Within-mother analysis of seasonal patterns in health at birth

Janet Currie; Hannes Schwandt

doi:10.1073/pnas.1307582110

New Research In

Edited by Kenneth W. Wachter, University of California, Berkeley, CA, and approved June 4, 2013 (received for review April 22, 2013)

This article has a Correction. Please see:

Correction for Currie and Schwandt, Within-mother analysis of seasonal patterns in health at birth - July 22, 2013

Abstract

A large literature describes relationships between month of birth, birth weight, and gestation. These relationships are hypothesized to reflect the causal impact of seasonal environmental factors. However, recent work casts doubt on this interpretation by showing that mothers with lower socioeconomic status are more likely to give birth in months that are associated with poorer birth outcomes. Seasonality in the numbers of conceptions in different months can also induce a mechanical correlation between preterm birth and month of birth. This paper analyzes the seasonality of health at birth using a large sample of 647,050 groups of US siblings representing 1,435,213 children. By following the same mother over time, we eliminate differences in fixed maternal characteristics as an explanation for seasonal differences in health at birth. We find a sharp trough in gestation length among babies conceived in May, which corresponds to an increase in prematurity of more than 10%. Birth weight conditional on gestation length, however, is found to be strongly hump-shaped over the year, with 8–9 additional g for summer conceptions. We examine several potential mechanisms for explaining seasonality in birth outcomes that have generally been dismissed in the literature on seasonality in rich countries, notably disease prevalence and nutrition. The May trough in gestation length coincides with a higher influenza prevalence in January and February, when these babies are nearing full term, whereas the hump shape in birth weight is associated with a similar pattern in pregnancy weight gain.

A venerable literature spanning many scientific fields has investigated the relationship between season of birth, health, and socioeconomic outcomes. Seasonal patterns have been found for birth outcomes, mental health, neurological disorders, body height, life expectancy, intelligence quotient, educational attainment, and income (1 ⇓⇓⇓⇓⇓⇓–8). As an indicator of the size of this literature, the work by Torrey et al. (4) identifies more than 250 studies solely on the relationship between season of birth and schizophrenia. Although winter births tend to be associated with poorer outcomes in most studies, seasonal patterns differ from country to country, over time, and between study populations. Still, it is often hypothesized that these relationships reflect causal effects of seasonal environmental factors.

However, several papers point out that women who give birth in different seasons tend to have different characteristics. For example, Lam et al. (9) show that there is greater birth seasonality in nonwhite than white births. Similarly, Darrow et al. (10) show that mothers with lower socioeconomic status tend to give birth in months with poorer average birth outcomes. Buckles and Hungerman (8) show that mothers' education, race, age, and marital status all fluctuate systematically over the year and argue that these fluctuations may explain much of the relationship between season of birth and outcomes. The issue of selection has long been recognized in the seasonality literature (11). Still, many season-of-birth studies do not control for maternal characteristics, or they include only broad socioeconomic proxies that are unlikely to capture the entire extent of selection.

In this paper, we analyze the seasonality of health at birth by comparing siblings conceived by the same mother at different times. Because the effects of seasonality are computed by following the same woman over time, they are not contaminated by socioeconomic differences between mothers who select into different conception months. We thereby contribute selection-free estimates to a literature that can be traced back for almost a century (12, 13).*

Furthermore, we shed light on potential mechanisms, especially infectious disease and nutrition, which have been downplayed in the previous literature (1). For example, influenza infections are known to trigger adverse birth outcomes (15 ⇓⇓–18), perhaps by causing inflammation, which has itself been linked to the cascade of events that trigger labor (19, 20). Additionally, vaccinated mothers have been shown to be less likely to deliver prematurely than unvaccinated mothers during influenza season (21, 22). However, to our knowledge, seasonal influenza epidemics have not been linked to seasonal patterns in health at birth in developed countries. Nutrition during pregnancy has been found to affect birth weight (23, 24), but the seasonality literature has hypothesized that nutrition is an unlikely driver of seasonal birth outcomes in developed countries, where food supply fluctuates little over the year (1).

Our birth data come from Vital Statistics Natality, comprising 3.2 million birth records for all births in New Jersey from 1997 to 2006, New York City from 1994 to 2004, and Pennsylvania from 2004 to 2010. These data include some information relevant to nutrition, including prepregnancy weight and weight gain during pregnancy, in addition to other characteristics of the mother such as race and education and health behaviors such as smoking. To test for the role of seasonal influenza, we merge influenza monitoring data collected by the Center for Disease Control from 1997 onward.

We focus on the month of conception rather than the month of birth, because seasonal fluctuations in the conception rate lead to a mechanical relationship between average gestation length and birth month (Discussion). We chose gestation length and birth weight as the outcomes of interest; these outcomes are the most commonly examined measures of infant health at birth, and also, these measures have been associated with child and adult outcomes (25).

Results

We restrict attention to single births with nonmissing information on gestation length. To compare siblings, we further exclude mothers with no more than one birth during the observation period. These exclusions yield a sample of 1,435,213 births. Table 1 shows descriptive statistics for this sample as well as all births in the United States over the past two decades. The fraction of white mothers is lower in the regional sample, but the other maternal and birth characteristics look fairly similar to those characteristics in the full sample of US births.

View this table:

Table 1.

Summary statistics for the study sample and the overall United States

Socioeconomic characteristics are strongly associated with conception months. As the ordinary least squares (OLS) results in Fig. 1, dashed lines, indicate, mothers conceiving in the first one-half of the year are significantly more likely to be nonwhite, less educated, less likely to be married, and more likely to smoke during pregnancy than those mothers conceiving in the second one-half of the year (P < 0.001 in each regression). Controlling for a broad set of observable mother and birth characteristics (Fig. 1, dotted lines) weakens the seasonality in mothers' socioeconomic status (SES). However, a significant pattern remains in all cases (equality of month effects rejected with P < 0.001 in each regression). This finding indicates that the inclusion of even detailed observables is not sufficient to control for confounding because of selection into conception months.

Fig. 1.

Mother characteristics by month of conception. The coefficients from regressions of (A) a zero/one indicator for white mothers, (B) an indicator for mothers with more than 12 y of schooling, (C) an indicator for married mothers, and (D) an indicator for mothers smoking during pregnancy on month of conception indicator variables are displayed. January serves as the reference month. OLS regressions (dashed lines) control only for state-specific time trends. OLS + controls regressions (dotted lines) further control—except if coinciding with the dependent variable—for maternal race, education, age, marital status, and newborn's parity and sex. Within-mother regressions (solid lines) further add mother indicators. Numerical regression results are displayed in Table S1 (n = 1,435,213).

Fig. 1, solid lines, shows the results from within-mother comparisons. We can think of these comparisons as regressions with a perfect control for the mother’s type, which by construction, cannot vary with season of conception, because it is a fixed constant for each mother. Evidently, if we regress fixed characteristic of the mother, such as race, on this fixed constant for each mother, we would not expect to find any significant effect. Indeed, if it were not for a small amount of measurement error in measured race, it would not be possible to estimate such an equation. Hence, it is not surprising that including the mother fixed effects eliminates seasonality in maternal race (Fig. 1A). Similarly, there are only a small number of women who increase their education, and therefore, including the mother fixed effect eliminates seasonality in maternal education (Fig. 1B). These examples show that within-mother comparisons effectively control for selection into conception months.

Marital status and smoking behavior, however, might vary over time. For example, if mothers have a preference for certain conception months and if marital status and smoking correlate with whether a conception was planned, then one would expect seasonality in these characteristics to remain, even in within-mother comparisons. As the solid lines in Fig. 1 C and D show, however, within-mother comparisons eliminate the seasonality in these characteristics.

In Fig. 2A, we repeat these regressions with gestation length as the dependent variable. Fig. 2A, dashed line, shows that the seasonal pattern in gestation length matches the seasonal pattern in maternal characteristics, in that there are worse outcomes in the first 5 mo of the year than in the second one-half of the year (P < 0.001). The gestation length decreases by about 0.1 wk from January to May and jumps back to the January level in June. Thereafter, it moves around a slightly lower level. The similarity between seasonality in maternal characteristics and seasonality in gestation length suggests that there may be little to learn from these OLS results about potential effects of the conception month on birth outcomes, because the observed seasonality in birth outcomes could be largely driven by selection into conception.

Fig. 2.

Gestation length and influenza prevalence at birth by month of conception. The coefficients from regressions of (A) gestation length and (B) the fraction of patients visiting a healthcare center with influenza-like illnesses on the month of conception zero/one indicator variables are displayed. Numerical regression results are displayed in Table S2. Fig. 1 has additional notes.

When controlling for maternal and birth characteristics (Fig. 2A, dotted line) and within-mother comparisons (Fig. 2A, solid line), the magnitude of the estimated conception month effects decreases slightly. However, the qualitative seasonal pattern is quite similar. The average gestation length decreases significantly by 0.8 wk (P < 0.001) during the first 5 mo of the year and returns to the January level in June, where it remains for the rest of the year. This finding indicates that the seasonal pattern in gestation length is not driven by mothers of different socioeconomic background selecting differently into conception months. The May decrease in gestation length by 0.8 wk leads to an increase in premature births of about 1 percentage point (Fig. S1A). Given an average prematurity rate of 7.6%, this estimate indicates a 13.2% increase. This result holds across maternal SES for both males and females and when excluding firstborn children (Fig. S2). The similarity of the seasonal pattern in gestation length across specifications and subgroups suggests that external environmental factors affecting the society as a whole might play an important role. One such potential factor is seasonal influenza.

Fig. 2B illustrates the average fraction of patients in reporting healthcare centers diagnosed with influenza-like illnesses for each conception month (this measure is the Center for Disease Control's standard influenza monitoring measure; additional details are in Materials and Methods) during the month of birth. The fraction of influenza patients at birth increases during the first conception months of the year, reflecting the start of the influenza season in late August. It peaks for May conceptions, which are scheduled to be born in mid-February and tend to suffer a shortened gestation, with deliveries taking place in late January and early February when seasonal influenza is at its peak (Fig. S3A). In the following conception months, the fraction strongly declines until August and then remains at a level slightly below January. The strong correlation of gestation length and the prevalence of influenza (the correlation coefficient is −0.71) is remarkable.

The relationship between seasonal influenza and gestation length is further explored in Fig. 3, which exploits year to year variation in both the extent and the timing of influenza epidemics. The fall of 2009 saw the arrival of the H1N1 pandemic, which not only began much earlier (and peaked earlier) than a regular influenza season but also involved many more cases (Fig. S3B). If influenza leads to preterm birth, then we might expect to see both a stronger seasonality in gestation length and a shifting of the pattern to earlier months in 2009 relative to other years. Fig. 3 shows exactly this pattern: infants conceived in February to May had shorter gestations in 2009. The seasonal effect is about 1.5 times as large in 2009 as in other years, and the trough is shifted to February/March. This pattern is evident in both the OLS (Fig. 3A) and the within-mother specification (Fig. 3B). Corresponding prematurity results are Fig. S1B.

Fig. 3.

Gestation length by month of conception (1994–2010 vs. 2009). The coefficients from regressions of gestation length on month of conception indicator variables are displayed. The solid line shows the coefficients for the conception year 2009, and the dashed line is the estimated effect for the years 1994–2010 (net of the 2009 effect). January serves as the reference month. The regression in A controls for a quadratic time trend, maternal race, education, age, marital status, and newborn's parity and sex, and the regression in B adds mother indicators.

In Fig. 4, we turn to the second birth outcome: birth weight. The OLS results (dotted line in Fig. 4A) indicate a seasonal pattern in birth weight that matches the seasonality in maternal characteristics. Newborns conceived in the first 5 mo of the year have significantly lower birth weight than newborns conceived in the second one-half of the year (P < 0.001). Average birth weight decreases by 10 g during the first 5 mo of the year and then increases by more than 20 g in June. It remains at this high level for the summer months and declines slightly during the fall. This seasonal pattern coincides with the socioeconomic selection into conception months (Fig. 1) even more strongly than in the case of gestation length.

Fig. 4.

Birth weight, pregnancy weight gain, and prepregnancy weight by month of conception. The coefficients from regressions of (A) birth weight, (B) birth weight conditional on gestation length, (C) maternal weight gain during pregnancy, and (D) prepregnancy weight on month of conception zero/one indicator variables are displayed. B regressions include zero/one indicator variables for each week of gestation length. Negative weight gain is coded as zero weight gain in the New Jersey and New York City data. Prepregnancy weight is not reported in New Jersey. Numerical regression results are displayed in Table S2. Fig. 1 has additional notes.

Fig. 4A, dashed line, shows that controlling for a rich set of controls does little to dampen the strong seasonal pattern. However, Fig. 4A, solid line, shows the effects when comparing different births within the same mother. The decrease during the first 5 mo is less pronounced when we compare infants born to the same mother and is not significantly different from zero. For summer conceptions, however, average birth weight remains significantly higher, with about 8 additional g compared with January conceptions. This seasonal pattern is found across SES, child sex, and birth order groups (Fig. S4).

Because many low birth weight babies are premature, the seasonal pattern in birth weight might be driven by the strong seasonal pattern in gestation length shown in Fig. 2A. In particular, the jump in birth weight between May and June might reflect the dramatic change in average gestation length between these two months. In Fig. 4B, we repeat the birth weight regressions and include indicators for each week of gestational age. These figures continue to show a hump shape in birth weight with a gain of 8–9 additional g for summer conceptions compared with January conceptions. However, the jump in average birth weight between May and June disappears. Instead, birth weight begins to increase between April and May. This result suggests that shorter gestation length—potentially triggered by the prevalence of seasonal influenza at the month of birth—depresses the birth weight of May conceptions. However, the seasonal pattern in gestation does not serve as an explanation for the higher birth weights of summer conceptions.

One well-established determinant of birth weight is nutrition during pregnancy (23, 24), which can be proxied by maternal weight gain during pregnancy. Fig. 4C shows that there is a strong seasonal pattern in pregnancy weight gain. From January to June, average weight gain increases by about 0.8 lb. It remains at this level from June to August, before it declines back to about the level of January. Remarkably, this sizable hump shape hardly changes when additional controls are included and when we do within-mother comparisons. Additionally, the conception months of highest weight gain correspond to the months with highest birth weight.

A natural question to ask is whether these changes in weight gain over the year reflect differential maternal weight at birth or just a differential catch-up process because of seasonality in female body weight at conception. In Fig. 4D, we analyze prepregnancy weight. Indeed, prepregnancy weight decreases by 0.3–0.4 lb during the first months of the year and increases back to the initial level in November. However, prepregnancy weight remains constant in the middle of the year, indicating that changes in weight gain across these months do not seem to be related to differences in prepregnancy weight.

Discussion

Seasonal patterns in health at birth have been documented for almost a century. What has been unknown is the extent to which these associations are driven by socioeconomic selection into conception months. We address this issue by comparing birth outcomes for the same mother at different points in time. Our first set of results emphasizes the relevance of this empirical strategy. We show that the inclusion of observable mother characteristics is not sufficient to eliminate the strong seasonal selection found in our data. In contrast, comparing the effects of seasonality within mothers effectively controls for maternal characteristics that are associated with month of conception. We find strong seasonality in two central birth outcomes. Gestation length is lower for conceptions during the first months of the year, with a sharp minimum for May conceptions. Birth weight, however, is hump-shaped over the year, with the highest average weight for summer conceptions.

Some recent studies have argued that the seasonality of birth and later outcomes might be entirely driven by selection. Our results suggest the opposite conclusion. The seasonal effects on birth outcomes are so strong that the bias because of selection is relatively small. In other words, a high SES mother getting pregnant in an unfavorable month will, on average, experience similarly poor birth outcomes as the typical (lower SES) mother conceiving in this unfavorable month.

The seasonal pattern in birth outcomes that we find is similar to the results of other studies that examine seasonality at the level of the conception month (26). Findings from studies that analyze seasonality at the level of birth months, however, provide a broader range of results (reviews of the literature are in refs. 1, 6, and 7). One reason for these heterogeneous findings might be the fact that few papers adjust for the confounding role of fluctuations in the number of conceptions. It has long been known that human conception rates fluctuate considerably over the year and that these fluctuations are different across countries and times (9, 27, 28). To take an extreme example, if all conceptions occurred in mid-January, then all premature births would occur in September. We analyze this issue using data on all births in the United States over the past two decades and find that 21.8% of the seasonality in gestation length at the birth month level is mechanically caused by fluctuations in the conception rate over the year (Fig. S5). Our analysis suggests that an examination of seasonality at the birth month level that does not correct for seasonality in conceptions is likely to be severely biased, even if selection on SES is not a problem. Hence, this analysis provides a justification for focusing on month of conception rather than month of birth.

Many other mechanisms have been proposed for explaining seasonality in birth and later outcomes (4). Possibilities include seasonal variation in viral infections (29), seasonal allergies (30), pollution levels (31), eating patterns (24, 32, 33), temperature (6, 27, 34), and early fetal loss (9). One limitation of this research is that we cannot investigate all of the potential mechanisms that might generate seasonal effects. For example, given that our data are regional, it is not ideally suited for looking at the effects of variations in temperature or climate change, although these effects might be important.^†

Given the data available on birth records, we can examine some maternal health behaviors that are known to affect birth outcomes, such as smoking (35, 36) or use of medical care. We showed above that there was little evidence of a seasonal pattern in smoking within mothers or marital status, two factors that could correlate with whether a conception was planned. We have also investigated seasonality in use of medical care. We find that mothers with conceptions in October or November are the least likely to initiate prenatal care in the first trimester, a pattern that does not match well with the seasonality in birth outcomes (Fig. S6A). There is also a study that has found a spike in Cesarean sections in December (37); this spike might matter for the seasonality in gestation length and birth weight, but we find little evidence of a seasonal effect within mothers (Fig. S6B). Similarly, although there is a lengthy literature examining the sex ratio at birth as an outcome (38), we do not find that sex ratio is related to season of birth (Fig. S6C). Finally, some studies have highlighted the fact that season of birth could have effects after birth through, for example, social phenomena that intervene after birth, such as schooling laws (8). This later point suggests that it is useful to start with an analysis of season-of-birth effects on health at birth.

Seasonal influenza has received little prior attention in the literature on seasonality of birth outcomes, despite the strong effects of influenza infections on health at birth found in previous studies (15 ⇓–17, 39, 40). We find a strong and negative correlation of influenza with gestation length. The estimated correlation coefficient of −0.71 is even more notable given the discontinuous monthly pattern in both variables, which makes a spurious relationship unlikely. To our knowledge, this finding provides the strongest evidence to date that seasonal influenza might be a driver of the seasonality in gestation length.

An important caveat is that, in emphasizing the effect of influenza in apparently triggering preterm birth, we do not mean to dismiss the possibility that exposure earlier in the pregnancy could also have harmful effects on the developing fetus that are not reflected in shorter gestations or lower birth weights. For example, the works by Almond (39) and Kelly (40) suggest that surviving infants who were exposed to influenza during the first trimester of pregnancy may suffer lasting cognitive deficits.

Influenza-induced reductions in gestation seem to cause the relatively low levels of birth weight that we documented for conceptions during the first one-half of the year (Fig. 4 A and B). However, seasonal influenza fluctuations do not explain the high average birth weight of summer conceptions. We find strong seasonal effects on weight gain during pregnancy, with small effects on prepregnancy weight. Women gain almost 1 lb more when they conceive in June, July, or August than when they conceive in January, suggesting that gains in birth weight are driven, in part, by higher maternal weight gain during pregnancy.

In conclusion, by focusing on births to the same mother, our work provides evidence that there are seasonal patterns in birth weight and gestation that are not entirely driven by the fact that women with different characteristics tend to give birth at different times. We are also able to investigate several possible mechanisms for these seasonal effects. We find a strong relationship between influenza prevalence in the month of birth and prematurity: infants conceived in May are likely to be due in mid-February, which is the height of the flu season. Because influenza is known to cause premature labor, these infants are at higher risk of short gestation. Because they are of short gestation, they also tend to be lower birth weight. In addition, there seems to be another mechanism driving seasonal patterns in birth weight, which is that mothers who conceive during the summer months have higher pregnancy weight gain and hence, give birth to heavier infants.

Our results may have some implications for public policy, because they suggest that seasonal variations in nutrition matter for birth outcomes, even in rich countries, and that flu shots might be effective in fighting the seasonal deterioration in length of gestation. However, whether specific interventions are effective is a subject for future research.

Materials and Methods

Birth Data.

The regional birth data come from individual birth records and provide detailed information on birth outcomes, mother characteristics, and a code that allows us to match births to the same mother. The birth date is reported by month of birth, whereas gestation is coded in weeks. We calculate the month of conception, our key variable, by subtracting the rounded number of gestation months (gestation in weeks × 7/30.5) from the month of birth.

In addition to the sample restrictions described in the text, we exclude births that were conceived more than 6 mo before the beginning of the sample and less than 10 mo before the end of the sample, because for these conception months, only particularly long and short gestation lengths are observed, respectively [the work by Strand et al. (41) discusses this issue]. Controls are also included for missing values of explanatory variables.

Fig. S7 plots the number of conceptions over months by education group. Fig. S7 illustrates the two confounders involved in the relationship between season of birth and birth outcomes. Conception rates fluctuate strongly over the year, which leads to seasonality in gestation lengths, even in the absence of a causal effect of season of birth. Additionally, the composition of mothers conceiving over the year changes, such that seasonal differences in birth outcomes are confounded by differences in mothers’ SES.

Influenza Data.

The influenza data used in this study are collected by the Center for Disease Control's US Outpatient Influenza-Like Illness Surveillance Network. Each week, ∼1,800 outpatient healthcare providers around the country report data to the Center for Disease Control on the total number of patients seen and the number of those patients with influenza-like illness (ILI). We restrict the data to US Department of Health and Human Services regions 2 and 3, which include New Jersey, New York City, and Pennsylvania. The standard influenza measure derived from these data is the fraction of patients in reporting healthcare centers diagnosed with ILI. ILI is defined as fever [temperature of 100 °F (37.8 °C) or greater] and a cough and/or a sore throat without a known cause other than influenza. A plot of average ILI fractions by calendar week is shown in Fig. S3.

Analyses.

The analyses are estimated using three types of linear regression models:

where i indexes the newborn, c is the month of conception, m is the mother, Y_i is a characteristic of the newborn's mother or a birth outcome, μ_c is a vector of zero/one indicators for each month of conception, T_r is a region-specific time trend at the monthly level, X_i is a vector of control variables, ν_m is an indicator variable for each mother, and ε_i is a residual. Control variables include (unless the variable listed is identical to the dependent variable) indicators for mother's age (<20, 20–24, 25–29, 30–34, and 35+ y), education (<12, 12, 13–15, and 16+ y), race, Hispanic ethnicity, and marital status as well as the newborn's parity and sex.

We estimate all three equations using both maternal characteristics and birth outcomes as dependent variables. The three maternal characteristics regressions test whether mothers select into conception months (Eq. 1) and the extent to which additional controls (Eq. 2) and mother indicators (Eq. 3) control for this selection. The birth outcome regressions estimate the conception month effects given these different sets of controls.

The inclusion of mother indicators in Eq. 3 (so-called mother fixed effects) is equivalent to differencing all equation variables within different births of the same mother (42). This procedure controls for any characteristics of the mother that are constant over time. Notice that, given the inclusion of the mother indicators, the time trend T_r controls for the interval between conceptions (i.e., the maternal aging between pregnancies). [The inclusion of this control for maternal aging is necessary, because in the multiple birth sample, mothers are systematically younger at the beginning of the sample than at the end. Additionally, because one has to start the sample with some months (e.g., January, February, and March) and end it with some other months (e.g., November, December, and January), there will be a mechanical relationship between month of birth, maternal age, and other characteristics that are correlated with maternal age.]

Footnotes

↵¹To whom correspondence should be addressed. E-mail: jcurrie{at}princeton.edu.

Author contributions: J.C. and H.S. designed research, performed research, analyzed data, and wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1307582110/-/DCSupplemental.
↵*The sole previous paper using sibling comparisons that we are aware of is by Gavrilov and Gavrilova (14), who collected a sample of centenarians, their siblings, and spouses through internet search and found that centenarians are more likely than shorter-lived siblings to have been born in the fall. There may be some measurement issues involved with reconstructing siblingship groups in this fashion. More importantly, their analysis is at the birth month level and therefore, prone to the potentially strong conception rate confounder as discussed below.
↵^†There is still much uncertainty concerning the association between temperature and birth outcomes. The work by Strand et al. (6) has a review.

View Abstract

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(1985) Trend and variation in the seasonality of U.S. fertility, 1947-1976. Demography 22(1):89–100.
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1. Payne CM,
2. Ray CG,
3. Borduin V,
4. Minnich LL,
5. Lebowitz MD
(1986) An eight-year study of the viral agents of acute gastroenteritis in humans: Ultrastructural observations and seasonal distribution with a major emphasis on coronavirus-like particles. Diagn Microbiol Infect Dis 5(1):39–54.
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1. Wjst M,
2. et al.
(2005) Latitude, birth date, and allergy. PLoS Med 2(10):e294.
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1. Currie J,
2. Neidell M,
3. Schmieder JF
(2009) Air pollution and infant health: Lessons from New Jersey. J Health Econ 28(3):688–703.
OpenUrl CrossRef PubMed
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1. Olsen J,
2. Frische G,
3. Poulsen AO,
4. Kirchheiner H
(1989) Changing smoking, drinking, and eating behaviour among pregnant women in Denmark. Evaluation of a health campaign in a local region. Scand J Soc Med 17(4):277–280.
OpenUrl PubMed
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1. Siega-Riz AM,
2. Savitz DA,
3. Zeisel SH,
4. Thorp JM,
5. Herring A
(2004) Second trimester folate status and preterm birth. Am J Obstet Gynecol 191(6):1851–1857.
OpenUrl CrossRef PubMed
↵
1. Basu R,
2. Malig B,
3. Ostro B
(2010) High ambient temperature and the risk of preterm delivery. Am J Epidemiol 172(10):1108–1117.
OpenUrl Abstract/FREE Full Text
↵
1. Kyrklund-Blomberg NB,
2. Cnattingius S
(1998) Preterm birth and maternal smoking: Risks related to gestational age and onset of delivery. Am J Obstet Gynecol 179(4):1051–1055.
OpenUrl CrossRef PubMed
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2. Day MC
(2000) Perinatal complications associated with maternal tobacco use. Semin Neonatol 5(3):231–241.
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2. Chandra A
(1999) Taxes and the timing of births. Journal of Political Economy 107(1):161–177.
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1. Trivers RL,
2. Willard DE
(1973) Natural selection of parental ability to vary the sex ratio of offspring. Science 179(4068):90–92.
OpenUrl Abstract/FREE Full Text
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1. Almond D
(2006) Is the 1918 influenza pandemic over? Long-term effects of in utero influenza exposure in the post-1940 U.S. population. Journal of Political Economy 114(4):672–712.
OpenUrl CrossRef
↵
1. Kelly E
(2011) The scourge of Asian Flu: In utero exposure to pandemic influenza and the development of a cohort of British children. Journal of Human Resources 46(4):669–694.
OpenUrl Abstract/FREE Full Text
↵
1. Strand LB,
2. Barnett AG,
3. Tong S
(2011) Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes. BMC Med Res Methodol 11(1):49.
OpenUrl CrossRef PubMed
↵
1. Wooldridge JM
(2002) Econometric Analysis of Cross Section and Panel Data (MIT Press, Cambridge, MA).

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Proceedings of the National Academy of Sciences: 110 (30)

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Social Sciences
Social Sciences

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[89] Miron JA

[90] ↵
Seiver DA
(1985) Trend and variation in the seasonality of U.S. fertility, 1947-1976. Demography 22(1):89–100.
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[91] Seiver DA

[92] ↵
Payne CM,
Ray CG,
Borduin V,
Minnich LL,
Lebowitz MD
(1986) An eight-year study of the viral agents of acute gastroenteritis in humans: Ultrastructural observations and seasonal distribution with a major emphasis on coronavirus-like particles. Diagn Microbiol Infect Dis 5(1):39–54.
OpenUrl CrossRef PubMed

[93] Payne CM,

[94] Ray CG,

[95] Borduin V,

[96] Minnich LL,

[97] Lebowitz MD

[98] ↵
Wjst M,
et al.
(2005) Latitude, birth date, and allergy. PLoS Med 2(10):e294.
OpenUrl CrossRef PubMed

[99] Wjst M,

[100] et al.

[101] ↵
Currie J,
Neidell M,
Schmieder JF
(2009) Air pollution and infant health: Lessons from New Jersey. J Health Econ 28(3):688–703.
OpenUrl CrossRef PubMed

[102] Currie J,

[103] Neidell M,

[104] Schmieder JF

[105] ↵
Olsen J,
Frische G,
Poulsen AO,
Kirchheiner H
(1989) Changing smoking, drinking, and eating behaviour among pregnant women in Denmark. Evaluation of a health campaign in a local region. Scand J Soc Med 17(4):277–280.
OpenUrl PubMed

[106] Olsen J,

[107] Frische G,

[108] Poulsen AO,

[109] Kirchheiner H

[110] ↵
Siega-Riz AM,
Savitz DA,
Zeisel SH,
Thorp JM,
Herring A
(2004) Second trimester folate status and preterm birth. Am J Obstet Gynecol 191(6):1851–1857.
OpenUrl CrossRef PubMed

[111] Siega-Riz AM,

[112] Savitz DA,

[113] Zeisel SH,

[114] Thorp JM,

[115] Herring A

[116] ↵
Basu R,
Malig B,
Ostro B
(2010) High ambient temperature and the risk of preterm delivery. Am J Epidemiol 172(10):1108–1117.
OpenUrl Abstract/FREE Full Text

[117] Basu R,

[118] Malig B,

[119] Ostro B

[120] ↵
Kyrklund-Blomberg NB,
Cnattingius S
(1998) Preterm birth and maternal smoking: Risks related to gestational age and onset of delivery. Am J Obstet Gynecol 179(4):1051–1055.
OpenUrl CrossRef PubMed

[121] Kyrklund-Blomberg NB,

[122] Cnattingius S

[123] ↵
Andres RL,
Day MC
(2000) Perinatal complications associated with maternal tobacco use. Semin Neonatol 5(3):231–241.
OpenUrl CrossRef PubMed

[124] Andres RL,

[125] Day MC

[126] ↵
Dickert‐Conlin S,
Chandra A
(1999) Taxes and the timing of births. Journal of Political Economy 107(1):161–177.
OpenUrl CrossRef

[127] Dickert‐Conlin S,

[128] Chandra A

[129] ↵
Trivers RL,
Willard DE
(1973) Natural selection of parental ability to vary the sex ratio of offspring. Science 179(4068):90–92.
OpenUrl Abstract/FREE Full Text

[130] Trivers RL,

[131] Willard DE

[132] ↵
Almond D
(2006) Is the 1918 influenza pandemic over? Long-term effects of in utero influenza exposure in the post-1940 U.S. population. Journal of Political Economy 114(4):672–712.
OpenUrl CrossRef

[133] Almond D

[134] ↵
Kelly E
(2011) The scourge of Asian Flu: In utero exposure to pandemic influenza and the development of a cohort of British children. Journal of Human Resources 46(4):669–694.
OpenUrl Abstract/FREE Full Text

[135] Kelly E

[136] ↵
Strand LB,
Barnett AG,
Tong S
(2011) Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes. BMC Med Res Methodol 11(1):49.
OpenUrl CrossRef PubMed

[137] Strand LB,

[138] Barnett AG,

[139] Tong S

[140] ↵
Wooldridge JM
(2002) Econometric Analysis of Cross Section and Panel Data (MIT Press, Cambridge, MA).

[141] Wooldridge JM

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