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For peer review onlyOutcome of the 2016 United States Presidential Election and

the Subsequent Sex Ratio at Birth in Canada

Journal: BMJ Open

Manuscript ID bmjopen-2019-031208

Article Type: Original research

Date Submitted by the Author: 22-Apr-2019

Complete List of Authors: Retnakaran, Ravi; Mount SInai Hospital, Leadership Sinai Centre for DiabetesYe, Chang; Mount Sinai Hospital, Leadership Sinai Centre for Diabetes

Keywords: OBSTETRICS, PUBLIC HEALTH, EPIDEMIOLOGY

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1

Outcome of the 2016 United States Presidential Election and the

Subsequent Sex Ratio at Birth in Canada

Ravi Retnakaran MD1-3, Chang Ye MSc1

1. Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Ontario, Canada2. Division of Endocrinology, Department of Medicine, University of Toronto, Toronto,

Ontario, Canada 3. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada

Correspondence: Dr. Ravi Retnakaran Professor of Medicine, University of Toronto Leadership Sinai Centre for Diabetes, Mount Sinai Hospital 60 Murray Street, Suite-L5-039, Mailbox-21 Toronto, ON Canada M5T3L9 Phone: 416-586-4800-Ext-3941 Fax: 416-586-8853 Email: [email protected]

Running title: US Election and the Sex Ratio in Canada

Tables: 2 Figures: 2 Online Tables: 1

Text words: 2862

Key words: Sex ratio, fetal loss, societal stress, population

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ABSTRACT

Background: The sex ratio at birth (proportion of boys to girls) generally shows a slight male

preponderance but may decrease in response to societal stressors. Discrete adverse events such as

terrorist attacks and disasters typically lead to a temporary decline in the sex ratio 3-5 months later,

followed by resolution over the ~5-months thereafter. We hypothesized that the unexpected

outcome of the 2016 US presidential election may have been a societal stressor for liberal-leaning

populations and thereby precipitated such an effect on the sex ratio in Canada.

Methods: We determined the sex ratio at birth in Canada’s most populous province (Ontario) for

each month from April/2010 to October/2017 (n=1,079,758) and performed segmented regression

analysis to evaluate the seasonally-adjusted sex ratio for the following 3 time periods: before the

November/2016 election; following the election to before the anticipated impact; and from the

anticipated impact to 5-months thereafter.

Results: In the 12-months following the election, the lowest sex ratio occurred in March/2017 (4-

months post-election). Compared to preceding months, the sex ratio was lower in the 5-months

from March-July/2017 (p=0.02) during which time it was rising (p=0.01), reflecting recovery from

the nadir. Both effects were seen in liberal-leaning regions of Ontario (lower sex ratio (p=0.006)

and recovery (p=0.002) in March-July/2017) but not in conservative-leaning areas (p=0.12 and

p=0.49, respectively).

Conclusion: The 2016 US presidential election preceded a temporary reduction in the sex ratio at

birth in Canada, with the time course of changes therein matching the characteristic pattern of a

discrete societal stressor.

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Strengths and Limitations of this Study

This population-based study evaluated all births in Canada’s most populous province for

each month from April 2010 to October 2017, thereby enabling comprehensive assessment

of the pattern of changes in the sex ratio in this population.

The ecological study design enabled evaluation of this population outcome (sex ratio) and

its precise monthly pattern in the year following the 2016 US presidential election, while

accounting for seasonal changes therein.

Population-level data provides limited capacity for inference to the level of the individual

and hence causality cannot be definitively ascertained.

This population-based analysis cannot ascertain an individual woman’s political

preferences or whether her perception of the election outcome contributed to fetal loss and

thereby impacted the sex ratio.

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INTRODUCTION

The human sex ratio at birth (i.e. proportion of boys to girls) typically shows a slight male

preponderance.1-3 Although its physiologic determinants are not well understood,3,4 it is recognized

that this ratio can be modified by adverse societal conditions. As there is no conclusive evidence

of variability in the sex ratio at conception,1 such variation in the analogous ratio at birth is believed

to reflect sex-specific differences in the likelihood of fetal demise at various times during

pregnancy.5,6 Indeed, adverse societal stressors such as natural and man-made disasters,7-9 terrorist

attacks,10-13 and economic collapse14 have all been reported to decrease the proportion of boys at

birth, likely reflecting greater spontaneous loss of male fetuses in response to these conditions.5,6

Notably, discrete events, such as terrorist attacks, have typically resulted in a temporary decline in

the sex ratio 3-5 months after the event, followed by recovery in ~5 months thereafter.10-13

The outcome of the 2016 United States (US) presidential election on Nov. 8, 2016 was

perceived by most observers as a completely unexpected and stunning event, with unclear

domestic and international ramifications that raised widespread societal concerns about the future.

Given its global implications, we hypothesized that the unanticipated election of the nationalist

right-leaning Republican nominee would be perceived by left-leaning populations outside the US

as an adverse societal event and could thereby have affected the sex ratio in such countries. With

its historically liberal society coupled with close geographic, economic, and socio-political ties to

the US, Canada provides the prototypical example of such a country. Thus, in this context, we

hypothesized that (i) the outcome of the US presidential election on Nov. 8, 2016 may have

precipitated a temporary decline in the sex ratio at birth in Canada’s most populous province

(Ontario) 3-5 months later and (ii) that this effect may relate to the political preferences of the

population.

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METHODS

The Better Outcomes Registry & Network (BORN) collects comprehensive data on

pregnancies and births in the province of Ontario. Through BORN, we obtained data on all births

in Ontario from April 2010 to Oct 2017 (n=1,079,758 births). Specifically, we received the number

of births (total and live births) and sex breakdown thereof (numbers of boys and girls, respectively)

for each of the 91 months between April 2010 and Oct 2017 inclusive. As Ontario has 14

geographically-distinct Local Health Integration Networks (LHNs) through which healthcare is

delivered across the province, we obtained the same data stratified by LHIN of maternal residence.

This study was approved by the Mount Sinai Hospital Research Ethics Board.

All analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC). The sex ratio at delivery

was calculated as the ratio of males to females in each month from April 2010 to Oct 2017

inclusive. The time series of sex ratio thus comprised 91 timepoints. The analysis plan consisted

of the following two steps: seasonal adjustment and segmented regression.

Step 1: Seasonal Adjustment of Sex Ratio

As it is known that the sex ratio is subject to seasonality,10,15 we used box plots of the time series

of sex ratio by month to examine a possible seasonal pattern. An Autoregressive Integrated Moving

Average (ARIMA) model-based seasonal adjustment method Tramo (time series regression with

ARIMA noise, missing values, and outliers)16,17 was implemented with PROC X12 in SAS to

remove the seasonal component from the time series. ARIMA model is a generalization of

an autoregressive moving average (ARMA) model, which is a combination of the AR

(autoregressive) and MA (moving average) models. The approach consists of three stages: model

identification, model estimation, and model diagnosis.

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1. Model Identification -- We used Akaike’s information criteria (AIC) to determine (i) whether

log transformation should be applied for the outcomes (sex ratios), and (ii) whether the

corresponding additive mode or multiplicative model should be applied to decompose the seasonal

component. Furthermore, the procedure identified the order for the unseasonal and seasonal

autoregressive and moving average terms. A series of combinations of orders were generated and

ranked in the order of Bayesian information criterion (BIC), so that the procedure determined a

best-fitting ARIMA model (0,1,1) (0,1,1) for our sex ratio series.

2. Model Estimation -- Maximum likelihood method was used to estimate the seasonal

component in the best-fitting ARIMA model so that the seasonal component could be removed

from the time series and thereby enable determination of the seasonally-adjusted time series.

3. Model Diagnosis -- Residual analyses were conducted to check whether the identified model

was appropriate, and Freidman and Kruskal-Wallis tests were performed to assess the presence of

seasonality in the seasonally-adjusted time series. Based on the seasonally-adjusted time series of

sex ratio, we determined when the lowest monthly sex ratio occurred in the year after the

November 2016 election (Table 1).

Step 2: Segmented Regression Analysis

Segmented regression analysis was performed to estimate the potential impact of the US

election on the sex ratio in Ontario in the months thereafter. This method is powerful in that it can

(i) control the trend effect of sex ratio (i.e. to rule out the possibility that the observed decline in

March 2017 was due to a downward trend over time), (ii) reduce measurement bias by ensuring

concordance with population ratios rather than ratios at the LHIN/health region level, and (iii)

allow stratification analysis to evaluate the potential differential impact of the event between

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different groups.18

The time series were divided into three segments: (i) before the election (consisting of 79

months or timepoints from April/2010 to Oct/2016), (ii) the period from the election to before the

anticipated effect (consisting of 4 timepoints from Nov/2016 to Feb/2017), and (iii) the period

from the anticipated effect to the months thereafter (consisting of 8 timepoints from March/2017

onwards). We constructed the segmented regression model in the form below, assuming linearity

of the trend lines within each segment. We tested autocorrelation of residuals using the Durbin

Watson statistic to confirm that the time series have no serious autocorrelations. Figure 1 presents

the time series of the seasonally-adjusted sex ratio by month from April 2010 to October 2017,

with the predicted segmented regression line shown for the 3 segments. Since the decline in the

sex ratio after a discrete adverse societal event is a transient phenomenon, we assumed its presence

for 5 months (e.g. as occurred after the Sep 11, 2001 attacks11 and the April 1992 Los Angeles

riots19). For this reason, the third interval in the segmented regression analyses ran from March

2017 to July 2017.

The segmented regression model was constructed as follows:

Seasonally-adjusted sex ratio = β0 + β1*time + β2*event + β3*time after event + β4*effect +

β5*time after effect + error term,

where β0 estimates the level of the sex ratio before election (baseline level); β1 estimates the

change in sex ratio before election (baseline trend); β0+β2 estimates the level of the sex ratio after

the election but before the anticipated effect occurred; β1+β3 estimates the change in sex ratio

after the election but before the effect occurred; β0+β2+β4 estimates the level of the sex ratio after

the effect occurred; and β1+β3+β5 estimates the change in sex ratio after the effect occurred.

Finally, we conducted stratification analyses using the same segmented regression model for the

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respective liberal-leaning and conservative–leaning areas of the province. To do so, we first

classified each LHIN as either liberal-leaning or conservative-leaning based on the political party

holding its constituent federal parliamentary ridings at the time of the US election in Nov 2016.

Ridings were classified as liberal-leaning if held by either the Liberal Party or the New Democratic

Party. Ridings were classified as conservative-leaning if held by the Progressive Conservative

Party. Based on the political parties holding the respective federal parliamentary ridings

comprising the geographic area of each LHIN, there were 11 liberal-leaning LHINs and 3

conservative-leaning LHINs in Ontario. Considering the unbalanced population of males and

females at birth in each LHIN, we pooled the births across the 3 conservative-leaning LHINs and

the 11 liberal-leaning LHINs, respectively, and then calculated the sex ratio for each of these two

groups for each month. We repeated ARIMA approach to obtain seasonally-adjusted male and

female series, and then calculated seasonally-adjusted sex ratio series for each of the two groups.

Patient and Public Involvement

Patients were not involved in development of the research question and outcome measures,

study design, or conduct of this study.

RESULTS

Table 1 shows the sex ratio at delivery for all births in Ontario for each of the 12 months from

the election onwards (Nov 2016 to Oct 2017). During this time, the lowest seasonally-adjusted sex

ratio occurred in March 2017, which was 4 months after the election and thus precisely within the

anticipated 3-5 months post-event interval. Figure 1 presents a time series of the seasonally-

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adjusted sex ratio by month from Apr 2010 to Oct 2017, with predicted segmented regression lines

shown for the following 3 intervals: (i) before the election (Apr 2010 to Oct 2016); (ii) from the

election to before the anticipated effect (Nov 2016 to Feb 2017); and (iii) from the anticipated

effect to the 5 months thereafter (Mar 2017 to July 2017). This plot shows that the fall in the sex

ratio in March 2017 was followed by a recovery in the 5 months thereafter, exhibiting the

anticipated transient nature and time course of the predicted effect. Indeed, segmented regression

analysis (Table 2) confirmed that, compared to the period from the election to before the

anticipated effect (Nov 2016 to Feb 2017), the sex ratio was lower in the months from March 2017

to July 2017 (β4=-0.0448, p=0.02). Moreover, the change in the sex ratio differed significantly in

the period from March 2017 to July 2017 (β5=0.0133, p=0.01), reflecting a rising slope in the latter

interval (i.e. recovery of the ratio). In contrast, neither the sex ratio nor the change therein differed

significantly between pre-election and the post-election period before the anticipated effect (Nov

2016 to Feb 2017). Thus, taken together, these data are indicative of a transient fall in the sex ratio

4 months after the election, with recovery in the 5 months thereafter.

To address the hypothesis that political preferences of the population may have affected the

degree to which the unexpected outcome of the election was perceived as an adverse societal event

and thereby contributed to the observed changes in the sex ratio, we classified each Local Health

Integrated Network (LHIN) in Ontario as either liberal-leaning or conservative-leaning, based on

the political party holding its constituent federal parliamentary ridings at the time of the US

election. As shown in Figure 2, the patterns of changes in the sex ratio differed markedly between

liberal- and conservative-leaning regions. Indeed, in the liberal-leaning regions, the findings

matched those observed in the entire population (Table 2). Specifically, compared to the period

from the election to before the anticipated effect, the post-effect interval from March 2017 to July

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2017 showed a significantly lower sex ratio (β4=-0.0539, p=0.006), coupled with a rising slope

(β5=0.0173, p=0.002). In contrast, in the conservative-leaning regions (Table 2), the analogous

comparisons showed no significant differences in either the sex ratio (β4=0.0823, p=0.12) or the

change therein (β5=-0.0103, p=0.49). The same findings were observed when the analyses were

limited to live births only (Online Table 1).

DISCUSSION

In this study, we demonstrate 2 main findings. First, Canada’s most populous province

experienced a decline in the sex ratio at birth 4 months after the 2016 US presidential election,

with subsequent recovery in the 5 months thereafter. This time course of changes in the sex ratio

matches that which has been previously described following adverse societal events, such as

terrorist attacks. Second, the transient decline in the overall proportion of boys to girls born in

Ontario in March 2017 was observed in politically liberal-leaning jurisdictions but not in

conservative-leaning regions of the province. Taken together, these data suggest that the

unanticipated outcome of the 2016 US presidential election was associated with a temporary

reduction in the sex ratio at birth in Canada that may have related to its perception as an adverse

societal event by the politically liberal-leaning population.

In humans, despite relative balance in the proportion of spermatozoa carrying a Y-chromosome

to those carrying an X-chromosome,20 there is typically a slight preponderance of boys at delivery.

This imbalance at birth has been attributed to sex-specific differences in fetal vulnerability during

specific time periods in pregnancy.21 Indeed, after initial balance at conception, the sex ratio in

humans varies at different timepoints across gestation, with total female mortality in utero

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ultimately exceeding male mortality (thereby yielding the slight excess of boys at delivery).21

Thus, changes in the sex ratio at birth can reflect the impact of sex-specific differences in fetal loss

during pregnancy.

In this context, enhanced loss of male fetuses has been proposed as the mechanistic basis by

which adverse societal stressors (such as disasters, terrorism, and economic collapse) may lead to

a reduction in the sex ratio at birth.3,5,6 From the perspective of evolutionary biology, it has been

suggested that, under adverse conditions, the loss of frail male fetuses may be beneficial to the

species by yielding a “culled cohort” of healthier males that are better able to reproduce and hence

increase the likelihood of survival of the population.5,6,22 Amongst such societal stressors in

humans, discrete events such as terrorist attacks have typically induced a characteristic pattern

consisting of a transient decline in the sex ratio 3-5 months later that is believed to reflect

comparatively greater male fetal loss during a vulnerable window in mid-pregnancy at ~20-25

weeks gestation.10,23 In other words, the greater loss of male fetuses who are within this vulnerable

window at the time of the event results in a depression of the sex ratio 3-5 months later when these

babies would otherwise have been born. For example, after the terrorist attacks of September 11,

2001, the sex ratio fell 3-5 months later in New York,11 California,12 and the entire US,13

accompanied by greater male fetal deaths in the intervening months.13 Indeed, this post-event loss

of male babies has emerged as an under-recognized contributor to the overall casualty toll

following terrorist attacks such as 9/11, the 2011 Norway attacks, and the 2012 Sandy Hook

Elementary School shooting.23

Against this background, we hypothesized that the unexpected victory of the nationalist, right-

leaning Republican nominee in the 2016 US election and its resultant uncertain global implications

could have been perceived as a societal stressor in left-leaning nations and thereby affected the sex

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ratio in a country such as Canada. Although we cannot definitively ascertain causality with the

current study design, three lines of evidence arising from these data support this hypothesis. First,

the hypothesized pattern of a transient decline in the sex ratio at birth followed by recovery

thereafter was indeed observed in Ontario. Second, although other unrecognized societal factors

may also affect the sex ratio, the anticipated decline occurred precisely within the predicted

window of 3-5 months following the election, as did the recovery in the 5 months thereafter. Third,

this effect was observed in liberal-leaning regions where the population may have perceived the

outcome of the election as an adverse societal stressor, but not in conservative-leaning jurisdictions

(where it may not have been perceived in this way). It is notable that the pattern of change in the

sex ratio in the liberal regions precisely matched that which would occur after a discrete adverse

event, with both the nadir 4-months post-election and continuous rise (recovery) over the 5-months

that followed (Figure 2A and Table 2). In contrast, the sex ratio pattern in conservative regions

showed neither of these characteristic features (Figure 2B and Table 2).

We recognize that a limitation of this study is that population-level data provides limited

capacity for inference to the level of the individual. Nevertheless, the ecological study design is

appropriate for evaluating the impact of a societal stressor on a population outcome such as the

sex ratio.24 Moreover, a strength of this study is its evaluation of all births in Ontario, such that the

apparent differential post-election sex ratio pattern in the 3 conservative-leaning LHINs (in

contrast to the 11 liberal-leaning LHINs) is not a reflection of limited power but instead indicative

of some difference between the respective populations (though neither individual political

preference nor the perception of stress in response to the election can be ascertained). Thus,

limitations notwithstanding, we believe that the current data are collectively supportive of the

hypothesis in question, owing to the precision of the predicted effect in both pattern and timing in

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both the entire provincial and politically-stratified populations.

In summary, there was a decline in the proportion of boys to girls born in Canada’s most

populous province 4 months after the 2016 US presidential election followed by recovery in the 5

months thereafter, reflecting the characteristic pattern of changes observed after an adverse societal

event. Moreover, this effect was observed in liberal-leaning jurisdictions of Ontario, but not in

conservative-leaning regions. It thus emerges that the unanticipated outcome of the 2016 US

presidential election may have held unrecognized implications for the populations of other

countries, where its perception as a societal stressor may have impacted the sex ratio at birth in the

months thereafter.

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FUNDING

This study was supported by intramural funds from the Leadership Sinai Centre for Diabetes. The

funding source had no role in study design, data collection, data analysis, data interpretation, or

writing of the report.

COPYRIGHT

The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf

of all authors, a worldwide licence to the Publishers and its licensees in perpetuity, in all forms,

formats and media (whether known now or created in the future), to i) publish, reproduce,

distribute, display and store the Contribution, ii) translate the Contribution into other languages,

create adaptations, reprints, include within collections and create summaries, extracts and/or,

abstracts of the Contribution, iii) create any other derivative work(s) based on the Contribution,

iv) to exploit all subsidiary rights in the Contribution, v) the inclusion of electronic links from the

Contribution to third party material where-ever it may be located; and, vi) licence any third party

to do any or all of the above.

ACKNOWLEDGEMENTS

R Retnakaran holds the Boehringer Ingelheim Chair in Beta-cell Preservation, Function and

Regeneration at Mount Sinai Hospital.

CONTRIBUTIONS

R Retnakaran conceived the hypothesis. R Retnakaran and C Ye designed the analysis plan. C Ye

performed the analyses. R Retnakaran wrote the manuscript. Both authors interpreted the data,

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critically revised the manuscript for important intellectual content, and approved the final

manuscript. Both authors had full access to all of the data in the study and can take responsibility

for the integrity of the data and the accuracy of the data analysis. The corresponding author attests

that all listed authors meet authorship criteria and that no others meeting the criteria have been

omitted.

TRANSPARENCY DECLARATION

R Retnakaran is guarantor and affirms that this manuscript is an honest, accurate, and transparent

account of the study being reported; that no important aspects of the study have been omitted; and

that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

DATA SHARING: Data are available on request and permission from the Better Outcomes

Registry & Network (BORN) (www.bornontario.ca)

ETHICS APPROVAL: This study was approved by the Mount Sinai Hospital Research Ethics

Board

COMPETING INTERESTS

Both authors have completed the ICMJE uniform disclosure form at

www.icmje.org/coi_disclosure.pdf and declare: Dr. Retnakaran reports grants and personal fees

from Novo Nordisk, grants from Boehringer Ingelheim, personal fees from Eli Lilly, personal fees

from Takeda, personal fees from Sanofi, outside the submitted work. Ms, Ye has nothing to

disclose.

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REFERENCES

1. Austad SN. The human prenatal sex ratio: a major surprise. Proc Natl Acad Sci USA 2015; 112:4839-4840.

2. Jacobsen R, Møller H, Mouritsen A. Natural variation in the human sex ratio. Hum Reprod 1999; 14:3120-3125.

3. James WH, Grech V. A review of the established and suspected causes of variations in human sex ratio at birth. Early Hum Dev 2017; 109:50-56.

4. Retnakaran R, Wen SW, Tan H, Zhou S, Ye C, Shen M, Smith GN, Walker MC. Maternal blood pressure before pregnancy and sex of the baby: A prospective pre-conception cohort study. Am J Hypertens 2017; 30(4):382-388.

5. Catalano R, Bruckner T. Secondary sex ratios and male lifespan: damaged or culled cohorts. Proc Natl Acad Sci USA 2006; 103:1639-43.

6. Bruckner T, Catalano R. The sex ratio and age-specific male mortality: evidence for culling in utero. Am J Hum Biol 2007; 19:763-773.

7. Fukuda M, Fukuda K, Shimizu T, Møller H. Decline in sex ratio at birth after Kobe earthquake. Hum Reprod 1998; 13:2321-2.

8. Catalano R, Yorifuji T, Kawachi I. Natural selection in utero: evidence from the Great East Japan Earthquake. Am J Hum Biol 2013; 25(4):555-9.

9. Mocarelli P, Brambilla P, Gerthoux PM, Patterson DG Jr, Needham LL. Change in sex ratio with exposure to dioxin. Lancet 1996; 348(9024):409.

10. Grech V, Zammit D. A review of terrorism and its reduction of the gender ratio at birth after seasonal adjustment. Early Hum Dev 2017; 115:2-8

11. Catalano R, Bruckner T, Marks AR, Eskenazi B. Exogenous shocks to the human sex ratio: the case of September 11, 2001 in New York City. Hum Reprod 2006; 21:3127-31.

12. Catalano R, Bruckner T, Gould J, Eskenazi B, Anderson E. Sex ratios in California following the terrorist attacks of September 11, 2001. Hum Reprod 2005; 20(5):1221-7.

13. Bruckner TA, Catalano R, Ahern J. Male fetal loss in the U.S. following the terrorist attacks of September 11, 2001. BMC Public Health 2010; 10:273.

14. Catalano R, Bruckner T, Anderson E, Gould JB. Fetal death sex ratios: a test of the economic stress hypothesis. Int J Epidemiol 2005; 34:944-8.

15. Lerchl A. Seasonality of sex ratio in Germany. Hum Reprod 1998; 13:1401–1402.

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16. Gomez V, Maravall A. (1997a), Guide for Using the Programs TRAMO and SEATS, Beta Version, Banco de España.

17. Gomez V, Maravall A. (1997b), Program TRAMO and SEATS: Instructions for the User, Beta Version, Banco de España.

18. Penfold R, Zhang F. Use of interrupted time series analysis in evaluating health care quality improvements. Acad Pediatr 2013; 13(6 suppl):S38-S44.

19. Grech V. The male-female birth ratio in California and the 1992 April riots in Los Angeles. West Indian Med J 2015; 64(3):223-5.

20. Boklage CE. The epigenetic environment: secondary sex ratio depends on differential survival in embryogenesis. Hum Reprod 2005; 20:583-7.

21. Orzack SH, Stubblefield JW, Akmaev VR, Colls P, Munné S, Scholl T, Steinsaltz D, Zuckerman JE. The human sex ratio from conception to birth. Proc Natl Acad Sci USA 2015; 112:E2102-11.

22. Trivers RL, Willard DE. Natural selection of parental ability to vary the sex ratio of offspring. Science 1973; 179(4068):90-2.

23. Masukume G, O’Neill SM, Kashan AS, Kenny LC, Grech V. The terrorist attacks and the human live birth sex ratio: a systematic review and meta-analysis. Acta Medica (Hradec Kralove). 2017;60(2):59-65.

24. Pearce N. Epidemiology in a changing world: variation, causation and ubiquitous risk factors. Int J Epidemiol 2011; 40(2):503-512.

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Table 1: Crude (unadjusted) and seasonally-adjusted sex ratio for all births in Ontario in each of the 12 months from November 2016 to Oct 2017.

Month

Number of Births

(n)

CrudeSex Ratio

(M:F)

Seasonally-adjustedSex Ratio

(M:F)Nov 2016 11309 1.027792720 1.043159510Dec 2016 11089 1.057710150 1.053889585Jan 2017 11534 1.082701336 1.085020254Feb 2017 10672 1.055865922 1.060867388Mar 2017 11782 1.028232054 1.027164337Apr 2017 11482 1.043787825 1.046988171May 2017 12243 1.069822485 1.056590659Jun 2017 12166 1.078592175 1.068903879Jul 2017 12410 1.076987448 1.074560743Aug 2017 12532 1.059152153 1.057795259Sep 2017 12284 1.042227764 1.048025503Oct 2017 11983 1.053641817 1.053431063

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Table 2: Segmented regression models evaluating the sex ratio and changes therein during the following 3 intervals: (i) before election (Apr 2010 to Oct 2016) (Segment 1); (ii) the period from election to before the anticipated effect (Nov 2016 to Feb 2017) (Segment 2) and (iii) the period from the anticipated effect to 5 months thereafter (Mar 2017 to July 2017) (Segment 3), respectively. Data are shown for the entire population of Ontario, the population in politically liberal-leaning regions at the time of the election, and the population in politically conservative-leaning regions at the time of the election, respectively.

Segment 1: Before Election

(Apr 2010 to Oct 2016)

Segment 2: From Election to Before Effect

(Nov 2016 to Feb 2017)

Segment 3:From Effect to 5 Months Thereafter

(Mar 2017 - July 2017)

Baseline level of sex ratio

before election

Baseline level of change in sex ratio

before election

Difference in sex ratio

compared to pre-election

Difference in change in sex ratio



compared to before effect



β0 p-value β1 p-value β2 p-value β3 p-value β4 p-value β5 p-valueEntire population 1.0603 <0.0001 -0.000131 0.092 0.0195 0.11 -0.001464 0.36 -0.0448 0.02 0.0133 0.01 Liberal-leaning regions 1.0605 <0.0001 -0.000133 0.096 0.0151 0.22 -0.000726 0.66 -0.0539 0.006 0.0173 0.002 Conservative-leaning regions 1.0591 <0.0001 -0.000067 0.76 -0.032 0.35 0.000585 0.9 0.0823 0.12 -0.0103 0.49

Notes re interpretation of level of sex ratio and change in sex ratio:β0 estimates the level of the sex ratio before the election (baseline level)β0+β2 estimates the level of the sex ratio after the election but before the anticipated effect occurredβ0+β2+β4 estimates the level of the sex ratio from the anticipated effect to 5 months thereafter (predicted duration)β2 = (β0+β2)-β0 = estimates the difference in sex ratio between after the election but before the anticipated effect occurred (segment 2) and before the election (segment 1)β4 = (β0+β2+β4) – (β0+β2) = estimates the difference in sex ratio between the time period from the anticipated effect to 5 months thereafter (segment 3) and the time period after the election but before the anticipated effect occurred (segment 2)

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β1 estimates the change in the sex ratio before the election (baseline trend)β1+β3 estimates the change in the sex ratio after the election but before the anticipated effect occurredβ1+β3+β5 estimates the change in the sex ratio from the anticipated effect to 5 months thereafter (predicted duration)β3 = (β1+β3)-β1 = estimates the difference in change in sex ratio between after the election but before the anticipated effect occurred (segment 2) and before the election (segment 1)β5 = (β1+β3+β5) – (β1+β3) = estimates the difference in change in sex ratio between the time period from the anticipated effect to 5 months thereafter (segment 3) and the time period after the election but before the anticipated effect occurred (segment 2)

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FIGURE LEGENDS

Figure 1: Time series of seasonally-adjusted sex ratio by month from Apr 2010 to Oct 2017. The

predicted regression line for the sex ratio is shown for the following 3 intervals: (i) before election

(Apr 2010 to Oct 2016), (ii) period from election to before the anticipated effect (Nov 2016 to Feb

2017), and (iii) the period from the anticipated effect to 5 months thereafter (Mar 2017 to July

2017), respectively.

Figure 2: Time series of seasonally-adjusted sex ratio by month from November 2016 (election)

to October 2017 in (Panel A) liberal-leaning regions and (Panel B) conservative-leaning regions.

Each panel shows the predicted regression line for the sex ratio for (i) the period from the election

to before the anticipated effect (November 2016 to February 2017) and (ii) the period from

anticipated effect to 5 months thereafter (March 2017 to July 2017)

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Figure 1: Time series of seasonally-adjusted sex ratio by month from Apr 2010 to Oct 2017. The predicted regression line for thesex ratio is shown for the following 3 intervals: (i) before election (Apr 2010 to Oct 2016), (ii) period from election to before the anticipated effect (Nov 2016 toFeb 2017), and (iii) the period from the anticipated effect to 5 months thereafter (Mar 2017 to July 2017).respectively.

0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

Sex

Ratio

(Mal

e:Fe

mal

e)

US election Nov 2016

Anticipated effect

Mar 2017

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Figure 2: Time series of seasonally-adjusted sex ratio by month from Nov 2016 (election) toOctober 2017 in (Panel A) liberal-leaning regions and (Panel B) conservative-leaning regions.Each panel shows the predicted regression line for the sex ratio for (i) the period from the election to before the anticipated effect (Nov 2016 to Feb 2017), and (ii) the period from the anticipated effect to 5 months thereafter (March 2017 to July 2017).

Panel Apvar rvar Date Sexratio Sexratio_seasonal1.049795 -0.01163 Nov-16 1.028674 1.038163 x y1.064429 -0.01533 Dec-16 1.057715 1.0491 Mar-17 0.81.063569 0.019988 Jan-17 1.091159 1.083558 Mar-17 1.15

1.06271 -0.00351 Feb-17 1.059451 1.0592031.025267 0.001651 Mar-17 1.024706 1.0269191.041709 0.001236 Apr-17 1.039618 1.042945

1.05815 -0.0085 May-17 1.065206 1.0496461.074591 0.006695 Jun-17 1.07903 1.0812871.091033 -0.00108 Jul-17 1.079712 1.0899541.057555 0.00348 Aug-17 1.062561 1.0610351.056696 -0.00551 Sep-17 1.041368 1.051191.055836 0.000874 Oct-17 1.056346 1.05671

Panel Bpvar rvar Date Sexratio Sexratio_seasonal1.053729 -0.07318 Nov-16 0.997392 0.9805511.022272 0.03807 Dec-16 1.067024 1.0603421.022791 -0.02726 Jan-17 0.975962 0.9955341.023309 -0.02247 Feb-17 0.997249 1.000836

1.0959 -0.05057 Mar-17 1.053817 1.0453261.086166 0.060456 Apr-17 1.090175 1.1466221.076433 0.013298 May-17 1.113415 1.089731.066699 -0.00567 Jun-17 1.093671 1.0610341.056965 -0.01751 Jul-17 1.040719 1.0394511.026418 0.013343 Aug-17 1.016611 1.0397611.026936 0.017711 Sep-17 1.053364 1.0446471.027454 -0.01939 Oct-17 1.018957 1.00806

0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

Sex

Ratio

(Mal

e:Fe

mal

e)

Anticipatedeffect

0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

Sex

Ratio

(Mal

e:Fe

mal

e)

Anticipatedeffect

Liberal areas

Conservative areas

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Online Table 1: Segmented regression models evaluating the sex ratio and changes therein for live births only during the following 3 intervals: (i) before election (Apr 2010 to Oct 2016) (Segment 1); (ii) the period from election to before the anticipated effect (Nov 2016 to Feb 2017) (Segment 2) and (iii) the period from the anticipated effect to 5 months thereafter (Mar 2017 to July 2017) (Segment 3), respectively. Data are shown for the entire population of Ontario, the population in politically liberal-leaning regions at the time of the election, and the population in politically conservative-leaning regions at the time of the election, respectively.

Segment 1:Before Election

(Apr 2010 to Oct 2016)


(Nov 2016 to Feb 2017)

Segment 3: From Effect to 5 Months Thereafter

(Mar 2017 - July 2017)


before election


before election









β0 p-value β1 p-value β2 p-value β3 p-value β4 p-value β5 p-valueEntire population 1.0595 <0.0001 -0.000126 0.11 0.018 0.14 -0.00136 0.4 -0.0403 0.03 0.0122 0.02 Liberal-leaning regions 1.0596 <0.0001 -0.000128 0.12 0.0151 0.24 -0.000695 0.68 -0.0505 0.01 0.0163 0.004 Conservative-leaning regions 1.0669 <0.0001 -0.000207 0.37 -0.0377 0.3 0.002138 0.65 0.0952 0.087 -0.0141 0.37 Notes re interpretation of level of sex ratio and change in sex ratio: β0 estimates the level of the sex ratio before the election (baseline level) β0+β2 estimates the level of the sex ratio after the election but before the anticipated effect occurred β0+β2+β4 estimates the level of the sex ratio from the anticipated effect to 5 months thereafter (predicted duration) β2 = (β0+β2)-β0 = estimates the difference in sex ratio between after the election but before the anticipated effect occurred (segment 2) and before the election (segment 1) β4 = (β0+β2+β4) – (β0+β2) = estimates the difference in sex ratio between the time period from the anticipated effect to 5 months thereafter (segment 3) and the time period after the election but before the anticipated effect occurred (segment 2)

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β1 estimates the change in the sex ratio before the election (baseline trend) β1+β3 estimates the change in the sex ratio after the election but before the anticipated effect occurred β1+β3+β5 estimates the change in the sex ratio from the anticipated effect to 5 months thereafter (predicted duration) β3 = (β1+β3)-β1 = estimates the difference in change in sex ratio between after the election but before the anticipated effect occurred (segment 2) and before the election (segment 1) β5 = (β1+β3+β5) – (β1+β3) = estimates the difference in change in sex ratio between the time period from the anticipated effect to 5 months thereafter (segment 3) and the time period after the election but before the anticipated effect occurred (segment 2)

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STROBE Statement—checklist of items that should be included in reports of observational studies

Item No Recommendation

Page number

(a) Indicate the study’s design with a commonly used term in the title or the abstract

3Title and abstract 1

(b) Provide in the abstract an informative and balanced summary of what was done and what was found

3

IntroductionBackground/rationale 2 Explain the scientific background and rationale for the investigation being

reported 4

Objectives 3 State specific objectives, including any prespecified hypotheses 4

MethodsStudy design 4 Present key elements of study design early in the paper 5Setting 5 Describe the setting, locations, and relevant dates, including periods of

recruitment, exposure, follow-up, and data collection 5

(a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-upCase-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controlsCross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants

5Participants 6

(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposedCase-control study—For matched studies, give matching criteria and the number of controls per case

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable

5-8

Data sources/ measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group

5-8

Bias 9 Describe any efforts to address potential sources of bias 6-8Study size 10 Explain how the study size was arrived at 5Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable,

describe which groupings were chosen and why 5-8

(a) Describe all statistical methods, including those used to control for confounding

5-8

(b) Describe any methods used to examine subgroups and interactions 5-8(c) Explain how missing data were addressed 5-8(d) Cohort study—If applicable, explain how loss to follow-up was addressedCase-control study—If applicable, explain how matching of cases and controls was addressedCross-sectional study—If applicable, describe analytical methods taking account of sampling strategy

5-8

Statistical methods 12

(e) Describe any sensitivity analyses 8Continued on next page

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Results(a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed

5,

(b) Give reasons for non-participation at each stage 5

Participants 13*

(c) Consider use of a flow diagram (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders

8-9

(b) Indicate number of participants with missing data for each variable of interest 8-9

Descriptive data

14*

(c) Cohort study—Summarise follow-up time (eg, average and total amount) 8-9Cohort study—Report numbers of outcome events or summary measures over time 8-9Case-control study—Report numbers in each exposure category, or summary measures of exposure

Outcome data 15*

Cross-sectional study—Report numbers of outcome events or summary measures(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were included

8-9

(b) Report category boundaries when continuous variables were categorized 8-9

Main results 16

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses

9

DiscussionKey results 18 Summarise key results with reference to study objectives 10Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or

imprecision. Discuss both direction and magnitude of any potential bias 12

1Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence

10-13

Generalisability 21 Discuss the generalisability (external validity) of the study results 10-13

Other informationFunding 22 Give the source of funding and the role of the funders for the present study and, if

applicable, for the original study on which the present article is based 14

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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the Subsequent Sex Ratio at Birth in Canada: An Ecologic Study

Journal: BMJ Open

Manuscript ID bmjopen-2019-031208.R1


Date Submitted by the Author: 29-Nov-2019


<b>Primary Subject Heading</b>: Obstetrics and gynaecology

Secondary Subject Heading: Public health, Epidemiology



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Subsequent Sex Ratio at Birth in Canada: An Ecologic Study







Text words: 3290


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ABSTRACT

Background: The sex ratio at birth (proportion of boys-to-girls) generally shows slight male


terrorist attacks and disasters typically lead to a temporary decline in the sex ratio 3-5 months

later, followed by resolution over ~5-months thereafter. We hypothesized that the unexpected

outcome of the 2016 US presidential election may have been a societal stressor for liberal-

leaning populations and thereby precipitated such an effect on the sex ratio in Canada.

Methods: We determined the sex ratio at birth in Canada’s most populous province (Ontario) for

each month from April/2010 to October/2017 (n=1,079,758) and performed segmented

regression analysis to evaluate the seasonally-adjusted sex ratio for the following 3 time periods:

before the November/2016 election; following the election to before the anticipated impact; and

from anticipated impact to 5-months thereafter.

Results: In the 12-months following the election, the lowest sex ratio occurred in March/2017

(4-months post-election). Compared to preceding months, the sex ratio was lower in the 5-

months from March-July/2017 (p=0.02) during which time it was rising (p=0.01), reflecting

recovery from the nadir. Both effects were seen in liberal-leaning regions of Ontario (lower sex

ratio (p=0.006) and recovery (p=0.002) in March-July/2017) but not in conservative-leaning

areas (p=0.12 and p=0.49, respectively).

Limitation: The ecologic design precludes ascertainment of causality.

Conclusion: The 2016 US presidential election preceded a temporary reduction in the sex ratio

at birth in Canada, with the time course of changes therein matching the characteristic pattern of

a discrete societal stressor.

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each month from April 2010 to October 2017, thereby enabling comprehensive

assessment of the pattern of changes in the sex ratio in this population.




The ecologic design with population-level data provides limited capacity for inference to

the level of the individual and hence causality cannot be definitively ascertained.


preferences or whether her perception of the election outcome contributed to fetal loss

and thereby impacted the sex ratio.

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INTRODUCTION


preponderance.1-3 Although its physiologic determinants are not well understood,3,4 it is

recognized that this ratio can be modified by adverse societal conditions. As there is no

conclusive evidence of variability in the sex ratio at conception,1 such variation in the analogous

ratio at birth is believed to reflect sex-specific differences in the likelihood of fetal demise at

various times during pregnancy.5,6 Indeed, adverse societal stressors such as natural and man-

made disasters,7-10 economic downturn,11 social unrest,10,12 and terrorist attacks10,13-17 have all

been reported to decrease the proportion of boys at birth, likely reflecting greater spontaneous

loss of male fetuses in response to these conditions.5,6 Notably, discrete events, such as terrorist

attacks, have typically resulted in a temporary decline in the sex ratio 3-5 months after the event,

followed by recovery in ~5 months thereafter.10,13-17 Indeed, this pattern has been seen after a

range of events including the Sep 11/2001 attacks,13-15 the 2004 Madrid bombings,10,17 the 2005

London bombings,10,17 the 2011 Norway attacks,16 and the 2012 Sandy Hook Elementary School

shooting.16 Moreover, this characteristic pattern of the sex ratio in the months thereafter has been

confirmed in a meta-analysis assessing the effect of these events on the sex ratio at birth.17



domestic and international ramifications that raised widespread societal concerns about the

future. Given its global implications, we hypothesized that the unanticipated election of the

nationalist right-leaning Republican nominee would be perceived by left-leaning populations

outside the US as an adverse societal event and could thereby have affected the sex ratio in such

countries. With its historically liberal society coupled with close geographic, economic, and

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socio-political ties to the US, Canada provides the prototypical example of such a country. Thus,

in this context, we hypothesized that (i) the outcome of the US presidential election on Nov. 8,

2016 may have precipitated a temporary decline in the sex ratio at birth in Canada’s most

populous province (Ontario) 3-5 months later and (ii) that this effect may relate to the political

preferences of the population.

METHODS



in Ontario from April 2010 to Oct 2017 (n=1,079,758 births). Specifically, we received the

number of births (total and live births) and sex breakdown thereof (numbers of boys and girls,

respectively) for each of the 91 months between April 2010 and Oct 2017 inclusive. As Ontario

has 14 geographically-distinct Local Health Integration Networks (LHINs) through which

healthcare is delivered across the province, we obtained the same data stratified by LHIN of

maternal residence. This study was approved by the Mount Sinai Hospital Research Ethics

Board.

All analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC). The sex ratio at

delivery was calculated as the ratio of males to females in each month from April 2010 to Oct

2017 inclusive. The time series of sex ratio thus comprised 91 timepoints. The analysis plan

consisted of the following two steps: seasonal adjustment and segmented regression.

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As it is known that the sex ratio is subject to seasonality,10,18 we used box plots of the time

series of sex ratio by month to examine a possible seasonal pattern. An Autoregressive Integrated

Moving Average (ARIMA) model-based seasonal adjustment method Tramo (time series

regression with ARIMA noise, missing values, and outliers)19,20 was implemented with PROC

X12 in SAS to remove the seasonal component from the time series. ARIMA model is a

generalization of an autoregressive moving average (ARMA) model, which is a combination of

the AR (autoregressive) and MA (moving average) models. The approach consists of three

stages: model identification, model estimation, and model diagnosis.

1. Model Identification – We used Akaike’s information criteria (AIC) to determine (i)

whether log transformation should be applied for the outcomes (sex ratios), and (ii) whether the

corresponding additive mode or multiplicative model should be applied to decompose the

seasonal component. Furthermore, the procedure identified the order for the unseasonal and

seasonal autoregressive and moving average terms. A series of combinations of orders were

generated and ranked in the order of Bayesian information criterion (BIC), so that the procedure

determined a best-fitting ARIMA model (0,1,1) (0,1,1) for our sex ratio series.

2. Model Estimation – Maximum likelihood method was used to estimate the seasonal



3. Model Diagnosis – Residual analyses were conducted to check whether the identified

model was appropriate, and Freidman and Kruskal-Wallis tests were performed to assess the

presence of seasonality in the seasonally-adjusted time series. Based on the seasonally-adjusted

time series of sex ratio, we determined when the lowest monthly sex ratio occurred in the year

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after the November 2016 election (Table 1).



election on the sex ratio in Ontario in the months thereafter. This method is powerful in that it

can (i) control the trend effect of sex ratio (i.e. to rule out the possibility that the observed

decline in March 2017 was due to a downward trend over time), (ii) reduce measurement bias by

ensuring concordance with population ratios rather than ratios at the LHIN/health region level,

and (iii) allow stratification analysis to evaluate the potential differential impact of the event

between different groups.21


months or timepoints from April/2010 to Oct/2016), (ii) the period from the election to before

the anticipated effect (consisting of 4 timepoints from Nov/2016 to Feb/2017), and (iii) the

period from the anticipated effect to the months thereafter (consisting of 8 timepoints from

March/2017 onwards). We constructed the segmented regression model in the form below,

assuming linearity of the trend lines within each segment. We tested autocorrelation of residuals

using the Durbin Watson statistic to confirm that the time series have no serious autocorrelations.

Figure 1 presents the time series of the seasonally-adjusted sex ratio by month from April 2010

to October 2017, with the predicted segmented regression line shown for the 3 segments. Since

the decline in the sex ratio after a discrete adverse societal event is a transient phenomenon, we

anticipated its presence for 5 months, as this was the time interval over which the sex ratio

recovered from its nadir after the Sep 11, 2001 attacks13 and the April 1992 Los Angeles riots12.

For this reason, the third interval in the segmented regression analyses ran from March 2017 to

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July 2017.




where time is a continuous variable indicating time in months from the start of the observation

period; event is an indicator taking value 0 before the election and 1 after it; and time after event

is a continuous variable counting the number of months after the election, taking value 0 before

the election and (time-80) after the election (which occurred at month 80); effect is an indicator

taking value 0 before the anticipated effect occurred and 1 after 1; time after effect is a

continuous variable counting the number of months after the anticipated effect, taking value 0

before the effect and (time-83) after the effect which occurred at month 84; β0 estimates the level

of the sex ratio before election (baseline level), which is the level at the beginning of the pre-

election period; β1 estimates the change in sex ratio before election, which is the slope of the

trend before election; β0+β2 estimates the level of the sex ratio after the election but before the

anticipated effect occurred; β1+β3 estimates the change in sex ratio after the election but before

the effect occurred; β0+β2+β4 estimates the level of the sex ratio after the effect occurred; and

β1+β3+β5 estimates the change in sex ratio after the effect occurred.

In addition, we conducted stratification analyses using the same segmented regression model

for the respective liberal-leaning and conservative–leaning areas of the province. To do so, we

first classified each LHIN as either liberal-leaning or conservative-leaning based on the political

party holding its constituent federal parliamentary ridings at the time of the US election in Nov

2016. Ridings were classified as liberal-leaning if held by either the Liberal Party or the New

Democratic Party. Ridings were classified as conservative-leaning if held by the Progressive

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Conservative Party. Based on the political parties holding the respective federal parliamentary

ridings comprising the geographic area of each LHIN, there were 11 liberal-leaning LHINs and 3






Finally, considering the limited data to fit the second line segment, we did two sensitivity

analyses (i) with the exclusion of the second segment (by removing the data from Dec 2016 to

Feb 2017), and (ii) with the aggregation of the first and second line segments, for the whole

population and the respective liberal-leaning and conservative-leaning areas. The segmented

regression model was then re-constructed as follows:

Seasonally-adjusted sex ratio = β6 + β7*time + β8*effect + β9*time after effect + error

term,

where time, effect and time after effect are defined same as model (1); β6 estimates the level of

the sex ratio before the anticipated effect occurred (baseline level); β7 estimates the change in

sex ratio before the anticipated effect occurred; β6+β8 estimates the level of the sex ratio after

the effect occurred; and β7+β9 estimates the change in sex ratio after the effect occurred.




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RESULTS


the election onwards (Nov 2016 to Oct 2017). During this time, the lowest seasonally-adjusted

sex ratio occurred in March 2017, which was 4 months after the election and thus precisely

within the anticipated 3-5 months post-event interval. Figure 1 presents a time series of the

seasonally-adjusted sex ratio by month from Apr 2010 to Oct 2017, with predicted segmented

regression lines shown for the following 3 intervals: (i) before the election (Apr 2010 to Oct

2016); (ii) from the election to before the anticipated effect (Nov 2016 to Feb 2017); and (iii)

from the anticipated effect to the 5 months thereafter (Mar 2017 to July 2017). This plot shows

that the fall in the sex ratio in March 2017 was followed by a recovery in the 5 months thereafter,

exhibiting the anticipated transient nature and time course of the predicted effect. Indeed,

segmented regression analysis (Table 2) confirmed that, compared to the period from the election

to before the anticipated effect (Nov 2016 to Feb 2017), the sex ratio was lower in the months

from March 2017 to July 2017 (β4=-0.0448, p=0.02). Moreover, the change in the sex ratio

differed significantly in the period from March 2017 to July 2017 (β5=0.0133, p=0.01),

reflecting a rising slope in the latter interval (i.e. recovery of the ratio). In contrast, neither the

sex ratio nor the change therein differed significantly between pre-election and the post-election

period before the anticipated effect (Nov 2016 to Feb 2017). Thus, taken together, these data are

indicative of a transient fall in the sex ratio 4 months after the election, with recovery in the 5

months thereafter.


degree to which the unexpected outcome of the election was perceived as an adverse societal

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event and thereby contributed to the observed changes in the sex ratio, we classified each Local

Health Integrated Network (LHIN) in Ontario as either liberal-leaning or conservative-leaning,

based on the political party holding its constituent federal parliamentary ridings at the time of the

US election. As shown in Figure 2, the patterns of changes in the sex ratio differed markedly

between liberal- and conservative-leaning regions. Indeed, in the liberal-leaning regions, the

findings matched those observed in the entire population (Table 2). Specifically, compared to the

period from the election to before the anticipated effect, the post-effect interval from March 2017

to July 2017 showed a significantly lower sex ratio (β4=-0.0539, p=0.006), coupled with a rising

slope (β5=0.0173, p=0.002). In contrast, in the conservative-leaning regions (Table 2), the

analogous comparisons showed no significant differences in either the sex ratio (β4=0.0823,

p=0.12) or the change therein (β5=-0.0103, p=0.49). The same findings were observed when the

analyses were limited to live births only (Online Table 1).

We also performed sensitivity analyses with two segments (before the anticipated effect and

the post-effect interval) in 2 ways: (i) by excluding the 3 months from December 2016 to

February 2017 and (ii) by including these 3 months in the pre-effect segment (Online Table 2).

With both approaches, the post-effect interval in the liberal-leaning regions showed a

significantly lower sex ratio with a rising slope, while the conservative-leaning regions showed

neither.

DISCUSSION




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In humans, despite relative balance in the proportion of spermatozoa carrying a Y-

chromosome to those carrying an X-chromosome,22 there is typically a slight preponderance of

boys at delivery. This imbalance at birth has been attributed to sex-specific differences in fetal

vulnerability during specific time periods in pregnancy.23 Indeed, after initial balance at

conception, the sex ratio in humans varies at different timepoints across gestation, with total

female mortality in utero ultimately exceeding male mortality (thereby yielding the slight excess

of boys at delivery).23 Thus, changes in the sex ratio at birth can reflect the impact of sex-specific

differences in fetal loss during pregnancy.





species by yielding a “culled cohort” of healthier males that are better able to reproduce and

hence increase the likelihood of survival of the population.5,6,24 Amongst such societal stressors

in humans, discrete events such as terrorist attacks have typically induced a characteristic pattern


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weeks gestation.10,17 In other words, the greater loss of male fetuses who are within this

vulnerable window at the time of the event results in a depression of the sex ratio 3-5 months

later when these babies would otherwise have been born. For example, after the terrorist attacks

of September 11, 2001, the sex ratio fell 3-5 months later in New York,13 California,14 and the

entire US,15 accompanied by greater male fetal deaths in the intervening months.15 Indeed, this

post-event loss of male babies has emerged as an under-recognized contributor to the overall

casualty toll following terrorist attacks such as 9/11, the 2011 Norway attacks, and the 2012

Sandy Hook Elementary School shooting.17

Against this background, we hypothesized that the unexpected victory of the nationalist,

right-leaning Republican nominee in the 2016 US election and its resultant uncertain global

implications could have been perceived as a societal stressor in left-leaning nations and thereby

affected the sex ratio in a country such as Canada. Although we cannot definitively ascertain

causality with the current study design, three lines of evidence arising from these data support

this hypothesis. First, the hypothesized pattern of a transient decline in the sex ratio at birth

followed by recovery thereafter was indeed observed in Ontario. Second, although other

unrecognized societal factors may also affect the sex ratio, the anticipated decline occurred

precisely within the predicted window of 3-5 months following the election, as did the recovery

in the 5 months thereafter. Third, this effect was observed in liberal-leaning regions where the

population may have perceived the outcome of the election as an adverse societal stressor, but

not in conservative-leaning jurisdictions (where it may not have been perceived in this way). It is

notable that the pattern of change in the sex ratio in the liberal regions precisely matched that

which would occur after a discrete adverse event, with both the nadir 4-months post-election and

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continuous rise (recovery) over the 5-months that followed (Figure 2A and Table 2). In contrast,

the sex ratio pattern in conservative regions showed neither of these characteristic features

(Figure 2B and Table 2).




sex ratio.25 Moreover, a strength of this study is its evaluation of all births in Ontario, such that

the apparent differential post-election sex ratio pattern in the 3 conservative-leaning LHINs (in

contrast to the 11 liberal-leaning LHINs) is not a reflection of limited power but instead

indicative of some difference between the respective populations (though neither individual

political preference nor the perception of stress in response to the election can be ascertained).

Thus, limitations notwithstanding, we believe that the current data are collectively supportive of

the hypothesis in question, owing to the precision of the predicted effect in both pattern and

timing in both the entire provincial and politically-stratified populations.


populous province 4 months after the 2016 US presidential election followed by recovery in the

5 months thereafter, reflecting the characteristic pattern of changes observed after an adverse

societal event. Moreover, this effect was observed in liberal-leaning jurisdictions of Ontario, but

not in conservative-leaning regions. It thus emerges that the unanticipated outcome of the 2016

US presidential election may have held unrecognized implications for the populations of other

countries, where its perception as a societal stressor may have impacted the sex ratio at birth in

the months thereafter.

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FUNDING

This study was supported by intramural funds from the Leadership Sinai Centre for Diabetes.

The funding source had no role in study design, data collection, data analysis, data interpretation,

or writing of the report.

COPYRIGHT







iv) to exploit all subsidiary rights in the Contribution, v) the inclusion of electronic links from

the Contribution to third party material where-ever it may be located; and, vi) licence any third

party to do any or all of the above.

ACKNOWLEDGEMENTS



CONTRIBUTIONS

R Retnakaran conceived the hypothesis. R Retnakaran and C Ye designed the analysis plan. C

Ye performed the analyses. R Retnakaran wrote the manuscript. Both authors interpreted the

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data, critically revised the manuscript for important intellectual content, and approved the final


for the integrity of the data and the accuracy of the data analysis. The corresponding author

attests that all listed authors meet authorship criteria and that no others meeting the criteria have

been omitted.

TRANSPARENCY DECLARATION: R Retnakaran is guarantor and affirms that this

manuscript is an honest, accurate, and transparent account of the study being reported; that no

important aspects of the study have been omitted; and that any discrepancies from the study as

planned (and, if relevant, registered) have been explained.




Board

COMPETING INTERESTS



from Novo Nordisk, grants from Boehringer Ingelheim, personal fees from Eli Lilly, personal

fees from Takeda, personal fees from Sanofi, outside the submitted work. Ms, Ye has nothing to

disclose.

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REFERENCES










10. Grech V, Zammit D. A review of terrorism and its reduction of the gender ratio at birth after seasonal adjustment. Early Hum Dev 2017; 115:2-8.





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16. Grech V. Terrorist attacks and the male-to-female ratio at birth: The Troubles in Northern Ireland, the Rodney King riots, and the Breivik and Sandy Hook shootings. Early Hum Dev 2015; 91: 837–840.










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19


Month

Number of Births

(n)

CrudeSex Ratio

(M:F)



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20



(Apr 2010 to Oct 2016)


(Nov 2016 to Feb 2017)


(Mar 2017 - July 2017)


before election


before election











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β1 estimates the change in the sex ratio before the electionβ1+β3 estimates the change in the sex ratio after the election but before the anticipated effect occurredβ1+β3+β5 estimates the change in the sex ratio from the anticipated effect to 5 months thereafter (predicted duration)β3 = (β1+β3)-β1 = estimates the difference in change in sex ratio between after the election but before the anticipated effect occurred (segment 2) and before the election (segment 1)β5 = (β1+β3+β5) – (β1+β3) = estimates the difference in change in sex ratio between the time period from the anticipated effect to 5 months thereafter (segment 3) and the time period after the election but before the anticipated effect occurred (segment 2)

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FIGURE LEGENDS

Figure 1: Time series of seasonally-adjusted sex ratio by month from Apr 2010 to Oct 2017.

The predicted regression line for the sex ratio is shown for the following 3 intervals: (i) before

election (Apr 2010 to Oct 2016), (ii) period from election to before the anticipated effect (Nov

2016 to Feb 2017), and (iii) the period from the anticipated effect to 5 months thereafter (Mar

2017 to July 2017), respectively.


to October 2017 in (Panel A) liberal-leaning regions and (Panel B) conservative-leaning

regions. Each panel shows the predicted regression line for the sex ratio for (i) the period from

the election to before the anticipated effect (November 2016 to February 2017) and (ii) the

period from anticipated effect to 5 months thereafter (March 2017 to July 2017)

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0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

Sex

Ratio

(Mal

e:Fe

mal

e)


Anticipated effect

Mar 2017

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1.06271 -0.00351 Feb-17 1.059451 1.0592031.025267 0.001651 Mar-17 1.024706 1.0269191.041709 0.001236 Apr-17 1.039618 1.042945




0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

Sex

Ratio

(Mal

e:Fe

mal

e)

Anticipatedeffect

0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

Sex

Ratio

(Mal

e:Fe

mal

e)

Anticipatedeffect

Liberal areas

Conservative areas

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Online Appendix


Online Table 2: Segment regression models evaluating the changes in sex ratio when comparing time interval before the anticipated effect to the interval after the anticipated effect (Mar 2017 to July 2017), with the pre-effect segment defined in the following ways: (i) by excluding Dec 2016 to Feb 2017 and (ii) by aggregating the pre-election interval with this 3 month segment. Data are shown for entire population, liberal-leaning regions and conservative-leaning regions, respectively.

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(Apr 2010 to Oct 2016)


(Nov 2016 to Feb 2017)


(Mar 2017 - July 2017)


before election


before election









β0 p-value β1 p-value β2 p-value β3 p-value β4 p-value β5 p-value Entire population 1.0595 <0.0001 -0.000126 0.11 0.018 0.14 -0.00136 0.4 -0.0403 0.03 0.0122 0.02 Liberal-leaning regions 1.0596 <0.0001 -0.000128 0.12 0.0151 0.24 -0.000695 0.68 -0.0505 0.01 0.0163 0.004 Conservative-leaning regions 1.0669 <0.0001 -0.000207 0.37 -0.0377 0.3 0.002138 0.65 0.0952 0.087 -0.0141 0.37 Notes re interpretation of level of sex ratio and change in sex ratio: β0 estimates the level of the sex ratio before the election (baseline level) β0+β2 estimates the level of the sex ratio after the election but before the anticipated effect occurred β0+β2+β4 estimates the level of the sex ratio from the anticipated effect to 5 months thereafter (predicted duration) β2 = (β0+β2)-β0 = estimates the difference in sex ratio between after the election but before the anticipated effect occurred (segment 2) and before the election (segment 1) β4 = (β0+β2+β4) – (β0+β2) = estimates the difference in sex ratio between the time period from the anticipated effect to 5 months thereafter (segment 3) and the time period after the election but before the anticipated effect occurred (segment 2)

1

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β1 estimates the change in the sex ratio before the election β1+β3 estimates the change in the sex ratio after the election but before the anticipated effect occurred β1+β3+β5 estimates the change in the sex ratio from the anticipated effect to 5 months thereafter (predicted duration) β3 = (β1+β3)-β1 = estimates the difference in change in sex ratio between after the election but before the anticipated effect occurred (segment 2) and before the election (segment 1) β5 = (β1+β3+β5) – (β1+β3) = estimates the difference in change in sex ratio between the time period from the anticipated effect to 5 months thereafter (segment 3) and the time period after the election but before the anticipated effect occurred (segment 2)

2

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Online Table 2: Segment regression models evaluating the changes in sex ratio when comparing time interval before the anticipatedeffect to the interval after the anticipated effect (Mar 2017 to July 2017), with the pre-effect segment defined in the following ways: (i) by excluding Dec 2016 to Feb 2017 and (ii) by aggregating the pre-election interval with this 3 month segment. Data are shownfor entire population, liberal-leaning regions and conservative-leaning regions, respectively. (i) Excluding Dec 2016 to Feb 2017:

β6 p-value β7 p-value β8 p-value β9 p-valueEntire population 1.0599 <0.0001 -0.000118 0.099 -0.0303 0.075 0.0118 0.02Liberal-leaning regions 1.0598 <0.0001 -0.000111 0.13 -0.0418 0.02 0.0166 0.002Conservative-leaning regions 1.061 <0.0001 -0.000132 0.51 0.0555 0.25 -0.0096 0.50

(ii) Aggregate Pre-effect Interval:


Note: β6 estimates the level of the sex ratio before the anticipated effect occurred (baseline level); β7 estimates the change in sex ratio before the anticipated effect occurred;β6+β8 estimates the level of the sex ratio after the effect occurred; β7+β9 estimates the change in sex ratio after the effect occurred.

Segment 1 -- Before Effect (Apr 2010 to Feb 2017)

Segment 2 -- After Effect (Mar 2017 - Jul 2017)


Baseline change in sex ratio

Difference in sex ratio compared to before

effect



Segment 1 -- Before Effect (Apr 2010 to Nov 2016)





effect



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1



Page number


1,2Title and abstract 1


2


reported 4

Objectives 3 State specific objectives, including any prespecified hypotheses 4,5




5Participants 6



5-9



5-9




5-9


5-9



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2


5,


Participants 13*


10,11

(b) Indicate number of participants with missing data for each variable of interest 10,11

Descriptive data

14*

(c) Cohort study—Summarise follow-up time (eg, average and total amount) 10.11

Cohort study—Report numbers of outcome events or summary measures over time 10,11Case-control study—Report numbers in each exposure category, or summary measures of exposure

Outcome data 15*


10,11

(b) Report category boundaries when continuous variables were categorized 10,11

Main results 16



11

DiscussionKey results 18 Summarise key results with reference to study objectives 12Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or



12-15






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the Subsequent Sex Ratio at Birth in Canada: An Ecologic Study

Journal: BMJ Open

Manuscript ID bmjopen-2019-031208.R2


Date Submitted by the Author: 08-Jan-2020


<b>Primary Subject Heading</b>: Obstetrics and gynaecology

Secondary Subject Heading: Public health, Epidemiology



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1


Subsequent Sex Ratio at Birth in Canada: An Ecologic Study







Text words: 3290


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ABSTRACT

Objectives: The sex ratio at birth (proportion of boys-to-girls) generally shows slight male


terrorist attacks and disasters typically lead to a temporary decline in the sex ratio 3-5 months

later, followed by resolution over ~5-months thereafter. We hypothesized that the unexpected

outcome of the 2016 US presidential election may have been a societal stressor for liberal-

leaning populations and thereby precipitated such an effect on the sex ratio in Canada.

Design: Ecologic study

Setting: Administrative data for Ontario (Canada’s most populous province)

Participants: All births in Ontario from April 2010 to Oct 2017 inclusive (n=1,079,758)

Primary and Secondary Outcome Measures: We determined the sex ratio at birth in Ontario

for each month from April 2010 to October 2017 and performed segmented regression analysis

to evaluate the seasonally-adjusted sex ratio for the following 3 time periods: before the

November 2016 election; following the election to before the anticipated impact; and from

anticipated impact to 5-months thereafter.

Results: In the 12-months following the election, the lowest sex ratio occurred in March 2017

(4-months post-election). Compared to preceding months, the sex ratio was lower in the 5-

months from March-July 2017 (p=0.02) during which time it was rising (p=0.01), reflecting

recovery from the nadir. Both effects were seen in liberal-leaning regions of Ontario (lower sex

ratio (p=0.006) and recovery (p=0.002) in March-July 2017) but not in conservative-leaning

areas (p=0.12 and p=0.49, respectively).

Conclusion: The 2016 US presidential election preceded a temporary reduction in the sex ratio

at birth in Canada, with the time course of changes therein matching the characteristic pattern of

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a discrete societal stressor.

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4



each month from April 2010 to October 2017, thereby enabling comprehensive

assessment of the pattern of changes in the sex ratio in this population.




The ecologic design with population-level data provides limited capacity for inference to

the level of the individual and hence causality cannot be definitively ascertained.


preferences or whether her perception of the election outcome contributed to fetal loss

and thereby impacted the sex ratio.

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5

INTRODUCTION


preponderance.1-3 Although its physiologic determinants are not well understood,3,4 it is

recognized that this ratio can be modified by adverse societal conditions. As there is no

conclusive evidence of variability in the sex ratio at conception,1 such variation in the analogous

ratio at birth is believed to reflect sex-specific differences in the likelihood of fetal demise at

various times during pregnancy.5,6 Indeed, adverse societal stressors such as natural and man-

made disasters,7-10 economic downturn,11 social unrest,10,12 and terrorist attacks10,13-17 have all

been reported to decrease the proportion of boys at birth, likely reflecting greater spontaneous

loss of male fetuses in response to these conditions.5,6 Notably, discrete events, such as terrorist

attacks, have typically resulted in a temporary decline in the sex ratio 3-5 months after the event,

followed by recovery in ~5 months thereafter.10,13-17 Indeed, this pattern has been seen after a

range of events including the Sep 11/2001 attacks,13-15 the 2004 Madrid bombings,10,17 the 2005

London bombings,10,17 the 2011 Norway attacks,16 and the 2012 Sandy Hook Elementary School

shooting.16 Moreover, this characteristic pattern of the sex ratio in the months thereafter has been

confirmed in a meta-analysis assessing the effect of these events on the sex ratio at birth.17



domestic and international ramifications that raised widespread societal concerns about the

future. Given its global implications, we hypothesized that the unanticipated election of the

nationalist right-leaning Republican nominee would be perceived by left-leaning populations

outside the US as an adverse societal event and could thereby have affected the sex ratio in such

countries. With its historically liberal society coupled with close geographic, economic, and

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socio-political ties to the US, Canada provides the prototypical example of such a country. Thus,

in this context, we hypothesized that (i) the outcome of the US presidential election on Nov. 8,

2016 may have precipitated a temporary decline in the sex ratio at birth in Canada’s most

populous province (Ontario) 3-5 months later and (ii) that this effect may relate to the political

preferences of the population.

METHODS



in Ontario from April 2010 to Oct 2017 (n=1,079,758 births). Specifically, we received the

number of births (total and live births) and sex breakdown thereof (numbers of boys and girls,

respectively) for each of the 91 months between April 2010 and Oct 2017 inclusive. As Ontario

has 14 geographically-distinct Local Health Integration Networks (LHINs) through which

healthcare is delivered across the province, we obtained the same data stratified by LHIN of

maternal residence. This study was approved by the Mount Sinai Hospital Research Ethics

Board.

All analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC). The sex ratio at

delivery was calculated as the ratio of males to females in each month from April 2010 to Oct

2017 inclusive. The time series of sex ratio thus comprised 91 timepoints. The analysis plan

consisted of the following two steps: seasonal adjustment and segmented regression.

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As it is known that the sex ratio is subject to seasonality,10,18 we used box plots of the time

series of sex ratio by month to examine a possible seasonal pattern. An Autoregressive Integrated

Moving Average (ARIMA) model-based seasonal adjustment method Tramo (time series

regression with ARIMA noise, missing values, and outliers)19,20 was implemented with PROC

X12 in SAS to remove the seasonal component from the time series. ARIMA model is a

generalization of an autoregressive moving average (ARMA) model, which is a combination of

the AR (autoregressive) and MA (moving average) models. The approach consists of three

stages: model identification, model estimation, and model diagnosis.

1. Model Identification – We used Akaike’s information criteria (AIC) to determine (i)

whether log transformation should be applied for the outcomes (sex ratios), and (ii) whether the

corresponding additive mode or multiplicative model should be applied to decompose the

seasonal component. Furthermore, the procedure identified the order for the unseasonal and

seasonal autoregressive and moving average terms. A series of combinations of orders were

generated and ranked in the order of Bayesian information criterion (BIC), so that the procedure

determined a best-fitting ARIMA model (0,1,1) (0,1,1) for our sex ratio series.

2. Model Estimation – Maximum likelihood method was used to estimate the seasonal



3. Model Diagnosis – Residual analyses were conducted to check whether the identified

model was appropriate, and Freidman and Kruskal-Wallis tests were performed to assess the

presence of seasonality in the seasonally-adjusted time series. Based on the seasonally-adjusted

time series of sex ratio, we determined when the lowest monthly sex ratio occurred in the year

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8

after the November 2016 election (Table 1).



election on the sex ratio in Ontario in the months thereafter. This method is powerful in that it

can (i) control the trend effect of sex ratio (i.e. to rule out the possibility that the observed

decline in March 2017 was due to a downward trend over time), (ii) reduce measurement bias by

ensuring concordance with population ratios rather than ratios at the LHIN/health region level,

and (iii) allow stratification analysis to evaluate the potential differential impact of the event

between different groups.21


months or timepoints from April/2010 to Oct/2016), (ii) the period from the election to before

the anticipated effect (consisting of 4 timepoints from Nov/2016 to Feb/2017), and (iii) the

period from the anticipated effect to the months thereafter (consisting of 8 timepoints from

March/2017 onwards). We constructed the segmented regression model in the form below,

assuming linearity of the trend lines within each segment. We tested autocorrelation of residuals

using the Durbin Watson statistic to confirm that the time series have no serious autocorrelations.

Figure 1 presents the time series of the seasonally-adjusted sex ratio by month from April 2010

to October 2017, with the predicted segmented regression line shown for the 3 segments. Since

the decline in the sex ratio after a discrete adverse societal event is a transient phenomenon, we

anticipated its presence for 5 months, as this was the time interval over which the sex ratio

recovered from its nadir after the Sep 11, 2001 attacks13 and the April 1992 Los Angeles riots12.

For this reason, the third interval in the segmented regression analyses ran from March 2017 to

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July 2017.




where time is a continuous variable indicating time in months from the start of the observation

period; event is an indicator taking value 0 before the election and 1 after it; and time after event

is a continuous variable counting the number of months after the election, taking value 0 before

the election and (time-80) after the election (which occurred at month 80); effect is an indicator

taking value 0 before the anticipated effect occurred and 1 after 1; time after effect is a

continuous variable counting the number of months after the anticipated effect, taking value 0

before the effect and (time-83) after the effect which occurred at month 84; β0 estimates the level

of the sex ratio before election (baseline level), which is the level at the beginning of the pre-

election period; β1 estimates the change in sex ratio before election, which is the slope of the

trend before election; β0+β2 estimates the level of the sex ratio after the election but before the

anticipated effect occurred; β1+β3 estimates the change in sex ratio after the election but before

the effect occurred; β0+β2+β4 estimates the level of the sex ratio after the effect occurred; and

β1+β3+β5 estimates the change in sex ratio after the effect occurred.

In addition, we conducted stratification analyses using the same segmented regression model

for the respective liberal-leaning and conservative–leaning areas of the province. To do so, we

first classified each LHIN as either liberal-leaning or conservative-leaning based on the political

party holding its constituent federal parliamentary ridings at the time of the US election in Nov

2016. Ridings were classified as liberal-leaning if held by either the Liberal Party or the New

Democratic Party. Ridings were classified as conservative-leaning if held by the Progressive

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Conservative Party. Based on the political parties holding the respective federal parliamentary

ridings comprising the geographic area of each LHIN, there were 11 liberal-leaning LHINs and 3






Finally, considering the limited data to fit the second line segment, we did two sensitivity

analyses (i) with the exclusion of the second segment (by removing the data from Dec 2016 to

Feb 2017), and (ii) with the aggregation of the first and second line segments, for the whole

population and the respective liberal-leaning and conservative-leaning areas. The segmented

regression model was then re-constructed as follows:

Seasonally-adjusted sex ratio = β6 + β7*time + β8*effect + β9*time after effect + error

term,

where time, effect and time after effect are defined same as model (1); β6 estimates the level of

the sex ratio before the anticipated effect occurred (baseline level); β7 estimates the change in

sex ratio before the anticipated effect occurred; β6+β8 estimates the level of the sex ratio after

the effect occurred; and β7+β9 estimates the change in sex ratio after the effect occurred.




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RESULTS


the election onwards (Nov 2016 to Oct 2017). During this time, the lowest seasonally-adjusted

sex ratio occurred in March 2017, which was 4 months after the election and thus precisely

within the anticipated 3-5 months post-event interval. Figure 1 presents a time series of the

seasonally-adjusted sex ratio by month from Apr 2010 to Oct 2017, with predicted segmented

regression lines shown for the following 3 intervals: (i) before the election (Apr 2010 to Oct

2016); (ii) from the election to before the anticipated effect (Nov 2016 to Feb 2017); and (iii)

from the anticipated effect to the 5 months thereafter (Mar 2017 to July 2017). This plot shows

that the fall in the sex ratio in March 2017 was followed by a recovery in the 5 months thereafter,

exhibiting the anticipated transient nature and time course of the predicted effect. Indeed,

segmented regression analysis (Table 2) confirmed that, compared to the period from the election

to before the anticipated effect (Nov 2016 to Feb 2017), the sex ratio was lower in the months

from March 2017 to July 2017 (β4=-0.0448, p=0.02). Moreover, the change in the sex ratio

differed significantly in the period from March 2017 to July 2017 (β5=0.0133, p=0.01),

reflecting a rising slope in the latter interval (i.e. recovery of the ratio). In contrast, neither the

sex ratio nor the change therein differed significantly between pre-election and the post-election

period before the anticipated effect (Nov 2016 to Feb 2017). Thus, taken together, these data are

indicative of a transient fall in the sex ratio 4 months after the election, with recovery in the 5

months thereafter.


degree to which the unexpected outcome of the election was perceived as an adverse societal

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event and thereby contributed to the observed changes in the sex ratio, we classified each Local

Health Integrated Network (LHIN) in Ontario as either liberal-leaning or conservative-leaning,

based on the political party holding its constituent federal parliamentary ridings at the time of the

US election. As shown in Figure 2, the patterns of changes in the sex ratio differed markedly

between liberal- and conservative-leaning regions. Indeed, in the liberal-leaning regions, the

findings matched those observed in the entire population (Table 2). Specifically, compared to the

period from the election to before the anticipated effect, the post-effect interval from March 2017

to July 2017 showed a significantly lower sex ratio (β4=-0.0539, p=0.006), coupled with a rising

slope (β5=0.0173, p=0.002). In contrast, in the conservative-leaning regions (Table 2), the

analogous comparisons showed no significant differences in either the sex ratio (β4=0.0823,

p=0.12) or the change therein (β5=-0.0103, p=0.49). The same findings were observed when the

analyses were limited to live births only (Online Table 1).

We also performed sensitivity analyses with two segments (before the anticipated effect and

the post-effect interval) in 2 ways: (i) by excluding the 3 months from December 2016 to

February 2017 and (ii) by including these 3 months in the pre-effect segment (Online Table 2).

With both approaches, the post-effect interval in the liberal-leaning regions showed a

significantly lower sex ratio with a rising slope, while the conservative-leaning regions showed

neither.

DISCUSSION




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13








In humans, despite relative balance in the proportion of spermatozoa carrying a Y-

chromosome to those carrying an X-chromosome,22 there is typically a slight preponderance of

boys at delivery. This imbalance at birth has been attributed to sex-specific differences in fetal

vulnerability during specific time periods in pregnancy.23 Indeed, after initial balance at

conception, the sex ratio in humans varies at different timepoints across gestation, with total

female mortality in utero ultimately exceeding male mortality (thereby yielding the slight excess

of boys at delivery).23 Thus, changes in the sex ratio at birth can reflect the impact of sex-specific

differences in fetal loss during pregnancy.





species by yielding a “culled cohort” of healthier males that are better able to reproduce and

hence increase the likelihood of survival of the population.5,6,24 Amongst such societal stressors

in humans, discrete events such as terrorist attacks have typically induced a characteristic pattern


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weeks gestation.10,17 In other words, the greater loss of male fetuses who are within this

vulnerable window at the time of the event results in a depression of the sex ratio 3-5 months

later when these babies would otherwise have been born. For example, after the terrorist attacks

of September 11, 2001, the sex ratio fell 3-5 months later in New York,13 California,14 and the

entire US,15 accompanied by greater male fetal deaths in the intervening months.15 Indeed, this

post-event loss of male babies has emerged as an under-recognized contributor to the overall

casualty toll following terrorist attacks such as 9/11, the 2011 Norway attacks, and the 2012

Sandy Hook Elementary School shooting.17

Against this background, we hypothesized that the unexpected victory of the nationalist,

right-leaning Republican nominee in the 2016 US election and its resultant uncertain global

implications could have been perceived as a societal stressor in left-leaning nations and thereby

affected the sex ratio in a country such as Canada. Although we cannot definitively ascertain

causality with the current study design, three lines of evidence arising from these data support

this hypothesis. First, the hypothesized pattern of a transient decline in the sex ratio at birth

followed by recovery thereafter was indeed observed in Ontario. Second, although other

unrecognized societal factors may also affect the sex ratio, the anticipated decline occurred

precisely within the predicted window of 3-5 months following the election, as did the recovery

in the 5 months thereafter. Third, this effect was observed in liberal-leaning regions where the

population may have perceived the outcome of the election as an adverse societal stressor, but

not in conservative-leaning jurisdictions (where it may not have been perceived in this way). It is

notable that the pattern of change in the sex ratio in the liberal regions precisely matched that

which would occur after a discrete adverse event, with both the nadir 4-months post-election and

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continuous rise (recovery) over the 5-months that followed (Figure 2A and Table 2). In contrast,

the sex ratio pattern in conservative regions showed neither of these characteristic features

(Figure 2B and Table 2).




sex ratio.25 Moreover, a strength of this study is its evaluation of all births in Ontario, such that

the apparent differential post-election sex ratio pattern in the 3 conservative-leaning LHINs (in

contrast to the 11 liberal-leaning LHINs) is not a reflection of limited power but instead

indicative of some difference between the respective populations (though neither individual

political preference nor the perception of stress in response to the election can be ascertained).

Thus, limitations notwithstanding, we believe that the current data are collectively supportive of

the hypothesis in question, owing to the precision of the predicted effect in both pattern and

timing in both the entire provincial and politically-stratified populations.


populous province 4 months after the 2016 US presidential election followed by recovery in the

5 months thereafter, reflecting the characteristic pattern of changes observed after an adverse

societal event. Moreover, this effect was observed in liberal-leaning jurisdictions of Ontario, but

not in conservative-leaning regions. It thus emerges that the unanticipated outcome of the 2016

US presidential election may have held unrecognized implications for the populations of other

countries, where its perception as a societal stressor may have impacted the sex ratio at birth in

the months thereafter.

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FUNDING

This study was supported by intramural funds from the Leadership Sinai Centre for Diabetes.

The funding source had no role in study design, data collection, data analysis, data interpretation,

or writing of the report.

COPYRIGHT







iv) to exploit all subsidiary rights in the Contribution, v) the inclusion of electronic links from

the Contribution to third party material where-ever it may be located; and, vi) licence any third

party to do any or all of the above.

ACKNOWLEDGEMENTS



CONTRIBUTIONS

R Retnakaran conceived the hypothesis. R Retnakaran and C Ye designed the analysis plan. C

Ye performed the analyses. R Retnakaran wrote the manuscript. Both authors interpreted the

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data, critically revised the manuscript for important intellectual content, and approved the final


for the integrity of the data and the accuracy of the data analysis. The corresponding author

attests that all listed authors meet authorship criteria and that no others meeting the criteria have

been omitted.

TRANSPARENCY DECLARATION: R Retnakaran is guarantor and affirms that this

manuscript is an honest, accurate, and transparent account of the study being reported; that no

important aspects of the study have been omitted; and that any discrepancies from the study as

planned (and, if relevant, registered) have been explained.




Board

COMPETING INTERESTS



from Novo Nordisk, grants from Boehringer Ingelheim, personal fees from Eli Lilly, personal

fees from Takeda, personal fees from Sanofi, outside the submitted work. Ms, Ye has nothing to

disclose.

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REFERENCES










10. Grech V, Zammit D. A review of terrorism and its reduction of the gender ratio at birth after seasonal adjustment. Early Hum Dev 2017; 115:2-8.





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16. Grech V. Terrorist attacks and the male-to-female ratio at birth: The Troubles in Northern Ireland, the Rodney King riots, and the Breivik and Sandy Hook shootings. Early Hum Dev 2015; 91: 837–840.










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Month

Number of Births

(n)

CrudeSex Ratio

(M:F)



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(Apr 2010 to Oct 2016)


(Nov 2016 to Feb 2017)


(Mar 2017 - July 2017)


before election


before election











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β1 estimates the change in the sex ratio before the electionβ1+β3 estimates the change in the sex ratio after the election but before the anticipated effect occurredβ1+β3+β5 estimates the change in the sex ratio from the anticipated effect to 5 months thereafter (predicted duration)β3 = (β1+β3)-β1 = estimates the difference in change in sex ratio between after the election but before the anticipated effect occurred (segment 2) and before the election (segment 1)β5 = (β1+β3+β5) – (β1+β3) = estimates the difference in change in sex ratio between the time period from the anticipated effect to 5 months thereafter (segment 3) and the time period after the election but before the anticipated effect occurred (segment 2)

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FIGURE LEGENDS

Figure 1: Time series of seasonally-adjusted sex ratio by month from Apr 2010 to Oct 2017.

The predicted regression line for the sex ratio is shown for the following 3 intervals: (i) before

election (Apr 2010 to Oct 2016), (ii) period from election to before the anticipated effect (Nov

2016 to Feb 2017), and (iii) the period from the anticipated effect to 5 months thereafter (Mar

2017 to July 2017), respectively.


to October 2017 in (Panel A) liberal-leaning regions and (Panel B) conservative-leaning

regions. Each panel shows the predicted regression line for the sex ratio for (i) the period from

the election to before the anticipated effect (November 2016 to February 2017) and (ii) the

period from anticipated effect to 5 months thereafter (March 2017 to July 2017)

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0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

Sex

Ratio

(Mal

e:Fe

mal

e)


Anticipated effect

Mar 2017

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1.06271 -0.00351 Feb-17 1.059451 1.0592031.025267 0.001651 Mar-17 1.024706 1.0269191.041709 0.001236 Apr-17 1.039618 1.042945




0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

Sex

Ratio

(Mal

e:Fe

mal

e)

Anticipatedeffect

0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

Sex

Ratio

(Mal

e:Fe

mal

e)

Anticipatedeffect

Liberal areas

Conservative areas

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Online Appendix


Online Table 2: Segment regression models evaluating the changes in sex ratio when comparing time interval before the anticipated effect to the interval after the anticipated effect (Mar 2017 to July 2017), with the pre-effect segment defined in the following ways: (i) by excluding Dec 2016 to Feb 2017 and (ii) by aggregating the pre-election interval with this 3 month segment. Data are shown for entire population, liberal-leaning regions and conservative-leaning regions, respectively.

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(Apr 2010 to Oct 2016)


(Nov 2016 to Feb 2017)


(Mar 2017 - July 2017)


before election


before election









β0 p-value β1 p-value β2 p-value β3 p-value β4 p-value β5 p-value Entire population 1.0595 <0.0001 -0.000126 0.11 0.018 0.14 -0.00136 0.4 -0.0403 0.03 0.0122 0.02 Liberal-leaning regions 1.0596 <0.0001 -0.000128 0.12 0.0151 0.24 -0.000695 0.68 -0.0505 0.01 0.0163 0.004 Conservative-leaning regions 1.0669 <0.0001 -0.000207 0.37 -0.0377 0.3 0.002138 0.65 0.0952 0.087 -0.0141 0.37 Notes re interpretation of level of sex ratio and change in sex ratio: β0 estimates the level of the sex ratio before the election (baseline level) β0+β2 estimates the level of the sex ratio after the election but before the anticipated effect occurred β0+β2+β4 estimates the level of the sex ratio from the anticipated effect to 5 months thereafter (predicted duration) β2 = (β0+β2)-β0 = estimates the difference in sex ratio between after the election but before the anticipated effect occurred (segment 2) and before the election (segment 1) β4 = (β0+β2+β4) – (β0+β2) = estimates the difference in sex ratio between the time period from the anticipated effect to 5 months thereafter (segment 3) and the time period after the election but before the anticipated effect occurred (segment 2)

1

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β1 estimates the change in the sex ratio before the election β1+β3 estimates the change in the sex ratio after the election but before the anticipated effect occurred β1+β3+β5 estimates the change in the sex ratio from the anticipated effect to 5 months thereafter (predicted duration) β3 = (β1+β3)-β1 = estimates the difference in change in sex ratio between after the election but before the anticipated effect occurred (segment 2) and before the election (segment 1) β5 = (β1+β3+β5) – (β1+β3) = estimates the difference in change in sex ratio between the time period from the anticipated effect to 5 months thereafter (segment 3) and the time period after the election but before the anticipated effect occurred (segment 2)

2

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Online Table 2: Segment regression models evaluating the changes in sex ratio when comparing time interval before the anticipatedeffect to the interval after the anticipated effect (Mar 2017 to July 2017), with the pre-effect segment defined in the following ways: (i) by excluding Dec 2016 to Feb 2017 and (ii) by aggregating the pre-election interval with this 3 month segment. Data are shownfor entire population, liberal-leaning regions and conservative-leaning regions, respectively. (i) Excluding Dec 2016 to Feb 2017:


(ii) Aggregate Pre-effect Interval:


Note: β6 estimates the level of the sex ratio before the anticipated effect occurred (baseline level); β7 estimates the change in sex ratio before the anticipated effect occurred;β6+β8 estimates the level of the sex ratio after the effect occurred; β7+β9 estimates the change in sex ratio after the effect occurred.

Segment 1 -- Before Effect (Apr 2010 to Feb 2017)





effect



Segment 1 -- Before Effect (Apr 2010 to Nov 2016)





effect



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1



Page number


1,2Title and abstract 1


2


reported 5

Objectives 3 State specific objectives, including any prespecified hypotheses 5,6




6Participants 6



6-10



6-10




6-10


6-10



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2


6,


Participants 13*


11,12

(b) Indicate number of participants with missing data for each variable of interest 11,12

Descriptive data

14*

(c) Cohort study—Summarise follow-up time (eg, average and total amount) 11.12

Cohort study—Report numbers of outcome events or summary measures over time 11,12Case-control study—Report numbers in each exposure category, or summary measures of exposure

Outcome data 15*


11,12

(b) Report category boundaries when continuous variables were categorized 11,12

Main results 16



12

DiscussionKey results 18 Summarise key results with reference to study objectives

12,13Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or



12-15






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bmjopen.bmj.com€¦ · for peer review only. 2. results (a) report numbers of individuals at each...

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