Data recorded at birth and death registration in England and Wales have been routinely linked with data recorded at birth notification since 2006. These provide scope for detailed analyses on ethnic differences in preterm birth (PTB).We aimed to investigate ethnic differences in PTB and degree of prematurity in England and Wales, taking into account maternal sociodemographic characteristics and to further explore the contribution of mother's country of birth to these ethnic differences in PTB.We analysed PTB and degree of prematurity by ethnic group, using routinely collected and linked data for all singleton live births in England and Wales, 2006‐2012. Logistic regression was used to adjust for mother's age, marital status/registration type, area deprivation and mother's country of birth.In the 4 634 932 births analysed, all minority ethnic groups except ‘Other White’ had significantly higher odds of PTB compared with White British babies (ORs between 1.04‐1.25); highest odds were in Black Caribbean, Indian, Bangladeshi and Pakistani groups. Ethnic differences in PTB tended to be greater at earlier gestational ages. In all ethnic groups, odds of PTB were lower for babies whose mothers were born outside the UK.In England and Wales, Black Caribbean, Indian, Bangladeshi, Pakistani and Black African babies all have significantly increased odds of being born preterm compared with White British babies. Bangladeshis apart, these groups are particularly at risk of extremely PTB. In all ethnic groups, the odds of PTB are lower for babies whose mothers were born outside the UK. These ethnic differences do not appear to be wholly explained by area deprivation or other sociodemographic characteristics. Study questionWhat are the ethnic differences in preterm birth in England and Wales, taking into account sociodemographic characteristics?How does mother's country of birth contribute to these differences?What is already knownRisk of preterm birth is high among mothers of Caribbean and West African origin compared with White mothers, whereas the risks in South Asian groups have been less consistent.Reasons behind disparities are not well understood.What this study addsBlack Caribbean, Indian, Bangladeshi, Pakistani and Black African babies all have increased odds of being born preterm compared with White British babies.Bangladeshis apart, these groups are particularly at risk of extremely preterm birth.Within each group, the odds of preterm birth are lower for babies whose mothers were born outside the UK.1Complications of preterm birth are the largest direct cause of neonatal deaths worldwide, accounting for 35% of 3.1 million neonatal deaths in 2010.Much of the research on ethnic differences in preterm birth has been carried out in the United States. This has shown that compared with White mothers, the risks of preterm birth are consistently higher for Black mothersPrevious studies in the United Kingdom (UK) have some limitations. Studies based on regional data have provided important insights but have generally not included all ethnic groups, others have used parents' countries of birth or presented only crude rates because of small numbers.National data about gestational age and ethnic group in England and Wales became available after 2005.22.1This was a population‐based study using routinely collected and linked national data on all singleton live births at gestational ages of 22 completed weeks and over in England and Wales between 2006 and 2012 inclusive. De‐identified live birth data for all babies who were born in 2006 through 2012 in England and Wales and those born in this period who died before their first birthday were provided by the Office for National Statistics (ONS) from linked civil registration and birth notification data.Birth and death registration data are routinely checked by ONS. However, ONS does not routinely exclude all implausible combinations of gestational age and birthweight. We therefore further checked and cleaned the data extract by excluding records with missing values or implausible combinations of gestational age and birthweight by sequentially removed births with: (a) a gestational age greater than or equal to 43 completed weeks; (b) a missing birthweight; (c) an implausible combination of birthweight and gestational age, defined as birthweight more than twice the interquartile range above or below the median birthweight of the sex‐gestation‐ethnic group‐specific stratum of the study dataset.2.2Staff notifying a baby's birth to the birth notification system are asked to record the baby's ethnic group as reported by the mother based on the ethnic categories used in the 2001 Census in England and Wales.These ethnic group categories were then recoded for analysis into White British, Other White, three Asian or Asian British groups , two Black or Black British groups (Black Caribbean and Black African), a ‘Mixed/Other’ group, which included all Mixed groups, Other Asian, Other Black, Chinese and Other, and a ‘Not stated’ group. The above individual minority ethnic groups were selected and included for analysis because they were the most common minority ethnic groups in England and Wales. All Mixed groups and ‘Other’ groups were aggregated because of small numbers and the complexity of the heterogeneous composition of the subgroups which would have made any results difficult to interpret. Details of the derivation of the ethnic group categories are reported elsewhere.Other explanatory variables included the baby's sex and year of birth, age of mother, area deprivation score of the mother's area of residence as coded according to the 2015 English Index of Multiple Deprivation (IMD)2.3The primary outcome was preterm birth, defined as the live birth of a baby at less than 37 completed weeks of gestation. Our outcome measure was the percentage of live births that were preterm. A secondary outcome was degree of prematurity: (a) late and moderately preterm births (32‐36 completed weeks); (b) very preterm births (28‐31 completed weeks); and (c) extremely preterm births (22‐27 completed weeks).2.4Logistic regression models were used to explore ethnic differences in preterm birth using the White British group as the reference group. We adjusted all models for the baby's sex and year of birth to account for differences between sexes and over time (models adjusted for these two variables only are referred to as the ‘base model’ hereafter). Further adjustments were made (referred to as ‘adjusted model’) to account for IMD quintiles, age of mother  and parents' marital status/registration type . As IMD scores are constructed differently in England and in Wales, we adjusted for the mother's country of residence  in all models that included IMD.Multinomial logistic regression was used to investigate the association between ethnic group and degree of prematurity, treating degree of prematurity as a categorical outcome variable and adjusting for the same covariates as above.To further explore the contribution of mother's country of birth to ethnic differences in the odds of preterm birth, we then included mother's country of birth (inside versus outside the UK) in the adjusted model, tested the interaction between ethnic group and mother's country of birth, and present stratified results as appropriate. Finally, to assist in the interpretation of findings, we carried out additional exploratory analyses in which we adjusted for covariates  individually. All analyses were conducted in STATA version 13.2.5The study was approved by ‘National Research Ethics Service (NRES) Committee South Central—Oxford B’ (Research Ethics Committee reference number: 15/SC/0493).3The original dataset included 4 744 666 singleton live births at gestational ages of 22 weeks or more in England and Wales from 2006 to 2012. We sequentially excluded 16 695 births with implausible gestational age, 20 999 with missing birthweight and 72 040 with implausible sex‐gestation‐ethnic group‐specific birthweight, adding up to 109 734 (2.3%) births excluded in total. The study population therefore consisted of 4 634 932 singleton live‐born babies at gestational ages of 22‐42 completed weeks. Around 65% of the study population was White British, 7% was Other White, 8% was South Asian , 4% was Black (consisting of Black Caribbean and Black African), 9% was in the ‘Mixed/Other’ group. The remaining 6% of the study population had an ethnic group ‘not stated’.3.1Ethnicity was strongly associated with characteristics of mothers and babies Table . South APakistani, Bangladeshi, Black Caribbean and Black African babies were more likely to be born to mothers living in the most deprived areas (between 50%‐60% in IMD quintile 1 and approximately 80% in IMD quintiles 1 and 2 combined) compared with White British babies .The majority of White British (96.1%) and Black Caribbean (63.3%) babies were born to mothers who themselves were born in the UK, while the majority of babies in the other ethnic groups were born to mothers born outside the UK, ranging from 62.9% in the Pakistani group to 92.7% in Black African babies. This reflects differences in migration histories and timescales among ethnic groups in England and Wales.Compared with parents of White British babies, parents of South Asian and Other White babies were more likely to be married or jointly registered as living at the same address while parents of Black babies, especially Black Caribbean babies, were less likely to be married or jointly registered as living at the same address.The absolute risk of preterm birth was also strongly associated with these characteristics, having decreased over time and plateaued in 2010. The risk was highest in male babies, in the youngest and oldest groups of mothers, those living in the most deprived areas, mothers born in the UK and mothers who were sole registrants or jointly registered as living at different addresses from the baby's father; these results were confirmed using adjusted odds ratios Table .3.2Overall, preterm births accounted for 5.6% n = 258 515) of the whole study population. Preterm birth rates were lowest among White babies, with 5.5% of White British babies and 4.6% of Other White babies being born preterm . For all other groups, the odds of preterm birth were higher than for White British babies with ORs ranging from 1.09 for Indian and Pakistani babies to 1.52  for Black Caribbean babies.After further adjustment, the relationships were broadly the same, but the adjusted ORs for Indians, Pakistanis and Bangladeshis increased in magnitude while the adjusted OR for Black Caribbean and Black African babies decreased in magnitude. Adjustment increased the odds in Other White babies, but odds in this group remained the lowest . Amongst the groups with increased odds, adjusted ORs ranged from 1.04  for Black African babies to 1.25  for Black Caribbean, 1.23  for Indian and 1.21  for Bangladeshi babies.3.3Compared with White babies, babies of other ethnic backgrounds were generally more likely to be born at earlier gestational ages. Rates of extremely preterm birth were particularly high among babies of Black Caribbean and Black African origin Table .In the adjusted multinomial model, the differences compared with the White British group were greater at earlier gestational ages for all groups except Other White and Bangladeshi groups Figure . The dif3.4P < .001) between ethnic group and whether the mother's country of birth was inside or outside the UK, we further stratified the study population into 18 subcategories by ethnic group (9 groups) and mother's country of birth, inside or outside UK, and compared the odds of preterm birth using White British babies born to mothers born in the UK as the reference group. Figure As we found strong interaction  compared with White British births. After adjustment, the highest odds are found in Black Caribbean and South Asian groups. Ethnic differences in preterm birth tend to be greater at earlier gestational ages. In all ethnic groups, the odds of preterm birth are lower for babies whose mothers were born outside the UK.4.2The complete coverage of birth registration data, the large number of births and the low proportion with missing data mean that selection bias, detection and observer bias are minimized. The dataset also included sufficient numbers of births in the smaller ethnic groups, such as Black Caribbean and Bangladeshi groups, to enable disaggregated analyses.4.3There is limited information available about the quality of ethnicity recording although it is possible that in some cases the mother may record her own ethnic group or that health care staff may record the mother or baby's ethnic group rather than asking the mother to report it.We lack data on how gestational age was measured and the accuracy of the measurement, but the birth notification data that we used are regarded as the best source for national data on gestational age.Although these national‐level results are applicable to the populations of England and Wales, the different compositions of ethnic groups in other countries may limit international generalisability. In particular, we have used a combined ‘Black African’ group which consists of a diverse range of people originating from a whole continent within which there are wide differences in risks of preterm birth.4.4Findings of higher odds of preterm birth in the Black groups are consistent with previous studies.Higher odds of preterm birth identified in all the three South Asian  groups in our study have been less well established.It is plausible that higher odds of preterm birth in the South Asian groups are due to a higher prevalence of medical conditions that are associated with medically indicated preterm birth in these groups, but this is not something we were able to investigate. A UK study found being South Asian was a risk factor for medically indicated but not spontaneous delivery before 34 weeks.Our finding that ethnic differences in the odds of preterm birth tend to be greater at earlier gestational ages has been less well studied as most studies only use one cut‐off for preterm birth. However, our results are broadly consistent with findings from the few studies that have used different cut‐off points for preterm birth to explore the changes in ethnic variation for Black groups.We cannot provide an explanation of the ethnic differences in the increased odds of preterm birth at earlier gestational ages. It is worth exploring this phenomenon and potential mechanisms in other studies as very preterm births  account for the majority of neonatal deaths and serious morbidity.Another important finding of our study is that within each ethnic group, babies of non‐UK‐born mothers had lower odds of preterm birth than those of UK‐born mothers. The implications of this are likely to vary as the proportion of minority ethnic mothers born outside the UK varies. Differences in mother's country of birth by ethnic group, as seen in our study, may reflect differences in migration history, length of residence in the host country and degree of acculturation.5In England and Wales, Black Caribbean, Indian, Bangladeshi, Pakistani and Black African babies all have increased odds of being born preterm compared with White British babies. Bangladeshis apart, these groups are particularly at risk of extremely preterm birth. In all ethnic groups, the odds of preterm birth are lower for babies whose mothers were born outside the UK. These ethnic differences do not appear to be wholly explained by area deprivation or other sociodemographic characteristics; therefore, other factors should be explored, including the causes and subtypes of preterm birth, particularly the contribution of medically indicated preterm birth. The higher odds of preterm birth observed in UK‐born compared with non‐UK‐born mothers in all ethnic groups may point to risk factors that increase with acculturation. Click here for additional data file.


Opuntia spp.), called Barbary fig, is a cultivated species springing from family Cactaceae. It is native to Mexico and has been naturalized in other continents, especially the Mediterranean countries (North Africa). The aim of the study was to investigate the physical, physicochemical, and biochemical criteria of peels of three Moroccan prickly pear varieties  growing in the Rhamna regions (dry area).  Prickly pear  and biochemical characteristics  were were studied according to previously reported methods. μg/g of FM. The total betalain content ranged from 6213.46 ± 58.86 to 8487.19 ± 51.71 μg/100 g of FM. The total polyphenolic content varied from 160 ± 3.55 to 243.79 ± 5.55 mg GA E/100 g of FM. The ascorbic content ranged from 58.21 ± 0.24 to 74.72 ± 0.17 mg/100 g of FM.  Regarding the physiochemical criteria, the moisture of the fresh peels of studied varieties ranged from 81.59 ± 0.02 to 83.47 ± 0.02%. The water activity (aw) ranged from 0.862 ± 0.001 to 0.872 ± 0.001. The values of Brix varied from 14.69 ± 0.05° Bx to 15.80 ± 0.03° Bx. pH values varied from 5.13 ± 0.01 to 5.32. The total titratable acidity values ranged from 0.130 ± 0.008 to 0.196 ± 0.014 g of citric acid/100 g of FM (fresh matter). The ash content values ranged from 8.92 ± 0.10 to 11.04 ± 0.06 g/100 g of FM. Regarding the biochemical criteria, the total carotenoid content ranged from 2.29 ± 0.01 to 2.87 ± 0.01  The findings of physicochemical and biochemical criteria of the investigated varieties growing in Moroccan drylands showed promising results in terms of studied parameters. Morocco is one of the several countries that have been subjected for many years to severe drought with adverse consequences on agriculture and general economic activities of the country. To face this environmental hazard, the targeted countries have obliged to develop agriculture more adapted and less influenced by climatic burdens .Drought has become one of the biggest challenges that affect negatively sustainable crop production. Climate change such as desertification is the major challenge faced by Moroccan zone where the rural landscape is most heavily affected. Therefore, the crops which can resist to such hard conditions like, drought, high temperatures, and low soils need more emphasis.Prickly pear crops are receiving increasing interest throughout the globe due to their unique criteria, which provide resilience to the hard ecological conditions. Prickly pear can grow on land where many other crops are unable to grow; it can be used to repair degraded land. It has the ability to adapt to stress even at low soil potential. It is the only plant that can flourish when others fail. Thus, it is an environment friendly plant which can resist long periods of dryness , 3.The synthesis of secondary metabolites by the plants is positively influenced when plants grow under climate stress with hard environmental conditions during varying environments. The increase of secondary metabolites diversity is frequently conducted by environmental components that comprise local geo-climatic. Furthermore, seasonal changes and external conditions like temperature, light, humidity, and developmental processes are intensively involved in secondary metabolites diversity , 5.Nowadays, the cultivation of prickly pear is intensively practiced in Morocco. Numerous efforts have been carried out by the state under the Green Morocco Plan to make Barbary fig an industrial crop for both animal and human feeding. In Morocco, the total area planted with prickly pear has increased from one year to the next. The extension of the cultivated area resulted in an increase in fruit production. The prickly pear crops and their affiliated services sectors can support large numbers of workers, provide them with sustainable jobs, and increase the prosperity of their neighbours and communities. An efficient agro-industry enhances economic stability for rural people, increases food safety, and helps achieve economic development .The current research work was conducted to evaluate three varieties of Barbary fig growing in Moroccan drylands. The current study was deeply assessing the physical, physicochemical, and biochemical characteristics of three Moroccan perkily pear verities growing in the Rhamna regions under hard ecological conditions.The plant material was harvested from the rural areas of Skhour Rhamna. This sampling area is located in the region of Marrakech-Safi, Morocco. It is located 99 km north of Marrakech city (32°29′N: 7°55′W) .A low rainfall characterizes the study area (Skhour Rhamna region) over the year (150 to 350 mm). The number of dry months ranges from 8 to 11. The climax vegetation in the study area is composed of species well adapted to lack of water and less heterogeneous. Diagram of Gaussen and Bagnouls applied in Rhamna region shows a dry study area over the year .Opuntia spp., locally called Aakria, Derbana, and Mles  currently describes the availability of “free” water in a product . 5 g of The current pH was determined at 25°C by a pH meter after necessary calibration with buffer solution twice, pH 4 and pH 7. The current pH was determined directly in ground peels.The total titratable acidity of fresh peels was measured as follows: an extract obtained from 25 g of test portion was supplemented with distilled water and heated for 30 minutes. After filtration, the filtrate was titrated again with a solution of NaOH (0.1 N) to pH = 8.1. The results were expressed in g of citric acid per 100 g of FM:The Brix scale is currently used to assess the soluble dry matter percentage in a product. 15 g of fresh peels was centrifuged at 12,000 rpm for 31 min. The obtained supernatant was used for the determination of Brix after the filtration procedure. Regarding dried peels analysis, the test portion was 1 g solubilized in 10 ml of distilled water at a ratio of 1 : 10 (powder of peels: distilled water). The findings were expressed in degree Brix (°Bx).2 g of fresh peels was placed for incineration at 500°C for 7 hours. The ash content was calculated as a percentage relative to the dry matter (DM) with respect to the following formula:μg equivalent β-carotene using the absorbance coefficient of 2500:μg eq. β-carotene/g) = A450∗V∗106/2500∗100∗Sw, A450: absorbance at 450 nm, V: analyzed volume of the sample (1 ml), and SW: sample weight (g).10 g of fresh peels of prickly pear was subjected to extraction ). The obtained mixture was centrifuged again at 6500 rpm at 6°C for 6 min. The hexane fraction possessing pigments was recovered for measuring the total carotenoid. The total carotenoid content was measured by reading the absorbance at 450 nm. The findings were expressed in Assessment of betalain content was carried out according to the method reported in earlier literature . 0.5 g oξ is the molar extinction coefficient (L/mol ∗ cm), Mw denotes molecular weight (g/mol), λ denotes wavelength (nm), and Sw represents the sample weight.Betalain content was measured according to the following equation:(1) Extraction. 0.5 g of fresh peels was extracted with 10 ml of acetone . The obtained extract was centrifuged at 5000 rpm for 31 min and then filtered. The total polyphenol and ascorbic acid contents were determined in the obtained extract.The total polyphenolic content was measured with respect to the Folin–Ciocalteu method described in earlier published data with slight modifications . The cur∗ 3 ml of distilled water. 0.5 ml of the current solution was dosed according to Folin–Ciocalteu method instructions. The findings were expressed in mg/100 g of FM.A small sample of the current extract was mixed with 3.5 ml of distilled water. 2 ml of the present mixture was put on an OASIS cartridge previously covered with 3 ml of MeOH +2 Quantitative data were analyzed using one-way ANOVA to compare the means for different studied varieties in the current work . The Student–Newman–Keuls method was used to search the homogeneous groups of means. Data of the present work were presented as means ± standard deviation.p < 0.001)  . These r ± 0.93% .The current levels of moisture content make these fresh peels highly perishable. Therefore, it is necessary to preserve them in order to avoid potential microbial deterioration. The difference in humidity level between the fruit peels of the three varieties could induce a significant difference in required energy and time for possible drying for better conservation.The values of water activity (aw) of fresh peels (0.862 ± 0.001 to 0.872 ± 0.001) were matched with moisture values. Both criteria of water activity and moisture are reflecting poor conservation ability due to microbial development at these intervals of water activity . In regaThe values of water activity reported in the current work were lower than those reported in earlier found data (aw = 0.895) regarding prickly peels of pear .p < 0.001). The highest value of Brix was attributed to the variety of Derbana (15.80 ± 0.03° Bx) while the lowest was attributed to the variety of Aakria (14.69 ± 0.05° Bx) (Opuntia ficus-indica (15.00 ± 0.50° Bx) [Analysis of variance showed a significant difference between the fresh peels of prickly pear varieties (.05° Bx) . Similarp < 0.001). The fruit peels of Aakria variety was the most acidic (pH = 5.13 ± 0.01) compared to the other varieties such as Derbana and Mles with pH values of 5.32 and 5.21, respectively (The pH of the fresh peels was weakly acidic. A significant difference was reported between the three varieties (ectively .Opuntia ficus-indica (4.00 ± 0.01) [Opuntia spp. was very close to that of orange peels (5.67 ± 0.01) [The current pH values were higher than those found previously in Algerian varieties of  ± 0.01) . This di ± 0.01) .p < 0.001)  (< 0.001) .Opuntia ficus-indica (0.12 ± 0.01 g citric acid/100 g FM) [The obtained values of total titratable acidity ranged from 0.130 ± 0.008 to 0.196 ± 0.014 g of citric acid/100 g of FM. These results were in accordance with those reported in earlier published data regarding the total titratable acidity of fresh peels of 00 g FM) . Due to p < 0.05).The statistical analysis showed a significant difference between the fresh peels of studied varieties  Total Carotenoid Content. Carotenoids are major determinants of organoleptic and nutritional qualities of fruits [p < 0.001)  . The res/g of FM . Hence, (2). Betalains. The attractive colors of prickly pear are due to betalains including betacyanins (red-purple pigments) and betaxanthins (yellow-orange pigments) [p < 0.001) (igments) . Analysi< 0.001) .μg/100 g of FM, respectively. These values were in agreement with previous research carried out on Opuntia sheeri [μg/100 g of FM.Betacyanin content (red pigment) contained in the fresh peels of Aakria variety fruits was higher than that contained in Derbana and Mles varieties. However, indicaxanthin content (yellow pigment) contained in the fresh peels of Mles variety was higher than that contained in both Aakria and Derbana varieties. The total betalain content of Aakria and Mles was 6213.46 ± 58.86 and 8487.19 ± 51.71 The important content of betalains revealed in peels of investigated varieties in this work could constitute a new way of prickly pear valorization further in the agribusiness and pharmaceutical sector. The betalains are considered as interesting natural dyes and powerful antioxidants . Betalai(3). Content of Phenolic Compounds. Regarding the content of phenolic compounds, a highly significant difference was noted between the fresh peels of all investigated varieties (p < 0.001). Indeed, the highest content was recorded in Aakria variety (243.79 ± 5.55 mg GAE/100 g of FM) (g of FM) .Opuntia was 226.2 mg GAE/100 g of FM. However, these results of phenolic content were relatively lower than those found in previous reports, which showed that the phenolic content of Opuntia ficus-indica fresh peels ranged from 250 to 300 mg EAG/100 g of FM [The current results were in agreement with those reported in earlier data ; it was The fresh peels of Aakria variety contained the highest rate of total polyphenols. Hence, this variety exhibits an important antioxidant potential compared to Derbana and Mles varieties. In addition, polyphenols could also play a protective role against chronic and cardiovascular diseases .(4). Ascorbic Acid Content. Analysis of variance showed a significant difference between ascorbic acid content of studied varieties (p < 0.05) ( < 0.05) . The valOpuntia ficus-indica fresh peels was 59.82 mg/100 g FM. The bioactive compounds contained in prickly pear peels such as ascorbic acid have attracted scientists from different disciplines for screening their biological, pharmacological, and technological properties.The biological properties of prickly pear peels attract researcher's attention for searching potentially interesting compounds . These vThe present study gives big data on the physicochemical and biochemical criteria of fresh peels of three Moroccan prickly pear verities . Both physicochemical and biochemical criteria of investigated varieties growing under stress climate in Moroccan drylands showed promising results in terms of studied parameters. Therefore, the present comparative study enables to underline that Darban variety was the best of the studied varieties in regard to its content of biochemical and physical parameters. All studied varieties may constitute a promising reservoir of natural compounds for potential use as natural food as well as in pharmaceutical fields.


Among European countries, optimal birth weight at which the mortality is minimal is shown to be different by country. We investigated this difference examining one geopolitical population, the U.S. term live births, born to the five groups of the same ethnic parents; White, Black, Hispanic, North Asian, and South Asian. North Asians and South Asians had more favorable maternal factors for birth weight. Yet, Whites had the highest mean birth weight and South Asians, the lowest. However, neonatal mortality rate in Whites was 0.78 per 1,000 live births, significantly higher than 0.36 and 0.72 per 1,000 live births in North Asians and South Asians, respectively. Other maternal factors hardly explained this ethnic disparity in birth weight or mortality. Optimal birth weight was greatest in Whites , and least in South Asians . However, neonatal mortality at optimal birth weight was significantly lower in North Asians. Adjustment of maternal factors except parental ethnicity changed little of this difference. Optimal birth weight and its mortality differ by ethnicity. On planning the best birth outcome in a population, one should consider the variable mortality risks by ethnicity. But the birth weight is a stronger predictor for mortality. Susser3 reported that when gestational age and birth weight were analyzed simultaneously, birth weight accounted for 90% of the variance of perinatal mortality, whereas gestational age accounted for barely 5%. In general, birth weight is greatest at term gestation, reflecting adequate time for intrauterine growth. However, a large birth weight at term is not necessarily favorable for survival. At term, risk for mortality by birth weight shows a U-shaped curve; the risk is minimal with birth weight at vertex, “optimal birth weight,” and the risk increases either with a decrease or with an increase in birth weight beyond that point.Infant mortality is closely related to both birth weight and gestational age5. Graafmans and his associates6 studied birth outcomes in seven Western European countries, Finland, Sweden, Norway, Denmark, Scotland, the Netherlands, and Belgium. Average birth weight and birth weight at minimal mortality varied among these countries. Compared to the U.S., these European countries may have less heterogeneous ethnic composition, but possibly by country more difference in their socioeconomic status and health care systems. The U.S. population is composed of varied heterogeneous ethnic groups living in one geopolitical area. In our study we asked two questions that (1) whether in one geopolitically defined country, in the U.S., different ethnic groups would have insignificant difference in optimal birth weight, and (2) whether mortality risk would be similar at this optimal birth weight among different ethnic groups. In this study, we wanted to minimize the impact of different provisions of health care system on mortality among different ethnic groups in different countries. Thus, we chose a large birth population of the U.S., where the health care system would have been rather similar across the country.Many maternal factors influence birth weight, such as ethnicity, age, parity, education, health conditions and her other socioeconomic status. When comparing birth weights of different populations, maternal ethnicity has the strongest influence on birth weightIn the U.S., Whites had the highest mean birth weight  and South Asians (S. Asians), the lowest . However, compared to Whites, South Asians had more favorable maternal factors for birth weight, namely better education and marital status, and lesser smoking and alcohol use, and with less maternal medical complications , while South Asians had the shortest mean gestational age (39.0 ± 1.1 wks). However, this difference in gestational length among all these ethnic groups was less than a day and half at most, hardly accountable for the difference in birth weight among these ethnic groups.Whites had higher mean birth weight than both North Asians and South Asians. Yet, its neonatal mortality rate was 0.78 per 1,000 live births, significantly higher than 0.36 and 0.72 per 1,000 live births in North Asians and South Asians respectively. Thus, it appears that within term births, maternal ethnicity prevailingly influence the mortality than their birth weights at term.Then, we further explored the difference in mortality among these five ethnic groups by their maternal sociodemographic and health care factors Table . When noIn all five ethnic groups, the pattern of mortality by birth weight showed a “u-shaped curve”, fitting best to a quadratic equation Fig. . From th8. This pattern of mortality is universal among all ethnic groups. Crude neonatal or infant mortality rate is determined by its two components, birth weight distribution, particularly a proportion of smaller birth weight groups, and birth weight-specific mortality rates of individual birth weight groups10. Blacks in the U.S. has higher crude neonatal and infant mortality rate primarily due to its unfavorable birth weight distribution, higher proportion of small birth weight groups, and by lesser, by mortality rate difference in individual birth weight groups14. However, in the U.S., there is little information on subtle difference in term infants, their birth weight distribution within term gestation and their mortality risk among different ethnic groups.Mortality in its relationship to birth weight follows a “reversed J” curve15 showed that in the U.S., optimal birth weight was higher and mortality risk at term was less in Whites compared to Blacks. When mortality risk was expressed by the relative birth weight (a z-score relative to mean birth weight), they were able to observe consistently high mortality risks in Blacks than in Whites at both below and above the zero z-score. These observations suggest that ethnic disparity in mortality at term could not be solely explained by either mean or optimal birth weight. It is determined by two factors by the ethnicity, (1) differences in proportional birth weight distribution at term, below, at, and above optimal birth weight, and (2) differences in mortality at respective birth weights, below, at, and above optimal birth weight. This concept of mortality at term is explored in our present study.Platt and his associates6 studied birth outcomes in seven Western European countries. They reported variable mode of birth weight distribution, ranged between 3,384 g in Flanders and 3,628 g in Finland. The birth weight at the minimal perinatal mortality also differed, but closely related to mean birth weight, about 170 g higher than mean birth weight. Compared to the U.S., Western European countries may have less heterogeneous racial composition, but possibly more differences in their socioeconomic environments and health care systems. In our study, we used the U.S. live birth population. We wanted to study the birth outcomes in more diverse ethnic groups. In the U.S., ethnic identification of births were newly refined and registered starting in the year 1995. To assure that we have an adequate number of live births in all individual ethnic groups, we included our study population of live births for the 12 years, 1995–2006. Thus, we had an adequate number of term singleton live births born to the same ethnic parents, ranging from 19 million births in Whites to half million births in South Asians. In the U.S., over the period, 1995–2006, there had been changes in socioeconomic status and health care system. This study did not examine the potential bias on birth outcomes arising from differential impact of these changes to individual ethnic groups over time. Only using the whole study population for all the years combined, we examined ethnic differences in mean birth weight, gestational age, optimal birth weight, and mortality by term birth weight distribution. As noted by others20, when controlled for maternal sociodemographic and health care status , ethnic differences in birth outcomes changed little. It indicates that the parental ethnicity is the strongest predictor of term birth outcomes, overwhelming all other parental factors.Graafmans and his associatesIn essence, our results from the U.S. births with diverse ethnicity confirm the observations made by Graafmans and his associates in seven Western European countries. However, magnitude of difference in mean or modal birth weight in the U.S. ethnic groups was larger, perhaps reflecting its diverse ethnic composition in the U.S. than in the Western European countires. In the present study, Non-Hispanic Whites had the highest mean birth weight followed by Hispanics, the North Asians , and non-Hispanic Blacks. The South Asians  had the lowest mean birth weight. Within each of individual ethnic groups, maternal sociodemographic and health status is significantly associated with their birth weight outcomes, but little impact was noted when examined the ethnic difference in birth outcomes.6.Mortality at the optimal birth weight varied more in the Western European countries than in different ethnic groups in the U.S., possibly reflecting variable health care system in the European countries. Among our studied ethnic groups, the rank order of optimal birth weight precisely followed that of mean birth weight. It was the heaviest in Whites and the lowest in the South Asians. Optimal birth weight in each group was about 8% to 18% higher than its mean term birth weight. These results confirm similar observations made by Graafmans and his associatesp, variance of birth weight-specific mortality)  had the least overall crude mortality rate with the least mortality risk at optimal birth weight and wider parabola of the mortality curve. This was followed by Hispanics, non-Hispanic Whites, and South and Southeast Asians . Non-Hispanic Blacks had the highest overall crude mortality rate with the highest mortality risk at optimal birth weight and the most narrow parabola of the mortality curve.Mortality risk at term by birth weight fits best to a quadratic curve. In this curve, mortality reaches the bottom  and increases more or less symmetrically to either side of the optimal birth weight (σty) Fig. 21. The pIn conclusion, our present study shows that different ethnic groups in the U.S. have varying mean birth weight at their term gestation. Ethnic difference in mean gestational age at term was little, less than a day and half at most, and was not responsible for the ethnic difference in mean birth weight. Optimal birth weight was about 8% to 18% higher than mean term birth weight in all ethnic groups. Magnitude of mortality risks both at optimal birth weight and at below and above the optimal birth weight was also different among different ethnic groups. Maternal sociodemographic and health factors failed to explain the ethnic difference in all the following measures, mean birth weight, optimal birth weight, and mortality risks at optimal birth weight and at below and above the optimal birth weight. Our study strongly suggests that optimal birth weight and term birth weight distribution of one ethnic group cannot be applied to other ethnic group in planning and delivering the perinatal health care.Our study population derives from the Birth Cohort Linked Birth/Infant Death Data of the U.S. National Center for Health Statistics (NCHS) from 1995 to 2006. It includes only singleton live births with gestational ages between 37 and 42 weeks . They were born to the five parental groups with both parents with the same ethnicity; (1) non-Hispanic White (Whites) , (2) non-Hispanic Black (Blacks) , (3) Hispanic (Hispanics) , (4) Korean, Japanese, and Chinese (North Asians) , and (5) Filipino, Vietnamese, and Asian Indian (South Asians) .Neonatal mortality was defined as the number of deaths under 28 days of life per 1,000 live births. Optimal birth weight was defined as the birth weight with the least neonatal mortality at term.We compared the following variables between the five ethnic groups: mean birth weight, mean gestational age, neonatal mortality, sex, maternal age, education, marital status, smoking, alcohol use, paternal age, adequacy of prenatal care, maternal medical risk factors(RF), neonatal medical risk factors(RF) at birth, neonatal chromosomal and congenital anomalies(CA). In these five ethnic groups, we estimated the optimal birth weight with and without adjustment of the following variables; maternal age, marital status, education, medical risks , prenatal care, alcohol use, smoking, and paternal age.Chi-square was used to test for significant difference in maternal characteristics among the ethnic groups. For the assessment of ethnic disparities in the neonatal mortality, we estimated odds ratios with confidence intervals by the multivariable logistic regression. For the estimation of optimal birth weight and birth weight-specific neonatal mortality curve, we used term birth weights by 250 g intervals. The best fit quadratic equation was derived from the data, neonatal mortality (y) and birth weight (x) using the quadratic fit model of CurveExpert 1.3. From this equation, we drew the birth weight-specific mortality curve of the five ethnic groups at term gestation. And also we estimated their individual optimal birth weight with minimal mortality by finding x and y values of vertex. All other statistical analyses, we used Stata (v.13).


The hydatid cyst (HC) is an endemic parasitic disease worldwide. Although the HC can locate in every part of a body, it rarely occurs over the abdominal wall. A 12-year-old female patient was brought to Department of Pediatric Surgery, Firat University School of Medicine, Elazig, Turkey in 2017. She had been suffering from abdominal pain for one week. A lump was determined underneath her skin in the suprapubic region. It was swollen, tense and movable. A cystic mass filling the midline was found in the radiological bladder superior. It was an anechoic cyst causing ondulation on the muscles of the anterior abdominal wall. The sizes of the mass were measured approximately as 9×7 cm (mesentery cyst?). The cystic mass was occurred in the urachal area of the anterior abdominal wall, not in the abdomen. After the cyst was emptied with applying mini median incision below the umbilicus, we saw the germinative membrane inside the cyst. Diagnosis of the HC was confirmed with the pathologic evaluation. For the differential diagnosis of a pure cystic mass, which can locate in every part of a body, diagnosis of the HC should be considered. The hydatid cyst (HC) is an endemic parasitic disease worldwide. It develops because of the Echinococcus granulosus larvae locating in the tissue. The HC is mostly seen in the communities where the livestock industry is widespread , there was anechoic cystic lesion with the sizes of 6.5×9.1 cm in her bladder superior. The thickness of the cyst wall was 2 mm and the vascularisation was not monitored on the wall. The differential diagnosis was mesenteric cyst, lymphocyst, and urachal cyst because of its existence in the midline. In MRI of the lower abdomen, a cystic mass, which completely filled the midline from the promontorium to the anterior abdominal wall, was determined . It was We decided to continue with open surgery. Anatomic layers were passed with mini median incision below the umbilicus. A smoothly finite cystic mass, occurred between the rectus muscles, was seen. It was lying until the periton and bladder. The sizes of the mass were approximately measured as 10×10 cm. It was much excised. There was no contact between the mass and bladder. There was germinative membrane inside the cyst . We washThe hydatid cyst is usually asymptomatic and late diagnosed. Therefore, it can reach large sizes . Its symBecause of its atypical settlement, diagnosis of the HC was not considered. Although the eosinophilia (34%) can be seen in the blood of a patient diagnosed as the HC, this symptom is not specific for the HC . In our The main goal of the treatment is removing the fluid and cavity of the cyst and preventing the contamination. Medical treatment , Puncture, Aspiration, Injection and Respiration (PAIR), endoscopic and open surgery are the possible applicable methods for the treatment. PAIR is not preferred for children because of sedation, contamination and the risk of anaphylaxis due to this contamination. However, it can be used for the patients who do not respond the medical treatment, have relapsed and are not operated –15. The Despite the improvements in the imaging methods, atypically located HC may not be determined clearly. Its asymptomatic course, lack of laboratory findings, and rare body parts where the HC occurs can cause late diagnosis. Especially in the endemic regions, when there is a pure cystic lesion anywhere in a body, the HC should be considered as a differential diagnosis.


Globally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings.This study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters commonly available at delivery to correctly categorize births as either preterm (<37 weeks) or term, compared to GA assigned by early ultrasound as the gold standard. Trained midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual period (LMP), and birth weight were used individually to assign GA at delivery and categorize each birth as either preterm or term. Additionally, machine learning techniques incorporated combinations of these measures with several maternal and newborn characteristics associated with prematurity and SGA to develop GA at delivery and preterm birth prediction models. The distribution and accuracy of all models were compared to early ultrasound dating. Within our live-born cohort to date (n = 862), the median GA at delivery by early ultrasound was 39.4 weeks (IQR: 38.3–40.3). Among assessed newborns with complete data included in this analysis (n = 468), the median GA by ultrasound was 39.6 weeks (IQR: 38.4–40.3). Using machine learning, we identified a combination of six accessible parameters  that can be used by machine learning to outperform current GA prediction methods. For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796.We identified a parsimonious list of variables that can be used by machine learning approaches to improve accuracy of preterm newborn identification. Our best-performing model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors associated with SGA. These variables are all easily collected at delivery, reducing the skill and time required by the frontline health worker to assess GA.ClinicalTrials.gov Identifier:NCT02738892 Preterm birth affects more than one in ten live births worldwide. It is thEarly prenatal ultrasound, widely regarded as the gold standard for GA dating, is unavailable in many LMIC settings. In its absence, providers must rely on other methods, such as last menstrual period (LMP), newborn assessment, or birthweight to classify infant GA at delivery. Each of these approaches has limitations. Reported LMP is subject to patient recall and can be very unreliable in settings where women present late for care.–11 NewboWe sought to develop a machine learning algorithm that can estimate GA at birth from readily obtained indicators in a setting where early ultrasound is not available. We were particularly interested in the simple, binary classification of preterm  versus term. We hypothesized that a model combining LMP, individual elements of the NBS, birthweight, and key pregnancy risk factors associated with SGA, would outperform any individual approach.This study was conducted using data from the Zambian Preterm Birth Prevention Study , an ongoing prospective obstetrical cohort at the Women and Newborn Hospital of the University Teaching Hospital (UTH) in Lusaka, Zambia. The rationale for our study, its procedures, and cohort characteristics have been described elsewhere. Briefly,The study employs midwives who attend to participants admitted to the labor ward or postpartum unit at UTH. Their duties include ensuring that relevant clinical information is captured in the study record, that babies are weighed at birth or shortly thereafter, and that the NBS is performed within 72 hours of delivery.th percentile for its corresponding GA.) developed in SAS version 9.4  along with the SAS procedures HPFOREST,[We used super learner to generHPFOREST, GENMOD,[HPFOREST, and GAM?HPFOREST, to perfoHPFOREST, which weHPFOREST, For our Kernel density plots and Pearson’s correlation coefficients were generated to compare the predicted GAs from each continuous outcome model to GAs by early ultrasound. For our primary analysis, we based accuracy of each predictive model on the model fit to the data in which we had complete data (n = 468). In a subset of births without NBS that were not used to train predictive models, we subsequently estimated predictive accuracy. We note that, while super learner utilizes cross-validation to reduce over-fit, the super learner predictions do not, themselves, estimate cross-validated accuracy; thus, this latter step is necessary to produce fair estimates of the out-of-sample accuracy of our approach. Receiver operating curves (ROCs) were generated and area under the curve (AUC) calculated for the diagnostic accuracy of preterm birth for each binary classification model. We also calculated the positive predictive value, negative predictive value, and percent correct classification for the identification of preterm infants using the best cutoff point for each model, as determined using the Youden method. The YoudBetween August 2015 and September 2017, 1450 pregnant women were consented and enrolled into the ZAPPS cohort. To date, 862 (59.4%) participants have had live births with deliveries captured by a study midwife. A total of 468 (53.1%) of these live births had newborns assessed at <72 hours of life by a trained nurse midwife and had complete data available to be included in subsequent preterm birth predictive modeling . Among aKernel density plots, with accompanying Pearson correlation coefficients, comparing the continuous GA distributions of the 8 models evaluated in this study to those calculated by ultrasound are shown in The accuracy of preterm birth classification by each GA dating method was assessed using ROCs and associated AUCs . The AUCPositive predictive value (PPV), negative predictive value (NPV), and correct classification of all models predicting prematurity are shown in In a sensitivity analysis, we tested NBS(-)LMP(+), our best-performing model, on the subset of women who were enrolled in the first trimester , as their ultrasound dating is expected to be most accurate. We found a PPV of 73.3%, NPV of 98.8%, correct classification of 94.6%, and AUC of 0.9679. Additionally, because our best-performing model excluded NBS–the variable most likely to cause study exclusion due to missingness–we were able to test our model in this population. In 245 newborns not included in our initial analysis, we found a PPV of 71.9%, NPV of 95.7%, correct classification of 90.2%, and AUC of 0.8776.In our urban Zambian cohort with early pregnancy ultrasound dating, we used machine learning to identify a parsimonious set of maternal and newborn variables associated with prematurity and small for gestational age that can improve discrimination between preterm and term newborns as compared to common gestational age dating methods . This exploratory study demonstrates the promising utility of machine learning techniques to optimize algorithms for the identification of preterm birth and other adverse birth outcomes in low-resource settings.Although a positive correlation between the number of parameters and accuracy of GA assessment has been established, increasist or 10th of month) and recall bias.[An interesting finding of our analysis was the strength of LMP as a predictor of GA and preterm birth. Limitations of LMP as a dating method are well-documented. Women wiall bias., 10 Consall bias.–41 Indeeall bias., 42, 43 Further, our best-performing prematurity prediction model excluded NBS. In fact, the addition of NBS components to our best-performing list of covariates decreased the PPV, percent correct classification, and AUC of the model. LMP outperformed NBS in prematurity prediction, both when assessed individually and in combination with other parameters. This finding supports a recent systematic review indicating that NBS has lower agreement with ultrasound dating than LMP. The exclThe calculated PPVs for all models demonstrate the limitations of our currently utilized, single parameter methods to correctly identify preterm newborns. Only 32.3% and 28.6% of newborns predicted to be preterm by LMP and NBS, respectively, were also classified as preterm by early pregnancy ultrasound dating. Many of our multiple parameter, machine learning models performed similarly, with comparable PPVs. Only our best-performing model, NBS(-)LMP(+), had a PPV greater than 50%. All GA dating models demonstrated a propensity for overestimating preterm birth rates and misclassifying term newborns as preterm; however, our best-performing model had a decreased tendency to misidentify preterm newborns in this way. Additionally, this model correctly classified 94% of newborns, including 30 out of 32 preterm newborns. Further, in both our sensitivity analysis of newborns with ultrasound dating <14 weeks gestation (the most accurate GA dating) and in our external validation cohort of newborns without NBS, our best-performing model had a PPV >70%. Although far from perfect as a preterm newborn algorithm, our parsimonious model significantly improves upon currently available identification methods, allowing clinicians to better direct care and resources to newborns in need, especially in low-resource, LMIC settings.A significant limitation of this current study is survival bias of assessed newborns, as demonstrated by the significant differences between our assessed and not assessed populations . PretermA further limitation of our current model is that it was developed to optimize the accuracy the average GA for a given set of covariates, which was then used to infer term (≥37 weeks) versus preterm (<37 weeks) births. This model results in limited accuracy in the prediction of the complete distribution of GA. Utilizing super learner capabilities to better model the distribution of GA, rather than just the mean, as a continuous outcome may be a helpful next step for neonatal providers desiring to better estimate accurate GA. Additionally, our current model requires all characteristics and measurements to assess preterm birth status be present for study inclusion. Consequently, if a woman does not know her LMP, her newborn is omitted from this model. Future work to assess novel GA and preterm newborn prediction models using machine learning techniques should include methods to impute missing data. Further, as validation of our model was limited to internal k-fold cross validation, preventing over-fitting the data, in addition to a small model validation cohort of newborns missing NBS, external validation in a larger dataset should be pursued in future work.In summary, by leveraging the capacity of cutting-edge machine learning algorithms and maternal parameters associated with prematurity and SGA newborns, we identified a parsimonious list of covariates that improves accuracy of preterm newborn identification. Our model incorporates six accessible maternal and newborn characteristics and metrics, reducing the skill and time required to assess gestational age. This exploratory study supports the need for further research into the use of machine learning techniques to improve the accuracy of gestational age assessment in low resource settings and to assist frontline health workers in identifying newborns who may require special care.


South Asia contributes substantially to global low birth weight population (i.e. those with birth weight < 2500 g). Synthesized evidence is lacking on magnitude of cognitive and motor deficits in low birth weight (LBW) children compared to those with normal birth weight (NBW) (i.e. birth weight ≥ 2500 g). The meta-analysis aimed to generate this essential evidence.Literature search was performed using PubMed and Google Scholar. Original research articles from south Asia that compared cognitive and/or motor scores among LBW and NBW individuals were included. Weighted mean differences (WMD) and pooled relative risks (RR) were calculated. All analyses were done using STATA 14 software.n = 5999) were included in the analysis. Children < 10 years of age born LBW had lower cognitive  and motor scores  compared to children with NBW. Within LBW children, those with birth weight < 2000 g had much lower cognitive  and motor scores .Nineteen articles  contains supplementary material, which is available to authorized users. Evidence is lacking from south Asian setting on magnitude of cognitive and motor deficits in low birth weight (LBW) individuals compared to those with normal birth weight (NBW).Our meta-analysis showed that LBW children < 10 years of age had 4.56 points lower cognitive and 4.16 points lower motor scores compared to children with NBW.Early child development interventions in south Asia should emphasize on children born LBWLower middle income countries (LMICs), as per the recent World Bank criteria, are those with a gross national income (GNI) per capita between USD 996 and 3895 . In LMICIn most of the south Asian regions, substantial thrust is still on improving survival, particularly in the neonatal period , 12. In n = 3276) from developed settings documented lower cognitive scores in school aged children born preterm, compared to controls born at term  [Data on neurodevelopment impairment from developed countries suggest that individuals born with LBW have a higher risk of lower cognitive function, tend to score lower on academic performance measures, have higher prevalence of mental disorders, serious emotional and behavioural problems and development delay compared to term healthy counterparts –18. Neur.2–12.5) . These f.2–12.5) . FurtherOur systematic review examined the degree of developmental impairment primarily in LBW children, compared with normal birth weights, in south Asia. Additionally, a similar comparison was also done for adolescent age group. Synthesizing such comparative evidence will be helpful in strategic planning of a health program aimed at improving child development. A question in deciding about such program is whether to reach all infants equally, irrespective of their birth weights or make additional inputs on LBWs. To address this question, we attempted to elucidate how NBW children in south Asian context grow developmentally compared to NBW children from upper-middle-income settings  [The primary objective of the systematic review was to compare cognitive and motor scores among children aged < 10 years born with normal and low birth weight in south Asian setting. It also encompassed a comparison of these outcomes between children born with a birth weight of < 2000 g and those with NBW. We further extended such comparisons until the adolescent age group (i.e. 10–19 years of age).The objective of the additional analysis was to compare cognitive and motor scores among NBW children born in south Asia and upper middle-high income settings, following the World Bank classification .A systematic search was performed by two authors independently  using PubMed and Google Scholar. Google Scholar was used as an adjunct resource to complement PubMed as it offers advantages in terms of its potential to provide access to the gray literature, theses, abstracts, conference proceedings, preprints and institutional repositories. Any discrepancy was discussed with a third author (RPU). Search strategies used subject headings and key words with no language and time restrictions. For abstracts/articles published in non-English language, we planned to use Google translator or involve a language expert to help the team in comprehending the study findings. The search strategy is presented in Table To be included, the study had to be an original research, either cross-sectional or cohort. Studies reporting outcomes of interest by birth weight in the control arm of a randomized controlled trial were also eligible. Included studies should have been conducted in south Asian setting and have compared outcomes of interest among normal and low birth weight individuals. After initial screening of titles and abstracts, full-text publications of potential studies were reviewed. Discrepancies about inclusion of studies and interpretation of data were resolved by discussion with the other authors . Data from all studies meeting the inclusion criteria were abstracted into a tabular form (RPU). Newcastle-Ottawa Quality Assessment Scale adapted for observational studies was used for quality assessment of included studies . The assFor the additional analysis, a search strategy was developed to identify most recent reviews that either presented pooled cognitive and/or motor scores for NBW individuals or compared cognitive and/or motor scores among normal and low birth weight individuals from upper-middle-high income settings. The key search terms included: “birth weight”, “low birth weight”, “preterm”, “cognition”, “intelligence”, “motor”, “psychomotor”, “neurocognitive”, “systematic reviews”, “meta-analysis”. The search strategy was run on PubMed and Google Scholar. Last date of search was 31st December, 2017. Data on cognitive and/or motor scores from each of the studies included in the identified review(s) were tabulated.2. I2 values > 50% were considered to represent substantial heterogeneity [All analyses were done using STATA 14 software. Heterogeneity of effects was assessed and quantified by the Iogeneity . In caseogeneity . In studogeneity , 26. ThiIn studies that reported an outcome at different points in time, only the outcome reported at the most recent point of assessment was considered for analysis. This was done to avoid the analyses of correlated data from repetitive and paired observations, and consequently compromising the reliability of the findings of this meta-analysis. In studies where the outcomes were reported as median (range), conversion into mean (standard deviation) was done using a reliable method . Where sWe did an additional analysis to compare pooled mean cognitive and motor scores among NBW children from south Asia and upper middle-high income settings. The pooled mean cognitive and motor scores for NBW individuals in these two settings were obtained separately and thereafter, compared for statistical significance of difference in means.n = 1631 and Google Scholar; n = 500). Out of these, 1967 were excluded based on titles and another 83 after reviewing the abstracts. We assessed 81 full text articles for eligibility and found 16 articles to be relevant for the review. Additional 3 articles were identified through cross-references of eligible studies. A total of 19 articles  were included in our final analysis [We screened 2131 titles of articles identified through electronic literature search   .The overall pooled weighted mean difference (WMD) in cognitive scores from infancy till adolescence in low birth weights, compared to NBW participants was − 6.14   .The proportion with low cognitive score, defined as IQ score of less than 25th percentile or a mental quotient of < 85, was 14%  and 5%  in LBW and NBW children aged < 10 years respectively (Data not shown). The risk of low cognitive score in children under 10 years was around 2.5 times higher in LBWs compared to those born with NBW      compared to their counterparts with NBW . In terms of motor performance, such children had around 6.5 points lower motor score compared to their NBW counterparts  . There was around 4 times higher risk of low motor performance in children born with birth weight of < 2000 g  compared to those with a weight of ≥2500 g at birth     and for south Asia it was 104.13  with a P-value for difference in means = 0.092)  and 101.75  respectively  in those with birth weight of < 2000 g. The risk of cognitive and motor deficits in LBWs seemed to persist throughout the transition from early childhood to adolescence. In the additional analysis, we found cognitive and motor scores of NBW children from south Asian settings to be similar to those in upper middle-high income settings.There is substantial difference in quantum of deficits that LBW individuals experience compared to normal birth weights, in upper middle-high income and south Asian settings. Recently published systematic reviews from upper middle-high income settings document LBW individuals to have around 8–12 points lower cognitive scores, as opposed to 5 points lower scores found by us in south Asia –51. SimiThe magnitude of cognitive deficit is often related to subsequent income. According to the World Bank 2006 report, studies from United States, Pakistan, Kenya and Tanzania reported 8–12, 6.5, 8 and 5% decreases in wages, respectively, for a 0.5 SD decline in cognitive score . In the South Asia is home to a large proportion of the World’s LBW infants . Until nTo the best of our knowledge, this is the first systematic review which documents the magnitude of deficits in cognitive and motor performance among individuals born from south Asia with LBW compared to those with NBW. The current review suffers from paucity of good quality studies from south Asia; only a third of the included studies had a score of ≥5 out of 10 . Another limitation of the review is that because of the limited data on cognitive and motor performance disaggregated by prematurity and SGA, we could not compare their separate neurodevelopment outcomes with term-appropriate for gestational age (term-AGA) infants. Further, the small number of studies reporting outcomes in adolescence led to wide confidence intervals of estimates. Most of the studies in the review were from India with lesser representation from other countries of south Asia. However, the social, cultural, economic and health care milieu in most of the south Asian countries has strong similarities and therefore, the generalizability of the findings will probably remain unaffected. We used Google Scholar to complement the findings of the search on PubMed. However, we acknowledge the limitations of Google Scholar such as lack of reliable advanced search functions, lack of controlled vocabulary (similar to MeSH terms in PubMed) and inadequate understanding of the exact scope of its coverage. The included studies varied in the nature of study design. Almost all the studies were observational in nature; however, pooling of studies with cohort and cross-sectional design might lead to some loss of reliability of the findings. In studies with prospective follow up, we would expect the birth weight to have been reliably measured while birth weight data may be prone to recall bias and consequent misclassification of birth weight categories in cross-sectional studies . In studies where standardized test was not used, the scores were converted into a standardized scale with mean of 100 and standard deviation of 15. The advantage of this conversion is that scores from different tests can be meaningfully interpreted and compared. However, there is a limitation that such conversions assume a normal distribution but in case this assumption is not met, the scores cannot be interpreted as a standard proportion of the distribution from which they were calculated.This metaanalysis from south Asian setting reveals significant deficits in cognitive and motor scores in children and adolescents born with low birth weight, compared to those born with normal birth weight. We also observed a dose effect relationship wherein among the LBW children; those with birth weight of less than 2000 g had much lower cognitive and motor scores. While NBW infants from south Asia appear to develop similar to their counterparts from upper middle-high income settings, the high degree of deficits among LBWs underscore the need for prioritizing the delivery of child development interventions to these children.Additional file 1:Table S1. Summarized additional findings in the studies included in the meta analysis. Table S2. Mean cognitive scores in normal birth weight children and adolescents from upper middle-high income settings and south Asia. Table S3. Mean motor scores in normal birth weight children from upper middle-high income and south Asian setting. Figure S1. Pooled mean cognitive scores in children < 10 years of age born with normal birth weight (≥2500 g) from upper middle-high income settings. Figure S2. Pooled mean cognitive scores in children < 10 years of age born with normal birth weight (≥2500 g) from south Asian setting. Figure S3. Pooled mean cognitive scores from infancy till adolescence in individuals born with normal birth weight (≥2500 g) from upper middle-high income settings. Figure S4. Pooled mean cognitive scores from infancy till adolescence in individuals born with normal birth weight (≥2500 g) from south Asian setting. Figure S5. Pooled mean motor scores in children < 10 years of age born with normal birth weight from upper middle-high income settings. Figure S6. Pooled mean motor scores in children < 10 years of age born with normal birth weight from south Asian setting. (DOC 221 kb)


FBA was performed to assess the relationship between FD, FC, FDC and PMA at scan, GA at birth, days on mechanical ventilation, days on total parenteral nutrition (TPN), birthweight z-score, and sex. FBA reveals fibre population-specific alterations in FD, FC and FDC associated with clinical risk factors. FD was positively correlated with GA at birth and was negatively correlated with number of days requiring ventilation. FC was positively correlated with GA at birth, birthweight z-scores and was higher in males. FC was negatively correlated with number of days on ventilation and days on TPN. FDC was positively correlated with GA at birth and birthweight z-scores, negatively correlated with days on ventilation and days on TPN and higher in males. We demonstrate that these relationships are fibre-specific even within regions of crossing fibres. These results show that aberrant white matter development involves both microstructural changes and macrostructural alterations.Diffusion MRI (dMRI) studies using the tensor model have identified abnormal white matter development associated with perinatal risk factors in preterm infants studied at term equivalent age (TEA). However, this model is an oversimplification of the underlying neuroanatomy. Fixel-based analysis (FBA) is a novel quantitative framework, which identifies microstructural and macrostructural changes in individual fibre populations within voxels containing crossing fibres. The aim of this study was to apply FBA to investigate the relationship between fixel-based measures of apparent fibre density (FD), fibre bundle cross-section (FC), and fibre density and cross-section (FDC) and perinatal risk factors in preterm infants at TEA. We studied 50 infants  born at 24.0–32.9 (median 30.4) weeks gestational age (GA) and imaged at 38.6–47.1 (median 42.1) weeks postmenstrual age (PMA). dMRI data were acquired in non-collinear directions with b-value 2500 s/mm  •Fixel-based analysis of effects of risk factors on preterm brain development•Differentiates micro- and macrostructural changes in individual fibre bundles•Fibre density and cross-section decrease with low gestation and longer ventilation.•A number of clinical risk factors are related to lower fibre cross-section. QuantitVolumetric tissue changes can be assessed using tensor-based morphometry (TBM), based on the spatial transformations derived from image registration of each subject to a common template. The determinant of the spatial derivative matrix (the Jacobian) describes the local expansion or contraction of each subject's image with respect to the template. However, this discards information regarding the spatial direction of volume change. With respect to white matter, a change perpendicular to the fibre bundle is more likely to reflect a functionally relevant change than a change in tract length. Fixel-based analysis (FBA) is a novel framework which provides quantitative measures of macro- and microstructure associated with a single fibre population, even in voxels containing crossing fibres . In FBA,Apparent fibre density (FD) estimates the volume of the restricted intra-axonal compartment for a particular fibre population direction within a voxel. A reduced FD may be due to fewer axons in that fibre population or due to smaller axons. This makes FD sensitive to changes in tissue microstructure. For the assessment of morphometric group differences, fibre cross-section (FC) captures fixel-specific changes in fibre-bundle cross-sectional area. FC is based on the transformations derived from ODF registration, analogous to the tensor-based method from Fibre density and cross-section (FDC) is the fibre density weighted by the change in the cross-sectional extent of the tract. Assuming that the total FD across a fibre bundle is a measure of its capacity to relay information, FDC allows comparisons of this capacity, irrespective of morphometry. Recently, The aim of this study was to investigate the relationship between fixel-based measures and a number of clinical risk factors including degree of prematurity, the number of days on mechanical ventilation, sex, the number of days on total parenteral nutrition (TPN), and birthweight. We hypothesise that greater exposure to these risk factors is associated with lower FD and FC. We expect FBA to provide new insights into whether risk factors affect whole-brain white matter or whether the effects are localised. Furthermore, in crossing fibre regions FBA will help to discern whether individual tracts are affected or whether these effects are regional, traversing multiple white matter tracts.22.1E-Prime) study of preterm brain development. Written parental consent was obtained prior to imaging. T1- and T2-weighted MR images were assessed for the presence of focal brain injury. The perinatal characteristics of the infants were obtained from clinical records at the time of MRI. A two-sample Kolomorov-Smirnoff test was applied to test for differences between male and female subjects in perinatal clinical characteristics. We found no significant sex differences in any of the perinatal characteristics studied.We studied 50 infants  born at 24.0–32.9 (median 30.4) weeks gestational age (GA) and imaged at 38.6–47.1 (median 42.1) weeks postmenstrual age (PMA). Infants were recruited as part of the Evaluation of Preterm Imaging , T2-weighted turbo spin echo  and high angular resolution diffusion imaging (HARDI) data  were acquired on a Philips 3 Tesla  system sited on the neonatal intensive care unit using an eight-channel phased array head coil. All examinations were supervised by a pediatrician experienced in MR imaging. Pulse oximetry, temperature and electrocardiography were monitored throughout the scan and ear protection was used, comprising earplugs molded from a silicone-based putty  placed in the external auditory meatus and neonatal earmuffs .2.3HARDI data were pre-processed by manual removal of motion-corrupted volumes, PCA-based denoising , susceptStructural MRI data were pre-processed by running bias field correction using the N4 algorithm . T2-weig2.42.4.1Each infant's ODF image was registered to an iteratively refined, group-averaged ODF template using nonlinear transformations . The reg2.4.2At high diffusion-weightings, the extra-axonal water is strongly attenuated and the total radial diffusion signal is proportional to the intra-axonal water. The amplitude of the ODF is proportional to the radial diffusion signal and therefore provides a measure of the intra-axonal volume fraction of the fibres aligned with the corresponding direction. FD was calculated for each fixel in each infant's warped ODF image. For each infant, the ODF lobes were segmented, and the FD of each lobe was calculated by numerically integrating the ODF lobe over the corresponding orientations . Fixels To achieve anatomical correspondence across subjects, each ODF in the template was segmented and a template fixel mask was defined within which to perform statistical analysis. The FD value from each infant's fixel was assigned to the corresponding fixel in the template.2.4.3At each point, the nonlinear mapping of each infant's ODF image to the template is given by the Jacobian matrix. The determinant of the Jacobian describes the local expansion or contraction with respect to the template. FC is a measure of the volume change in the direction perpendicular to the orientation of a fixel computed using the determinant of the Jacobian . FC is c2.4.4FD and FC can be combined to give a measure that reflects both changes in microscopic density and macroscopic morphology. For each fixel, FDC is calculated as FD multiplied by FC .2.5Whole-brain probabilistic tractography was performed in the ODF template, seeded from a whole-brain white matter mask to produce a tractogram of 100 million streamlines. From this tractogram, a subset of 10 million streamlines was selected that best fit the diffusion signal using the SIFT algorithm . ConnectFirst, in order to assess the relationship between fixel-based measures and age at scan, regression analysis was performed between FD, FC and FDC and PMA. We then undertook regression analyses to assess the relationship between FD, FC and FDC and perinatal risk factors: GA at birth, the number of days on mechanical ventilation, the number of days on total parenteral nutrition (TPN) and birthweight z-scores. We also assessed group differences between male and female subjects. We carried out these analyses controlling for PMA at scan and GA at birth (except when GA at birth was the variable of interest and only PMA was included as a covariate). Morphological and microstructural differences have been observed previously between male and female subjects  thereforFC and FDC may be associated with brain volume, and so we repeated our analyses with brain volume as a covariate. However, brain volume is also likely to be related to PMA at scan and all of the risk factors assessed here. We therefore assessed the relationship between brain volume and PMA, GA, days on ventilation, days on TPN, birthweight z-scores and sex differences using Pearson's correlation and report our findings in Supplementary Data .We did not assess the relationship between fixel-based measures and necrotising enterocolitis, chorioamnionitis or treatment for patent ductus arteriosus as there were few subjects in the study group .Table 1P33.1The perinatal characteristics of the infants are described in 3.2Focal brain injury was observed on conventional MRI in three infants. One subject had a unilateral cerebellar haemorrhage; one subject had a small unilateral haemorrhagic parenchymal infarct, and one subject had haemorrhage in the right temporal lobe, cerebellar haemorrhage, cerebellar hypotrophy and bilateral intraventricular haemorrhage.3.3PMA at scan was significantly positively correlated with FD, FC and FDC throughout the white matter. PMA was correlated with FD in the forceps minor and forceps major, lateral projections of the corpus callosum, the corona radiata, inferior fronto-occipital fasciculus, superior longitudinal fasciculus and inferior longitudinal fasciculus . There w3.4All the results reported below are bilateral, unless the hemisphere is stated, significant with FWE-corrected p-values <.05 and corrected for PMA at scan and GA at birth. The results are summarised in 3.4.1GA at birth was significantly positively correlated with FD, FC and FDC, after correcting for PMA at scan. GA was positively correlated with FD in the splenium 15 and tapetum of the corpus callosum, and a small region in the anterior portion of the left inferior fronto-occipital fasciculus . GA was 3.4.2All fixel measures were significantly negatively correlated with the number of days requiring mechanical ventilation, corrected for PMA and GA. FD in the cerebellum and the pons was negatively correlated with number of days on mechanical ventilation . FC was negatively correlated with number of days on ventilation in the genu, splenium and tapetum of the corpus callosum, the anterior commissure, corticospinal tract, anterior limb of the internal capsule, middle-to-anterior regions of the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus, fornix, thalamic regions, cerebellum and pons . Of note3.4.3There were no significant correlations between FD, FDC and the number of days requiring TPN. FC showed a negative correlation in the corticospinal tract, external capsule, cerebellum and pons . We perf3.4.4Birthweight z-scores were not correlated with FD. FC was significantly positively correlated with birthweight z-scores throughout the white matter including the genu, splenium, body and lateral projections of the corpus callosum, the anterior commissure, corticospinal tract, the right anterior limb of the internal capsule, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, inferior longitudinal fasciculus, fornix, thalamic regions, the cerebellum and pons . FDC was3.4.5There were no significant differences in FD between male and female subjects. Male subjects showed significantly higher FC in the splenium of the corpus callosum, corticospinal tract, the superior longitudinal fasciculus, inferior longitudinal fasciculus, cingulum, fornix and cerebellum  and high4This study reveals fibre population-specific associations between measures of fibre density and regional white matter fasciculi cross-section, and perinatal risk factors. Our findings are in line with previous dMRI studies by our group and others assessing white matter development in the preterm population, demonstrating abnormal white matter development associated with immaturity at birth, longer requirement for mechanical ventilation, parenteral nutrition, stress and illness severity . Using FIn general, the correlations between perinatal risk factors and FC were more widespread than the correlations with FD. Indeed, there were no correlations between FD and the number of days requiring TPN or birthweight z-scores, and no significant differences in FD were found between male and female subjects. These results suggest that the perinatal clinical variables studied here are more strongly associated with reductions in the cross-sectional area of white matter fasciculi rather than changes in apparent fibre density. It is possible that impaired white matter development previously attributed to microstructural changes in DTI studies may be due to alterations in fibre cross-sectional area of specific fibre bundles at the macroscopic scale. This study extends our knowledge of morphological changes associated with perinatal risk factors by demonstrating fibre-specific relationships. Using fixel-based measures it is possible to identify whether morphological changes are localised to specific fibre bundles or are related to changes across the whole of the white matter. This is of particular importance in regions of crossing fibres. For example, in the centrum semiovale, we observed that FC in the corticospinal tract was diminished with greater requirement for mechanical ventilation (when correcting for PMA and GA), however there was no relationship between duration of mechanical ventilation and FC in the association fibres traversing the region. By identifying which tracts, and therefore which functional pathways, are affected by specific risk factors it may be possible to anticipate future developmental impairment.After including total brain volume, correlations between fixel measures and GA at birth, days on TPN, birthweight z-scores and differences between male and female subjects were no longer statistically significant. However, brain volume was strongly correlated with age at scan and the perinatal risk factors. Including brain volume as a covariate, therefore, essentially removes the effect of interest. Future FBA studies may extend to multivariate analyses, taking into account the interdependencies of variables. After including sex as a covariate in addition to PMA and GA there were no changes to the results and no significant sex differences were observed in any of the risk factors studied here. Therefore, it is unlikely that changes in fixel measures observed across risk factors are driven by sex-specific morphological differences.Previous DTI studies have shown a dose-dependent relationship between GA at birth and white matter FA values  and pretRespiratory illness during the perinatal period is associated with poor developmental outcome  and, indUndernutrition is common in preterm infants and contributes to postnatal growth failure . RequireLower birth weight, specifically intrauterine growth restriction, is associated with reduced FA values in the corpus callosum , reducedWe also explored differences in white matter microstructure and fasciculi cross-section between male and female preterm infants. Male sex has been implicated as a risk factor for poorer outcome in preterm infants . In thisFD, FC and FDC measures were positively correlated with age at scan throughout the white matter. These results are in agreement with the findings from While our findings describe local white matter damage, they are likely to also reflect grey matter damage leading to abnormal thalamo-cortical connections . CorticaWe recognise that this study is limited by a relatively small study group of preterm infants and larger studies with neurodevelopmental outcome measures in later childhood are required. Nevertheless, this study demonstrates that the FBA approach offers new insights into white matter development and injury related to perinatal risk factors.5In summary, this study shows that greater exposure to the perinatal risk factors studied here was associated with more wide-spread reductions in cross-section of white matter fasciculi than with alterations in fibre density, offering additional information to that which can be obtained from traditional diffusion measures of anisotropy and diffusivity. We demonstrate fibre bundle-specific relationships between risk factors and fixel measures, providing new insight into white matter development and injury in this vulnerable population.