Interpregnancy intervals and adverse birth outcomes in high-income countries: An international cohort study

Gizachew A Tessema; M Luke Marinovich; Siri E Håberg; Mika Gissler; Jonathan A Mayo; Natasha Nassar; Stephen Ball; Ana Pilar Betrán; Amanuel T Gebremedhin; Nick de Klerk; Maria C Magnus; Cicely Marston; Annette K Regan; Gary M Shaw; Amy M Padula; Gavin Pereira

doi:10.1371/journal.pone.0255000

. 2021 Jul 19;16(7):e0255000. doi: 10.1371/journal.pone.0255000

Interpregnancy intervals and adverse birth outcomes in high-income countries: An international cohort study

Gizachew A Tessema ^1,^2,^*, M Luke Marinovich ¹, Siri E Håberg ³, Mika Gissler ^4,⁵, Jonathan A Mayo ⁶, Natasha Nassar ⁷, Stephen Ball ⁸, Ana Pilar Betrán ⁹, Amanuel T Gebremedhin ¹, Nick de Klerk ¹⁰, Maria C Magnus ^3,^11,¹², Cicely Marston ¹³, Annette K Regan ^1,¹⁴, Gary M Shaw ⁶, Amy M Padula ¹⁵, Gavin Pereira ^1,³

Editor: Antonio Simone Laganà¹⁶

¹Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia

²School of Public Health, University of Adelaide, Adelaide, South Australia, Australia

³Centre for Fertility and Health (CeFH), Norwegian Institute of Public Health, Oslo, Norway

⁴Information Services Department, Finnish Institute for Health and Welfare, Helsinki, Finland

⁵Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden

⁶Department of Pediatrics, March of Dimes Prematurity Research Center, Stanford University, Stanford, CA, United States of America

⁷Children’s Hospital at Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, New South Wales, Australia

⁸Curtin School of Nursing, Curtin University, Perth, Western Australia, Australia

⁹UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, Department of Reproductive Health and Research, World Health Organization, Geneva, Switzerland

¹⁰Telethon Kids Institute, University of Western Australia, Subiaco, Western Australia, Australia

¹¹MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom

¹²Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

¹³Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom

¹⁴School of Public Health,Texas A&M University, College Station, Texas, United States of America

¹⁵Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, CA, United States of America

¹⁶University of Insubria, ITALY

Competing Interests: The authors have declared that no competing interests exist.

^✉

* E-mail: gizachew.tessema@curtin.edu.au

Roles

Gizachew A Tessema: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Software, Validation, Writing – original draft, Writing – review & editing

M Luke Marinovich: Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Writing – review & editing

Siri E Håberg: Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Mika Gissler: Data curation, Formal analysis, Funding acquisition, Methodology, Software, Validation, Writing – review & editing

Jonathan A Mayo: Data curation, Formal analysis, Funding acquisition, Methodology, Software, Validation, Writing – review & editing

Natasha Nassar: Conceptualization, Funding acquisition, Methodology, Validation, Writing – review & editing

Stephen Ball: Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Ana Pilar Betrán: Methodology, Writing – review & editing

Amanuel T Gebremedhin: Conceptualization, Methodology, Writing – review & editing

Nick de Klerk: Conceptualization, Methodology, Writing – review & editing

Maria C Magnus: Conceptualization, Data curation, Methodology, Writing – review & editing

Cicely Marston: Conceptualization, Methodology, Writing – review & editing

Annette K Regan: Conceptualization, Methodology, Writing – review & editing

Gary M Shaw: Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Amy M Padula: Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Gavin Pereira: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing

Antonio Simone Laganà: Editor

PMCID: PMC8289039 PMID: 34280228

Abstract

Background

Most evidence for interpregnancy interval (IPI) and adverse birth outcomes come from studies that are prone to incomplete control for confounders that vary between women. Comparing pregnancies to the same women can address this issue.

Methods

We conducted an international longitudinal cohort study of 5,521,211 births to 3,849,193 women from Australia (1980–2016), Finland (1987–2017), Norway (1980–2016) and the United States (California) (1991–2012). IPI was calculated based on the time difference between two dates—the date of birth of the first pregnancy and the date of conception of the next (index) pregnancy. We estimated associations between IPI and preterm birth (PTB), spontaneous PTB, and small-for-gestational age births (SGA) using logistic regression (between-women analyses). We also used conditional logistic regression comparing IPIs and birth outcomes in the same women (within-women analyses). Random effects meta-analysis was used to calculate pooled adjusted odds ratios (aOR).

Results

Compared to an IPI of 18–23 months, there was insufficient evidence for an association between IPI <6 months and overall PTB (aOR 1.08, 95% CI 0.99–1.18) and SGA (aOR 0.99, 95% CI 0.81–1.19), but increased odds of spontaneous PTB (aOR 1.38, 95% CI 1.21–1.57) in the within-women analysis. We observed elevated odds of all birth outcomes associated with IPI ≥60 months. In comparison, between-women analyses showed elevated odds of adverse birth outcomes for <12 month and >24 month IPIs.

Conclusions

We found consistently elevated odds of adverse birth outcomes following long IPIs. IPI shorter than 6 months were associated with elevated risk of spontaneous PTB, but there was insufficient evidence for increased risk of other adverse birth outcomes. Current recommendations of waiting at least 24 months to conceive after a previous pregnancy, may be unnecessarily long in high-income countries.

Introduction

Both short and long interpregnancy intervals (IPI) have been associated with increased risk of adverse birth outcomes such as preterm birth (PTB, < 37 weeks of gestation) [1–6], small-for-gestational age (SGA) [7–9] and term low birth weight (LBW) [4, 10, 11], among other adverse perinatal outcomes [12–16]. Meta-analyses have reported that short (<6 months) and long (≥60 months) IPIs are associated with increased risk of PTB, SGA, term LBW compared to an IPI of 18–23 months [17]. In 2005, the World Health Organization recommended waiting at least 24 months before attempting pregnancy following a live birth [18]. This was based on limited evidence and analyses based on studies available at the time, mostly from low- and middle-income countries.

Although adverse effects of IPIs have been consistently reported for between-women (unmatched) cohort studies, results from more recent within-women (matched) studies that compare different pregnancies from the same women suggest bias in the between-women studies, due to unmeasured or inadequately controlled maternal characteristics that remain stable between pregnancies [19–22]. The first study of this type was conducted by Ball et al. [20] in Western Australia and reported that the increased risk of PTB, term LBW and SGA after a short IPI was lower when comparing subsequent pregnancies to the same women. Swedish [22] and Californian [23] studies have since reported substantially smaller associations between short intervals and adverse birth outcomes when using such a within-women design [22, 23]. A Canadian study comparing outcomes of pregnancies within the same women reported that short IPIs were associated with reduced, not increased odds of PTB [21]. A systematic review also indicated that results for the associations between IPI and adverse births outcomes were inconsistent [24]. Therefore, the direction and magnitude of the association between short intervals and adverse birth outcomes remains unclear.

We conducted an international longitudinal cohort study to investigate the association between IPI and adverse birth outcomes in four high-income countries, using within-women and between-women analyses.

Methods

Study design and population

We conducted an international longitudinal cohort study on the association between IPI and birth outcomes using individual-level perinatal records from four high-income countries: Australia (Western Australia [WA] and New South Wales [NSW]) (1980–2016), Finland (1987–2017), Norway (1980–2016), and the United States (California) (1991–2012). Descriptions of the perinatal data sources are described in the published protocol [25]. In summary, perinatal records of birth with complete or near-complete coverage were obtained from the Midwives Notifications System (WA), the Perinatal Data Collection (NSW), the Medical Birth Register of Finland, and the Medical Birth Register of Norway. For California, we obtained a linked birth cohort file that merged fetal death, birth, and infant death certificates provided by the Office of Statewide Health Planning and Development (OSHPD) [25]. These data sources contain unique identifiers for women and births that allow linkage of births to their mothers. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist to report our findings [26] (S1 Checklist).

Inclusion criteria

There were 11,383,557 births from the participating countries/states during the study period, 1980 to 2017 (Australia: 3,074,232; Finland: 1,850,446; Norway: 2,094,171; California: 4,364,708). We excluded birth records for multiple gestations, with gestational age <22 or >44 weeks, with missing birth weight or weight <500g, with unspecified sex, and records with missing maternal age or age <14 years at birth [25]. These exclusions resulted in 9,902,167 births, and of these we excluded records without prior registered births (i.e. no IPI), leaving 5,521,206 births to 3,849,191 women for the between-women analyses (S1 Fig). Finally, we identified births to women with at least two or more IPIs resulting in a cohort of 2,905,703 births to 1,233,688 women for within-women analyses. Study entry was defined for all women as the earliest birth during the study period with an IPI.

Outcomes

We investigated three birth outcomes: PTB, spontaneous PTB and SGA. PTB was defined as birth <37 completed weeks of gestation. For most countries, spontaneous PTB was defined as PTB with spontaneous onset of labour, while for California, spontaneous PTB was defined as a PTB with any of the following three criteria: premature rupture of membranes, premature labour or tocolysis. SGA was defined as a birthweight in the lowest 10th centile based on the national birthweight distributions in each country by final week of gestational age and sex.

Exposure

IPI was the main exposure and calculated based on the time difference between two dates—the date of birth of the first pregnancy and the date of conception of the next (index) pregnancy (birth date minus gestational length) [27]. In all countries, ultrasound was routinely used to confirm gestational age and where unavailable it was calculated based on the date of the menstrual period. IPI was categorised as: 0–5, 6–11, 12–17, 18–23, 24–59, 60–119, and ≥120 months, with 18–23 months used as the reference category [20].

Statistical analyses

All analyses were adjusted using a prognostic score derived with logistic regression of the birth outcome regressed on time-varying covariates. Prognostic score adjustment [28, 29] was adopted to minimise the within-women collinearity between the exposure and the time-varying adjustment variables. The variables included when creating prognostic score were maternal age (<20, 20–24, 25–29, 30–34, 35–39, and ≥40 years), parity (1, 2, 3, ≥4) and year of birth (5-year categories). We also assessed sensitivity to (i) inclusion of stillbirths in the IPI definitions [30, 31]; (ii) between-women analyses for women with at least two IPIs (at least three births); and (iii) additional adjustment for socioeconomic status (SES) for the cohorts with this information available: Australia, Finland, and California. For Australia, SES was derived based on scores from the Index of Relative Socio-Economic Disadvantage (quintiles), a composite of education, skilled occupation status, and household income [32]. For Finland, SES was recorded using maternal occupational status at the time of birth and included four categories (blue collar, lower-white collar, upper-white collar workers, and ‘other’). For California, maternal educational attainment was considered as a proxy measure of SES and comprised four categories (some high school or less, high school diploma/equivalent, some college, college graduate or more). SES adjustment was not possible for Norway for which no SES information was available. Furthermore, we conducted additional analyses to assess sensitivity of our results for the inclusion of births of from higher-order parity women by restricting our cohort to the first three births (parity 0, 1 and 2) in Australia and Norway and for the inclusion of births with gestational ages estimated using LMP by restricting births from 1990 onwards in Norway when ultrasound is being widely used to estimate gestational ages.

Conventional logistic regression was used for between-women analyses and conditional logistic regression applied for within-women analyses that compared the outcomes and exposures of subsequent pregnancies to the same women. First, analyses were conducted for each country and each outcome measure separately. Second, country-specific adjusted odds ratios (aOR) for the between-women and within-women analyses were pooled using the inverse variance method with random intercepts for country [33]. Heterogeneity in the estimates between countries was identified with the I² statistic [34]. Analyses were done with SAS version 9.4 and Stata version 14.

Ethical statement

Ethics approval was obtained from Human Research Ethics Committees and Institutional Review Boards from the Departments of Health in Western Australia and New South Wales, Curtin University, Stanford University, and the Norwegian Regional Committees for Medical and Health Research Ethics of South/East Norway. Each committee provided a waiver of consent for use of participants’ data. For Finland, ethical approval was not required for studies based on registry data.

Results

Cohort characteristics at study entry

Approximately 89–93% of women entered the study cohort at parity 1 in Australia, Finland, Norway, and 98% in California for the within-women study (Table 1). For the between-women study, except in California, 87–90% of women entered the study cohort at parity 1 (S1 Table). In the within-women study, at study entry, the highest proportion of women were between the ages of 25 and 29 years in Australia (36%), Finland (43%), and Norway (45%); but were between 20 and 24 years in California (35%).

Table 1. Maternal characteristics at study entry (first birth in the included cohort)^* for the within-women analysis (n = 1,233,688).

Characteristics	Australia	Finland	Norway	California
Characteristics	N (%)	N (%)	N (%)	N (%)
Total women	306,639	202,252	219,770	505,027
Parity^**
1	273,911 (89.3)	186,615 (92.3)	205,117 (93.4)	495,390 (98.1)
2	20,649 (6.7)	9,352 (4.6)	10,851 (4.9)	9,222 (1.8)
≥3	12,079 (3.9)	6,285 (3.10)	3,802 (1.7)	415 (0.1)
Maternal age (years)
<20	12,764 (4.2)	2,749 (1.4)	2,194 (1.0)	50,034(9.9)
20–24	75,219 (24.5)	48,734 (24.1)	51,674 (23.5)	177,492 (35.2)
25–29	109,210 (35.6)	86,098 (42.6)	98,248 (44.7)	148,185 (20.3)
30–34	85,271 (27.8)	52,316 (25.9)	56,904 (25.9)	100,803 (20.0)
35–39	22,686 (7.4)	11,442 (5.7)	10,215 (4.65)	26,882 (5.3)
≥40	1,489 (0.5)	913 (0.5)	535 (0.2)	1,631 (0.3)
Birth year
1980–1989	29,804 (9.7)	9,400 (4.6)	54,220 (24.7)	-
1990–1999	79,256 (25.8)	87,752 (43.4)	79,037 (36.0)	199,063 (30.5)
2000–2009	147,576 (48.1)	75,221 (37.2)	70,198 (31.9)	290,640 (57.5)
2010 or later	50,003 (16.3)	29,879 (14.8)	16,315 (7.4)	15,324 (3.0)
Socioeconomic index for areas (quintile)
Lowest	71,259 (23.2)	N/A	N/A	N/A
Low	62,479 (20.4)	N/A	N/A	N/A
Middle	56,814 (18.5)	N/A	N/A	N/A
High	52,143 (17.0)	N/A	N/A	N/A
Highest	47,595 (15.5)	N/A	N/A	N/A
Other/unknown/missing	16,349 (5.3)	N/A	N/A	N/A
Maternal occupation
Upper-white collar worker	N/A	22,675 (13.9)	N/A	N/A
Lower-white collar worker	N/A	74,147 (36.7)	N/A	N/A
Blue collar worker	N/A	32,702 (16.2)	N/A	N/A
Other or unknown	N/A	67,728 (33.5)	N/A	N/A
Highest level of maternal education
Some high school or less	N/A	N/A	N/A	99,696 (19.7)
High school diploma/Equivalent	N/A	N/A	N/A	169,195 (33.5)
Some college	N/A	N/A	N/A	120,038 (23.8)
College graduate or more	N/A	N/A	N/A	109,357 (21.7)
Missing	N/A	N/A	N/A	6,741 (1.3)
Number of IPIs
2	220,175 (71.8)	144,141(71.3)	177,636 (80.8)	385,449 (76.3)
≥3	86,464 (28.2)	58,111 (28.7)	42,134 (19.2)	119,578 (23.7)

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*Study entry is defined as the earliest birth (second birth) at which the women appeared within the included cohort during the study period.

**Women parity at the birth when entering to the cohort (i.e the second birth after interval). IPI -_Interpregnancy intervals. N/A—data were not available in these countries.

Country specific analysis

We observed that 6.7% of all births had IPI <6 months, ranging from 3.8% of births in Norway to 7.9% in California (Table 2). The proportion of births after an IPI of 18–23 months was similar across countries ranging from 12.9% (California) to 14.6% (Australia). The prevalence of PTB ranging from 3.9% (Finland) to 8.6% (California); spontaneous PTB was between 2.9% and 4.0% for all countries; and SGA was similar for all countries ranging from 6.7% (Norway) to 8.0% (Australia) (S2 Table).

Table 2. Interpregnancy interval distribution for births in within-women analyses across the four countries (n = 2,905,703).

IPI category (months)	Australia N (%)	Finland N (%)	Norway N (%)	California N (%)	Total N (%)
<6	54,847 (7.4)	30495 (5.6)	18,617 (3.8)	91,473 (7.9)	195,432 (6.7)
6–11	132,930 (18.0)	94,415 (18.6)	58,987 (12.0)	172,445 (14.8)	458,778 (15.8)
12–17	144,015 (19.5)	94,069 (18.5)	75,168 (15.2)	185,782 (16.0)	499,035 (17.2)
18–23	107,880 (14.6)	66,998 (13.2)	66,934 (13.6)	150,103 (12.9)	391,916 (13.5)
24–59	230,998 (31.3)	159685 (31.4)	194,292 (39.4)	414,553 (35.6)	999,529 (34.3)
60–119	60,090 (8.1)	54622 (10.7)	67,966 (13.8)	134,038 (11.5)	316,723 (10.9)
≥120	8,337 (1.1)	8749 (1.7)	11,157 (2.3)	16,058 (1.4)	44,301 (1.5)

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*Numbers in brackets are column percentages for IPI categories in each country

The between-women analyses showed elevated odds of PTB ranging from 1.42 (California) to 2.06 (Norway); spontaneous PTB ranging from 1.67 (California) to 2.04 (Norway); and SGA ranging from 1.07 (Finland) to 1.42 (California) for short IPIs of <6 months compared to IPI of 18–23 months in all countries. For long IPIs, the odds of PTB, sPTB, and SGA from between-women analyses were elevated for all countries. In the within-women analyses, compared to an IPI of 18–23 months, there were elevated odds of PTB after IPIs of <6 months and 6–11 months in Australia and California, but no association for Finland and Norway. The odds of spontaneous PTB after an IPI <6 months were elevated for all countries, ranging from 1.21 (Norway) to 1.66 (Australia). Except for California, the odds of SGA were reduced in Australia, Finland and Norway after an IPI of <6 months compared with an IPI of 18–23 months. For long IPIs, the odds of PTB, sPTB, and SGA from within-women analyses were elevated for all countries (Table 3).

Table 3. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) between interpregnancy interval and adverse birth outcomes among births in between-women^* and within-women^** analyses across the four countries.

Outcome by country	Interpregnancy interval
Outcome by country	<6 months	6–11 months	12–17 months	18–23 months	24–59 months	60–119 months	≥120 months
PTB	aOR (95% CI)
Australia
Between-women	1.68 (1.63, 1.73)	1.13 (1.10, 1.16)	0.99 (0.97, 1.02)	Ref	1.16 (1.13, 1.19)	1.59 (1.55, 1.64)	2.14 (2.03, 2.27)
Within-women	1.22 (1.15, 1.29)	1.08 (1.03, 1.14)	1.00 (0.95, 1.05)	Ref	1.06 (1.02, 1.11)	1.35 (1.27, 1.43)	1.71 (1.52, 1.92)
Finland
Between-women	1.62 (1.53, 1.70)	1.06 (1.01, 1.10)	0.99 (0.95, 1.10)	Ref	1.12 (1.08, 1.16)	1.41 (1.35, 1.47)	1.73 (1.61, 1.86)
Within-women	1.00 (0.91, 1.09)	1.02 (0.95, 1.10)	0.95 (0.88, 1.02)	Ref	1.00 (0.94, 1.07)	1.26 (1.16, 1.37	1.67 (1.44, 1.95)
Norway
Between-women	2.06 (1.96, 2.17)	1.17 (1.12, 1.22)	1.02 (0.98, 1.06)	Ref	1.11 (1.08, 1.15)	1.51 (1.45, 1.57)	1.91 (1.82, 2.02)
Within-women	0.97 (0.88, 1.06)	1.01 (0.93, 1.09)	1.03 (0.96, 1.12)	Ref	1.08 (1.02, 1.15)	1.38 (1.29, 1.49)	1.71 (1.51, 1.93)
California
Between-women	1.42 (1.39, 1.45)	1.16 (1.14, 1.18)	1.06 (1.05, 1.08)	Ref	1.12 (1.10, 1.14)	1.41 (1.39, 1.44)	1.82 (1.76, 1.88)
Within-women	1.13 (1.09, 1.18)	1.10 (1.06,1.14)	1.07 (1.03,1.11)	Ref	1.00(0.97, 1.03)	1.17 (1.13, 1.22)	1.42 (1.31, 1.54)
Spontaneous PTB
Australia
Between-women	1.93 (1.85, 2.01)	1.22 (1.18, 1.27)	1.03 (0.99, 1.07)	Ref	1.13 (1.10, 1.17)	1.56 (1.50, 1.63)	1.99 (1.84, 2.16)
Within-women	1.66 (1.54, 1.79)	1.30 (1.22, 1.39)	1.07 (1.00, 1.14)	Ref	0.96 (0.90, 1.02)	1.10 (1.02, 1.19)	1.28 (1.09, 1.51)
Finland
Between-women	1.77 (1.66, 1.88)	1.14 (1.09, 1.20)	1.04 (0.99, 1.09)	Ref	1.15 (1.10, 1.20)	1.45 (1.37, 1.52)	1.76 (1.62, 1.92)
Within-women	1.32 (1.19, 1.46)	1.22 (1.12, 1.34)	1.04 (0.96, 1.14)	Ref	1.05 (0.96, 1.13)	1.35 (1.22, 1.49)	1.77 (1.47, 2.13)
Norway
Between-women	2.04 (1.92, 2.18)	1.22 (1.16, 1.28)	1.04 (0.99, 1.09)	Ref	1.06 (1.02, 1.10)	1.40 (1.34, 1.47)	1.78 (1.64, 1.90)
Within-women	1.21 (1.08, 1.36)	1.10 (1.00, 1.21)	1.04 (0.95, 1.14)	Ref	0.93 (0.86, 1.01)	1.12 (1.02, 1.23)	1.29 (1.09, 1.51)
California
Between-women	1.67 (1.62, 1.72)	1.15 (1.12, 1.18)	1.03 (1.00, 1.06)	Ref	1.15 (1.12, 1.17)	1.52 (1.48, 1.56)	1.96 (1.88, 2.05)
Within-women	1.34 (1.26, 1.42)	1.12 (1.06, 1.18)	1.06 (1.00, 1.13)	Ref	1.05 (1.00, 1.10)	1.35 (1.27, 1.43)	1.65 (1.47, 1.86)
SGA
Australia
Between-women	1.10 (1.07, 1.13)	0.99 (0.97, 1.01)	0.98 (0.96,1.01)	Ref	1.18 (1.15, 1.20)	1.65 (1.61, 1.69)	2.08 (1.98, 2.19)
Within-women	0.94 (0.87, 0.99)	0.99 (0.95, 1.04)	1.03 (0.99,1.08)	Ref	1.04 (1.00, 1.09)	1.24 (1.17, 1.31)	1.58 (1.40, 1.78)
Finland
Between-women	1.07 (1.03, 1.12)	0.95 (0.92, 0.98)	0.98 (0.95, 1.01)	Ref	1.09 (1.06, 1.12)	1.35 (1.31, 1.40)	1.72 (1.63, 1.81)
Within-women	0.89 (0.82, 0.96)	0.99 (93, 1.05)	1.00 (0.95, 1.07)	Ref	1.08 (1.02, 1.13)	1.24 (1.16, 1.32)	1.80 (1.59, 2.03)
Norway
Between-women	1.28 (1.22, 1.35)	1.08 (1.05, 1.12)	1.02 (0.99, 1.05)	Ref	1.11 (1.08, 1.14)	1.42 (1.37, 1.46)	1.83 (1.75, 1.92)
Within-women	0.88 (0.81, 0.97)	0.98 (0.91, 1.05)	0.97 (0.91, 1.03)	Ref	1.05 (1.00, 1.11)	1.26 (1.18, 1.34)	1.63 (1.45, 1.82)
California
Between-women	1.42 (1.39, 1.46)	1.08 (1.06, 1.10)	1.00 (0.98, 1.02)	Ref	1.13 (1.11, 1.15)	1.37 (1.34, 1.40)	1.64 (1.59, 1.70)
Within-women	1.26 (1.20, 1.31)	1.05 (1.01, 1.09)	1.00 (0.96, 1.04)	Ref	1.03 (1.00, 1.07)	1.17 (1.12, 1.22)	1.52 (1.39, 1.67)

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aOR—adjusted odds ratio. CI—confidence interval. IPI—interpregnancy interval. PTB—preterm birth. SGA—small for gestational age.

*Odds ratios calculated using between-women analyses for women with ≥2 births/ ≥1 IPI after prognostic score adjustment for maternal age, parity, and year of birth.

**Odds ratios calculated using within-women analyses for women with ≥3 births/ ≥2 IPIs after prognostic score adjustment for maternal age, parity, and year of birth

Pooled analyses

Pooled adjusted between-women analyses indicated association between IPI and adverse birth outcomes. Elevated odds of all adverse outcomes were observed for short IPIs (<6 months and 6–11 months) and long IPI categories (24–59 months, 60–119 months, and ≥120 months) (S2–S4 Figs).

Pooled adjusted within-women analyses indicated no or weak association between IPIs of <6 months (aOR 1.08, 95% CI: 0.99, 1.18) and PTB, compared with 18–23 months of IPI (Fig 1). Odds of spontaneous PTB were elevated for IPIs <6 months (aOR = 1.38, 95% CI: 1.21, 1.57) (Fig 2). For SGA, there was no observed association for IPIs of <6 months (aOR = 0.99, 95% CI: 0.81, 1.19) (Fig 3). The pooled estimate for long IPI categories of 60–119 months and ≥120 months were consistently associated with elevated odds of all adverse birth outcomes (Figs 1–3).

Fig 1 — IPI—Interpregnancy interval. *Adjusted Odds ratios (aOR) and corresponding 95% confidence intervals, adjusted for maternal age, parity, and birth year; the reference IPI category is 18–23 months. **Weights are derived from inverse-variance.

Fig 2 — IPI—interpregnancy interval. *Adjusted Odds ratios (aOR) and corresponding 95% confidence intervals, adjusted for maternal age, parity, and birth year; the reference IPI category is 18–23 months. **Weights are derived from inverse-variance.

Fig 3 — IPI—interpregnancy interval. *Adjusted Odds ratios (aOR) and corresponding 95% confidence intervals, adjusted for maternal age, parity, and birth year; the reference IPI category is 18–23 months. **Weights are derived from inverse-variance.

For between-women and within-women analyses, the direction of association between IPI and the adverse birth outcomes was mostly consistent between countries, except for IPI <6 months for PTB and SGA in the within-women analyses (Figs 1 and 3). The magnitude of associations varied across countries for both between-women and within-women analyses. Heterogeneity between countries (I²) generally exceeded 50% for all outcomes (Figs 1–3, S2–S4 Figs).

Sensitivity analyses

Additional adjustment for SES resulted in negligible change to the results (S3 Table). Restriction to live births, also had negligible influence on the observed associations for most countries (S4 Table), and an increase in the odds of PTB in Norway (0.97 to 1.32) for IPI <6 months. The results of the between-women analyses restricted to women with at least two IPIs (at least three births) were consistently similar with the results of between-women analyses for women with at least one IPI (S5 Table). Moreover, our additional analysis restricting our cohorts in Australia and Norway to the first three births (parity 0, 1, and 2) for accounting the inclusion of births from women in the higher-order parity did not significantly change the results (S6 Table). Our analysis restricting births from 1990 onwards in Norway, when ultrasound has been widely used to precisely estimate gestational ages, had negligible impact on our results (S7 Table).

Discussion

This international cohort study of nearly five and half million births indicated associations between both short and long IPIs and adverse birth outcomes in four high-income countries, when pregnancies to different women were compared. After comparing pregnancy outcomes after IPIs within the same women with at least three pregnancies, we observed smaller odds ratios, but consistently elevated odds of all outcomes (PTB, spontaneous PTB, and SGA) for long IPIs. Associations with short IPIs were limited to the spontaneous PTB outcome.

Consistent with previous literature, results from our between-women analyses indicated elevated odds of adverse birth outcomes for both short and long IPIs [1, 8, 10, 17, 35]. Estimates were smaller in within-women analyses, indicating bias away from the null attributed from confounders that vary between-women.

Associations between IPI and adverse birth outcomes have been investigated by comparing pregnancies to the same women in two independent cohorts from Canada and Sweden [21, 22]. Our finding of a lack of association between short IPIs and SGA and PTB was consistent with the results from the Canadian study [21]. However, the Canadian study reported a reduced odds ratio (0.85) for PTB. The Swedish study reported a slightly elevated odds ratio (1.23) of PTB for short IPI [22]. In our study, evidence of associations were less apparent for overall PTB than for spontaneous PTB. Medical intervention shortens the pregnancy length [36] and it is possible that IPI is not as strongly associated with the clinical indications for PTB as it is with spontaneous onset of preterm labour. This hypothesis is supported by results from other between-women studies in Canada and California [1, 23], that found greater odds of a spontaneous PTB (2.41 for Canada, 1.81 for California) than the odds for medically indicated PTB (1.88 for Canada, 1.35 for California).

We observed elevated odds of adverse birth outcomes for long IPIs for our cohort. While the study from Sweden showed similar results with long IPIs [22], the Canadian study did not [21]. Despite consistent evidence there are currently no recommendations to limit long IPI.

The explanation for the association between IPI and adverse birth outcomes is not well-understood. Given the lack of observed associations between overall PTB and SGA with short IPI, our findings do not provide support for the maternal depletion hypothesis (nutritional depletion) that has been previously suggested, at least in low- and middle-income countries [11, 37, 38]. A potential mechanism for the association between short IPI and spontaneous PTB may be the failure of uterine contraction associated chemicals/proteins, such as G protein coupled receptors, to return back to pre-pregnancy levels, which could predispose women of short IPI for preterm onset of labour [3, 39]. Short IPIs also do not permit adequate time to recover from infections that cause inflammatory disease such as endometritis [40], and leave less time for the uterine incision to heal after caesarean section.[37, 41] Short IPIs may leave women with insufficient time to lose excess weight gained during pregnancy. Maternal overweight or obesity may result in increased risk of inflammatory up-regulation [42], and increased levels of inflammatory proteins (cytokines) may lead to cervical ripening and cause weakening of the membranes and preterm myometrial contractions through prostaglandin activation [43]. Unfortunately, owing to the administrative nature of study data these and other potential mechanistic hypotheses could not be investigated.

Our observation of increased risk of adverse birth outcomes after long IPIs might be explained by the potential gradual decline in uterine adaptation over time, such as changes to uterine blood flow that can result in regression back to a primigravida state [10]. A previous study observed that outcomes of births conceived after a long IPI are similar to perinatal outcomes of births born to women who are pregnant for the first time [10]. In addition, the increased risk of adverse birth outcomes after a long IPI may be due to underlying causes such as sexually transmitted infections or maternal chronic illnesses that lead to both secondary sub-fertility and adverse perinatal outcomes [35, 44]. While partner change has previously been suggested as a potential factor explaining the association between IPI and adverse pregnancy outcomes [45], a recent study in Western Australia indicated that partner changes between pregnancies did not influence the association between IPI and risk of preeclampsia [46].

We could not analytically explore a range of time-varying potential covariates such as pre-pregnancy body mass index, diet during pregnancy, maternal medical conditions and fertility treatment due to lack of complete information in the cohorts across countries. Notably, women who conceive quickly after childbirth might be relatively healthy and relatively more fertile than women who conceive later and therefore less prone to adverse birth outcomes [30]. Fertility treatment is associated with adverse birth outcomes [47, 48] and longer IPIs due to the requirement of a longer period of time to conception in sub-fertile couples [49]. Although we expect that only small proportion of women (1–4%) received fertility treatment in the cohort periods of this study [50–52], the uptake of reproductive technology is rapidly increasing in some countries [53] which may suggest that future studies need to account for assisted reproductive technology. As the primary design for this study was within-women (matched) analysis, conditioning on the outcome (i.e adverse birth outcomes) prior to the interval would introduce potential bias with estimated effect sizes because for those women with two intervals, the outcome of the second pregnancy would also be an effect modifier of the effect of IPI on the third pregnancy. However, a previous analysis using between-women analysis suggested that previous PTB may modify the effect of IPI on PTB in the subsequent pregnancies [54]. Our study also did not adjust for maternal ethnicity/race due to lack of complete information across cohorts and it is possible that the between-women results are prone to confounding by these variables. Since maternal ethnicity/race would not change between pregnancies, our within-women analysis will account this time-invariant confounder and therefore not adjusting for maternal ethnicity/race would not significantly change within-women results. Nonetheless, generalisability to other study populations might still be affected by within-country heterogeneity in race/ethnicity. However, a Californian study reported that the effect of short IPI on the risks of PTB did not substantially differ between non-Hispanic White and non-Hispanic Black women [2]. Given the within women analysis included small number of women with at least two IPIs (three or more births), these women may represent highly selected cohort of women and hence may not be generalisable to the general population. However, the results of the between-women analyses comprising these women were consistent with the results from the between-women analyses comprising at least one IPI (two or more births) over the study period suggesting it is unlikely that our results are necessarily reflective of differences in the two cohorts of women.

Undetected pregnancy loss (miscarriage, induced abortion and stillbirth) inflates IPI and is a marker for adverse birth outcomes in subsequent pregnancies [55]. Unlike previous studies that restricted analyses to consecutive live births [20, 22, 56], we included both live births and stillbirths, but could not account for pregnancy loss before 22 weeks. The associations between IPI and preterm birth might differ by presentation based on timing of delivery (extreme PTB (<28 weeks), very PTB (28–31 weeks), moderate PTB (32–37 weeks)) [3], however, the scope of this study was limited to separately investigate the association between IPI and PTB classified by clinical presentation (spontaneous and overall PTB).

Although there was substantial heterogeneity in the magnitude of the effects, the direction of associations was generally consistent. Part of the heterogeneity in the magnitude of results could be due to the difference in the characteristics of the population. For example, unlike Australia and California, the population in Norway and Finland represent relatively ethnically homogenous populations. An alternative explanation for the large observed heterogeneity (I²) is that most of the results were similar between countries, but I² appears large because within-study variability was small, which is a natural consequence of large registry-based studies that benefit from large sample sizes [17, 57]. To accommodate potential heterogeneity, we conducted random effects meta-analysis.

In conclusion, our international study from four high-income countries indicates insufficient evidence for association between short IPI and adverse birth outcomes, except for a slightly elevated odds of spontaneous PTB for IPIs <6 months. We found consistently elevated odds of adverse birth outcomes for births following long IPIs. Previous recommendations for parents to wait at least 24 months may be unnecessarily long in high-income countries, while recommendations regarding long IPI are required.

Supporting information

S1 Checklist. RECORD checklist.

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S1 Fig. Flowchart of study inclusions and exclusions–Australia, Finland, Norway and California.

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S2 Fig. Adjusted odds ratios for the between-women analysis for the association between interpregnancy interval and preterm birth as compared to 18–23 months of interpregnancy interval by country.

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S3 Fig. Adjusted odds ratios for the between-women analysis for the association between interpregnancy interval and spontaneous preterm birth as compared to 18–23 months of interpregnancy interval by country.

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S4 Fig. Adjusted odds ratios for the between-women analysis for the association between interpregnancy interval and small-for-gestational age birth as compared to 18–23 months of interpregnancy interval by country.

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S1 Table. Maternal characteristics at study cohort entry (first birth in the included cohort)* for the between-women analyses across the four countries (n = 3,849,193).

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S2 Table. Distributions of interpregnancy intervals by adverse birth outcomes in the cohort for the within-women analyses across the four countries.

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S3 Table. Sensitivity analysis–additional adjustment for socioeconomic status*** in three countries.

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S4 Table. Sensitivity analysis–analysis excluding stillbirths in the interpregnancy interval calculation for within-women* analyses across the four countries.

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S5 Table. Sensitivity analysis–analysis considering women with ≥3 births/ ≥2 IPI cohort in the between-women analyses* across the four countries.

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S6 Table. Sensitivity analysis–analysis considering the first three births (parity 0, 1, and 2) in between-women* and within-women** analyses in Australia and Norway.

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S7 Table. Sensitivity analysis–analysis considering births from 1990 onwards in Norway in between-women* and within-women** analyses (1990–2016).

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Acknowledgments

The authors would like to thank the Linkage and Client Services Teams at the Data Linkage Branch (Department of Health Western Australia) as well as the Data Custodian for the Midwives Notification System. The authors would also like to thank members of the Healthy Pregnancies Consumer Reference Group who provided community input and guidance in this research.

Data Availability

The data underlying this study are not publicly available due to agreements that restrict our ability to share the unit record data on ethical and legal grounds. The data are sensitive, and de-identified but potentially re-identifiable. Requests to access these data can be submitted to the following entities: New South Wales, Australia - NSW Centre for Health Record Linkage website: https://www.cherel.org.au/. Western Australia - Data Linkage Western Australia website: https://www.datalinkage-wa.org.au/. Norway, websites: https://helsedata.no/. Finland: Social and Health Data Permit Authority Findata website: https://findata.fi/en/.

Funding Statement

This work was supported by funding from the National Health and Medical Research Council, including an Early Career Fellowship [#GNT1138425 to AR], investigator grant [#APP1195716 for GAT, #APP1173991 for GP], Career Development Fellowship [#GNT1067066 to NN] and project grants [#GNT1099655 to GP, NN and SJB; # GNT1141510 to AKR and GP, # GNT1173991 to GP]. MCM works at the MRC Integrative Epidemiology Unit at the University of Bristol which receives infrastructure funding from UK Medical Research Council (MC/UU/12013/5), and she is also supported by the following fellowship from the UK Medical Research Council (MR/M009351/1). This work was partly supported by the Research Council of Norway through its Centres of Excellence funding scheme [# 262700 to SEH and GP]. This work was partly supported by the March of Dimes Prematurity Research Center at Stanford University School of Medicine and by the National Institutes of Health (R03HD090243). The funders have no role in the design, analysis and interpretation of the results in the study.

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PLoS One. doi: 10.1371/journal.pone.0255000.r001

Decision Letter 0

Antonio Simone Laganà

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

16 Apr 2021

PONE-D-21-07581

Interpregnancy intervals and adverse birth outcomes in high-income countries: an international cohort study

PLOS ONE

Dear Dr. Tessema,

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PLOS ONE

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Additional Editor Comments :

The topic of the manuscript is interesting. Nevertheless, the reviewers raised several concerns: considering this point, I invite authors to perform the required major revisions.

[Note: HTML markup is below. Please do not edit.]

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

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: No

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

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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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: It might be worthwhile to include demographic characteristics stratified by IPI as well as country. Including raw OR results would also help clarify the effect of these demographic variables on this issue.

Socioeconomic status is discussed in the methods, but is not actually presented as an adjusted variable in the results. Further, since records of socioeconomic status vary by country, and do not provide a valid basis for comparison, you could consider dropping discussion and presentation of these variables altogether for a more efficient paper.

Reviewer #2: Dear Madam/Sir,

Re. PONE-D-21-07581_ORI

Thank you for inviting me to review this highly interesting and impressive manuscript (MS). As the current Senior scientist of one of the referenced papers (#19) and a citizen of one of the countries involved, it is a privilege to contribute. I also give beforehand credit to the 16 authors for their considerable time and efforts leading up to this MS.

Authors’ conclusions

In short, the overall and most clinically relevant conclusion is that a standard recommendation of 1½ to 2 years’ interpregnancy interval (IPI) may be open for revision towards a shortening, in particular for women in high income countries. That conclusion is based on a thorough follow up of national birth registry data covering more than 5 million pregnancies in four countries on three continents. By use of a robust design, meticulous and advanced data management and analyses, the authors make a very good case in support of their hypotheses.

Study outcomes and Gestational age

The study outcomes are a) Overall preterm birth (PTB), b) Spontaneous preterm birth (sPTB), and c) Small-for-Gestational Age (SGA) birth. All of them hinge on an ascertainment of gestational age that is as accurate as possible. The basis of the ‘exposure’ (IPI in months) is birth year of the mothers’ very first offspring and was collected over a period from 1980 to 2017. In most cases, Expected Date of Delivery (EDD) was ascertained by use of ultrasound (ULS). In case such information was unavailable, the peculiar (and hitherto novel) variable ‘expected date of conception’ was ascertained based on the woman’s self-reported menstruation history (LMP). Both EDD methods are probability predictions, but LMP is definitely more imprecise than ULS due to the normal variability of menstrual periods between 21 and 35 days. Moreover, routine ULS was no prenatal routine in Norway until well after a National Consensus conference in 1986.

Hence (1), I suggest a sensitivity analysis that compares births before 1985 and after 1990 to rule out any uncertainties as regards equivalent comparisons of the outcomes under study.

Maternal overall reproductive history

Nulliparae are exceptional since they, unlike parous women, have never given birth before. On the other hand, it is a frequent speculation that women tend to repeat the same outcome of consecutive births. In fact, the overall study hypothesis of Ref #19 (above) was to compare outcomes across sibships and therefore, selectively included women who expected their 2nd or 3rd child and whose earlier birth outcomes were known (Bakketeig et al., 1993).

Hence (2), I would like to see some clinical and other background information related to the ‘nullip’ pregnancies, i.e. in addition to the date when the delivery took place. This argument is relevant since any future modification of advice about IPI in a clinical setting normally must take that perspective into account. I have accessed the registration form for all births reported to the Norwegian Birth Registry, the most recent revision of which occurred in 1998. Three important variables that are recorded and should be considered are: a) Pre-eclampsia (varying from slight hypertension to full-blown eclampsia) is a strong predictor of SGA, b) PTB (overall) is recorded as one of the reasons for transfer of the child to the neonatal intensive care unit, and c) the mother’s current occupation (including branch) is recorded and listed as none, part-time or full time. The latter runs counter to the reported “N/A” in Table S1 (maternal occupation)

Maternal ethnicity

The overall part of the current study population is most likely Caucasian white, with US Californians as a potential exception. On the other hand, it seems to be a fact that for instance Afro-Americans have a slightly different and left-skewed gestational age profile. This may have an impact on all study outcomes that rely on predicted gestational length.

Hence (3), as a minimum I would like to see a comment about generalizability of study outcomes across ethnicity.

Extremely long IPIs and other reproductive aspects

I endorse the discussion related to the influence of IPIs of 60 months (= 5 years) and over. One aspect is the potential change of partner, a result of which is that the fetus is exposed to an entirely novel mix of maternal/paternal epigenetic imprint that in turn may influence the outcome. One example is the ‘return to null’ in terms of an increased risk for preeclampsia (PE; cf. Ref. 43) that add to the effect an advanced maternal age has on PE and other reproductive outcomes.

Hence (4), as PE is risk for SGA, a comment is requested about any potential effect of a consistent fatherhood or not.

Further, with an observed prevalence of about 1%, the authors claim that there is but a negligible influence on the outcomes under study in pregnancies after assisted reproduction. Their current comment is that the IPI has been inadvertently extended. However, recent data from the Norwegian Birth Registry indicates that close to one in ten pregnancies belongs to that category and should be taken into account in future analyses. On the other hand, this is a situation where the ‘date of conception’ mentioned earlier can be confirmed quite precisely.

Hence (5), please add or modify the current text. You may also consider to pay less attention to the cost issue?

Study design

PLoS ONE is a journal that aims broadly towards a general medical and other scientific readership. My impression and concerns after some 30 years are that the two words ‘retrospective’ and ‘cohort’ used together may (repeat: MAY) seem unfamiliar and contra-intuitive to a number of readers.

Hence (6), I ask the authors to present the study design as in the title and the abstract = ‘..an international longitudinal cohort study..’ In so doing, it underscores that these are the results of a standard follow up study of a single exposure (= ordinal levels of IPI) and three definite birth outcomes (= PTB, SPTB and SGA). The use of historic data - as shown by how they were collected - are in my view, obvious and sufficient.

In conclusion, this is an important and most valuable paper that merits publication in PLoS ONE. I enjoyed reading it and have already commended the authors for their initiative and the impressive amount of work they have laid down.

Data have been analysed according to state-of-art and the results presented precisely and diligently.

I hope my comments may be helpful in the upcoming process, whether they be taken into account or rebutted. My grading advice = Minor revision

Finally, I see no reason why these comments are kept secret.

Reviewer #3: In this manuscript the authors combine data from four countries to examine the effects of interpregnancy intervals on adverse outcomes, focusing on preterm birth, spontaneous preterm birth, and small for gestational age. The manuscript is presented in a straightforward manner, and in well-written scientific English. There are a few questions and concerns for the authors to consider.

First, with respect to definitions of IPI, the authors give readers a bit more detail. its a fairly complicated topic actually, on which knowledgeable researchers might disagree. Consider consulting one of two papers from an expert panel convened by the US Office of Population Affairs:

Hutcheon, Jennifer A., Susan Moskosky, Cande V. Ananth, Olga Basso, Peter A. Briss, Cynthia D. Ferré, Brittni N. Frederiksen, Sam Harper, Sonia Hernández-Díaz, Ashley H. Hirai, Russell S. Kirby, Mark A. Klebanoff, Laura Lindberg, Sunni L. Mumford, Heidi D. Nelson, Robert W. Platt, Lauren M. Rossen, Alison M. Stuebe, Marie E. Thoma, Catherine J. Vladutiu, Katherine A. Ahrens, “Good Practices for the Design, Analysis, and Interpretation of Observational Studies on Birth Spacing and Perinatal Health Outcomes”, Paediatric and Perinatal Epidemiology, 33,1 (January 2019), O15-O24. DOI: 10.1111/ppe.12512 PMID: 30311958

Ahrens, Katherine A., Jennifer A. Hutcheon, Cande V. Ananth, Olga Basso, Peter A. Briss, Cynthia D. Ferré, Brittni N. Frederiksen, Sam Harper, Sonia Hernández-Díaz, Ashley H. Hirai, Russell S. Kirby, Mark A. Klebanoff, Laura Lindberg, Sunni L. Mumford, Heidi D. Nelson, Robert W. Platt, Lauren M. Rossen, Alison M. Stuebe, Marie E. Thoma, Catherine J. Vladutiu, Susan Moskosky, “Report of the Office of Population Affairs’ Expert Work Group Meeting on Short Birth Spacing and Adverse Pregnancy Outcomes: Methodological Quality of Existing Studies and Future Directions for Research”, Paediatric and Perinatal Epidemiology, 33,1 (January 2019), O5-O14. DOI:10.1111/ppe.12504 PMID: 30300948

Either of these papers will provide readers with more context than is provided solely by reference 20 on p 5.

On p 6, first sentence of results, reading Table 1 it appears that the range of women at parity 1 outside the US was 89-93%, not 87-93% as stated. It would be helpful however to clarify how the term parity is used in the study. Are women of parity 1 giving birth to their first-born child, that is, nulliparous, or have they all had a previous live birth. This is unclear from the manuscript.

On a related point however, if we assume that parity=1 are women now giving birth to their second baby, it would be helpful to see the substantive analyses in Table 3 run also only on these births, as that would limit the analysis to birth intervals only for time from first birth to conception of the next pregnancy. As many other studies in the literature do this, more comparable results would then be available.

Also unclear is whether the analysis file links births into sibships, or if it is a strictly cross-sectional analysis across the years contributed for each country.

In conducting sensitivity analyses, it would be interesting also to control for race/ethnic variation. This probably can't be done in the pooled analysis, but should surely be possible in the US and Australia. Analyses including nativity would also be helpful.

Addressing these issues will result in a more broadly useful contribution to the international literature.

Reviewer #4: This manuscript presents some important findings. It uses large samples from 4 developed countries to examine IPA, using both between women and within women analyses. The later is important as it allows the analysis to adjust for unobserved selection bias. That this bias is present is confirmed by the fact that the effects are consistently smaller for the within women analyses. This analysis provides strong support that the current recommendation for a 24 month IPI is not supported for developed countries.

I have no concerns with the methods; they are solid and appropriate.

The results and concussions are clearly stated and the conclusions are supported by the results.

While the results as presented are fine, this reviewer found himself asking for some additional results that were not presented. Specifically,

1. Was very preterm considered as an outcome, in addition to preterm? The consequences of very preterm delivery are many times more serious than moderately preterm delivery. The authors have sufficient sample size to examine this, and it would be an important addition. That said, there is a lot of results in the current manuscript, and adding preterm may be beyond the scope of this study. If so, would encourage the authors to including it in an additional study.

2. The full regression results, including those used to generate the prognostic score, were not presented in the supplementary material. t is appropriate to not report these in the main text, as it would result in huge tables. But, the full results should be made available.

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Reviewer #1: No

Reviewer #2: Yes: Geir W. Jacobsen, MD DrPH. Professor emeritus, Norwegian University of Science and Technology NTNU, Trondheim, Norway

Reviewer #3: Yes: Russell S. Kirby

Reviewer #4: No

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PLoS One. 2021 Jul 19;16(7):e0255000. doi: 10.1371/journal.pone.0255000.r002

Author response to Decision Letter 0

22 Jun 2021

Given that our responses included tables to address some of the comments by reviewers, we have provided our responses to reviewers as an attached file.

Attachment

Submitted filename: Response to reviewers_R1_17062021.docx

Click here for additional data file.^{(96.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0255000.r003

Decision Letter 1

Antonio Simone Laganà

8 Jul 2021

Interpregnancy intervals and adverse birth outcomes in high-income countries: an international cohort study

PONE-D-21-07581R1

Dear Dr. Tessema,

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.

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Kind regards,

Antonio Simone Laganà, M.D., Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Authors performed the required corrections, which were positively evaluated by the reviewers. I am pleased to accept this paper for publication.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: (No Response)

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: No

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Reviewer #2: I commend (again) the authors for their intelligent responsiveness and their comprehensive efforts to accommodate my queries and demands

Reviewer #3: The authors have adequately addressed concerns of mine and the other reviewers. I have no other concerns at this time.

Reviewer #4: (No Response)

**********

7. 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 #2: Yes: Geir Wenberg Jacobsen

Reviewer #3: Yes: Russell S. Kirby

Reviewer #4: No

PLoS One. doi: 10.1371/journal.pone.0255000.r004

Acceptance letter

Antonio Simone Laganà

9 Jul 2021

PONE-D-21-07581R1

Interpregnancy intervals and adverse birth outcomes in high-income countries: an international cohort study

Dear Dr. Tessema:

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. Antonio Simone Laganà

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 Checklist. RECORD checklist.

(DOCX)

Click here for additional data file.^{(40KB, docx)}

S1 Fig. Flowchart of study inclusions and exclusions–Australia, Finland, Norway and California.

(DOCX)

Click here for additional data file.^{(43.7KB, docx)}

S2 Fig. Adjusted odds ratios for the between-women analysis for the association between interpregnancy interval and preterm birth as compared to 18–23 months of interpregnancy interval by country.

(DOCX)

Click here for additional data file.^{(100.6KB, docx)}

(DOCX)

Click here for additional data file.^{(102.5KB, docx)}

(DOCX)

Click here for additional data file.^{(95.8KB, docx)}

S1 Table. Maternal characteristics at study cohort entry (first birth in the included cohort)* for the between-women analyses across the four countries (n = 3,849,193).

(DOCX)

Click here for additional data file.^{(30.8KB, docx)}

S2 Table. Distributions of interpregnancy intervals by adverse birth outcomes in the cohort for the within-women analyses across the four countries.

(DOCX)

Click here for additional data file.^{(28KB, docx)}

S3 Table. Sensitivity analysis–additional adjustment for socioeconomic status*** in three countries.

(DOCX)

Click here for additional data file.^{(31.5KB, docx)}

S4 Table. Sensitivity analysis–analysis excluding stillbirths in the interpregnancy interval calculation for within-women* analyses across the four countries.

(DOCX)

Click here for additional data file.^{(28.6KB, docx)}

S5 Table. Sensitivity analysis–analysis considering women with ≥3 births/ ≥2 IPI cohort in the between-women analyses* across the four countries.

(DOCX)

Click here for additional data file.^{(28.9KB, docx)}

S6 Table. Sensitivity analysis–analysis considering the first three births (parity 0, 1, and 2) in between-women* and within-women** analyses in Australia and Norway.

(DOCX)

Click here for additional data file.^{(28KB, docx)}

S7 Table. Sensitivity analysis–analysis considering births from 1990 onwards in Norway in between-women* and within-women** analyses (1990–2016).

(DOCX)

Click here for additional data file.^{(26.2KB, docx)}

Attachment

Submitted filename: Response to reviewers_R1_17062021.docx

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Data Availability Statement

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PERMALINK

Interpregnancy intervals and adverse birth outcomes in high-income countries: An international cohort study

Gizachew A Tessema

M Luke Marinovich

Siri E Håberg

Mika Gissler

Jonathan A Mayo

Natasha Nassar

Stephen Ball

Ana Pilar Betrán

Amanuel T Gebremedhin

Nick de Klerk

Maria C Magnus

Cicely Marston

Annette K Regan

Gary M Shaw

Amy M Padula

Gavin Pereira

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Study design and population

Inclusion criteria

Outcomes

Exposure

Statistical analyses

Ethical statement

Results

Cohort characteristics at study entry

Table 1. Maternal characteristics at study entry (first birth in the included cohort)* for the within-women analysis (n = 1,233,688).

Country specific analysis

Table 2. Interpregnancy interval distribution for births in within-women analyses across the four countries (n = 2,905,703).

Table 3. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) between interpregnancy interval and adverse birth outcomes among births in between-women* and within-women** analyses across the four countries.

Pooled analyses

Fig 1. Adjusted odds ratios of within-women analysis for the association between interpregnancy interval and preterm birth as compared to 18–23 months interpregnancy interval by country.

Fig 2. Adjusted odds ratios for within-women analysis for the association between interpregnancy interval and spontaneous preterm birth as compared to 18–23 months of interpregnancy interval by country.

Fig 3. Adjusted odds ratios for the within-women analysis for the association between interpregnancy interval and small-for-gestational age birth as compared to 18–23 months of interpregnancy interval by country.

Sensitivity analyses

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Antonio Simone Laganà

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Antonio Simone Laganà

Roles

Acceptance letter

Antonio Simone Laganà

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Table 1. Maternal characteristics at study entry (first birth in the included cohort)^* for the within-women analysis (n = 1,233,688).

Table 3. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) between interpregnancy interval and adverse birth outcomes among births in between-women^* and within-women^** analyses across the four countries.