We found that rainfall only increases fertility in higher altitude locations . This suggests the possibility of a combined or multi-factorial mechanism underlying the female fertility – birth season relationship. We discuss other environmental and sociological factors on the birth season – female fertility relationship. Future research should focus on the role of birth season and female fertility adjusting for additional factors that modulate female fertility as discussed in this comprehensive review.Globally, maternal birth season affects fertility later in life. The purpose of this systematic literature review is to comprehensively investigate the birth season and female fertility relationship. Using PubMed, we identified a set of 282 relevant fertility/birth season papers published between 1972 and 2018. We screened all 282 studies and removed 131 non-mammalian species studies on fertility and 122 studies that were on non-human mammals. Our meta-analysis focused on the remaining 29 human studies, including twelve human datasets from around the world . The main outcome was change in female fertility as observed by maternal birth month and whether this change was correlated with either temperature or rainfall. We found that temperature was either strongly correlated or anti-correlated in studies, indicating that  European countries show similarly high infertility rates with Great Britain at 17%3. In developing countries, the rate of infertility is estimated at 1 in 4 couples5. In addition to rising infertility rates among humans, increases in infertility and reproductive disorders were observed in dairy cows6. Dairy researchers found that a cool, low-temperature, environment appeared to preserve fertility and reduce the risk of reproductive disorders6 furthering support for a relationship between temperature and female fertility. Although at least one study points to other factors besides temperature that play a role more significant role at the country-level on female fertility7.Over 12% of reproductive age women (15–44 years) in the United States of America suffer from infertility or impaired fecundity8, including in both hunter/gather societies 9 and industrialized societies10. Birth season can affect later risk of disease through alterations in the prenatal environment that alter growth patterns and processes11. In addition, the known relationship between temperature and fertility6 could also manifest itself in a birth season relationship.Maternal birth season effects female fertility outcomes14. Importantly, the female reproductive system, unlike males, is established early with females being born with their lifetime maximum number of oocytes17. Oocyte count is thought to be linked to fertility18. Therefore, researchers posit that this initial reduction in oocyte volume at the time of the woman’s birth thereby reduces a woman’s fertility when she goes to bear children of her own later in life. In addition, there are many studies among cows kept for dairy purposes, demonstrating the important role of temperature in female conception rates21.The main biological mechanism underlying a birth season relationship with female fertility remains the hypothesis that oocytes’ exposure to high temperatures at birth and shortly thereafter results in increased oocyte loss early in lifeThis systematic review will comprehensively explore the literature underlying the maternal birth season – impaired female fertility paradigm while drawing from research conducted in humans. We will also discuss hypotheses supported by the literature and corresponding biological mechanisms to understand the effects observed in the literature and to place these findings in broader scientific context.22.This project was conducted using PRISMA guidelinesOur literature review focuses on fertility effects resulting from birth seasonal mechanisms. Therefore, we searched for relevant literature articles in PubMed as it is a freely and publically accessible database. All research funded by the National Institutes of Health within the United States of America are required to be deposited within PubMed. We used PubMed Central, an open access subset of PubMed, to obtain the full text of articles when possible. We also used site licenses from the University of Pennsylvania libraries to obtain the full texts of manuscripts retrieved via our initial query from PubMed. The following search query was used to identify relevant fertility/birth season papers:“("fertility" OR "reproductive") AND ("birth season" OR "birth month" OR "high stress season")”.23. After querying PubMed using the query detailed above, we then manually reviewed all retrieved abstracts and titles to identify the species studied in the papers. In addition to the species information and species type , we also captured the location where the study was conducted (which can differ from the affiliations of the researchers), the time-scale of seasonal information captured , whether the study was a laboratory study, field research or epidemiological study, and whether the full-text of the paper was available and whether the data was readily available. These details were annotated by two personnel (MRB and a work study assistant) for all studies retrieved using the query above by two personnel.We decided on the query detailed above after iteratively refining our query based on preliminary review of the search results obtained. The query interface within PubMed automatically maps the terms to their respective Medical Subject Headings (MESH) terms using PubMed Automatic Term MappingWe then chose to focus on human-only studies and to exclude studies involving non-human mammals and non-mammals . If the full-text of the manuscript was unavailable after multiple avenues had been explored  then the manuscript was not included in our review. We also excluded studies where birth month was used as a control variable because in those studies the relationship between birth month/season and fertility was not being studied explicitly .We investigated all studies involving human data as these were the focus on this research. We also gleaned some helpful insights from the non-human mammal studies where appropriate. However, we used very specific criteria for a study to be included in our meta-analysis. First the study had to involve human data . Second, the study had to involve a seasonal investigation on a 12-month time scale . Seasonal studies on a 2-month season  are not readily translatable to a 12-month timescale without making a series of assumptions and therefore these were excluded from the meta-analysis. Third the study had to have data readily accessible to our research team. This could be in the form of a figure detailing the rates by 12-month season, or in the form of a table or downloadable.csv or.txt file that contained information at the 12-month timescale.24 was used to compute the correlation between the exposure  and female fertility. The DSL method transforms each datasets correlation coefficient to a Fisher Z value with a standard error determined by the site-specific sample size. Using the sample size allows sites with larger sample sizes for the female fertility outcome to be weighted higher than sites with lower sample sizes. A summary correlation coefficient was computed and represents the overall correlation obtained from the meta-analysis across all sites included from the retrieved studies. We used the DSL method incorporated in the R ‘metacor’ library25 with widespread use among the research community26 and implemented based on Schulze method27.Meta-analysis was used to determine whether or not the birth season results reported demonstrated an overall correlation with temperature across all datasets included. The DerSimonian-Laird (DSL) random-effect meta-analytical method29. However, data on humidity was not available across all included sites and therefore rainfall was used instead. We also investigated the relationship between rainfall as measured in millimetres (mm) per month and female fertility outcomes. The same meta-analysis method was used (DSL) to investigate the relationship between rainfall and female fertility.Humidity is another important factor in the female fertility – birth season relationship22. This resulted in an initial set of 280 papers. We added two birth season studies that we know to have fertility-related birth month data, but were from studies exploring multiple birth season outcomes in general and not explicitly ‘fertility’ and ‘reproductive’ studies30. Our final dataset contained 282 studies. Within these 282 reviewed studies, 122 were non-human mammalian studies that we excluded. In addition, 131 studies were investigating birth season affects in either non-mammalian species  or plant species (N = 49 studies). We excluded these from further analysis because the underlying biological mechanisms underlying the role of a birth season mechanism on fertility were morphologically distinct. In total 29 studies were conducted in humans, we further reviewed these for inclusion in our meta-analysis.Our method for selecting and including studies in our review is shown in Fig. Six studies were excluded from the 29 human studies because either a.) birth month was used as a control variable or b.) it was not possible to access the text. Of the remaining 23 relevant human studies, one was about the birth seasonality of males and its effects on reproductive performance. While the focus of this study is on female reproductive performance and birth season, we discuss male studies in section 4.10 because male factor infertility contributes to the total number of offspring born to a woman. However, a sub-analysis of male studies was not possible because of the paucity of studies on male birth season and fertility later in life.Of the 29 human studies, 6 were not relevant because either a.) birth month was used as a control variable or b.) it was not possible to access the full text, resulting in 23 human studies with accessible data. Of these 23 relevant human studies, one was about the birth seasonality of males and its effects on reproductive performance while the remaining 22 studies were on females. We were focusing our meta-analysis on the relationship between female birth month and later fertility. We carefully reviewed each of these 22 studies and included all studies reporting data at the 12-month level that were investigating the effect of maternal birth month on either: a.) total number of offspring or b.) risk of having a child . From the 22 studies, we were able to extract data on female birth month (in 12-month increments) and later fertility from 6 distinct studies. These 6 studies contained 11 separate datasets. To augment our analyses, we included an additional dataset from our collaborators (co-authors LHJ and PRJ). In total, our meta-analysis consisted of twelve datasets for use in investigating the role of maternal birth season, temperature and rainfall.Four datasets contain sites from the Asia and the Pacific, four datasets contain sites from Europe and four datasets contain data from the United States of America. Data from 5 sites were reported as the relative risk (RR) of birth given the population Table . For puret al. studies) or purple . Six studies demonstrate positive correlation between temperature and female fertility with perinatal increases in temperature increasing the woman’s fertility later in life . Depending on the climate for a given region, some regions are very moist  and cold while other climates are moist and hot31. Similarly, there are climates that are dry and hot and others that are dry and cold. Therefore, the relationship between temperature and humidity depends on the climate of the region, and are not necessarily directly correlated31.We also investigated the relationship between rainfall and female fertility because humidity was another important factor in the female fertility – birth season relationshipincreases in female fertility. The overall correlation was not significant with an R = 0.158, p = 0.187. The meta-analysis results for temperature are given in Fig. increases fertility in higher altitude locations .Figure et al.30 those studies used data obtained from Electronic Health Records regarding conditions diagnosed among women who are delivering babies. The birth months of these women were assessed and the curves presented in Figs. et al.35. In the Huber et al. studies the results are the average number of live-born children per woman by birth month. The Huber et al. studies followed women longitudinally over time and therefore it is likely they have a greater capture over a woman’s lifetime reproductive success . Therefore, these two sets of studies differ somewhat in their data collection methods and are not completely comparable. However, it is important to note that findings from the NYC CUMC dataset from Boland et al. correlated well with Austria’s data from the Huber et al. study indicating that while the studies use different methods the results appear similar.Five of the included studies shown in Fig. et al. Romanian study tried to address this issue by separating out the low vs. high education women as a proxy for socioeconomic status. However, education as a proxy for socioeconomic status is somewhat limiting. The studies using the Electronic Health Records did not have adequate socioeconomic confounder adjustment and therefore this represents a significant confounder in these analyses. Other limitations are discussed in the limitations section.A major issue recognized by all of the authors of these studies is the lack of adequate confounder adjustment with regards to socioeconomic status and a woman’s choice regarding her desire to have a child (or the lack thereof). The Huber 36 and humans37. Some studies also point to the role of the photoperiod over temperature itself as the main driver behind conception rate increases38. However, conception rates are notoriously difficult to compare. Studies have shown that the probability of an offspring being born in certain birth months also depends on the birth month of the mother39. The authors demonstrated that while May – July born babies were uncommon in general, women who were already born in May – July were more likely to give birth to their offspring during the same months39. It is difficult to tease out some of these relationships because foetal loss and conception is generally difficult to capture.Our comprehensive review of the literature on the birth season relationship among women with later reproductive performance has revealed that temperature and rainfall are not enough to explain the relationship observed in multiple sites throughout the entire world. However, this review does reveal that there appears to be strong support for a relationship between temperature and reproductive success, including conception rates in bovinesOur meta-analysis of 12 datasets from around the world has revealed the there is a birth seasonal mechanism underlying female fertility. In addition, it does appear to have a negative correlation with temperature in some regions Fig. . In partBirth season has been linked with female fertility in multiple studies. In addition, while not statistically significant, there appears to be correlation in certain sites between a.) increased temperature at birth and later reductions in fertility and b.) increased rainfall at birth and increased fertility later in life. Therefore, we synthesize the literature on the birth season – female fertility relationship to explore other factors that modulate this relationship that have not been investigated in current human fertility-birth season studies.37. Others have found seasonal changes in environmental light intensity preceded changes in conception rates in both Germany and the Netherlands38. These findings point to the photoperiod being the primary driver underlying differences in conception rates across the seasons40. These studies were investigating conception rate and how it varies seasonally. However, our primary interest is the relationship between maternal birth season and later fertility outcomes. Perhaps women born in less favoured seasons  are biological different then those from favoured seasons . Some research suggests that successful conceptions that occur in less biologically favoured time periods  may have different characteristics, which could possibly explain some birth season effects39.The seasonality of female fertility varies by region. A Tanzanian study in humans found that conceptions were highest among the hot months when compared to the rainy season (Tanzania only has 2-seasons)41 and menopause42 in women. A study conducted in China consisting of 132,373 females with natural menopause investigated the relationship between birth season and both age at menopause  and reproductive timespan43. In addition, they had 285,186 females with timing of menarche  to investigate any relationship between menarche and birth season43. They found that timing of both menarche and menopause was birth season dependent with those born in the spring months  having early menarche and early menopause43. This would likely indicate that these women had shorter reproductive timespans. Our meta-analysis did not include the timing of menarche or menopause because these data were not collected for the datasets included in our study. This is a factor to consider in future birth season studies.Other fertility-related processes have been linked to birth season including the time of menarche44, which affected the age of the woman at the time of her first childbirth. Therefore, studies that investigate the relationship between maternal birth month and probability of having one child will likely show a birth month relationship. This is because there is a relationship between the first-born child and maternal birth month but not completed fertility rate. The completed fertility rate is the total number of children born to a woman at the time when she enters menopause . The completed fertility rate was not affected by birth month indicating that women can ‘catch up’ later in life by having more children in their later years44. Some studies included in our meta-analysis consisted of the total number of offspring born per woman, which is believed to be the completed fertility rate , which may be affected by the timing of marriage (this information was not available to the study authors). Importantly, birth season associations were found in the Huber et al. studies for completed fertility, which appears to contradict the findings from this Swedish study44.The completed fertility of a given woman  is affected by a number of sociological factors, including a woman’s choice or desire for children. Furthermore, timing of school and age relative to one’s peers has been shown to affect timing of marriage44. For example, if a woman were to complete her Medical Degree (MD) at an age of 26 years (which is the custom in the United States of America), the data from Sweden would suggest that this woman would give birth to her first child at 31.5 years of age. On the other hand, a woman graduating with a high-school degree, at 18 years, with no intentions of completing any additional schooling would then be expected to have her first child at approximately 23.5 years of age. The age of a woman at the time of her first childbirth would be expected to affect her total fertility rate with those having their first child at a younger age having more children over their lifetime. However, this is not what the researchers found. They found that women having children at older ages were able to ‘catch up’ and had similar family sizes to those who gave birth at a younger age. However, it is possible that in some countries and areas of the world, it may not be possible for women to ‘catch up’ and therefore, birth season effects may still be observed in those regions.The Swedish study found that women tended to have their first child on average 5.5 years after completing their highest level of educationet al. datasets included in our meta-analysis looked at completed fertility  while the 5 Boland et al. datasets investigated the relative risk (RR) of having a child. The RR of having a child includes those having their first child and also those having subsequent children. Therefore, the timing of marriage and the likelihood of advanced degrees would be expected to affect the Boland et al. datasets 48. A study in the early twentieth century in Japan, investigating records from 1925 through 1960, found that the least common time to be born was May – July49. However, women that were born between May and July had offspring that were randomly distributed across the birth months, this result was similar to what was observed in the early French-Canadian dataset49. The less season-sensitive women (born between May – July) were more fertile overall49, however, there were fewer women born in that period.A historical study using French-Canadian data from 1621 through 1765 found that women born in May through July were more likely to conceive quickly following marriage49. In addition, the authors suggest that perhaps women born between May – July are immune to the infection  that predominates in that season. Their justification for this hypothesis was that while May – July born babies were uncommon in general, women who were already born in May – July were more likely to have an offspring also born during the same birth period39. Suggesting that women born in that period may be immune to the factors causing spontaneous foetal loss among women born during other birth months39.The dearth of babies born between May – July could result from selective foetal loss due to some seasonal infection or other seasonal mechanisms50. This indicates the possibility of some type of family-based birth month selection among larger families50. Taken together with the prior studies conducted in French-Canada and Japan, there is the possibility that women born in fertile months  are having more offspring in general (and having larger families) and are also more likely to carry a baby due in May through July to term . All of these factors could manifest in a preferential family-based selection of birth months among offspring born in large families, as reported in the Spanish study50. However, the lower rates of foetal loss among May – July offspring born to May – July mothers indicates that there are birth season affects on the offspring that are due both to the child’s birth month and also its’ mothers birth month.A separate study with seemingly concordant results conducted in Spain found that large families tended to have less variability among birth months then smaller families51. The ratio of blood type O over A (O/A ratio) was lower among O mothers then A mothers among those born during August-January51. Typically, O mothers would be expected to have a higher O/A ratio among their offspring then blood type A mothers. Therefore, having a lower ratio among those born during August through January indicates the possibility of some underlying seasonally dependent infection or immunological response51.Other birth season-dependent affects that modulate the mother – offspring relationship include studies conducted on blood type. Seasonal variation among blood types was observed for all ABO blood groups52. The likelihood of a primiparous woman to breastfeed also depended on the birth season of her offspring53. They also found that women giving birth in the spring-summer months were more likely to report milk insufficiency as the reason for introducing bottle feeding54. Furthermore, being breastfed for a longer period of time, which appears to be seasonal, has been correlated with a number of beneficial outcomes later in life, including lower burden of respiratory illnesses55. Therefore, the seasonality of female fertility and birth season could be related to whether or not the female was breastfed as an infant.An Israeli study of Bedouin Arabs found that infants born between January and May were more likely to still be exclusively breastfed  at 4-months old than those born between July-October with a difference of 29.4% vs. 19%in vitro fertilization (IVF) success rates depend on season of implantation56. These findings have led some to suggest that season of conception should be considered for women who are considering using Assistive Reproductive Technologies (ART) in general57. Furthermore, many studies investigating the outcomes of ART now use birth season and year to match cases and controls, indicating the importance of these variables in the outcomes58. The highest fertilization rates and also quality-A embryo rates were observed during the spring with the lowest rates being observed during autumn in humans56.Studies have shown that 59. Women born in the spring and summer months had higher levels of Estradiol on the 22nd day of their cycle and higher levels of Sex Hormone-Binding Globulin (SHBG) on both the 11th and 22nd days of their cycle59. Physical activity between 13–15 years of age was inversely related to Estradiol levels, even after controlling for age, cycle length, BMI and birth month59.Female birth season also affected circulating serum Estradiol levels59. In addition, high Estradiol levels increase the desirability of the female60 and also the likelihood of conception61. Therefore, the high Estradiol levels associated with certain birth months may increase female fertility by decreasing the time to marriage, which can affect the total number of children born to a female44.Circulating hormone levels are important for understanding the role of birth season in increasing female fertility. Higher levels of circulating Estradiol were found among women born in spring and summer months35. This was the only study demonstrating a birth seasonal relationship with fertility among men in humans.Among men, birth season is also a significant factor in fertility. One study from Austria found that men born in the autumn months had fewer offspring and a higher risk of remaining childless than men born in spring59. In addition, studies in animals show that male birth season affects a plethora of male factors, including sperm motility62 with one human study showing that male birth season can affect reproductive performance35. There could be interactions between these two mechanisms, resulting in non-random mating between males and females based on their respective hormonal signatures. At this point, there are not any well-controlled studies in humans looking into this facet of birth season effects in depth.Female birth season has been shown to alter Estradiol levels (section 4.8)63. However, if born on the northern side of the river, the optimal birth season was during the fall63. Women who migrated to different sides of the river appeared to lose their survival advantage indicating that the advantage of birth season on survival may be limited to a certain region63. Another group showed that longevity depended on birth month in both men and women64. The overall longevity of a woman could impact her complete fertility rate, as she may not live long enough to finish having the children she intended to have. The issue of female mortality on overall fertility rates is still discussed in developing countries65 and would be critically important to consider in those countries.In addition to the role of birth month on female fertility, researchers have also found that birth month conferred a survival advantage among women born in a certain region. A study using historical data from the Saint Lawrence River region in Canada from women born before the year 1750 found that being born in the winter conferred optimal survival prospects if the child was born in the southern part of the river66. A recent systematic review found that several air pollutants can increase female infertility and related conditions67. A study in Barcelona, Spain found a statistically significant reduction in fertility rates with increases in traffic-related air pollution and especially coarse air particulates68. Air pollution is important to consider because it also varies seasonally69. Therefore, since air pollution varies seasonally, there could be adverse consequences for female babies born in months corresponding to peak exposure to air pollution.Air pollution has also been shown to affect fertility-related outcomes, including pre-term birth72. This seasonal variation in water quality may affect the developing foetuses born during certain birth months more than other birth months depending on when the exposure occurred . This is especially true for certain water exposures, such as Arsenic, which are known to affect the risk of certain adverse reproductive outcomes  and vary seasonally74.Variation in water quality can be observed seasonally in many countries76. In addition, food availability is often seasonally dependent. Therefore, this seasonal process could result in birth month affects for offspring born in periods with low food availability. Stress is another major factor affecting female reproductive performance. One human study found that stress (as measured using cortisol) was important in foetal loss rates77.Food availability in humans is often related to reproductive success78 and we satisfy this criterion, however it is still a limitation of our work given that some manuscripts may not appear on PubMed Central or PubMed. We were able to locate manuscripts from Austria, Sweden, and many other non-USA countries suggesting that our use of PubMed and PubMed Central was not USA-centric. The major reason that we utilized PubMed and PubMed Central is due to cost, many other online databases such as SCOPUS (https://www.elsevier.com/solutions/scopus) have heavy licensing fees associated with their usage.A major limitation of this review is our interrogation of two related databases – namely PubMed and PubMed Central. PubMed Central contains a subset of papers from PubMed that are open access. Our initial query was through PubMed and then we sought to obtain papers that were either open access  or obtainable via site license agreements through the University of Pennsylvania libraries. PRISMA guidelines recommend that at least one database we searched for relevant articlesIn this meta-analysis, we investigate the relationship between female fertility and birth season in published and already collected datasets. We discuss in our discussion section many other variables that were not included in our meta-analysis as these data were not readily available to the researchers conducting the prior studies. These factors are also important in the female fertility – birth season relationship, including socioeconomic factors, timing of menarche, timing of menopause, timing of marriage, a woman’s choice regarding her desire for a specific number of children and so forth. We discuss these other related studies here in our discussion because it is likely that these factors also interact with the female fertility – birth season relationship and may reduce our ability to find a statistically significant p-value on a global scale that adequately explains the female fertility-birth season relationship. Future studies should investigate more fully how these factors may modulate the female fertility-birth season relationship.), especially mountainous regions  could differ from urban areas  with respect to either airborne particulates  or waterborne contaminants, such as arsenic or lead . Furthermore, work is needed to investigate if inherited genes are tied to environmental factors and whether or not these factors also play a role in lactation and female fertility. More research is warranted to tease out the gene-environment interactions that may be underlying the female fertility – birth season associations seen throughout the world.Future steps should involve research in teasing out the various culprit exposures that appear to affect fertility rates, including stress, heat, and air pollution. Also more work should be conducted on the role of rainfall and female fertility, as some regions appear to demonstrate positive correlations. It is possible that the rainfall in certain regions Fig. , especiain certain regions. However, the overall meta-analysis result showed an insignificant positive correlation between increases in temperature at birth and increases in female fertility . We also investigated the relationship between rainfall at birth (in millimetres per month) and female fertility. We found that the overall meta-analysis result was again insignificant with a slight positive correlation . However, we found that rainfall only increases fertility in higher altitude locations . This suggests the possibility of a combined or multi-factorial mechanism underlying the female fertility – birth season relationship. We provide an overview of several factors that also vary seasonally by birth month that are involved in the birth season – fertility relationship that may alter the effects observed in certain locales. More work is needed to tease out any potential gene-environment interactions that may pertain to female fertility and birth season and to further explore other confounding variables  that are birth season dependent that could modulate the birth season – fertility relationship.In conclusion, we systematically reviewed the literature on birth season and female reproductive performance. We performed a meta-analysis on the relationship between temperature at birth and female fertility later in life based on birth month using datasets from 12 sites around the world. We found that temperature at birth was strongly negatively correlated with female fertility later in life only


The study aimed to assess the effect of inter pregnancy interval on preterm birth in Northern Ethiopia: prospective cohort study.This study showed that, total incidence of premature birth was 10.4%. Among mothers with short inter pregnancy interval the incidence of preterm birth was 39 (25.9%).Whereas, among mothers who had recommended inter pregnancy interval was 9 (2.9%). Short inter-pregnancy interval , antenatal care (ANC) visit 1–3 times , complication during pregnancy  and birth defect  were predictors of premature birth. Preterm birth (PTB) is defined as a delivery which occurs at less than 37 completed weeks of gestation. It is classified as extremely preterm < 28 weeks), very preterm (28 to < 32 weeks) and moderate to late preterm (32 to < 37 weeks) . Globall8 weeks, Systemic review done in 2007 showed that, 9.6% of all births were preterm, which translates to about 12.9 million births definable as preterm. Approximately 85% of this burden was concentrated in Africa and Asia, where 10.9 million births were preterm . Based oOther studies indicates that history of abortion, still birth, preterm birth, Premature rupture of fetal membrane, history of bleeding during the pregnancy, pregnancy induced hypertension, HIV infection, substance intake, history of low birth weight and short inter-pregnancy interval (IPI) were the factors leading to preterm birth –8.Furthermore, women with short interval between pregnancies are at increased risk of preterm birth. However, whether this association is confounded by other risk factors, including various aspects of socioeconomic status, ethnicity, demographics, and lifestyle is unclear . TherefoHealth facility based prospective cohort study design was conducted in Tigray Region, Northern Ethiopia, located at distance of 1087 km from Addis Ababa, the capital city of Ethiopia. Source population was child bearing mothers who are attending delivery in north western zone health facilities of Tigray.The sample size of 480 pregnant women was calculated using Epi-info-Calc. statistical software with the assumption of 95% CI, power of 90%, ratio of unexposed to exposed was 2:1 and percentage of exposed among preterm birth of 8.5% (14), odds ratio of 2.7, Loss to follow-up rate was estimated to be 10%.Four hospitals and eight health centers were selected randomly from North West zone health facilities. Study participants were proportionally allocated to each health facilities based on eligible pregnant mothers for ANC. Lastly, participants were included through systematic sampling method until the desired sample achieved.Data was collected using standardized, structured and face to face interviewer questionnaire and card review. Data quality was managed by trained three Bachelors degree of science holders fluent in the local language (Tigrigna). Pre-test, daily supervision, spot checking and reviewing completed questionnaire was conducted.Gestational age: was estimated as the interval in completed weeks from the last normal menstrual period (LNMP) to the child’s date of birth. When there are extra days it is counted to the near lowest gestational age but if the mother didn’t remember or recall her LNMP; gestational age was estimated from ultra sound result through reviewing the mothers chart.Inter-pregnancy interval: was documented as the time interval between delivery of the first pregnancy and the conception of the subsequent pregnancy.Short inter-pregnancy interval: mothers with inter-pregnancy interval from 0 to 24 months.Recommended inter-pregnancy interval: mothers with inter-pregnancy interval from 24 to 36 months or longer.Censor: mothers with normal weight, low birth weight, still birth, and congenital anomaly.Event: preterm birth.Data was entered into Epi Data 3.5.1 statistical software and analyzed using SPSS version 20.0™ package. The results were presented in tables and texts using descriptive statistics such as mean, standard deviation, frequency and percentages.The relationship between the event and explanatory variables was analyzed using Cox bivariate proportional regression model. Before fitting the covariate into the model proportional hazard assumption was checked using Log (−Log) S (t) plots.In order to identify independent predictors a multivariate Cox-proportional adjusted model was fitted with those variables p-value ≤ 0.25 in bivariate cox proportional regression analysis. Crude and adjusted hazard ratios with their 95% Confidence interval (CI) were estimated and p-value less than 0.05 were used to declare the presence of significant association.Four hundred eighty pregnant women were followed with 20 4.17%) lost in follow-up until delivery. The mean age of mothers was 30 (SD ± 5.92) ranging from 15 to a maximum of 48 years and above half 260 (56.5%) were between 25 and 34 years. Almost all or 450(97.8%) of the participants were married. Two-fifth 164 (35.7%) of the study participants had no formal education. About maternal occupation four out of ten participants 205(44.6%) were housewife and above half 273 (59.3%) of the participants come from Urban (Table 7% lost iAmong the cohort women two-third 308 (67.0%) of them had recommended inter pregnancy interval and three-fourth 346 75.2%) of them had ≤ 4 pregnancy with two-third 312 (67.8%) of them were multiparous. One out of ten (n = 418) women experience perinatal death in their preceding pregnancy. Around three-fourth 332 (72.2%) of the participants had planned pregnancy. Majority 445 (96.7%) of the pregnant women had at least one ANC checkup and four hundred thirteen (89.8%) of the pregnant women had maternal obstetrical complication during their current pregnancy. Eight out of ten (n = 375) of the study participants had hemoglobin level above 11 g/dl and eighty percent (n = 374) participants had delivered through spontaneous vaginal delivery for their current baby with a majority, 281 (61.1%) of them gave male baby by sex  and this indicates that around 3 out of 10 mothers who are exposed for short inter pregnancy interval have premature baby. But the incidence among mothers with recommended inter-pregnancy interval was 9 (2.9%).The predictors for premature birth were mothers with short inter-pregnancy interval had around seven times  hazard of bearing premature birth than those with recommended inter-pregnancy interval; also mothers with 1-3 times ANC visit had two times  likelihood to have premature birth than those who have greater than or equal to 4 visits and pregnant mother who had any problem during her current pregnancy had 3 times  more hazard to give premature birth than their counter parts. And those pregnant who gave birth a newborn with birth defect was eight times  more hazardous than those with normal birth. However, maternal residence, planned pregnancy, history of perinatal death and maternal hemoglobin level were significant at bi-variable but in significant in multivariable regression analysis  which is similar with studies done in Ethiopia –8, GhanaHaving birth defect was the other predictor of preterm birth which is comparable with the study done in Ethiopia , New-YorThis study indicates that incidence of premature birth is decreasing when compared with studies done in other parts of Ethiopia. Our findings, in conjunction with those of other studies, strongly suggest that a short inter pregnancy interval is a causal factor for preterm birth. Number of ANC visit, complication during pregnancy and being birth defect are also the predictors for premature birth.Therefore, the evaluation of the outcomes of primary prevention programs based on this putative causal factor would resolve the matter and health professionals should target to improve maternal education and counseling on recommended ANC visits and the adverse effect of shorter IPIs on subsequent birth, better access to contraception to increase intervals between births and therefore reduce the risk of preterm birth.Enhancing social marketing approaches emphasized the need to seek early and focused antenatal care to those non-booked mothers is also mandatory. Better management of obstetric complications and research to elucidate the mechanisms by which they cause preterm birth, offers a practical approach of reducing the high preterm birth rates.There are some limitations on this study. There was lost to follow-up of participants and it was not possible to enroll mothers who had difficult to estimate their gestational age that is unknown last normal menstrual period (LNMP) and missed the ultra sound result in her chart.


P < 0.01) from day 0 to 60 and for time to the first episode of BSI was 1.76  from day 0 to 36. Having the heavier neonates group as reference, the HR for time to the first episode of BSI was 2.94  from day 37 to 90. Late-onset neonatal sepsis prevention measures should consider the differences in risk during time, according to neonates' birth weight.A cohort study was performed from January 2014 to December 2016 in a Brazilian neonatal intensive care unit, including neonates with high risk for infection and death. We estimated bloodstream infection (BSI) incidence and conducted a survival analysis, considering the time to death and to the first episode of BSI as outcomes, comparing very low birth weight (VLBW) neonates with the remaining neonates. An extended Cox model was performed and the hazard ratio (HR) was calculated for different time periods. The study had 1560 neonates included, the incidence and the incidence density of BSI was 22% and 18.6 per 1000 central venous catheter-days, respectively. Considering VLBW neonates as the reference group, the HR for time to death was 4.06 (95% CI 2.75–6.00,  Sistema de Informação sobre Mortalidade – SIM), are prematurity, congenital anomalies and infection, the latter being the main cause of death in the late neonatal period (7–27 days of life) [Brazil is a middle-income country that successfully reduced child mortality in the last decades, achieving the fourth millennium development goal in 2015. Nonetheless, it was based mainly on the reduction of post-neonatal mortality. As a result, neonatal mortality currently accounts for 69% of deaths in children below 1 year of age in the country [Neonatal sepsis is defined as a systemic infection occurring in the first 28 days of life. There are two patterns of disease: early-onset neonatal sepsis (EOS), variably defined as occurring within 48–72 h after birth and late-onset neonatal sepsis (LOS), occurring thereafter. Microorganisms responsible for EOS are of maternal origin, acquired hours or days before or during delivery from the birth canal. Neonates with EOS may have a history of prolonged rupture of membranes, preterm onset of labour, chorioamnionitis and peripartum maternal fever . In BrazTherefore, in a post-2015 development agenda, recognising who are the neonates dying and becoming infected in the NICU and when these outcomes happen is important to establish strategies to reduce infant mortality in a middle-income country, such as Brazil. Thus, the objectives of this study were: (1) to estimate the incidence of LOS and its characteristics, distribution and the antimicrobial resistance profile of the microorganisms involved; (2) to estimate mortality rate according to birth weight; and (3) to compare the survival time from birth to death and from birth to the first bloodstream infection (BSI) episode among neonates admitted in a regional referral NICU, according to birth weight.This was a prospective cohort study, which included patients admitted from January 2014 to December 2016, at a public maternity hospital, located in Brasilia, Brazil. The hospital is a reference for care of preterm neonates and neonatal surgery, receiving high-risk pregnant women and patients from other services, covering an area of Brasilia and its surroundings, with approximately 4 million inhabitants . The neoAgência Nacional de Vigilância Sanitária – ANVISA) guidelines [Infections were actively detected through the result of laboratory cultures, antimicrobial prescription and during a multidisciplinary visit with the neonatology team. The infection control team performed HAI surveillance daily according to the Brazilian Agency of Sanitary Surveillance . Gestational age was assessed primarily by the date of the last menstrual period. When this information was not reliable, gestational age was assessed by a first trimester gestational ultrasound when available, otherwise by postnatal examination, according to Capurro score or the new Ballard score if very preterm , 17.® Becton Dickinson Microbiology System). Then, an automated bacterial identification panel  was used to identify the bacterial species and perform the antibiotic susceptibility. The laboratory used the Clinical and Laboratory Standards Institute (CLSI) manual for interpretation of the minimal inhibitory concentration [Neonates with clinical suspicion of infection had their blood samples collected in the volume of 1 ml and immediately inoculated in bottles for blood culture. Samples were initially submitted to a continuous automated system for bacterial detection . The antimicrobials resistance profile of microorganism isolated from blood cultures were described.The total cumulative incidence of primary BSI was calculated using two different numerators: the total number of episodes of BSI or the number of patients with BSI (regardless of the number of episodes of BSI per patient), divided by the total number of patients admitted to the unit during the study period, multiplied by 100. The incidence density of BSI was calculated dividing the number of BSI by the total CVC-days, multiplied by 1000. HAI rates and descriptive results were stratified in two groups, according to birth weight, <1500 and ⩾1500 g. The results were compared, considering a statistically significant value of A survival analysis was performed with two different outcomes: (1) the time from birth to death and (2) the time from birth to the onset of the first episode of BSI. The independent variable was birth weight, which was categorised into two distinct groups: VLBW neonates (<1500 g birth weight) and remaining neonates (⩾1500 g birth weight). When the outcomes of interest were not observed during the period of follow-up (until discharge or up to 90 days of life), these observations were censored. Death was considered a competing risk event when the outcome was the time until the first episode of BSI.The cumulative probability of each outcome of interest, death and presenting at least one episode of BSI, was calculated using the Kaplan–Meier method . The medP < 0.05). When this assumption was violated, an extended Cox model was performed, allowing the hazards to vary over time. The HR was considered constant at two different times of follow-up and the HR was calculated for each period separately. Ninety-five per cent confidence intervals were calculated for HR measures, considering a statistically significant value of P < 0.05. The database was built using EpiInfo 7 software  and the analysis was performed on STATA software version 11 .The basic premise of the model, the proportionality of HRs between the two groups was verified by the log–log survival curves and by the Harrel and Lee test, which uses Schoenfeld's residuals for the variables included in the models , 23. In The institution's medical ethics committee approved the study .From January 2014 to December 2016, 1506 neonates, who met the criteria for inclusion in HAI surveillance, were admitted to the neonatal unit and included in the study, a total of 34 906 patient-days. Of these, 943 (63%) had a birth weight of ⩾1500 g and 563 (37%) <1500 g. The median gestational age was 33 weeks (IQR 30–37 weeks) and the median birth weight was 1820 g (IQR 1240–2680 g). There were 714 late-onset HAI, and 318 (45%) were laboratory-confirmed BSI, 122 (17%) conjunctivitis, 117 (16%) clinically-confirmed BSI, 58 (8%) surgical site infections, 24 (3%) meningitis, 23 (3%) pneumonia, 18 (3%) necrotising enterocolitis and 34 (5%) other infections.P < 0.01).Of a total of 435 episodes of BSI, 271 neonates had one episode of BSI, 52 had two episodes, 12 had three episodes and six had four episodes. The total of CVC-days was 23 391. The incidence of BSI was 29 episodes or 22 patients infected per 100 admissions. VLBW neonates had 243 BSI episodes and 189 were infected; an incidence of 43 episodes or 34 patients infected per 100 admissions. Among neonates born with birth weight ⩾1500 g, there were 192 episodes and 152 infected patients; an incidence of 20 episodes or 16 patients infected per 100 admissions .The overall incidence density of BSI was 18.6 per 1000 CVC-days. It was 21.4 per 1000 CVC-days among VLBW neonates and 15.9 per 1000 CVC-days among neonates with birth weight ⩾1500 g  were resistant to amikacin, 27 (34%) were resistant to third- and fourth-generation cephalosporins and no acquired resistance to carbapenems was observed.There were 318 episodes of laboratory-confirmed BSI in this study, a total of 209 (66%) were Gram-positive bacteria, 80 (25%) were Gram-negative bacteria and 29 (9%) were fungi. P < 0.01). The median age at death was 8 days of life (IQR 3–21 days) among all neonates; 6 days (IQR 2–12 days) among VLBW neonates; and 10 days (IQR 3–44 days) among neonates with birth weight ⩾1500 g (P < 0.01).There were 201 (13%) deaths, and of these, 54 (27%) occurred during antimicrobial treatment and without other apparent cause. Mortality was higher among VLBW neonates compared to the remaining neonates, 22% and 8%, respectively . In the analysis of the death as outcome, at the 7th and 28th day of life, the survival probabilities were 87% and 80% among VLBW neonates and 95% and 91% among the remaining neonates, respectively. However, it equalled at the 60th day of life, and at the end of the 90th day of follow-up, the probability of survival was 72% among VLBW neonates and 55% among the remaining neonates. In the analysis of the first episode of BSI as outcome, at the 7th and 28th day of life, the probabilities of survival without BSI were 89% and 59% among VLBW neonates and 97% and 75% among the remaining neonates, respectively. However, it equalled at the 36th day of life and at the end of the 90th day of follow-up, the probability of survival without BSI among VLBW neonates was 33% and 23% among the remaining neonates.For the survival analysis, we included 1237 neonates who were born in the hospital; admissions from transfers from other hospitals were excluded from this analysis. P < 0.01). Comparing heavier neonates with VLBW neonates during the days 61–90 yielded an HR of 2.22 . The estimated adjusted HR of the time from birth to the first episode of BSI (considering death as a competing risk) comparing VLBW neonates with the remaining neonates during the first 36 days of life was 1.76 . Comparing heavier neonates with VLBW neonates during the days 37–90 yielded an HR of 2.94 .As the survival curves intersected, the premise of proportionality of the hazards between the two groups was violated . Therefore, an extended Cox model was performed to evaluate the HR in two different moments according to the outcome: for time from birth to death, the HR was calculated from 0 to 60 days and from 61 to 90 days; and for time from birth to the first episode of BSI, the HR was calculated from 0 to 36 days and from 37 to 90 days. The HR  estimated for time from birth to death among VLBW neonates compared to the remaining neonates during the first 60 days of life was 4.06  when considering only VLBW neonates, with a median occurrence in the 13th day of life, being a clear threat to those neonates who survived the complications of preterm birth in the first 2 weeks of life. These figures are lower than those reported by the Brazilian Neonatal Research Network, which found an incidence of 47% of BSI among VLBW neonates . The incvs. 8%) and affected this population earlier (median at the sixth day of life). In fact, among VLBW neonates, the hazard for the time to death was four times higher compared to heavier neonates during the first 60 days of life, with no difference thereafter. This fact highlights the urgent need to prevent preterm birth as a strategy to reduce neonatal mortality. This is especially important in Brazil, where preterm birth has increased over the last years [We hypothesised that death and the development of BSI affected neonates differently according to birth weight during their stay in the NICU. Therefore, we performed a survival analysis that found that mortality among VLBW neonates was much higher than that observed in other neonates  and 43 different empirical antibiotics regimens were noted . Thus, LOur results are important because they can refine HAI prevention strategies according to birth weight, since the risk of infection varies differently during NICU stay in these two groups. Additionally, they cover the unfinished agenda of reducing neonatal deaths and disabilities worldwide, especially in countries that expanded access to health care and reduced post-neonatal mortality, such as Brazil. The WHO published ‘Every Newborn: an action plan to end preventable deaths’, to reach the target of 10 or less neonatal deaths per 1000 live births by 2035  and the Our study had limitations, although the great majority of births in Brazil occur in the hospital, we restricted our population of study to neonates who had access to intensive care medicine, risking losing cases that died before reaching the NICU. Additionally, we lost follow-up of neonates transferred to other hospitals that could have experienced one of our outcomes: death or an episode of BSI. However, neonates were only transferred to other hospitals to continue their treatment when they had already improved clinically. We did not have a protocol for blood culture sampling, which may explain the high proportion of clinically confirmed BSI and CoNS isolates that may have overestimated the number of infections. We rarely had two or more blood samples for culture, making it difficult to establish when a CoNS was a cause of infection or a sample contaminant . FurtherIn conclusion, the rates of BSI reported in the NICU are high when compared to that observed in developed countries, and pose a challenge for middle-income countries that successfully reduced post-neonatal mortality, such as Brazil. If they want to keep with the reduction in infant mortality rates, they must invest in the quality of care and HAI prevention strategies must be adapted and evaluated, taking into account the different moments of risk that exist during hospitalisation between different groups of neonates according to birth weight.


Ovis aries) that are born to primiparous ewe lambs (8 to 9 months old at breeding). This experiment investigated the effect of being born from either a ewe lamb or mixed age dam as either a single or twin on the lifetime performance of ewe progeny. Lifetime performance was measured in terms of the life time live weights of the ewes, the weight and number of lambs born and weaned, the efficiency of production (kilograms of lamb weaned / predicted pasture intake (kgDM) of the ewes), and ewe survival. The study followed the lifetime production of 17 single and 41 twin female lambs born to mature ewes , and 28 single and 29 twin lambs born to ewe lambs . Over their lifetime L2 ewes were lighter (P<0.05) but had similar body condition scores to the other three ewe groups. There was no difference in average progeny weaning weight or total progeny litter weaning weights between groups. The M1 ewes had the greatest longevity (P<0.05) of the four groups. Even though L2 ewes were lighter than the other three groups, this was insufficient to increase their lifetime efficiency of production (kg lamb weaned/predicted pasture consumption), relative to the other groups. These results suggest farmers could select replacements born to ewe lambs without sacrificing animal production.Greater rates of genetic gain can be achieved by selecting animals born to younger parents. However, little is known about the lifetime performance of dual purpose ewes ( Ovis aries) in New Zealand are bred for the first time as ewe lambs, at 8–9 months of age [Currently, 30–43% of dual purpose breeding ewes  11 km north of Masterton, and Keeble Farm  5 km south of Palmerston North, New Zealand, with the approval of the Massey University Animal Ethics Committee (MUAEC12/21). The experiment ran from April 2009 until January 2017. Animals were managed at Riverside Farm from September 2009 (birth) to January 2010 (weaning), then moved to Keeble Farm for the remainder of the experiment.The study used Romney nulliparous ewe lambs  and multiparous mature Romney ewes  as the parents of the experimental ewes. They were naturally bred with Romney composite rams (at a ram to ewe ratio of 1:40) as one cohort for a 34-day mating interval in April 2009 , 9, 16. Ewes were first bred in 2011, at 18 months of age . RebreedAt birth, the ewe progeny had their live weight, thoracic girth, and crown-rump length measured within 12 hours of birth. The midpoint of the lambing period was defined as day 1 (d1). Further live weights (LWT) were recorded at day 41, weaning d99), and monthly until their first breeding (d552) 9, and mo) were mePregnancy diagnosis via transabdominal ultrasonography to count the number of foetuses present occurred each year between 72 and 86 days after ram introduction. Ewes were checked twice daily during the lambing period, beginning seven days before the planned start of lambing, and the birth weight, crown-rump length, and girth of their progeny were measured. Lambs had additional live weights measured at day 40 of lactation and weaning (average age of 99 days). No culling of ewes occurred unless on welfare grounds. Ewe deaths were recorded.Live weight of ewes during pregnancy were adjusted via Gompertz equation  to calcuProgeny weights were added together to form a total litter weight per ewe per year for birth, day 40 of lactation, and weaning weights. Total litter weight of lambs at weaning divided by the weight of the ewe at breeding, for each year to determine a ratio of progeny weaning weight to ewe breeding weight. If the ewe was present at breeding, but did not wean a lamb, a litter weaning weight of zero was given.Predicted daily live weights from the spline models were used to determine the daily nutritional requirements for each ewe for their maintenance and growth/loss . Litter As the ewes were only culled based on welfare grounds, hypothetical culling was imposed retrospectively on ewes, if a particular ewe was barren at pregnancy diagnosis, day 40 of lactation, or weaning, as per commercial farming conditions. This allowed survival analysis to be carried out for actual survival of the ewes, and the expected survival of the ewes if they were managed under commercial farming conditions. All ewes alive for actual survival or imposed-culling survival at the end of the experiment (Day 2623) were censored at that date for survival analysis.Statistical analysis was carried out using SAS version 9.4 software . The aim of this study was to classify production consequences of retaining single or twin ewes born to either a ewe lamb or a mature ewe. Thus, in all models, even if the interaction between dam age and birth rank was non-significant (P>0.05), the two-way interaction remained in the model, to allow for testing of the experimental question. Ewe live weights and some production traits up to 2.5 years of age have been previously reported by Loureiro  and LourEwe weight at birth, 41 days of age, and weaning, crown rump length, thoracic girth, progeny litter birth weight, progeny weight of the litter at 40 days of age, progeny litter weaning weight, ratio of progeny weaning weight to ewe breeding weight, total lifetime progeny weaning weight, total lifetime pasture consumed, and efficiency were analysed using a mixed linear model. The model included the fixed effects of age of dam (mature ewe vs ewe lamb) and ewe birth rank (single vs twin), and their interaction. Date of birth was included as a covariate in the model for the analysis of ewe weight at birth, 41 days of age, and weaning, crown rump length and thoracic girth. The model to analyse progeny litter weights at birth, 40 days of lactation, and weaning, and the ratio of progeny weaning weight to ewe breeding weight considered the fixed effect of year, and the random effect of ewe.Least squares means for predicted ewe live weights every 50 days during their productive life were obtained with a linear model that included the fixed effects of day, age of dam, ewe birth rank, interaction between age of dam and ewe birth rank, and the interaction of day, age of dam and ewe birth rank.Least squares means for predicted ewe live weight at breeding were obtained with a linear model that included the fixed effects of age of dam, ewe birth rank and year, and the interaction of age of dam and ewe birth rank.Body condition score at breeding, number of lambs born per year, number of lambs weaned per year, and total lifetime number of lambs weaned were analysed using a generalised linear model, assuming a Poisson distribution and logit transformation. The model included the fixed effects of age of dam, ewe birth rank, and their interaction. Body condition score at breeding, number of lambs born per year, and number of lambs weaned per year also included the fixed effect of year, and the random effect of animal to account for repeated measures.Lamb survival was analysed using a generalised linear model, assuming a binomial distribution and logit transformation. It included the fixed effects of age of dam, ewe birth rank, year, sex of lamb, and lamb birth rank, and the interaction of age of dam and ewe birth rank.Survival analysis was carried out using exit data of all ewes that died or were removed prior to the end of the experiment (Day 2623). All ewes alive at the weaning of their 6th lamb (Day 2623) were censored at that date for the survival analysis. In addition, hypothetical culling was imposed, retrospectively, if a particular ewe was barren at pregnancy detection, day 40 of lactation, or weaning, as per commercial farming conditions. All ewes that were alive, and not culled at the end of the experiment were censored on day 2623 for the survival analysis.There was no significant interaction (P>0.05) between age of their dam and their birth rank for birth weight, crown rump length, or thoracic girth . The eweThe M1 ewes were consistently heaviest (P<0.05) throughout the experimental period, while M2 and L1 ewes were not significantly different (P>0.05) from each other, but were heavier (P<0.05) than L2 ewes .The interaction of dam age group and birth rank was not significant (P>0.05) for live weight or body condition score at joining . Ewes boNumber of lambs born, number of lambs weaned, and lamb survival did not differ (P>0.05) between ewe groups . There wThere was no significant interaction (P>0.05) between dam age group and ewe birth rank on estimated total volume of pasture eaten, weight of lamb weaned, and efficiency of conversion of pasture to lamb growth over the eight years . CalculaWhen no culling occurred, the L2 ewes had the lowest (P<0.05) proportion of survival of all the ewes . The M1 The aims of this experiment were to determine the effects of dam age and birth rank on the lifetime performance of female offspring and to extend the data of Loureiro  which onPrevious studies have shown that ewe reproductive traits, such as ovulation rate , reproduEven though the L2 ewes were 10 kg lighter than the M1 ewes at breeding, this was not enough to significantly increase the ratio of litter weaning weight to ewe weight at breeding. The L2 ewes also had a lower predicted pasture consumption, but weaned fewer lambs for their lifetime, resulting in a similar efficiency to the M1 ewes. While the L2 ewes were not more efficient, there was no loss of production from having lighter live weights. However, because of the small group sizes further investigation, with larger group sizes, is warranted, to increase the statistical power, especially with binomial traits. Given the results in this study, a sample size of 189 per group would be required to show a statistically significant difference in efficiency between the groups.The lighter live weights of L2 ewes with similarity in production indicates farmers could have more of these types of ewes on their farm for a given total feed availability, and subsequently produce greater weight of lamb per hectare. However, a more powerful study, with an economic evaluation would be warranted to determine the economic benefits for farmers. In New Zealand, the Sheep Improvement Limited (SIL) dual purpose index indicates ewe live weight for feed to have a value of $-0.14/kg, indicating lighter ewes are more economical to feed . AdditioEwe survival tended to be lowest in L2 ewes compared to the other ewe groups, with losses occurring earlier in these ewes than in the other groups. This may have decreased their lifetime number and weaning weight of lambs produced, but it will also decrease their lifetime estimated pasture consumption. With sharp decreases in ewe numbers associated with lambing time, there are many ewes being fed from the weaning of their previous lamb until lambing, without any lambs being weaned, which may decrease efficiency of the L2 ewes.Previous studies , 35 haveEwes that are born to ewe lambs as twins are lighter than ewes born to ewe lambs as singles and ewes that are born to mature ewes as twins, which are lighter than ewes born to mature ewes as singles, for their lifetime to six-and-a-half years of age. The L2 ewes have the fastest rate of mortality, and M1 ewes have the slowest rate of mortality. The live weights of L2 ewes may not affect litter weight at weaning, producing similar weights of lambs weaned as the M1 ewes. However, this is insufficient to increase the efficiency of the L2 ewes compared to the other ewe groups, but consequently may not impair their production. Therefore, farmers could reasonably select lambs born to ewe lambs as replacements for their flock, without compromising their production. Further investigation is warranted, with a larger dataset and greater statistical power, to confirm the effects of selecting lambs born to ewe lambs, on their production and survival. An economic analysis is now required to determine whether there is sufficient financial benefit to farmers to warrant them implementing a policy of mating ewe lambs.S1 Appendix(DOCX)Click here for additional data file.


Linear regression analysis indicated a strongly significant influence of BW on serum hs-CRP level and SBP. Every 1 g increase in BW results in a decrease of serum hs-CRP level of 0.001 mg/L. Every 1 g increase in BW is attributed to 0.004 mmHg decrease in SBP. An increase in hs-CRP by 1 mg/L increases the SBP by 4.99 mmHg and DBP by 2.88 mmHg. LBW significantly correlates with hs-CRP level and higher SBP. A comprehensive education must be undertaken for the families who have children with LBW to reduce the risk of developing hypertension later in their life.Over the past years, low birth weight (LBW) has been proven to be attributed to a wide variety of long-term morbidities, including hypertension. This study aimed to investigate the association between high-sensitivity C-reactive protein (hs-CRP) and blood pressure (BP) in children with a history of LBW appropriate for gestational age (LBW AGA), LBW small for gestational age (LBW SGA), and normal birth weight appropriate for gestational age (NBW AGA). The study cohort comprised children aged 9–12 years who were born in 2007–2010 at Prof. Dr. R. D. Kandou General Hospital Manado and resided in the city of Manado from March to August 2019. The children who met the inclusion criteria were evaluated for BP and hs-CRP level. A total of 120 children who met the inclusion criteria were enrolled in this study. Analysis for the association between LBW and NBW with systolic blood pressure (SBP) showed statistical significance ( Low birth weight (LBW) newborns represent both term babies who experienced intrauterine growth restriction (IUGR) and preterm babies with and without IUGR . Until rOver the past years, LBW has been proven to be attributed to a wide variety of long-term morbidities, including hypertension, end-stage renal disease, insulin resistance, and cardiovascular diseases , 6. InflStudies addressing hs-CRP and its role in cardiovascular diseases have been performed in adult patients. However, such studies conducted in pediatric population are still limited, particularly in children with a history of low birth weight. Hence, a study investigating the association between hs-CRP and blood pressure in children with a history of low birth weight appropriate for gestational age (LBW AGA), low birth weight small for gestational age (LBW SGA), and normal birth weight appropriate for gestational age (NBW AGA) is necessary.This study is an observational study performed using cross-sectional design. This study was designed to answer a hypothetical theory which states that there is significant correlation between hs-CRP and blood pressure in children with a history of low birth weight.The study protocol was reviewed and approved by the Research Ethical Committee and Health Development of Prof. Dr. R. D. Kandou General Hospital with the reference number 228/EC-KEPK/XI/2018.The study involved children born at Prof. Dr. R. D. Kandou General Hospital Manado from 2007 to 2010 and resided in the city of Manado from March–August 2019. Prof. Dr. R. D. Kandou is a Teaching Hospital as well as the National Referral Center for the eastern region of Indonesia.The study cohort comprised children aged 9–12 years who were born in 2007–2010 at Prof. Dr. R. D. Kandou General Hospital Manado and resided in the city of Manado from March–August 2019. The inclusion criteria were children with a history of low (<2500 grams) and normal (2500–4000 grams) birth weight, complete medical record, presented with normal clinical and nutritional status, and whose parents agreed to participate and provided their written informed consent for participation. The exclusion criteria consisted of babies with multiple congenital anomalies, diabetes mellitus, history of medical therapy within the last 2 weeks, and history of renal disease . To estimate the sample size, the correlative sample size formula was used and a minimum of 97 samples suffice to generate statistical significance.n is the estimated size of the study sample; Zα1− is the value in table Z   = 5%, then Zα1− = 1.64); Zβ1− is the value in table Z (if β (standard deviation beta) = 20%, then Zβ1− = 0.84); ln is the natural logarithm; and ρ0is the alleged correlation coefficient of the studied variable = 0.25.Sample-size estimation for correlation coefficients :(1)n=Z1−Medical records of subjects with normal and low birth weight who were born at Prof. Dr. R. D. Kandou General Hospital Manado from 2010 to 2013 and resided in the city of Manado were reviewed to obtain the required data for analyses. The parents were contacted and informed about the purpose and the benefits of their participation in this study. Once the parents provided the consent, an experienced physician visited their home to perform a physical examination, anthropometric assessment, and blood pressure measurement and was blinded to the subjects' birth weight data. The children who met the inclusion criteria were enrolled in the cohort and were subsequently evaluated for the hs-CRP level. Blood samples were drawn by experienced laboratory staff. Blood sample collection was performed with sterile instruments and started by disinfecting the area for needle insertion using 70% alcohol swab, followed by taking a total of 3 ml venous blood samples drawn from the medial cubital vein using a syringe connected to a vacutainer. The special blood sample tube was labeled with the patient's identity. All the regarded data obtained through a series of interview, physical examination, anthropometric assessment, and hs-CRP measurement were subsequently analyzed.t-test was employed for assessing the association between low birth weight and normal birth weight groups with both hs-CRP level and blood pressure. Simple linear regression and multivariate regression analysis was performed to investigate the correlation between birth weight with both blood pressure and hs-CRP level. All the statistical analyses were performed using SPSS software version 25.0.Descriptive analysis was conducted in all study variables that were classified into three main birth weight categories: low birth weight appropriate for gestational age (LBW AGA), low birth weight small for gestational age (LBW SGA), and normal birth weight appropriate for gestational age (NBW AGA). LBW AGA is defined as babies with birth weight <2500 grams and gestational age at the 10th and 90th percentile of the Lubchenko curve. LBW SGA is defined as babies with birth weight <2500 grams and gestational age below the 10th percentile according to the Lubchenko curve. NBW AGA is defined as babies with birth weight between 2500–4000 grams and gestational age at the 10–90th percentile of the Lubchenko curve. Comparison among the three birth weight groups with blood pressure and hs-CRP level was done using the ANOVA test. Independent The study was conducted from April 2019 to September 2019 in children aged 9–12 years who were born around 2007–2010 at Prof. Dr. R. D. Kandou General Hospital and resided in Manado. A total of 120 children who met the inclusion criteria were enrolled in this study. All of the eligible children were further grouped based on their birth weight which resulted in the following categorization: 40 children with low birth weight small for gestational age, 40 children with low birth weight appropriate for gestational age, and 40 children with normal birth weight. Baseline characteristics of this study are shown in Hs-CRP levels can be classified as low risk of chronic subtle inflammation if hs-CRP levels are <1 mg/L, moderate risk of chronic subtle inflammation if hs-CRP levels are 1–3 mg/L, and high risk of chronic subtle inflammation if hs-CRP levels are >3 mg/L. Serum hs-CRP evaluation showed that out of 40 children with a history of LBW SGA, 19 children (47.5%) were considered having mild risk, 17 children (42.5%) with moderate risk, and 4 children (10%) with high risk. On the other hand, serum hs-CRP analysis in 40 children with a history of LBW AGA revealed 20 children (50%) with mild, 17 children (42.5%) with moderate, and 3 children (7.5%) with high risk. The last group showed all the 40 children (100%) with low risk regarding their serum hs-CRP level.p=0.76) and diastolic (p=0.109) blood pressure. The graphic presentations of these results are shown in Figures olic p=0.09 blood t-test have statistical significance with p=0.007. Meanwhile, no significant results were found for the association with diastolic blood pressure (p=0.254) as shown in The results from the analysis for the association between low and normal birth weight with systolic blood pressure using independent p value of <0.0001 (p=0.949). There is a significant difference between serum hs-CRP level in the LBW SGA and NBW AGA group (p < 0.0001). There is a significant difference between serum hs-CRP level in the LBW AGA and NBW AGA group (p < 0.0001).The results from the analysis regarding the association between children with LBW SGA, LBW AGA, NBW AGA, and serum hs-CRP level using ANOVA test showed the  <0.0001 . The grat-test analysis for the difference between the low birth weight group and the normal birth weight group counterpart with serum hs-CRP level resulted in a significant result with the p value of <0.0001 .Simple linear regression analysis indicated a strongly significant influence of birth weight (BW) on the serum hs-CRP level. The result showed that the heavier the birth weight, the lower the serum hs-CRP level . Every 1r = 0.044, p=0.011).Simple linear regression analysis demonstrated a significant influence of BW on SBP as shown in r = 0.000, p=0.472).On the contrary, the linear regression analysis showed no significant influence of BW on the DBP  .Simple linear regression analysis showed a significant positive correlation between hs-CRP and SBP . The higr = 0.155, p < 0.0001).Simple linear regression analysis resulted in a significant positive correlation between hs-CRP and DBP . The higThe multivariate regression analysis of hs-CRP and birth weight with systolic and diastolic blood pressure with control variables such as child's age, sex, weight, and height is presented in Tables p < 0.0001), whereas the diastolic blood pressure is influenced by birth weight and hs-CRP variables .The results of multivariate regression analysis showed that only the hs-CRP variable is associated with systolic blood pressure in children  and diastolic (p=0.109) blood pressure showed no significant association. On the contrary, the analysis regarding the relationship between low and normal birth weight children with the systolic blood pressure succeeded in reaching statistical significance with the p value of 0.007. This result indicates a significant difference between the two birth weight groups in terms of systolic blood pressure. As for the diastolic blood pressure, statistical analysis showed a nonsignificant association (p=0.254).Statistical analysis for the association between each birth weight group  and both systolic (β-Adrenergic receptors (β-AR) play an important role in the regulation of various organ systems, including cardiovascular, pulmonary, endocrine, and sympathetic nervous systems. Polymorphism of the β-AR gene is attributed to several variables associated with increased risk of cardiovascular events, including hemodynamic parameters such as heart rate, vasodilation, and blood pressure. Moreover, the β-AR gene is attributed to preterm birth in children [Recent evidence proves that low birth weight reflects a poor intrauterine environment, which is associated with the disappearance of nephron endowment and other pathophysiologic mechanisms predisposing to elevated blood pressure. The average number of the nephron in normal individuals ranges from 300,000 to 1.1 million, and its normal development reaches 60% by the end of the last trimester . β-Adrenchildren .Formation and development of a perfect kidney in the fetus are achieved at 36 weeks gestational age, and no more nephrons will be formed afterward. Intrauterine nephron formation is strongly influenced by intrauterine fetal growth that is linked with maternal nutritional status, vitamin intake, intrauterine infection, exposure to antibiotics, and smoking and alcohol intake during pregnancy . BrennerIn a study conducted by Ediriweera et al., in 2017, LBW showed a significant effect on hypertension, particularly its correlation with elevated systolic blood pressure in adulthood, but not directly with high diastolic blood pressure . Stock eLinear regression analysis showed a significant influence of BW on SBP. The higher the BW, the lower the SBP. Every 1 g increase in BW correlates with a reduction in SBP by 0.004 mmHg. In the Bogalusa Heart study, birth weight negatively correlated with blood pressure level in adulthood and long-term blood pressure level . This fip < 0.0001). Our results support the results of the previous study by Turoni et al. which reported that serum hs-CRP level is higher among children with a history of low birth weight compared to the control group [The result from the present study indicates a significant positive correlation between birth weight status and hs-CRP level during childhood. Similar results were also discovered for both low birth weight and normal birth weight groups (ol group .p=0.949). There was a significant difference in hs-CRP level between children with LBW SGA and children with NBW AGA (p < 0.0001). There was also a significant difference in hs-CRP level between the LBW AGA and NBW AGA groups (p < 0.0001). Turoni et al. also reported no significant difference in hs-CRP level between the LBW SGA and LBW AGA groups. The difference was only found when the serum hs-CRP level was compared with the control group. Hs-CRP is not only considered a marker for cardiovascular risk, but its increase also reflects a cardiovascular damage [The results of this study showed no significant difference in serum hs-CRP level between children with LBW SGA and children with LBW AGA (r damage .p < 0.01), and the level was higher in the hypertension group compared to the prehypertension group (p < 0.05) [p=0.03) [Regression analysis resulted in a highly significant positive correlation between hs-CRP and SBP and DBP. The higher the serum hs-CRP level, the higher the SBP and DBP. An increase in hs-CRP by 1 mg/L increases the SBP by 4.99 mmHg and DBP by 2.88 mmHg. These results support the previous discovery by Wasilewska et al. in Poland that involved children aged 10–19 years with prehypertension and hypertension where the hs-CRP level among these groups were higher than the control group ( < 0.05) . Lande e < 0.05) . A study[p=0.03) .The mechanisms on how CRP affects the vascular damage would be best attributed to angiogenesis, endothelial damage, oxidative stress, and vascular remodeling. Inflammation is considered a key mechanism in the pathogenesis of atherosclerosis and inflammatory process and also significantly associated with the development of arterial hypertension, heart failure, and valvular heart disease, as well as atrial fibrillation. The inflammatory cells increase the metabolic activities around the vascular wall making a more acidic medium that accelerates cellular apoptosis. By activating the angiotensin-aldosterone system, angiotensin-I, and angiotensin-II receptors, CRP induces angiotensin-mediated proatherogenic activities, directly and indirectly stimulates structural and functional modification of the arterial wall, vascular and cardiac remodeling, vascular stiffening, an increase in peripheral vascular resistance, and impairs regulatory mechanism of arterial blood pressure. CRP induces matrix metalloproteinase (MMP) activities (leading to the destruction of collagen) in macrophages and endothelial cells, as well as suppressing the MMP inhibitors within the tissue .Preterm babies live outside the uterine cavity during the normal fetal developmental period from their birth until reaching the adequate gestational age  and usually spend several weeks to months after birth in a neonatal intensive care unit. Poor perinatal condition also affects BP through fetal programming mechanism  Shah et Several limitations of this study include blood pressure measurement was only carried out once at a time (morning BP) so that the possibility of white coat effect might have caused bias, whereas several studies recommend performing an ambulatory blood pressure monitoring (24 hours). The serum hs-CRP level was measured only once. In adults, the AHA and CDC recommend performing CRP level for at least twice with a minimum of a 2-week interval to determine the cardiovascular risk. This study did not include both maternal and environmental factors in the analyses that might also contribute to increasing the risk of having increased blood pressure in children.Children with a history of low birth weight are significantly associated with higher systolic blood pressure during childhood. Low birth weight also significantly correlates with serum hs-CRP level. The higher the birth weight, the lower the hs-CRP level. An increase in hs-CRP by 1 mg/L increases the SBP by 4.99 mmHg and DBP by 2.88 mmHg. A comprehensive education must be given to the families who have children with low birth weight to reduce the risk of developing hypertension later in their life.