #7: Finally, reading it from São Paulo, Brazil, where the mean GA is 273, and for cesareans in the private sector is 268, it is great to see that in some regions in Sweden it is still 282, but at least 278. Adding granularity to GA is so feasible, so explanatory that should be widely available data. This is why I just approve the paper and I am eager to see it published.

Response: We thank the reviewer for your time and the approval.

Availability of data: under conditions.

English: very good for my non-native eyes.

6. PLOS authors have the option to publish the peer review history of their article. If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?

Reviewer #1: Yes: Carmen Simone Grilo Diniz

References:

1. Kullinger M, Granfors M, Kieler H, Skalkidou A. Adherence to Swedish national pregnancy dating guidelines and management of discrepancies between pregnancy dating methods: a survey study. Reprod Health

2. Persson PH, Weldner BM. Reliability of ultrasound fetometry in estimating gestational age in the second trimester. Acta Obstet Gynecol Scand. 1986;65(5):481–3.

Attachment

Submitted filename: Response to Reviewers.docx

Decision Letter 1

Frank T Spradley

23 Oct 2020

Changes in data management contribute to temporal variation in gestational duration distribution in the Swedish Medical Birth Registry

PONE-D-20-26656R1

Dear Dr. Modzelewska,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Frank T. Spradley

Academic Editor

PLOS ONE

Acceptance letter

Frank T Spradley

28 Oct 2020

PONE-D-20-26656R1

Changes in data management contribute to temporal variation in gestational duration distribution in the Swedish Medical Birth Registry

Dear Dr. Modzelewska:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Frank T. Spradley

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Variations in the expected gestational duration among Swedish counties, Swedish Medical Birth Register, 1983–2012.

    The table shows the year in which there was an observed change in the expected gestational duration. The first column shows the year in which there was a drop from the initial expected duration of 281 to 280 days. The second column shows the year in which there was a drop in the expected duration of 280 to 279 days. Sample size: n = 3 940 577. Analyses were limited to available counties. The counties where a change in the expected gestational duration was not observed during 1983–2012 (such as Uppsala and Kronoberg) are not included.

    (DOCX)

    Attachment

    Submitted filename: Response to Reviewers.docx

    Data Availability Statement

    The Swedish Medical Birth Registry is a national dataset; therefore, it is considered as public property. However, access to the data is given only to the researches with permission from a Swedish regional ethical review board and after approval of the research plan by the data manager. The data request may be sent to The Swedish National Board of Health and Welfare (https://www.socialstyrelsen.se/).


    Articles from PLoS ONE are provided here courtesy of PLOS

    RESOURCES