Fertility is a key demographic parameter influenced by disaster. With the growing risk of disasters, interest in the fertility response to a disaster is increasing among the public, policy makers and researchers alike. As yet, a synthesis of the current evidence on how fertility changes after disaster does not exist. We reviewed 50 studies retrieved from a systematic search based on a pre-registered protocol. We found an overall negative impact of disasters on fertility. If any, increases in fertility were mostly linked with weather-related physical disasters. We also identified 13 distinct mechanisms which researchers have considered as underlying the fertility effects of disaster. By contrast to the common belief that disasters are more likely to increase fertility in contexts with already high fertility, we found little evidence to suggest that the total fertility rate of the studied populations was an important predictor of the direction, timing or size of fertility impacts. While this may be because no relationship exists, it may also be due to biases we observed in the literature towards studying high-income countries or high-cost disasters. We summarize the methodological limitations identified from the reviewed studies into six practical recommendations for future research. Our findings inform both the theories behind the fertility effects of disasters and the methods for studying them. Beyond the proximate perils of displacement, death, disease and injury, disasters are often accompanied by broader challenges such as economic crisis, reduced access to health facilities and other services, and food insecurity. Since childbearing is one of the most consequential choices people make, it is not surprising that disasters affect whether and when people have children, and how many. With the growing frequency and severity of disasters ,2 especiYet in spite of the importance of and broad interest in the topic, a synthesis of the current evidence on how disasters affect fertility (the number and timing of births) does not yet exist. Doing so is a complex task, for multiple reasons. First, ‘disaster’ is a blanket term for many types of catastrophic events, each of which may vary from one another in their impact on fertility. Second, even within disaster type, the direction and magnitude of the effects thereof differ depending on population characteristics . For exahttps://www.cdc.gov/outbreaks/index.html). We focused on studies that operationalized disasters as relatively concentrated and unanticipated exposure  and fertility [outcome]. Disaster is a difficult concept to define  and rangWe selected studies that were: (a) either published or in press in peer-reviewed journals as of early February 2022, (b) on human populations regardless of publication year and languages, as long as (c) information on the direction or magnitude (or both) of the relationship between disaster and fertility was reported. We restricted studies to those on (d) the relatively acute impact of a disaster, not extending to studies that had assessed exposure to post-disaster environments created by a disaster itself, such as displacement camps or food insecurity, or the intergenerational impact of disaster on fertility. We focused on studies which examined (e) one of the disaster types—both natural and technological—listed in the International Disasters Database  and (f)  (b)population-level fertility response to significantly and unexpectedly abrupt disruptions to routine. This operationalization of disaster had three implications for the scope of this review. First, exposure to disaster is not reduced to individual-level exposure. For example, we omitted studies that compared fertility between infected and uninfected individuals, as the literature on the fertility effect of HIV commonly does  or larger [positive] than 0, or not different from the control group [null]. Where possible, we extracted standardized effect sizes (e.g. percentage difference or change). If there were multiple effect sizes, we identified a summary effect, which is either the effect size reported from an author's preferred analysis or the effect size mentioned as the main finding of a study.post-disaster fertility of the affected population with the unaffected population, making it impossible to separate the impact of disaster from any pre-existing difference between the compared populations.From the 50 studies, we extracted 108 data points on fertility effects, of which 82 were summary effects. Out of the 108 effects, only 76 had further information about magnitude because some studies did not report fertility effects in numeric forms for multiple reasons. For example, a multi-country study reported effect values from the study's preferred analysis for only select countries with large and statistically significant effect , or, in It was not feasible to summarize effect sizes via meta-analysis, due to differences in research designs, effect size types, duration of effect measurement, as well as the unavailability of information necessary for meta-analysis. Instead, we conducted various alternative analyses. First, we used a vote-counting method , which c (f)Out of the 50 studies, 36 postulated at least one mechanism underlying the fertility effects of disaster.We classified mechanisms into 13 groups, which were also categorized as either behavioural or physiological mechanisms . Of the 13, the behavioural mechanisms were: child/community replacement effect (no. 1), economic conditions/uncertainty (no. 2), migration (no. 3), psychological (no. 4), partnership dynamics (no. 5) and postponement/reduction of family size (no. 8). The physiological mechanisms were: disruption of reproductive services (no. 9), preterm births (no. 12), and mortality of men or women of reproductive age (no. 13). There were two additional mechanisms that could be behavioural or physiological depending on contexts: conception given intercourse would be affected behaviourally via (no. 6) contraceptive use but also physiologically via (no. 10) infertility, and successful delivery given conception would be affected behaviourally via (no. 7) abortion but also physiologically via (no. 11) miscarriage.Although it is challenging to integrate the 13 mechanisms into one framework, we follow the approach taken by a previous review on fertility after wars, humanitarian crises and displacement  and late. 3 (a) (i)The 50 studies were published between 1975 and 2022 and covered 24 different disasters, which occurred between 1882 (the measles outbreak in Iceland) and 2020 (the COVID-19 pandemic) across 52 countries. Our study sample is biased toward studies that examined certain disaster types and regions. Twenty-two out of the 50 studies (44%) were on either the Zika epidemic, the 1918 influenza pandemic, or the COVID-19 pandemic, and the majority of these studies were based on samples from Brazil, Italy, Japan and the US. In this regard, 44% of the research on the disaster–fertility nexus has thus far focused on disease outbreaks from middle- and high-income countries. Indeed, 10 out of the 50 studies covered the US, and more than half (52%) covered either Brazil, Italy, Japan or the US.The literature is relatively young, as most works were published over the last two decades  and year of disaster occurrence. https://www.humanfertility.org) and the Short-Term Fertility Fluctuations Data Series (www.humanfertility.org/Data/STFF). Studies on more recent disasters  also concern relatively low fertility contexts with TFR below two ( (ii)In nearly two-thirds of the studies, one population serves as its own comparison group  for both case and control—the information required for meta-analysis of effect sizes on counts or rates data. Other less common ways to calculate effect sizes included correlating mortality at its peak with fertility 9–10 months after ,39,61 ora priori, if (i) a disaster is unanimously said to have occurred on a specific day or across a few days (e.g. earthquake) or (ii) the researcher makes an explicit assumption about when the disaster likely had the most impact on fertility. The latter was a frequent choice if there was a lag between the emergence of hazards and their development into a disaster, as in the case of the COVID-19 pandemic where the lockdowns are often regarded as its onset of impact in respective countries. Second, some studies analysed monthly data on proxies of exposure to disaster , to inductively identify the month at which the exposure (or the perceived exposure) peaked [The majority of the reviewed studies (84%) presented information on the onset of a disaster at least in monthly units, a prerequisite for defining the post-disaster period. The onset of a disaster was defined in two ways. First, researchers defined the onset of disaster prior to observing data, by referring to the time point when a disaster is known to have created an acute and major shock. Information on the timing was available ) peaked ,41,42,61 (b) (i)p = 0.006). Similarly, among the subset of 81 summary effect sizes (effect sizes upon which each study based its main conclusion), the proportion of negative effects was 0.71 . The tendency for negative effects became weaker when we further restricted to 46 effect sizes that adjusted for time trends, but the majority of effect sizes (59%) were still negative .The effect of disasters on fertility was generally negative, even when controlling for secular time trends. Out of the 108 effect sizes for which information on direction was available, 77 were negative and 26 were positive. According to the binomial test of the assumption that the counts of negative and positive effects are even, the proportion of negative effects was 0.66 , and the same was true within disease outbreaks (figure 4b) and physical disasters (figure 4c). This comparison was not possible within technological disasters, for which no positive effects were reported (figure 4d). We further examined the counts of effects by direction (positive versus negative), TFR and disaster type using log-linear models. Among the models with different combinations of variables, the model with the interaction between TFR and disaster type yielded the highest goodness of fit as suggested by lowest model accuracy metrics . This finding is likely driven by the effect sizes from physical disasters, because the majority (78%) of positive effects reported from physical disasters pertained to contexts with the TFR above 2.0 (figure 4c). These results again suggest that the direction of effects may not differ by the TFR of the studied populations.Effect directions by time points.Among the 13 studies that reported effect sizes across multiple time points since the onset of a disaster , only onMagnitude of fertility effects. There were 18 effect sizes that reported effects as percentage changes and adjusted for seasonality and time trends in fertility . Most of them were negative, ranging from −1.7 to −43%. Only one effect was positive, showing a 9.5% increase. From these studies, two interesting patterns emerge. First, for studies that examine precisely the same context and disaster, effect sizes and directions agree very closely. One example is the 2011 Japanese earthquake, where two studies found a 9–10% reduction in fertility. Second, for studies that analyse the same disaster and country context but different sub-national regions, effect sizes for smaller areas tend to be larger in absolute terms. For example, four studies examine the effect of Zika in Brazil: a study on the Northeast region alone found a 25% decline in births, whereas a broader analysis of the 36 largest cities found only a 7.7% decline, and two yet broader analysis  found a very similar 1.7% and 1.9% decline. This is possibly due to publication bias towards studies which find large effects—regional analyses with null effects are less likely to be published, leaving only those with large effects. Similar patterns are found among the studies on the 1918 influenza pandemic in the US and COVID-19 in Italy. (c)overall negative effect based on their analyses.Among the 50 reviewed studies, 36 (72%) referred to at least one possible mechanism to explain the observed effects of disasters on fertility. Among the 20 studies on disease outbreaks that mentioned at least one mechanism and reported a negative impact on fertility, over half considered behavioural mechanisms. These included contraceptive use, postponement of childbearing, reductions in completed family size, or abortion, as well as psychological factors . Some of the studies also considered physiological mechanisms, such as those preventing successful delivery (e.g. miscarriage), reproductive age mortality and preterm births. Most of these 20 studies on disease outbreaks were on the Zika epidemic and the 1918 influenza pandemic, but notably, the former was mostly concerned with behavioural mechanisms (87% of the time) and the latter with physiological mechanisms (80% of the time). This diverging pattern reflects how different characteristics of risks associated with each disaster can result in the consideration of different mechanisms by researchers.This pattern also manifests itself in technological and physical disasters. For these disaster types, the mechanisms considered in each study were dominated by either behavioural or physiological mechanisms, but not both . Researc. 4This systematic literature review demonstrates that disasters reduce fertility, albeit heterogeneously by disaster type. Specifically, while physical disasters resulted in either positive or negative fertility responses, for disease outbreaks and technological disasters the summary effects were uniformly negative. When positive effects were found for physical disasters, they were more likely to occur in higher fertility contexts (TFR > 2.0). However, we find this tendency was not strong enough to support the oft-postulated notion that positive fertility effects are in general more likely in higher TFR countries ,7. In adThe concentration of positive effects from physical disasters may reflect differences in how risks develop and are perceived during these events. Physical disasters are characterized by a relatively acute, recognizable shock. As such, one can postulate that the waning of risks would also be perceived relatively easily, leading to a higher probability of fertility increase during the recovery period. By contrast, disease outbreaks and technological disasters bring risks that are often unclear, prolonged or difficult to track . Such uncertainty, together with the direct and often long-term physiological effects of virus infection or toxic materials on fecundity and health in general, may underlie the predominant pathways by which disease outbreaks and technological disasters are thought to reduce fertility.rapid outbreaks, and excluded endemic diseases such as HIV/AIDS. In other words, the study operationalized epidemics conceptually similar to physical disasters in terms of the time scale of risk development. It can thus be summarized that disasters involving higher uncertainty about risks may contribute to fertility reduction, whereas the evidence of fertility decline is weaker for relatively short-duration disasters. As such, the study suggests that the general findings from our review may hold even in relatively high fertility contexts such as in Africa.Our findings are corroborated by studies that were not included in the present review because they did not meet our eligibility criteria –34. A reWhile we find clear uniformity in the direction of summary effects for some disaster types, overall the results were heterogeneous. Among physical disasters, the direction of effects was variable, but also even within the same disaster, and even within the same population groups. As an example of between-population heterogeneity, one study on the 2004 Indonesian tsunami conducted in the most severely affected regions found a strong negative effect , while aa priori.We also note that the current literature is biased toward research on certain contexts and disasters, in spite of a substantial expansion of research on the disaster–fertility nexus during this period. Particularly noticeable were gaps for low-income countries, and for certain high-casualty disasters which occurred during the last two decades. This is consistent with a preoccupation with disasters causing high economic losses rather than high casualties . SimilarIn the remainder of this paper, we identify common limitations from the reviewed literature and summarize them into six practical recommendations for future research. While not prescriptive, these recommendations can help researchers produce and evaluate evidence on causal effects—essential for a field involving humans where experimental evidence is problematic. Whenever possible, we highlight examples of methods from reviewed studies that can be usefully applied for future research.First, data infrastructures for collecting data in low-resource settings should be supported, particularly during disasters (Recommendation 1). Conducting regular yet well-spaced population censuses is essential for monitoring the effect of disaster on fertility and mortality; however, doing so remains difficult for many low-income countries . ResearcImproved data availability, in turn, will lay the groundwork for more evidence building on how disaster affects fertility in a global perspective. Findings that do not meet the conventional threshold of ‘statistical significance’ should be published (Recommendation 2), as long as the data, analytic approach, and estimated effects are clear . Doing Future research should present more detailed information on the population characteristics of comparison groups, or at least indicate if such information is not available (Recommendation 3), and wherever possible, analytically adjust for differences between compared groups that may confound the disaster–fertility relationship (Recommendation 4). Researchers, either implicitly or explicitly, assume that groups that are being compared are generally similar aside from the exposure to disaster, otherwise known as the ‘homogeneity assumption’. Satisfying this assumption helps rule out alternative explanations that attribute observed fertility changes to factors unrelated to disaster. When comparing fertility within populations before and after a disaster, researchers should recognize that pre-disaster fertility differences across subpopulations may contribute to post-disaster gaps. Likewise, researchers should acknowledge that the effects of disasters on fertility may be direct and indirect. The frequency of sexual intercourse in a population may be lowered in the aftermath of a disaster due to physical damages or poor health (direct effect). At the same time, birth rates may decline due to changes in population structure (indirect effect), for example, if individuals of reproductive age were more likely to die due to the disaster than the overall population. Less than 10 out of the 50 studies tested specific threats to population homogeneity before and after the disaster, or even presented background characteristics of each compared group. These threats included both spatial (spillover of fertility effect to control cohorts ) and temDisasters do not happen in a vacuum, and fertility within countries is rarely stable. For example, fertility can rapidly fall in countries undergoing the demographic transition, and oscillate in countries that have already achieved low fertility. Many of the studies reviewed here did consider and account for these context-specific fertility trends, and such efforts would continue to benefit the literature in future (Recommendation 5). One common technique is to analytically adjust for seasonal fertility patterns and long-term fertility time trends. In our reviewed studies, the majority (80%) addressed seasonality, mostly by comparing fertility at least one year apart matched in months or by including month dummies in regression analyses. However, only half (52%) reported effect sizes that took time trends into account. This is problematic, given that a still large portion of the studied disasters occurred in contexts of declining fertility.post hoc, by subtracting 9–10 months from the period associated with the largest or statistically significant fertility effect. This approach might be useful as an exploratory technique, but is subjective and vulnerable to questionable research practices such as post hoc hypothesizing.Lastly, one of the most basic requirements for causal inference in this area is to establish that the disaster actually preceded the observed fertility change . Howeverlonger than 9–10 months, the average length of gestation. However, among such studies, only one provided a rationale for why a longer lag was chosen [Instead, some authors made explicit their beliefs about the timing and magnitude of fertility impacts, a practice that could aid in evaluating the evidence at hand and building theory (Recommendation 6). Some studies stated how large an effect is expected to be, in the form of either qualitative statements (‘little to no effect’ ) or evens chosen , demonst. 5Across the 50 studies examining the changes in live birth counts after disasters, we found that disasters generally have a negative fertility impact, depending on disaster type. If any, studies showing an increase in fertility were more likely to come from studies on physical disasters, which included avalanche, drought, earthquake and tsunami, flood, hurricane and typhoon in the reviewed studies. We also identified 13 distinct mechanism groups which researchers have considered as underlying the fertility effects of disaster. By contrast to the common belief that disasters are more likely to increase fertility in contexts with already high fertility, we found little evidence to suggest that the underlying fertility level was an important predictor of the direction, timing or size of fertility impacts. While this may be because no relationship exists, it may also be due to biases in the literature towards studying high-income countries or high-cost disasters. This bias may also arise from a lack of standardization by methods and timeframes in this literature, leading to a small number of studies across which to compare. We thus need more studies from high fertility contexts, and also more efforts to measure fertility effects comparably and across multiple time points. Doing so will also help disentangle key mechanisms of fertility effects which are numerous and complex. To encourage moves toward a more robust literature on this critical topic, and eventually toward evidence-informed theory and policy, we offer six recommendations for future research: (1) increase efforts to build and maintain data infrastructure for the collection of quality data in resource-limited settings; (2) nurture a publication culture that incentivizes reporting of results (including null results) to be compared across contexts; (3) collect and report characteristics of compared groups; (4) analytically address threats to the homogeneity assumption; (5) adjust for seasonality and time trends in fertility; (6) be explicit about when and how the expected fertility effects of disasters are likely to occur. Given the predicted increase in disasters—particularly in the context of global climate change—even subtle changes in fertility in response to disasters may have far-reaching consequences in population structure. Thus, the importance of studying how disasters affect fertility is higher than ever, and should remain a major priority in the research community across disciplines, from demography to population studies, economics, anthropology, sociology, public health and epidemiology.


Here, we summarize the characteristics of cranial sutures and SMSCs, and the important functions of the FGF signaling pathway in SMSC and cranial suture development as well as diseases caused by suture dysfunction. We also discuss emerging current and future studies of signaling regulation in SMSCs.Suture mesenchymal stem cells (SMSCs) are a heterogeneous stem cell population with the ability to self-renew and differentiate into multiple cell lineages. The cranial suture provides a niche for SMSCs to maintain suture patency, allowing for cranial bone repair and regeneration. In addition, the cranial suture functions as an intramembranous bone growth site during craniofacial bone development. Defects in suture development have been implicated in various congenital diseases, such as sutural agenesis and craniosynostosis. However, it remains largely unknown how intricate signaling pathways orchestrate suture and SMSC function in craniofacial bone development, homeostasis, repair and diseases. Studies in patients with syndromic craniosynostosis identified fibroblast growth factor (FGF) signaling as an important signaling pathway that regulates cranial vault development. A series of  Different from long bones, which are formed through endochondral ossification, most cranial bones are formed through intramembranous ossification directly from mesenchymal cells without a cartilaginous template . As showFGFR2 gain-of-function mutation) and Muenke syndrome (FGFR3 gain-of-function mutation)  . In addinostosis . In humanostosis . The remnostosis . HoweverMost cranial bones, such as the nasal bone, frontal bone, and part of the interparietal bone, are derived from neural crest (NC) cells, whereas the parietal bone and most of the occipital bones originate from paraxial mesoderm cells . Reportsin vivo. Gli1+ SMSCs and their derivatives were detectable in cranial suture mesenchyme, periosteum, dura mater, and quite a few osteocytes in the calvaria bones  mice displayed coronal suture dysgenesis, characterized by the reduced or absent overlap of frontal and parietal bones seen in wildtype mice with little or no intervening suture mesenchyme, resulting in more closely apposed OFs in the coronal suture  with coronal synostosis, suggesting the potential functions of Six2+ SMSCs in suture patency , the PI3 kinase/AKT pathway, and the phospholipase Cγ (PLCγ) kinase pathway . The actFGF signaling contributes to the development of most craniofacial structures, such as the development and outgrowth of the facial primordia, craniofacial skeletogenesis, palatogenesis, as well as development of submandibular salivary gland, teeth, eye lids, craniofacial muscles, and muscular tongue . PerturbThroughout cranial suture, SMSCs participate in cranial bone growth and development, homeostatic maintenance, injury repair, and cranial suture patency or fusion, which are precisely orchestrated by fine-tuned signals. Studies on patients with syndromic craniosynostosis and a series of mouse studies have indicated a pivotal role of FGF signaling in the development of cranial sutures.Fgf18-deficient mice exhibited delayed suture closure with decreased proliferation of osteogenic mesenchymal cells and delayed terminal differentiation of osteoblasts  repressed FGF2-induced cranial suture closure in cultured mouse calvaria, and decreased osteoblast differentiation . Repressnostosis . A studyant mice . Wang etl suture . Howeverl suture . Additioeoblasts . TogetheFGFR2 gene in the D2-D3 linker region, which leads to FGFR2 gain-of-function in a ligand-dependent manner (FGFR1 (Pro252Arg) or FGFR2  (FGFR1 (Pro252Arg) mutation leads to a bulkier residue that enhances the binding affinity of the receptor to the ligand to increase receptor activation  . The FGFtivation . The FGFide bond . Muenke s ligand , such ass ligand . Howevern humans .FGF2, FGF3, and FGF4 overexpression lead to suture synostosis, and FGF18 loss-of-function results in delayed suture closure. Gain-of-function mutations of FGFR1 and FGFR2 in mice also impact cranial suture development  in humans, leads to the premature fusion of calvarial sutures including frontal, sagittal, and coronal sutures  mutant mice also showed partial premature fusion of coronal sutures and impaired frontal bones, suggesting important roles of FGFR3 in suture patency and membranous ossification , a genetic model for Muenke syndrome, show a rounded skull and shortened snout with dental malocclusion which are similar to Muenke syndrome features in humans. However, coronal craniosynostosis in human patients is not reliably reproduced in this mouse model  with open fontanelles. Similarly, open fontanelles were also observed in Runx2+/− mutant mice with disturbed sagittal suture formation  expression to promote mineralization. BMP2 inhibition could reduce the induction of FGF2 to later-stage osteoblast differentiation of cranial suture cells (FGFR2+/S252W) mouse models  expression in cultured cells isolated from coronal sutures of FGFR2+/S252W mice, indicating an interaction between FGFR2 and Wnt/β-catenin signaling (As a pivotal regulatory signaling that functions during cranial suture development, FGF signaling broadly crosstalks with many other transcription factors and signals to orchestrate complicated processes. For example, Twist, a basic helix-loop-helix transcription factor, is expressed in SMSCs and regulates osteoblast differentiation and cells apoptosis . The hapl suture . In cont closure . In addiype mice . In wildsenchyme . Accordisenchyme . MSX1 ansenchyme . MSX2 ovfication . In mousormation . Interesof SMSCs . They fuelopment . Additioelopment . Sasaki mutation . BMP sigmutation . Moreovemutation . FGF2 trre cells . Recentle models . Lrp5 anignaling . The FGFignaling . HoweverSMSCs are located in the cranial suture and are characterized as a heterogeneous stem cell population. SMSCs have a distinct ability to self-renew and differentiate into multiple cell lineages, including osteoblasts and chondrocytes, in a tempo-spatial dependent manner. SMSCs make significant contributions to craniofacial development, suture patency maintenance, and cranial bone repair and regeneration. It has been established that the proliferation, differentiation, and apoptosis of SMSCs and their derivatives are associated with multiple factors and signaling pathways, including Twist, Msx1/2, Gli1, Axin2, as well as FGF, Wnt, Hedgehog, NOTCH, Hippo, and mechanical signaling to orchestrate SMSCs and cranial suture development. Any perturbation of these factors and pathways may open a window for an array of diseases caused by abnormal development of SMSCs and cranial sutures, especially those characterized by craniosynostosis.FGFR gain-of-function mutations, either in a ligand-dependent manner by altering the ligand-binding affinity or specificity, or in a ligand-independent manner through stabilizing intermolecular disulfide bonds to constitutively activate the receptor and signaling. Reports have shown that the majority of craniosynostosis syndromes were related to FGFR2 gain-of-function mutations. However, it is worth noting that different sutures respond to FGF-FGFR signaling differently. Compared with other sutures, craniosynostosis mainly occurs in the coronal sutures in FGFR gain-of-function mutant animal models, such as FGFR2+/S250W and FGFR2+/S252W transgenic mice, and FGFR3Y367C/+ mutant murine models (The FGF signaling pathway is a highly conserved, fundamental pathway that regulates numerous processes, ranging from embryonic development and organogenesis to adult tissue repair and regeneration. Dysfunction of FGF signaling has been linked to multiple human diseases , such ase models . This ma