Women with a history of gestational diabetes mellitus (GDM) are at higher risk of developing type 2 diabetes (T2DM); however, little is known about the association between other common pregnancy complications  and T2DM risk. We examined the associations between first-pregnancy preterm, postterm birth, low birth weight, and macrosomia with subsequent risk of T2DM.We conducted a prospective cohort study of Nurses’ Health Study II (NHSII) participants; 51,728 women in the study had a single live birth and complete pregnancy history. NHSII confirmed incident diabetes mellitus through supplemental questionnaires. Participants were followed from year of first birth until 2005. We defined gestational age as very preterm (20 to ≤32 weeks), moderate preterm (33 to ≤37 weeks), term (38 to ≤42 weeks), and postterm (≥43 weeks). We defined low birth weight as an infant born at term weighing less than 5.5 pounds, and we defined macrosomia as an infant born at term weighing 10 pounds or more. We used Cox proportional hazards models, adjusting for potential confounders.Women with a very preterm birth (2%) had an increased T2DM risk . This increased risk emerged in the decade following pregnancy. Macrosomia (1.5%) was associated with a 1.61 increased T2DM risk, after adjusting for risk factors, including GDM . This association was apparent within the first 5 years after pregnancy. Moderate preterm and term low birth weight did not significantly increase the risk of T2DM over the 35-year follow-up time.Women who experienced a very preterm birth or had an infant that weighed 10 pounds or more may benefit from lifestyle intervention to reduce T2DM risk. If replicated, these findings could lead to a reduced risk of T2DM through improved primary care for women experiencing a preterm birth or an infant of nonnormal birth weight. A growing body of research suggests that pregnancy and the period surrounding it may provide unique information about a woman’s future risk of chronic disease ,2. For eDespite the well-established association between GDM and future risk of T2DM ,8, less The objective of our study was to examine the association between gestational age, birth weight, and T2DM in mothers. We evaluated these associations in a large cohort study, adjusting for potential confounders, including maternal and paternal history of diabetes, pre-pregnancy body mass index (BMI), and smoking during pregnancy. We also adjusted for lifestyle and reproductive factors as well as pregnancy complications known to be predictors of T2DM. Finally, we explored the time trends in risk over the decades following birth of a preterm infant or an infant of nonnormal birth weight to suggest potential windows for prevention and glucose tolerance screening after complicated pregnancies.Study participants were from the Nurses’ Health Study II (NHSII) population, a cohort study of 116,678 female nurses who were aged 25 to 42 years at the start of the study in 1989. NHSII follows participants biennially by questionnaire to obtain both health-related behavior information and data on the occurrence of diseases, including diabetes. The follow-up rate has been approximately 90% for each biennial questionnaire. For purposes of this study, we followed women from their first pregnancy (the earliest pregnancy in our study population occurred in 1964) through 2005, the last reported follow-up for incident diabetes.Our study population is based on a subset of the NHSII population who had detailed data on pregnancy history and T2DM. More specifically, in 2001, NHSII sent a supplemental questionnaire to study participants who were deemed good responders — those women who typically responded to the first or second mailing of the biennial questionnaires. This supplemental questionnaire included detailed data on history of pregnancies that lasted at least 12 weeks; 68,376 women  returned the questionnaire . We exclStudy participants indicated the length of each of their 5 most recent pregnancies lasting at least 12 weeks in the following categories: 12 to less than 20 weeks, 20 to less than 24 weeks, 24 to less than 28 weeks, 28 to less than 32 weeks, 32 to less than 37 weeks, 37 to 42 weeks, and 43 or more weeks. Pregnancies were categorized as very preterm (20 to ≤32 weeks), moderate preterm (33to ≤37 weeks), term  and postterm (≥43 weeks).Participants were asked the birth weight for each of their 5 most recent pregnancies . Low birth weight was defined as less than 5.5 pounds at term and macrosomia as 10 pounds or more at term.In the NHSII 1989 baseline questionnaire and subsequent biennial questionnaires study participants indicated whether they had ever been diagnosed with GDM by a physician. A validation of GDM showed high validity of self-report of this condition compared with medical records .Study participants responded to “Have you ever had toxemia/pre-eclampsia (raised blood pressure and proteinuria) with any pregnancy?” on the NHSII baseline questionnaire and biennially. A recent validation of pre-eclampsia compared with medical record review showed self-reported preeclampsia is a moderately good indicator of hypertensive disorders of pregnancy (HDOP)  .NHSII study participants received a supplemental questionnaire if they reported having been diagnosed with diabetes mellitus on the biennial questionnaires between 1991 and 2005. These supplemental questionnaires collected information to distinguish between type 1 and type 2 diabetes mellitus on the basis of diabetes diagnosis and treatment. The validation of this method of case confirmation has been reported previously . The 6% 2) was calculated from reported height and weight data. BMI was derived for ages at which weight was not reported  from a formula using weight at age 18 and weights reported on biennial questionnaires, as well as somatograms at ages 20, 30, and 40 to assign BMI at each age starting at 18 years and through the end of the study period.On the 1989 baseline questionnaire, study participants reported their current weight and height and their weight at age 18 . In addition, the 2001 supplemental questionnaire asked about smoking status during pregnancy . Participants were also asked at baseline to report their menstrual regularity at age 18 to 22 years, which was categorized as regular, irregular, or no menstrual periods.We used Cox proportional hazards models to examine the association between preterm and postterm birth, as well as low birth weight and macrosomia with T2DM risk, modeling gestational age and birth weight separately. Very preterm birth, moderate preterm birth, and postterm birth were evaluated as indicator variables, with the reference group of term. For the association between birth weight and T2DM, we did not have enough information on birth weights under 5.5 pounds to create small-for-gestational-age categories among preterm births. Therefore, we restricted our analysis to women who had a term birth . Models were constructed among all term births by using indicator variables for low birth weight, macrosomia, and normal birth weight (reference group).Women were followed from their first birth through 2005 or up to 35 years after their first pregnancy; we were able to follow more than 95% of the cohort for up to 34 years after their first birth. First, we evaluated the association between gestational age, infant birth weight, and T2DM risk in first pregnancies for the entire 35-year follow-up period. Second, we explored time since first birth in 5-year intervals up to 35 years after the first pregnancy to determine at what time points the differences between gestational age or birth-weight groups were significant. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. We adjusted for potential confounders, those factors that preceded pregnancy complications of interest and were associated with diabetes mellitus. Finally, we used the Breslow estimator to calculate the age-adjusted risk differences from the Cox proportional hazards models. All analyses were conducted using SAS statistical software .In our study population of NHSII participants, births occurred between 1964 and 2001, with 95% occurring before 1993. We examined pregnancy complications and risk factors for all births and for term births in the study population .In age-adjusted analysis, we observed a graded association of very and moderate preterm birth with risk of T2DM, as well as elevated risks among women delivering their first child postterm . HoweverWe explored the association between gestational age and T2DM in 5-year intervals following first pregnancy . In the We also explored the findings using risk differences. We found that during the 6 to 10 years following the first pregnancy, only 12 excess T2DM cases per 10,000 women who experienced a moderate preterm birth occurred compared with women who delivered at term. By 26 to 30 years after the first pregnancy, 298 excess T2DM cases per 10,000 women who delivered a very preterm birth occurred compared with those who delivered at term.Although term low birth weight was not associated with T2DM in the overall population , an increased risk for women who delivered a term low birth weight infant was seen among those without a history of GDM or HDOP . Women who gave birth to a first infant that weighed 10 pounds or more had a 1.61 increased risk of T2DM  . RestricIn our exploratory analysis evaluating time trends in risk of T2DM by 5-year intervals since first birth, we found women who had a term, low birth weight infant had an inconsistently elevated risk of developing T2DM within the first 2 decades after the first pregnancy ; this asWhen evaluating absolute risk of T2DM for women who delivered an infant that weighed 10 pounds or more, we found an excess of 115 T2DM cases per 10,000 women in this group compared with women who delivered a normal weight infant in the 21 to 25 years following the first pregnancy. After 30 years, the elevated risk associated with having delivered a large infant had disappeared.Because the prevalence of screening for GDM and HDOP during pregnancy has changed over the period of the births in this cohort (1964–2001), we repeated the analysis among first births occurring after the baseline 1989 questionnaire, because after that time period, screening was more widely practiced as standard care and gestational age-dating of pregnancies had probably improved with the advent of ultrasound. The hazard ratios estimated by the fully adjusted models (including GDM and HDOP diagnoses) were stronger for birth weight and T2DM, which mitigated the concern that macrosomia in the earlier pregnancies might merely be a marker for undiagnosed GDM. However, the association of very preterm delivery with T2DM was no longer detectable when limiting the analysis to pregnancies occurring after 1989.In this study, the approximately 9% of women whose first infant was delivered preterm had excess risk of developing T2DM, even after accounting for potential medical and lifestyle confounders. The 2% of women who experienced a very preterm birth had a 34% increased risk of developing T2DM over the 35-year follow-up period. In an exploratory analysis, we found that the elevation in risk first became evident at 11 to 15 years after the first pregnancy. Postterm birth was associated with a slight, significant increase in risk of T2DM over the entire 35-year period. A history of having borne a first infant who was term low birth weight or macrosomic conferred an almost 2 to 3-fold increased risk of T2DM, which gradually waned over time. These findings held even after adjusting for GDM and HDOP, known predictors of T2DM ,20–22. IIn a previous study by Lykke et al based on vital statistics registry data from Denmark , pretermThe increased risk of T2DM among women who experience a preterm birth may be due to chronic low-level inflammation ,26. SeveStrengths of this study include use of a large cohort of nurses with detailed information on both pregnancy history and diabetes and information on pre-pregnancy and reproductive risk factors for diabetes. We were also able to control for GDM and HDOP as strong predictors of T2DM. In addition, this study had an average follow-up time after first birth of 22 years, which allowed for sufficient time for a substantial proportion of participants to develop the disease (approximately 4% of the population). Furthermore, we were able to explore these research questions by using different cut points in total study time. This technique allowed us to explore periods in which certain pregnancy complications may have the most predictive value for future development of T2DM. Future studies are needed to further explore and confirm these associations based on time since pregnancy.Our study has several limitations. First, data on gestational age and birth weight were unvalidated self-reports. However, validation studies demonstrated good self-report of related pregnancy factors ,19. AlsoWomen who experience a preterm birth or have an infant with nonnormal birth weight are not followed up for lifestyle intervention or disease prevention after re-entry into the standard health care system for nonpregnant women. Both the American Diabetes Association and American College of Obstetrics and Gynecology recommend screening for T2DM for women with a history of GDM ,33. If o


The objective of this study was to evaluate the mediating role of maternal early pregnancy plasma levels of long chain polyunsaturated fatty acids (LCPUFAs) in the association of interpregnancy interval (IPI) with birth weight and smallness for gestational age (SGA) at birth.We analysed a subsample of the Amsterdam Born Children and their Development (ABCD) cohort, comprising 1,659 parous pregnant women recruited between January 2003 and March 2004. We used linear and logistic regression to evaluate the associations between fatty acid status, interpregnancy interval and pregnancy outcome.p = 0.005) and a twofold increased risk of SGA . Adjustment for maternal fatty acid concentrations did not affect these results to any meaningful extent.Low plasma phospholipids concentrations of eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and dihomo-gamma-linolenic acid (DGLA), and high concentrations of arachidonic acid (AA) during early pregnancy were associated with reduced birth weight and/or an increased risk of SGA. Short IPIs  were associated with a mean decrease of 207.6 g (SE: ± 73.1) in birth weight (Despite the observed association of maternal early pregnancy LCPUFA status with birth weight and SGA, our study provides no evidence for the existence of an important role of maternal EPA, DHA, DGLA or AA in the association of short interpregnancy intervals with birth weight and SGA. Interpregnancy intervals (IPIs) shorter than six months are linked with increased risks of preterm birth, lower birth weight and smallness for gestational age (SGA) -3. TheseThe mechanisms underlying the unfavourable effects of short IPIs on pregnancy outcomes are not well understood. Some causal hypotheses have been put forward, including postpartum hormonal imbalance, maternal stress and the maternal nutritional depletion hypothesis -10. The Nutrients that could play a similar role in the relationship between short interpregnancy interval and adverse pregnancy outcome are the longer-chain, more unsaturated derivatives of the essential fatty acids α-linolenic acid and linoleic acid, also known as long chain polyunsaturated fatty acids (LCPUFAs). This particularly holds for docosahexaenoic acid , eicosapentaenoic acid , arachidonic acid  and its precursor, dihomo-gamma-linolenic acid , which are structural components of cell membranes and are involved in a range of physiologic processes relevant to fetal growth, development and immune system -17. ConsA decrease in biochemical LCPUFA status is common in pregnant women ,19. ThisIn the present study, we evaluated the potential role of the concentrations of DHA, AA, DGLA and EPA in the relationship between interpregnancy interval and adverse pregnancy outcome in a population-based cohort of 1,659 multiparous pregnant women.http://www.abcd-study.nl). The study design has been described in detail elsewhere [Data for this study were obtained from the Amsterdam Born Children and their Development (ABCD) study. This prospective, population-based cohort study examined the relationship between maternal lifestyle, ethnicity, psychosocial conditions and nutritional status during pregnancy and the child’s health at birth and in later life . Of this group, 7,738 women gave birth to a single live infant about whom information on birth weight and pregnancy duration was available. For the present study, we excluded all primiparous women  and women who delivered preterm . In addition, we excluded 1,656 women (49.9%) who did not participate in the biomarker study and therefore lacked data on fatty acid status, as well as 20 women for whom the interpregnancy interval was not known. Consequently, data about 1,659 women and their infants were available for analysis  and SGA, which was defined as birth weight below the 10th percentile for gestational age based on sex- and parity -specific standards from the Dutch Perinatal Registry (available from the authors upon request). Gestational age at birth was determined by ultrasound  or, if unavailable (< 10%), based on the first day of the last menstrual period.Interpregnancy interval was computed as the amount of time between current and previous delivery dates, diminished by the duration of the current pregnancy. We divided interpregnancy interval into five categories; < 6, 6–11, 12–17, 18–23 and ≥ 24 completed months, and used 18–23 months as the reference category [Infant sex, birth weight and gestational age at birth (in weeks) were obtained from the Youth Health Department of the Public Health Service in Amsterdam. The date of the previous delivery was collected via questionnaire.Fatty acid analysis in the ABCD study was previously described . In shor2) were obtained from the questionnaire. A random imputation procedure using linear regression analysis on the available variables was used to complete missing data on height (3.4% missing) and weight (9.5% missing) [A number of variables were considered and treated as potential confounders. Maternal age in years   .Maternal pregnancy-related variables were pregnancy intention (by inquiring whether participants did or did not want to become pregnant) , spontanLifestyle variables included self-reported smoking habits  and alcoFinally, the selected socio-demographic factors were cohabitant status  , years oWe examined the relationship between maternal LCPUFA concentrations and pregnancy outcome by means of logistic regression (SGA) and linear regression (birth weight). Because we expected potential associations in the extreme values , we usedTo evaluate the association between IPI and pregnancy outcome, we performed a logistic regression analysis (SGA) and a linear regression analysis (birth weight). Confounding factors were evaluated by performing multivariate, adjusted analyses in which each of the above-mentioned covariates was separately added to the model. In the SGA analyses, infant sex and gestational age were omitted from the multivariate analyses since they were already part of the definition of SGA. In the logistic regression analyses, a covariate was considered to be a confounder when the odds ratio of the univariate analyses deviated more than 10% after adding that specific covariate. In the linear regression analyses, we regarded a covariate to be a confounder when the deviation of the regression coefficient was greater than 10%. Using this confounder selection strategy, none of the aforementioned factors were identified as confounders, and therefore the final multivariate model only contained interpregnancy interval as an independent variable.Finally, we assessed the potential influence of maternal LCPUFA concentrations on the relationship between interpregnancy interval and pregnancy outcome by repeating the adjusted logistic and linear regression analyses. In these analyses, LCPUFAs were treated as independent variables, first each LCPUFA separately and then all LCPUFAs simultaneously. Associations were considered statistically significant at p < 0.05. All analyses were conducted with the use of SPSS software .Characteristics of the woman-infant pairs n = 1659) are presented in Table 659 are pBlood samples for fatty acid analyses were taken at an average gestational age of 13.8 (± 3.5) weeks. Characteristics of the woman-infant pairs who did not take part in the biomarker study  were comparable to those of the women and infants who did. The distributions of quintiles of maternal fatty acid concentrations in plasma phospholipids are listed in Table A statistically significant association between maternal AA concentrations and birth weight and SGA was also present. Women with AA concentrations in the highest quintile gave birth to infants with birth weights 145.0 g (SE: 39.1) lower and a nearly two times increased chance of SGA in comparison with women with AA concentrations in the middle quintile (OR = 1.88 (95% CI: 1.19–2.95).We did not observe any statistically significant associations between short interpregnancy intervals and maternal LCPUFA concentrations Table . CompareBoth short (< 6 months) and long (≥ 24 month) IPIs were significantly associated with lower birth weight Table . The shoAfter adjusting for each LCPUFA separately, the regression analyses on interpregnancy interval and birth weight/SGA did not show any important changes in regression coefficients or in odds ratios. When the four selected fatty acids were adjusted for simultaneously, there was no meaningful change in the observed relationships either Table . ResultsThis study examined the potential role of maternal LCPUFA status during early pregnancy in the association between short interpregnancy intervals with birth weight and SGA. Our results showed an association between IPIs of less than six months (and ≥ 24 months) and adverse pregnancy outcome Table . These fOur analyses of the relationship between maternal essential fatty acids and pregnancy outcome Table  showed aOur analyses of interpregnancy interval and maternal LCPUFA concentrations did not reveal any meaningful associations between IPIs and individual LCPUFA concentrations Table . In partAfter adjusting for the selected maternal LCPUFA concentrations, we did not observe any meaningful changes in the relationship between interpregnancy interval and birth weight and/or risk of SGA Table . The resAlthough 50% of the potential study population was excluded because of a lack of information on their fatty acid concentrations, a comparison of maternal and infant characteristics between included and excluded participants indicated that the resulting study population was representative for all. Because of a potential lack of power, we also decided not to investigate less common outcomes such as preterm birth  or infant death. Since this study focused on birth weight and SGA, which are clinically relevant outcome measures, restricting the study to term-born infants made it possible to investigate birth weight as a reflection of health outcomes without the distorting effect of preterm delivery .None of the preselected maternal and infant covariables acted as confounders in the association between IPI and birth weight/SGA. However, we cannot exclude the possibility of residual confounding due to unmeasured confounders or misclassification as a result of recall problems.Measurement of essential fatty acid and LCPUFA concentrations in early pregnancy reflects the potential shortage due to a short interpregnancy interval. Moreover, this early stage of pregnancy is vitally import for fetal development ,44,45 anIn conclusion, the study results do not support the idea that maternal early pregnancy DHA, AA, EPA or DGLA levels play an important role in the maternal depletion hypothesis. Although maternal supplementation may be beneficial for individual cases, based on the results of the current study we do not expect that routine periconceptional maternal fatty acid supplementation will mitigate the adverse effects of short IPIs on pregnancy outcomes.None of the authors declare having financial or non-financial competing interests.LS conceived of the study and developed the design of the analyses, HE performed the statistical analyses and drafted the manuscript, MvE participated in the design and data collection from the ABCD study, and all authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2393/13/23/prepub


Infants born to HIV-negative women between 26-44 weeks gestation were analyzed to construct birth weight for gestational age charts. These data were compared with published norms for black infants in the United States.st quartile 38, 3rd quartile 40 weeks), and the median birth weight was 3100 grams . We constructed estimated percentile curves for birth weight by gestational age which demonstrate increasing slope during the third trimester and leveling off beyond 40 weeks. Compared with black infants in the United States, Botswana-born infants had lower median birth weight for gestational age from weeks 37 through 42 (p < .02).During a 29 month period from 2007-2010, birth records were reviewed in real-time from 6 hospitals and clinics in Botswana. Of these, 11,753 live infants born to HIV-negative women were included in the analysis. The median gestational age at birth was 39 weeks (1in utero.We present birth weight for gestational age norms for Botswana, which are lower at term than norms for black infants in the United States. These findings suggest the importance of regional birth weight norms to identify and define risk factors for higher risk births. These data serve as a reference for Botswana, may apply to southern Africa, and may help to identify infants at risk for perinatal complications and inform comparisons among infants exposed to HIV and antiretrovirals  Each year over 4 million infants die in the first four weeks of life . Ninety-eight percent of neonatal deaths take place in the developing world, and the highest risk is in Africa, where an average of 41 neonatal deaths occur per 1000 live births [Botswana is in the midst of a generalized HIV epidemic, and up to a third of infants are born to HIV-infected women . HIV infth percentile for gestational age) are at risk for complications such as peripartum asphyxia, birth trauma, hypoglycemia, impaired neurological development and perinatal mortality [Low birth weight infants < 2500 grams) are at risk for early death ,21. Weig00 grams ortality -30. Becaortality ,31,32.In this report we describe birth weight for gestational age for a large cohort of infants born to HIV-negative women in 6 hospitals in Botswana. Botswana is well-suited for development of these norms as the majority of births occur in hospital settings where information on birth weight, gestational age, and maternal HIV status is available. We compare these data with U.S. birth weight data for black infants with the goal of providing reference data for assessing birth weight for gestational age for both HIV-exposed and unexposed infants in Botswana.Birth weight and gestational age were recorded for live births at six government facilities over a 29 month period from October 19, 2007 to March 16, 2010. Surveillance started at Princess Marina Hospital in Gaborone, the largest hospital in Botswana. Surveillance began in 2008 at Scottish Livingstone Hospital in Molepole and at Broadhurst and Old Naledi clinics in Gaborone. In 2009, surveillance expanded to Deborah Reteif Hospital in the village of Mochudi in southern Botswana, Ghanzi Primary Hospital in western Botswana, Letsolathebe Hospital in Maun in northwestern Botswana, and to Nyangabgwe Hospital in Francistown, the largest city in northern Botswana.Princess Marina Hospital is the largest hospital in Botswana and serves as a tertiary referral center for the southern part of the country; the Broadhurst and Old Naledi clinics are smaller clinics in Gaborone. Scottish Livingstone Hospital and Debora Reteif hospitals are district-level referral centers. The hospitals in Francistown and Ghanzi serve as referral centers for northern and western Botswana, respectively.This study was approved by the Office of Human Research Administration at the Harvard School of Public Health, by the Health Research Development Committee in Botswana, and by all participating hospitals.Birth weights were abstracted  from obstetric records and delivery registries completed by maternity nurses. Extracted data included birth weight, infant gender, delivery date, gestational age, maternal age, and documented HIV status. In the event of multiple gestations, the outcome of the first-born infant was recorded. Clinical staff assessed and recorded gestational age at delivery using last normal menstrual period and fundal height assessment. Gestational ages are not confirmed by ultrasound in Botswana's public hospitals.Our analyses included live births to HIV-negative mothers between 26 and 44 weeks of gestation with no known congenital abnormality. Cases for which a live birth could not be confirmed (including still-births and those of unknown status) and births to women of positive or unknown HIV-status were excluded. Births with documented congenital abnormalities were also excluded. Because of concerns regarding data accuracy, infants with recorded birth weight less than 450 grams or greater than 6500 grams across all gestational ages and those with a recorded birth-weight that was 20% lower than the first percentile or 20% higher than the ninety-ninth percentile of published distributional norms of black infants born in the U.S. were also excluded . This U.Smoothed birth weight percentiles for gestational ages 26-44 weeks were estimated using quantile regression analyses with natural cubic B-spline with knots at weeks 24, 26, 28, 38, 40, 44 -35. KnotWilcoxon rank sum test, stratified by gestational age, was used to compare birthweight for gestational age among girls and boys. Birthweight at each gestational age was compared with the U.S. median weight for that gestational age using Wilcoxon signed rank test. All analyses were performed in SAS 9.2 for UNIX.The initial dataset  was first restricted to 11,814 live births, born to HIV-negative women, without congenital abnormality, between 26 and 44 weeks of gestation. An HIV status was recorded for 94% of mothers in the initial dataset. Of the excluded outcomes, 5,141 (72%) were births to HIV-positive women and 1,224 were to women of unknown HIV status. Among the excluded births to HIV-negative women, 181 were stillbirths, 175 had a congenital abnormality, 435 had unrecorded birth or congenital abnormality outcome, and 27 had gestational age less than 26 or greater than 44 weeks. In addition, sixty-one outliers were removed as described in the methods, leaving a total of 11,753 in the analysis dataset  values are observed to be higher for pre-term babies in Botswana than for pre-term U.S.-born babies, but to be similar at and beyond term.Table in utero.This report describes infant birth weight for gestational age for the largest cohort of infants born to HIV-negative women reported from southern Africa. These data may serve as a reference for identifying infants at risk for early complications and for assessing potential risk factors for adverse infant outcomes, including maternal HIV infection and exposure to antiretrovirals These data may be representative for births in Botswana. Based on annual and estimated birth rates for Botswana, there were 114,620 births over the 29 months of surveillance . The iniBirth weight monitoring is complicated in countries where the majority of births occur outside healthcare facilities. For most developing countries, birth weight data are collected via annual Demographic Health Survey records and birth weight for gestational age is generally unavailable ,41. InstThe Botswana-born infants had higher average birth weights pre-term  and lower birth weights at term  than U.S.-born infants in the referent dataset. Several studies have explored the potential inaccuracies of gestational age dating by last menstrual period. Dating is often inaccurate due to recall bias, variable menstrual cycles, and misinterpretation of bleeding at the time of embroyo implantation. The summation of these errors results in underestimation of prematurity (thus higher birth weights pre-term) and overestimation of post-dates (thus lower birth weights post-term) . Distribth percentile cut-offs were lower. By convention, the 10th percentile is the cut-off to define small for gestational age [th percentile or if in this setting a larger proportion of infants are at risk for early mortality.Because Botswana-born term babies were smaller than U.S.-born babies, the 10onal age . AdditioThere are several limitations to these data. Reported data were collected from six different hospitals, however 68% of the data were from Princess Marina Hospital, a tertiary referral site. These women may represent a wealthier urban population, as well as women referred with complicated pregnancies. In addition, these data were not restricted to singleton births, thus potentially skewing the data towards lower birth weights. Third, estimated gestational age may be inaccurate when estimated from last menstrual period ,45. We aWe present a reference for birth weight for gestational age for a large sample of infants born to HIV-negative women in hospitals in Botswana, and demonstrate lower median term birth weights than reported for black infants born in the United States. These data should prove useful for future research investigating the determinants of neonatal mortality and for assessing the effects of HIV and antiretrovirals on infant birth weight.HIV: Human immunodeficiency virus; ARVs: antiretrovirals.The authors declare that they have no competing interests.LTM was responsible for analysis, presentation and primary authorship of the work presented here. HR was primarily responsible for conception, design, analysis, interpretation and contributed to writing. NP and JYC acquired the data; KB contributed data collection and analysis. AO, JM, SS, SL, ME were involved in conception and study design. RLS was involved in all phases of the work. All authors reviewed and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2431/11/115/prepub


Autism is a complex genetic disorder with multiple etiologies whose molecular genetic basis is not fully understood. Although a number of rare mutations and dosage abnormalities are specific to autism, these explain no more than 10% of all cases. The high heritability of autism and low recurrence risk suggests multifactorial inheritance from numerous loci but other factors also intervene to modulate risk. In this study, we examine the effect of birth rank on disease risk which is not expected for purely hereditary genetic models.We analyzed the data from three publicly available autism family collections in the USA for potential birth order effects and studied the statistical properties of three tests to show that adequate power to detect these effects exist. We detect statistically significant, yet varying, patterns of birth order effects across these collections. In multiplex families, we identify V-shaped effects where middle births are at high risk; in simplex families, we demonstrate linear effects where risk increases with each additional birth. Moreover, the birth order effect is gender-dependent in the simplex collection. It is currently unknown whether these patterns arise from ascertainment biases or biological factors. Nevertheless, further investigation of parental age-dependent risks yields patterns similar to those observed and could potentially explain part of the increased risk. A search for genes considering these patterns is likely to increase statistical power and uncover novel molecular etiologies. Autism is a genetic disorder whose molecular basis is incompletely understood. The characterized genetic component of autism, to date, accounts for only <10% of all cases A central feature of most genetic models is that the risk of affection depends on genotype but not position within the family. The latter is rare for a Mendelian disorder with exceptions occurring where disease mutations are strongly dependent on parental age and/or the mutation is dynamic A birth order effect can arise from both biological (genetic) or demographic  causes. Irrespective of etiology, this risk may increase or decrease with birth rank so that the last-borns or first-borns, respectively, are more likely to be affected The simplest demographic cause of an apparent birth order effect is the curtailment of reproduction after the birth of a child with a serious illness. More generally, one might consider social reproduction models where the probability of future births is dependent on disease severity and the number of pre-existing affected children, thus giving rise to patterns where affected offspring are skewed towards later births. A second demographic explanation for the birth order effect can be attributed to intense disease surveillance and curtailment within particular reproductive age-groups so that almost all incident cases occur outside that age-group. For example, in Down syndrome the risk increases with maternal age but in western societies with maternal-age based screening most affected births occur to younger women.de novo gene mutations, such as in achondroplasia, increase with paternal age In addition to demographic considerations, there are several well-documented biological causes for birth order effects that act through age. First, the risk of disease may depend on paternal or maternal age and thus increase with increasing birth rank https://www.nimhgenetics.org/), and SSC (Simons Simplex Collection) directly examine deviations from the expected uniformity in the distribution of affected offspring across birth ranks. The proposed testing procedures differ in their efficiency depending on the shape of the birth order effect as shown by the results of our systematic simulation studies. Having access to large autism collections with hundreds of families allows a test for birth order effect in both simplex and multiplex families. We demonstrate that these effects are statistically significant and that different collections vary in the patterns they manifest. Consequently, these effects impact the search for autism genes.To the best of our knowledge, a comprehensive formal assessment of overall birth order effect in autism, at least in the families now being extensively used for genetic investigations, has not been attempted. Consequently, in this study, we explore whether such birth rank trends exist in the three major and widely-available US autism family collections, namely, AGRE (Autism Genome Resource Exchange) https://www.nimhgenetics.org/), and the Simons Foundation (Simons Simplex Collection (SSC)) This study was conducted under ethics approval by the Johns Hopkins University School of Medicine Institutional Review Board (IRB) and was deemed to be exempt from full IRB review under exemption category 4. The families examined in this study were ascertained by the Autism Genome Resource Exchange  The two multiplex collections used in this study were the NIMH and AGRE collections. For the most part, both sets consist of families with at least two offspring affected with an autism spectrum disorder. The NIMH data comes from the NIMH distribution 6.0 pedigree files while the AGRE data was obtained from the AGRE January 2010 pedigree files. For AGRE, year of birth data was obtained from Vlad Kustanovich at AGRE . Across all families, we counted all those with a diagnosis of autism or ASD as affected. In the AGRE collection, 346 families met these criteria and contained 744 individuals with autism (60%) among a total of 1,234 children. In the NIMH collection, 396 families met these criteria and contained 840 individuals with autism (60%) among a total of 1,393 children. These two collections have families in common so that we combined the data sources to obtain a single non-duplicated dataset of 485 families containing 1,030 individuals with autism (60%) among a total of 1,716 children. The Simons Simplex Collection (SSC) consists of simplex families with at least two offspring only one of whom had a diagnosis of ASD. We obtained the data from the SSC v.7.1 data release to identify 1,119 individuals with autism (47%) among a total of 2,392 children. The high frequency of autism in all families show the ascertainment bias induced in these collections.We extended and generalized three different tests for the birth order effect and assessed their statistical power by computer simulations under different, but biologically relevant, types of disease risk profiles. A software package implementing these tests is freely available from the authors. A brief description of each test follows.The rank-sum test Although the above test may be useful for linear changes in risk, it is inefficient whenever the risk distribution across births is V-shaped 2-type test The third test of the birth order is a direct comparison of the observed and expected numbers of affecteds at each birth rank using a χuses the actual counts of affected offspring at each birth position and not the ranks . The tes2 distribution because the observations are not independent. The observed counts for the z was computed as a linear function of birth ranks as:Before analysis of the real data, we first assessed statistical power and the optimal behavior of the three tests under a variety of biological models of birth order effect. We performed extensive Monte-Carlo simulations to easily accommodate a variety of risk models. In the first set of simulations, we assumed five simple models where disease risk was (a) independent of birth rank, (b) linearly dependent on birth rank , (c) V-shaped birth order effect; (d) and (e) were mixtures of linear but opposite birth rank trends in 9∶1 (increasing/decreasing) and 3∶7 proportions. To generate family structures, we used a Poisson distribution for sibship sizes with mean λ = 2.39 . The probability of being affected at each birth rank where N is the sibship size. Both intercept and slope were selected such that the increased risk was substantial but not exceedingly large.2–type test proves to be a good compromise between the two other tests and is capable of detecting both types of nonuniform birth effects in the data. Its robust performance makes it the test of choice for the birth order effect, although it is less powerful than at least one of the other tests when the risk effects are either linear or V-shaped. In contrast, one has to keep in mind that both the rank-sum and inverse rank-sum test are likely to miss more complicated patterns of birth order effects.We estimated statistical power for 500 and 1,000 families under the five linear models. These results are shown in b (the maximum risk) and a :Although the previous power calculations are encouraging, we wanted to model risk using known genetic mechanisms. In other words, we know of many genetic mechanisms that vary risk by age as opposed to birth order. Thus, we wanted to assess how age-dependent risks would manifest themselves as birth rank effects. A commonly accepted biological risk model is logistic where risk of disease is age-dependent and the precise dependence is modeled by three parameters: β (intrinsic increase/decrease rate), b assumed to be 0.1 for all cases. Empirically observed risk rates for Down Syndrome that shows a maternal-age effect are also plotted in open circles providing a reasonable benchmark for the four models. To generate family structures, we again used a Poisson distribution for sibship sizes with mean λ = 2.39. Parameter selection for the four models was motivated by presenting reasonable yet somewhat different risk patterns. In the no-birth effect model both β and a are set to 0; for the linear model, β = 0.05 and a = 30, and for the two logistic curves, a = 45 is selected as the age at which the risk reaches half of its maximum b at two different rates (β = 0.2 and β = 0.5). The model with β = 0.5 and a = 45 is clearly the one closest to the observed risk pattern in Down Syndrome and is of primary interest as a potential ‘model’ for the autism age-dependent risk profile.We considered four sets of parameters to model three classes of risk, namely, where disease risk was (a) independent of age, (b) linearly increasing with age , (c) logistic increase in risk with lower and higher rates of increase at later ages. These risk profiles are shown in a = 45. The proportion of affected offspring by birth rank and the distributions of parental age for both affected and unaffected offspring are shown in birth rank, measured as proportion of affected at each rank, which directly corresponds to the underlying increase in risk by parental age. The expected and observed proportions of affected offsprings by birth rank differ substantially (top left) and the χ2-type test is highly significant (p<0.001). In a = 30 and is also plotted in red in 2-type test for the birth order effect in these 10,000 families is also highly significant (p<0.001) although the departure of observed to expected proportions is not as substantial as in the previous logistic example. Nevertheless, we still observe an increase in risk at each sibship size and the overall test confirms this effect. To estimate the statistical power for these logistic models we again resort to Monte-Carlo simulations. The estimated power for sample sizes 150, 500 and 1,000 families based on 100 simulations is shown in 2-type test. As expected, the inverse rank-sum test has the least power for monotonic patterns of risk increase and should not be used in such cases.To examine the nature of birth rank effects recovered we simulated 10,000 families with logistic risks and the parameters β = 0.5 and 2–type test was significant in all three autism collections. Third, the results from the multiplex collections are more significant than those from the simplex families.Finally, we utilized all three statistical tests and 10,000 permutations each to assess birth order effects in the multiplex AGRE and NIMH data sets and the simplex SSC data set; these results are presented in Based on the results of power analysis and assumed birth order effects, we can infer that the multiplex collections are not significant for the linear effect (rank-sum test) but very significant for the V-shaped effect (inverse rank-sum test), whereas the SSC showed an opposite pattern. In the combination dataset (AGRE + NIMH), shown in Due to large gender differences in autism prevalence, gender specific analyses in each of the collections were performed. Intriguingly, as reported in To examine both collections for parental age we also studied the parental ages for all individuals for whom this information was available. Overall, the distribution of affected children and unaffected children across parental ages did not differ  but the 2 –type tests to be best at detecting linear, V-shape, and general types of patterns, respectively. The χ2 –type test proved to be the most robust test, capable of identifying arbitrary deviations from the expected distribution, although slightly less efficient when the true risk profiles are either linear or V-shaped.In this study, we analyzed three autism datasets  for familial birth order effects in multiplex and simplex families. To accomplish this we generalized and implemented three different statistical tests for birth order effects and carried out a series of simulations studies to assess their performance under different birth order patterns. We demonstrated the proposed rank-sum, inverse rank-sum and the χ2 –type tests. Looking at the results visually confirms an increasing linear effect that peaks around rank 2 (de novo mutations (both copy number and substitutions) in individuals with autism. Maternal-fetal incompatibility such as in Rh-disease are difficult to detect with current genomic studies: with increased risk to subsequent births this is a potential consideration for autism etiology. This type of effect should be carefully studied through the development of novel genetic methods. Lastly, epigenetic changes across pregnancies may exist but remain an enigma.Application of each test to the simplex collection identified significant linear birth order effects confirmed by both the rank-sum and the χd rank 2 . A demog2 –type test were both significant and indicated an inverse V-shape birth order effect in which the middle ranks are more commonly affected as can be seen in In the multiplex families, the inverse-rank sum test and the χde novo mutation associated with parental age hypothesis has been put forth in the recent past in particular for simplex families. de novo parental age effects in combination with genetic segregation patterns. One should not immediately discount other possibilities such as ascertainment bias or sibship curtailment. In addition, with the mysterious nature of autism causation it may be time to consider newer models and possibilities such as maternal-fetal incompatibility and/or epigenetic changes. Perhaps this can lead to specific studies of maternal genotype and fetal environment.This study shows birth order effects do exist in autism and that some underlying biological cause may account for these effects. The Figure S1The expected and observed numbers of affected males and females by birth rank in simplex and multiplex autism families.(TIFF)Click here for additional data file.Figure S2Distribution of parental age at birth of affected (red) and unaffected (black) offspring in simplex and multiplex autism families.(TIFF)Click here for additional data file.Figure S3Proportion of affected children of all children at each parental age.(TIFF)Click here for additional data file.Table S1The inverse ranking scores used for the inverse rank-sum test. Offspring that are either early born or late born are given larger linear ranks as compared to the middle born children.(DOC)Click here for additional data file.Table S2Observed number of affected individuals for each sibship size and birth rank.(DOC)Click here for additional data file.


During the last decades there has been a steady increase of twin births. A combination of improved medical treatment of preterm and small-for-gestational age children has contributed to a higher number of surviving twins. Prematurity is known to affect reproduction in a negative way. Few studies have focused on the potential effect twinning may have on future reproduction. Thus, the aim of this study was to investigate the effect of being born a twin compared to being born a singleton have on future reproduction.In a national population-based register study, all individuals born between 1973–1983 who were alive and living in Sweden at 13 years of age (n = 1 016 908) constituted the sample. Data on each study subject’s own birth as well as the birth of their first offspring, and parental socio-demographic factors were collected from Swedish population based registers. Hazard ratios and corresponding 95% CI was calculated using Cox proportional hazards model.Twins, both men and women, had a reduced likelihood of reproducing compared to singletons . This difference in birth rates can only partly be explained by diverging birth characteristics. Amongst men and women born very preterm, twins had an increased likelihood of reproducing compared to singletons .Twins have lower reproduction rates compared to singletons, which only to a certain degree can be explained by diverging birth characteristics. Worldwide there has been a steady increase of twin births in Sweden the rate rose from 8.2 births/1000 women in 1973 to 16.3 births/1000 women in 2003 . This inEkholm et al.  showed tOther researchers have found that growth restriction and preterm birth can alter reproductive organs and functioning and hence, in theory, can affect future fertility and therefore have an impact on reproduction ,10. AlsoVery few studies have focused on the potential effect twinning may have on future reproduction. However, studies have shown that twins marry/form relationships later in life in comparison to singletons, which could have an impact on their reproduction . In addiIt is of importance to verify if the reproductive pattern in twins truly differs from the reproductive pattern amongst singletons. If so, it is of interest to establish whether this may be due to the prematurity/low birth weight of twins or the twinning itself. Using national registers, we were able to identify 16561 persons as being twins – which approximately equals 8000 twin pairs. These data provide a unique source for exploring the importance of being born a twin, compared to singletons, and its effect on the reproduction.The primary purpose of this study was to examine whether twins exhibited a different outcome in terms of future reproduction and to what extent a potential twinning effect could be explained by differences in birth-characteristics.“Small for gestational age” (SGA) was defined as a birthweight < −2 SD of the mean weight for the gestational length [“Large for gestational” (LGA) age was similarly defined as a birthweight > +2 SD of the mean weight for the gestational length according to the Swedish standard [“Low birthweight” was defined as birthweight below 2500 g and “very low birthweight” as a birthweight below 1500 g. “Moderate preterm birth” was defined as being born between gestational week 32 and 36, and “very preterm birth” as being born before gestational week 32. “Educational level” was separated in to three categories: ‘elementary school’, ‘high school’ and ‘graduate/postgraduate’. “Parental country of origin” was dichotomized into ‘both parents from Nordic countries’ and ‘one or both from non-Nordic countries’. “Mother’s marital status” had three levels: ‘married’, ‘unmarried’ and ‘divorced/widowed’. “Parity” was defined as either primiparity’ or ‘multiparity’. “Mother’s age when giving birth” was separated into four categories: 13–19 years, 20 – 26 years, 27 – 33 years and ≥ 34 years. “Non-optimal birth characteristics” include SGA, LGA and preterm delivery and “optimal birth characteristics” those not SGA, LGA or preterm. Missing values on country of origin and marital status were all imputed with the most common value for each variable.Several different definitions of non-optimal birth-characteristics were used, each definition reflecting different aspects of non-optimal birth characteristics. l length . “Large standard . “Low biThe data for this study were retrieved from Swedish population-based registries. All Swedish residents are assigned unique personal identification numbers, which enables us to individually link the information in the different registers. All registers have been validated.· The Swedish Medical Birth Register (MBR): Medical information on all births since 1973 and onwards has been stored in the MBR and is held by the Swedish National Board of Health and Welfare .· The Total Population Register (TPR) and the Multi-Generation Register: The TPR is held by Statistics Sweden and was established in 1968 ,16. The · The Causes of Death Register: The Causes of Death Register, which is held by the Swedish National Board of Health and Welfare, contains information on the cause of death and was established in 1961 .· The Education Register and the Population and Housing Census: Since 1985, Statistics Sweden has continuously collected information on the educational level of the population in the Education Register ,19.Our study population was ‘twins and singletons born and still living in Sweden at the age of 13’. However, MBR registers all births in Sweden and hence an extensive and careful cleaning of the data was required to remove individuals who did not reside in Sweden at the age of 13 as well as to remove individuals with missing or invalid data. A total of 1 070 380 birth were registered in 1973 – 1983 according to both MBR and TPR, of these 10 811 individuals were deceased before age 13 . Persons with missing values on birthweight (n = 2114) and on gestational length (n = 4840) were excluded. Individuals (n = 663) that were considered to have an extremely high birthweight with respect to gestational length were excluded. Also, 3629 cases with an unlikely low birthweight with respect to gestational length were excluded . Furthermore, we had to exclude persons who had emigrated, and not returned to Sweden, before the age of 13 (n = 20 507) or immigrated to Sweden at age 14 and onwards (n = 5485). Finally, 5423 individuals were removed due to lack of information on the father of the child. The final data set, after removal of triplets (n = 220), comprised of 1 016 688 individuals born as singleton or twin, out of these individuals a total of 16 561 twins could be identified. These individuals were followed until the end of 2006.In order to investigate whether being born as a twin was related to later childbearing (measured as the hazard ratio for giving birth) we analysed the data through Cox’s proportional hazards model. Individuals exited from the models when they gave birth to the first child, emigrated, died or reached the end of the follow-up. Adjustments were made for socio demographic characteristics such as parental educational level, age at giving birth, marital status and parity, and in a separate analysis adjustments were also made for their own birth characteristics. To account for the possible age effect on the outcome analyses were also performed on data stratified into three groups based on year of birth . The analyses were done by gender. All analyses were performed using SPSS, version 19.0 .Human Research Ethics Committee. Faculty of Health Sciences, Linköping University no. 03–556, 03–557, 07-M66 08 – 08-M 233–8.“size for gestational age”, “birthweight” and “gestational week” between twins and singletons  for females and 0.89 (95% CI = 0.85-0.94) for males. Estimated reproductive rates till the age of 30 were amongst female twins and singletons 40% and 44%, respectively. Corresponding rates for male twins and singletons were 24% and 26%.Increasing maternal age and no previous children were associated with a lower likelihood of reproducing amongst female twins as well as singletons Table . AdjustiAs for females, increasing maternal age and low maternal parity were associated with a lower likelihood of reproducing amongst male twins and singletons Table . Male twTwins are very often excluded from epidemiological studies due to a suspicion that they behave differently compared to singletons ,20-22. IWe found that twins have a lower reproductive rate compared to singletons. One possible explanation is that other birth characteristics such as prematurity and size for gestational age are of more importance when analysing reproduction rates. Socio-demographic and birth characteristics do have an effect on reproduction rates. Nevertheless, a difference still remains between twins and singletons, which has not been accounted for. It could be that twins rely on one another for companionship and hence, as studies have reported, marry/form relationships later in life compared to singletons, which could affect their reproductive pattern ,23. AlsoFurthermore, male and female twins, born at term or moderately preterm, were found to have a reduced likelihood of reproducing compared to singletons of the same category. However, twins, male and female, born very preterm had an increased likelihood compared with singletons born very preterm. The reason for these findings can be related to differences in aetiology for preterm birth and thus the risk for complications, later related to reproductive capacity. Singletons are mostly born preterm due to pregnancy complications e.g. infections, preeclampsia etc. whilst twins more often are delivered preterm due to mechanical reasons .In the analysis stratified by year of birth we found that twins had lower reproductive rates than singletons in all cohorts. The lower rate in twins appear to persist amongst females also after the age of 25 but it should be remembered that in this study the individuals are aged between 23 to 33 at end of follow-up. We are therefore only following the women through about half of their reproductive years and less so for males since they, on average, become parents at an older age than females.In 2011 deKeyser et al.  reportedThis is a population based study in which all children born in 1973–1983 who were still alive and living in Sweden at 13 years of age were included. Due to the aim of the study data related to the study populations’ own births as well as the birth of their firstborn were collected from The Swedish National Board of Health and Welfare. The major strength of this study is that it is a population based and therefore all analyses are based on a large number of individuals. Another strength is that all data are prospectively collected from registers maintained by The Swedish National Board of Health and Welfare and Statistics Sweden. These registers are evaluated regularly and in the last evaluation, performed in 2003, it was confirmed that MBR covers 99% of all newborns, birth weight is missing for only 0.04-1.95%, gestational length has a 97% correspondence. However, there is a possibility that gestational length have been miscalculated in both ways, i.e. both overestimating as well as underestimating the gestational age at time of delivery. This is most markedly evident in the post-term group. The reporting to NPR is complete for inpatient hospital care and 98.6% of all incidents are correctly classified. Also, through the unique personal identification number assigned to each individual we were able to link MBR-data to socio-demographic variables, which allowed for thorough/extensive analysis.This data did not allow us to control for monozygotic and dizygotic twins since the registers did not contain this information. Hence, in the comparison of male singleton with male twin, the male twins’ co-twin is in most cases a female . On the other hand the primary goal in this study was to analyse potential differences between twins and singletons, regardless of the twin being homozygote or heterozygote. Also, though not part of the aim of the study, another limitation of this study is that no analysis has been performed comparing the reproductive rates between twins raised as twins and twins raised as singletons. Finally, since the study population was defined as persons born between 1973 and 1983 the twinning rate in this study was not affected by assisted reproductive treatments.Twins, both males and females, were found to have a lower reproduction rate compared to singletons, when adjusting for socio-demographic factors. This difference in birth rates can partly be explained by diverging birth characteristics.For future research it will be of great interest to follow the growing population of twins born after IVF-treatment and their reproductive patterns.The authors declared that they have no competing interests.GS and OF had the original idea for the study. All authors planned the study. MB analysed the data and drafted the paper. All authors contributed to the interpretation of the data, revisions and gave input at all stages of the study. All authors have approved to the final version of the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2393/13/6/prepub


The Indigenous population of Australia was estimated as 2.5% and under-reported. The aim of this study is to improve statistical ascertainment of Aboriginal women giving birth in New South Wales.This study was based on linked birth data from the Midwives Data Collection (MDC) and the Registry of Births Deaths and Marriages (RBDM) of New South Wales (NSW). Data linkage was performed by the Centre for Health Record Linkage (CHeReL) for births in NSW for the period January 2001 to December 2005. The accuracy of maternal Aboriginal status in the MDC and RBDM was assessed by consistency, sensitivity and specificity. A new statistical variable, ASV, or Aboriginal Statistical Variable, was constructed based on Indigenous identification in both datasets. The ASV was assessed by comparing numbers and percentages of births to Aboriginal mothers with the estimates by capture-recapture analysis.Maternal Aboriginal status was under-ascertained in both the MDC and RBDM. The ASV significantly increased ascertainment of Aboriginal women giving birth and decreased the number of missing cases. The proportion of births to Aboriginal mothers in the non-registered birth group was significantly higher than in the registered group.Linking birth data collections is a feasible method to improve the statistical ascertainment of Aboriginal women giving birth in NSW. This has ramifications for the ascertainment of babies of Aboriginal mothers and the targeting of appropriate services in pregnancy and early childhood. The Indigenous population of Australia was estimated as 2.5% 517,200) of the Australian population as of 30 June 2006 [7,200 of In a report by the Australian Institute of Health and Welfare (AIHW), 35,264 women who identified as being of Indigenous origin gave birth to 35,682 babies  between 2001 and 2004 [However, it was found that maternal Indigenous status was under-reported in the New South Wales (NSW) MDC and the Registry of Birth Deaths and Marriages (RBDM) birth -9. In thTo adjust for under-enumeration, capture-recapture methods were used to estimate the number of Indigenous mothers and babies ,7. The cHowever, the summary numbers calculated by the capture-recapture method were not on an individual level and could not be used for some data analyses such as multivariate regression analysis. An improved statistical variable of Aboriginality is needed to more accurately reflect the Aboriginal population in NSW.The under-identification of Indigenous patients was also apparent in other states and territories in Australia . A reporThe aim of this study is to improve the statistical ascertainment of Aboriginal women giving birth in New South Wales. The method can be a reference for improving the data quality of Indigenous populations in other states and territories in Australia. It can also be used for quality improvement of other variables which are available in different data collections by data linkage.This study is based on linked population data of births (MDC-RBDM) from January 2001 to December 2005 in New South Wales. The MDC was recorded by midwives and other health care professionals. The scope of the MDC was all live and stillbirths of at least 400 grams birthweight or at least 20 weeks gestation in NSW. Comparatively, the RBDM was recorded by the parents of the child and lodged with the Registrar of Births Deaths and Marriages. It covered all births registered in New South Wales and included information on demographic factors and some pregnancy outcomes.The Indigenous status in the MDC only includes that relating to the mother. The Indigenous status in the RBDM birth includes that of both the mother and the father.The values of maternal Indigenous status in both birth data collections include: (1) Aboriginal, (2) Torres Strait Islander, (3) both Aboriginal and Torres Strait Islander and (4) neither Aboriginal nor Torres Strait Islander.There are few births in NSW reported by Torres Strait Islander mothers, so the combined value of Indigenous status was used in the study.Centre for Health Record Linkage (CHeReL) performed the data linkage using probabilistic record linkage methods and choiceMaker software. At the completion of the process, each record in the Master Linkage Key was assigned a record identification number and a Master Linkage Key person ID (Person Project Number (PPN)) to allow linked records for the same individual to be identified and extracted. Checked by 1,000 person IDs selected at random, the false positive rate of the linkage was 0.3% and false negative < 0.1%.Indigenous status refers to Aboriginal and Torres Strait Islander origin. Maternal Indigenous status refers to women who have given birth and identify themselves to be of Aboriginal and Torres Strait Islander origin .The accuracy of maternal Indigenous status in the MDC and RBDM birth can be assessed for consistency, sensitivity and specificity. For the purposes of this study, the sensitivity and specificity are based on the assumption that the Indigenous status reported in the MDC and RBDM are correct. So the Indigenous status in the MDC can work as the 'standard' for the calculation of the sensitivity and specificity in the RBDM birth and vice versa. Because the sensitivity and specificity are calculated based on assumptions rather than 'gold standard', the sensitivity in this paper is a 'relative sensitivity' and the specificity is a 'relative specificity'.The formulas for calculating consistency, sensitivity and specificity are as below. Table Consistency refers to the probability of the same identification of maternal Aboriginality between MDC and RBDM in the Indigenous population (Consistency (%) = (A+D)/T*100). Sensitivity refers to the probability that maternal Aboriginality is identified in Indigenous population (true positive). Sensitivity of MDC (%) is A/(A+C)*100; sensitivity of RBDM (%) is A/(A+B)*100. Specificity refers to the probability that maternal non-Aboriginality is identified in the non-Indigenous population. Specificity of MDC (%) is D/(B+D)*100, and specificity of RBDM (%) is D/(C+D)*100. Completeness refers to the extent to which individual variables have non-missing records.An improved variable, ASV was created by linking the MDC and RBDM birth. The ASV is based on the following assumptions:1. Mothers self-reported as Indigenous in either the MDC or RBDM are recorded as Indigenous in ASV.2. Mothers self-reported as non-Indigenous in both the MDC and RBDM are recorded as non-Indigenous.3. Mothers self-reported as non-Indigenous in the MDC are recorded as non-Indigenous when the value in the RBDM birth is not stated, and vice versa.4. Mothers, where the Indigenous status is not stated in the MDC or the RBDM, are classified as missing.The ASV is an aggregate variable transformed through the linkage of the MDC and RBDM.Using capture-recapture analysis, an estimate of the population prevalence of births with Aboriginal and/or Torres Strait Islander mothers can be calculated. The conditions for using the capture-recapture method are met in this study ,20. The 1 is the number of births to Aboriginal mothers identified by the MDC; n2 is the number of births to Aboriginal mothers identified by the RBDM birth; and n12 is the number of births to Aboriginal mothers identified by both the MDC and RBDM birth.Where nApproximate unbiased estimate of the variance of N ,20,21:The 95% confidence intervals (95%CI) of N are:The estimates of Aboriginal mothers by capture-recapture are assumed to represent the true number or rate of Aboriginal mothers. The maternal Indigenous status was under-reported in the MDC and RBDM and the numbers of Aboriginal mothers were significantly below the estimates calculated by the capture-recapture method. If the ASV can improve the under-reported numbers, it should be higher than the reported number on the MDC and RBDM birth and closer to the estimates by capture-recapture analysis; the closer, the better. If there is no significant difference between the results of the ASV and the estimates from capture-recapture analysis, the ASV is considered to be improved to an optimal level and can represent the true number of Aboriginal mothers.The chi-square test and 95% confidence interval were used to assess the differences between the percentages analysed by the ASV and the estimates calculated by the capture-recapture method.The linkage study was approved by the Aboriginal Health and Medical Research Council, NSW Population & Health Services Research Ethics Committee and the Human Research Ethics Committees of The University of Sydney and the University of New South Wales. Identifying information such as name, address, date of birth and gender obtained from the MDC baby and RBDM birth datasets was included in the Master Linkage Key which was constructed by the (CHeReL).There were a total of 440,994 birth records from the MDC and RBDM between January 2001 and December 2005 in NSW. There were 434,467 birth records in the MDC and 404,734 birth records in the RBDM birth. Theoretically, all births in the NSW MDC can be linked with those from the NSW RBDM birth. However, some birth records cannot be linked. There were 398,207 records which could be linked between the MDC and RBDM births with a linkage rate of 91.65% in the MDC and 98.39% in the RBDM birth.There were some babies who had more than one record in the data collections, so the duplicate records of 677  were excluded from the analysis. Eleven records did not state maternal Indigenous status in either the MDC or the RBDM birth and were excluded from the analysis.Table The mother's Indigenous statuses were highly consistent between the MDC and RBDM. However, the relative sensitivities in both MDC and RBDM were low in both the MDC and RBDM birth, and the sensitivities in the MDC were lower than the RBDM birth. At least one-third of Indigenous mothers were not identified in the MDC and one-seventh in the RBDM birth.Table Table The percentages of maternal Indigenous status from the ASV in the MDC and the ASV in the RBDM birth were significantly higher than the original maternal Indigenous statuses in the MDC and RBDM birth respectively. The ASV in the MDC and RBDM birth also reduced the number of missing cases significantly (p < 0.05), especially for the ASV RBDM birth (except for 2005). The improvement in the MDC was more than the RBDM birth. However, the percentages of maternal Indigenous status from the ASV in the MDC and the ASV in the RBDM birth were still significantly lower than the estimates by the capture-recapture method.Table The Aboriginal status of women giving birth in NSW was under-reported in both the MDC and RBDM birth -9. TableHowever, the under-ascertainment of maternal Aboriginal status can be improved significantly by creating the ASV through linkage of the MDC and RBDM birth. The ASV maximises ascertainment through accepting as valid Indigenous identification in both datasets. The linkage minimises missing values in both the MDC and RBDM birth. The ASV is more accurate and complete and is more suitable for use in advanced computation such as regression analysis.The ASV improves the data quality at an individual record level rather than providing an estimation of the total number calculated by the capture-recapture method. As a result the ASV can be used for more analyses which are based on individual records. For example, the variable is not only used to describe the rate and distribution of the Indigenous population but can also be used as a independent factor to show the association with other dependent factors such as low birthweight or stillbirth. However, the estimation by the capture-recapture method is an overall number which cannot be used for factor analysis.Interestingly, although the ASV improves ascertainment of Aboriginal women giving birth, it still under-enumerates estimates derived using a capture-recapture methodology. The impact of this limitation could be minimised by internal data linkage such as linking all of a mother's births to check the Indigenous identification. For example, if a mother had more than one birth, the missing or incorrect values of Aboriginality can be supplemented or confirmed with multiple birth records for that mother. But internal data linkage ideally involves a longer study period given that a mother may have more than one birth. The five-year study period was not long enough for internal linkage. This limitation may also be addressed through additional linkages of other data sources such as Medicare data collection, Admitted Patients Data Collection (APDC), the Emergency Department Data Collection (EDDC) or Register of Congenital Conditions (RCC). It is therefore possible to further improve the ascertainment of Aboriginality to an optimal level by linking the birth data with additional data collections of information on Aboriginality.Indigenous population was under-estimated not only in New South Wales but also in other states and territories in Australia . The ASVThe percentage of births to Aboriginal mothers in the registered birth group in 2005 was significantly lower than in the preceding four-year period, 2001 to 2004. This is because Aboriginal babies' registrations are more likely to be delayed . For exaThe number of births between 2001 and 2005 in the MDC Table  was margThe assumptions for using the capture-recapture method are described in Additional file All linkage studies have limitations. The sensitivity and specificity were calculated based on the assumption that the Indigenous status of women reported in the MDC and RBDM birth was the true and valid status, rather than a 'gold standard'. A validation study of these data would be needed to test this assumption. This is not possible as de-identified data made available from NSW Health is used. The sensitivity and specificity detail differences in the quality of the MDC and RBDM birth.Linkage of the birth data collections is a feasible method to improve the statistical ascertainment of Aboriginal women giving birth in NSW. Linkage results in enhanced ascertainment and the construction of a statistical Aboriginal variable. This can be applied to both of the birth collections and other population data. This has ramifications for the ascertainment of babies of Aboriginal mothers and the targeting of appropriate services in pregnancy care and early childhood. Increasing birth registration rate and additional data linkage may improve the ascertainment further.The authors declare that they have no competing interests.FX participated in study design, data analysis and paper writing. EAS participated in study design, coordination and paper writing. RCM conceived of the study, designed, coordinated and supervised the study. DAB participated in data analysis. LRJP oversaw the aspects of the analysis and reporting of information relating to Indigenous status. All authors read, revised the manuscript and approved the final version.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/12/8/prepubAssumptions for using the capture-recapture method. Five assumptions were considered when using the estimates by the capture-recapture method.Click here for file


Septic-shock-associated acute kidney injury (SSAKI) carries high morbidity in the pediatric population. Effective treatment strategies are lacking, in part due to poor detection and prediction. There is a need to identify novel candidate biomarkers of SSAKI. The objective of our study was to determine whether microarray data from children with septic shock could be used to derive a panel of candidate biomarkers for predicting SSAKI.A retrospective cohort study compared microarray data representing the first 24 hours of admission for 179 children with septic shock with those of 53 age-matched normal controls. SSAKI was defined as a >200% increase of baseline serum creatinine, persistent to 7 days after admission.n = 31) and patients without SSAKI (n = 148) were clinically similar, but SSAKI carried a higher mortality (45% vs. 10%). Twenty-one unique gene probes were upregulated in SSAKI patients versus patients without SSAKI. Using leave-one-out cross-validation and class prediction modeling, these probes predicted SSAKI with a sensitivity of 98%  = 81 to 100) and a specificity of 80% (95% CI = 72 to 86). Serum protein levels of two specific genes showed high sensitivity for predicting SSAKI: matrix metalloproteinase-8  and elastase-2 . Both biomarkers carried a negative predictive value of 95%. When applied to a validation cohort, although both biomarkers carried low specificity , they carried high sensitivity .Patients with SSAKI (Gene probes upregulated in critically ill pediatric patients with septic shock may allow for the identification of novel candidate serum biomarkers for SSAKI prediction. Septic shock leads to significant morbidity and mortality in critically ill adult and pediatric patients ,2. MeanwEffective therapies for septic-shock-associated acute kidney injury (SSAKI) are lacking. Detection schemes for SSAKI have been and still are dependent on serum creatinine, a flawed real-time marker of AKI ,11. DiagImportantly, the pathophysiology of SSAKI may be unique from that of ischemic or nephrotoxic AKI . TherapiWe have generated an extensive genome-wide expression database from children with septic shock by way of microarray technology and have now leveraged this database to identify candidate biomarkers for SSAKI -32. Heren = 11). Children ≤10 years of age admitted to the PICU and meeting pediatric-specific criteria for septic shock were eligible for enrollment [The study protocol was approved by the Institutional Review Boards of each participating institution . Microarray hybridization was performed as previously described using the Human Genome U133 Plus 2.0 GeneChip  -32.Measurements of serum MMP-8 and elastase-2 protein levels were performed using a Luminex 200 multi-plex instrument  and antibody-coated magnetic beads  following the manufacturer's specifications.Analyses were performed using one patient sample per chip. Image files were captured using an Affymetrix GeneChip Scanner 3000. CEL files were subsequently preprocessed using robust multiple-array average normalization and GeneSpring GX 7.3 software . All signal intensity-based data were used after robust multiple-array average normalization, which specifically suppresses all but significant variation among lower intensity probe sets . All chiDifferences in mRNA abundance between the study categories were measured using GeneSpring GX 7.3 software, as were the class prediction procedures. Receiver operating characteristic (ROC) curves and calculations of biomarker performance were performed using SPSS . Ordinal and continuous clinical variables not normally distributed were analyzed via analysis of variance on ranks. Dichotomous clinical variables were analyzed using a chi-square test .Based on complete microarray data from 179 children with septic shock, we used a multistage approach to begin deriving a panel of candidate biomarkers to predict SSAKI. All of the microarray data in the derivation process represent the first 24 hours of admission to the PICU with septic shock. Table n = 53), patients without SSAKI, and patients with SSAKI as the comparison groups, and corrections for multiple comparisons . This was followed by a post hoc test (Tukey) to isolate the gene probes differentially regulated between patients with and without SSAKI  were differentially regulated between patients with and without SSAKI. In step one we conducted a three-group analysis of variance using normal controls . Figure a priori goal of achieving a high sensitivity at the expense of specificity. The performance characteristics of the respective cut-off values are shown in Table We next constructed ROC curves to determine the ability of serum protein levels of MMP-8 and elastase-2, respectively, to predict SSAKI in the derivation cohort Figure . From thHaving demonstrated the individual performances of MMP-8 and elastase-2 as candidate biomarkers for predicting SSAKI in the derivation cohort, we next applied the same respective cut-off values to a separate validation cohort of patients. The validation cohort consisted of 59 patients without SSAKI and 11 patients with SSAKI. The clinical characteristics of the validation cohort were not significantly different from those of the derivation cohort, except that in the validation cohort the patients with SSAKI had a higher proportion of males and a lower proportion of patients with negative cultures compared with the patients without SSAKI Table .Table We have demonstrated that microarray data can be leveraged to identify gene expression patterns common in SSAKI. Although mRNA levels do not necessarily correlate with protein expression, analysis of MMP-8 and elastase-2 protein levels in the serum compartment indicates that our gene expression data can identify novel serum protein biomarkers for SSAKI.Although sepsis is the most important predictor of AKI in critically ill patients ,38, few NGAL has received considerable attention as a potential biomarker for AKI. NGAL derivation and validation studies were primarily performed in ischemic or nephrotoxic AKI with great efficacy , while iapoptosis rather than acute tubular necrosis [A variable that may complicate delineation of biomarkers for SSAKI is that the pathophysiology of SSAKI may be different from that of ischemic or nephrotoxic AKI. Although acute tubular necrosis has traditionally been considered the etiology of AKI in sepsis, no conclusive pathologic evidence has demonstrated that this is true . Animal necrosis . Nonhemonecrosis .did not normalize with standard resuscitation). The expression patterns of the 21 upregulated gene probes demonstrate excellent sensitivity and good specificity for SSAKI  in children. Our data represent the first 24 hours of presentation to medical care, a timeframe that can be considered a therapeutic window for acute intervention. It is important to note that our patients were restricted to having septic shock, an exclusion criterion not previously used in pediatric studies investigating AKI biomarkers. Additionally, our definition of SSAKI identifies patients with severe, persistent kidney injury up to day 7. These candidate biomarkers thus identify patients with resuscitation unresponsive AKI  were able to predict SSAKI with a high degree of sensitivity and a high negative predictive value in both a derivation cohort and a validation cohort.• The identified candidate genes could provide a foundation to robustly predict SSAKI.AKI: acute kidney injury; CI: confidence interval; IFN: interferon; IL: interleukin; MMP-8: matrix metalloproteinase-8; NGAL: neutrophil gelatinase-associated lipocalin; PICU: pediatric intensive care unit; RIFLE: Risk: Injury: Failure: Loss: End-Stage kidney injury; ROC: receiver operating characteristic; SSAKI: septic-shock-associated acute kidney injury.The authors declare that they have no competing interests.RKB assisted with data analysis and wrote the manuscript. SWS assisted with data analysis and database mining. NZC, GLA, NJT, RJF, NA, KM, PAC, RL, TPS, and MTB contributed patient samples and clinical data to the database. They also reviewed the manuscript prior to submission. DSW, PD, and SLG assisted in the initial study design, assisted with the data analysis, and edited the manuscript. HRW conceived the study, performed the majority of the analyses, and assisted with the writing of the manuscript. All authors read and approved the final manuscript.Gene probes differentially regulated between patients with and without acute kidney injury. Additional File Click here for file


Each year, more than 1 in 10 of the world’s babies are born preterm, resulting in 15 million babies born too soon. World Prematurity Day, November 17, is a global effort to raise awareness about prematurity. This past year, there has been increased awareness of the problem, through new data and evidence, global partnership and country champions. Actions to improve care would save hundreds of thousands of babies born too soon from death and disability. Accelerated prevention requires urgent research breakthroughs. Preterm birth has been the leading cause of neonatal mortality worldwide for about a decade. However, during 2012 new global estimates placed preterm birth as the number two cause of child mortality, behind pneumonia, with more than a million deaths each year [The problem of preterm birth is shared by all countries. While 60% of preterm births occur in South Asia and sub-Saharan Africa , the UniThis week marks World Prematurity Day, November 17, a global effort to raise awareness about prematurity . In celeBorn Too Soon: The Global Action Report on Preterm Birth , Africa-based Little Big Souls International Foundation and the National Premmie Foundation of Australia .World Prematurity Day 2012 promises much greater participation from the growing movement of partners. Awareness activities, such as the Global Illumination Initiative--where cities are lighting landmarks and buildings in purple in honour of preterm babies and their parents--and the World Prematurity Day Facebook page, aim to increase knowledge of this issue, and inspire action. Parent groups have a particular power in promoting action and accountability.Most importantly, multiple high burden countries are planning activities ranging from national events with government officials to local parent groups hanging posters at local hospitals. A number of countries will also announce major commitments to reduce preterm mortality. For example, Malawi, with the world’s highest estimated preterm birth rate, has a summit to champion change by continuing to scale up the use of kangaroo mother care and taking action to help ensure that antenatal steroids reach all who need them.Born Too Soon report, coalitions of stakeholders dedicated to improving care for preterm babies have been established and efforts are underway to establish well-funded and collaborative research networks into the causes of preterm birth.Evidence-based interventions for newborn survival have great potential to save newborn lives . ImproveThe Lancet medical journal, which shows that even with high coverage of five interventionsa, preterm birth rates in the 39 most developed countries could only be reduced by five percent by the year 2015 averting 58,000 preterm births and saving US$3 billion annually [Born Too Soon report, offer promise for prevention, particularly in lower-income countries where the causes of preterm birth may well be more amenable to prevention. These interventions, which can be delivered before and during pregnancy, include attention to birth spacing and addressing infections before and during pregnancy, such as malaria, HIV and syphilis [Prevention is the next frontier, especially for high-income countries where care is already being delivered to babies of extremely low gestational ages. An international team of experts has published a new analysis in nnually . This mayphilis ,15.The years ahead offer increased opportunities for more progress. As countries push towards the 2015 deadline for Millennium Development Goals 4 and 5 for child and maternal survival, and begin the dialogue for what comes next to address preventable deaths and promote sustainable development, progress in reducing preterm and newborn mortality have repeatedly been identified as major priorities .Immediate attention should focus on low- and middle-income countries, which have the highest mortality burden, particularly among babies born between 32–37 weeks completed gestation and who would benefit most from simple and affordable care. Yet, we cannot forget the needs of affected babies and their families in higher-income countries, where survival is now at the thresholds of extreme gestational age and those who survive, often face a lifetime of significant disability .Born Too Soon report was written to advance epidemiology and clinical practice - yet was also intended for a general audience including policy makers and parents. Its contents will be reframed as a series of scientific papers next year in Reproductive Health during 2013. We hope that the forthcoming series in Reproductive Health will inform wider scientific and policy discussions and increase the pace of progress.The Born Too Soon that we all have a role to play in addressing the problem of preterm birth. Academics are strengthening evidence, international organizations are supporting partners in countries, donors are allocating more funds although the baseline is very low [World Prematurity Day 2012 is more than one day of awareness – it is an acknowledgement of what has been achieved, especially in this last year, what still needs to be done and of the important message in ery low , professaThe five interventions included: smoking cessation, decreasing multiple embryo transfers during assisted reproductive technologies, cervical cerclage, progesterone supplementation, and reduction of non-medically indicated labour induction or caesarean delivery.