Iron is an essential micronutrient that is involved in many functions in humans, as it plays a critical role in the growth and development of the central nervous system, among others. Premature and low birth weight infants have higher iron requirements due to increased postnatal growth compared to that of term infants and are, therefore, susceptible to a higher risk of developing iron deficiency or iron deficiency anemia. Notwithstanding, excess iron could affect organ development during the postnatal period, particularly in premature infants that have an immature and undeveloped antioxidant system. It is important, therefore, to perform a review and analyze the effects of iron status on the growth of premature infants. This is a transversal descriptive study of retrieved reports in the scientific literature by a systematic technique. PRISMA  guidelines were adapted for the review strategy. The inclusion criteria for the studies were made using the PICO  model. Consequently, the systematic reviews that included studies published between 2008–2018 were evaluated based on the impact of iron status on parameters of growth and development in preterm infants. Iron is involved in many key cellular functions and processes in humans due to its role as an essential micronutrient. This ubiquitous mineral plays a critical role in growth and the development of the central nervous system. It is essential for energy metabolism, cell differentiation, and a host range of physiological processes for the normal functioning of the brain . In spitPreterm infants are prone to develop iron deficiency anemia (IDA) in the first 4 months of life due to lower iron stores at birth compared with term infants, rapid growth and iron losses. Most fetal iron is transferred from the mother during the third trimester of gestation. This transfer is interrupted by preterm birth, resulting in iron stores at birth being proportional to birth weight. Despite low iron stores at birth, growth velocity of premature infants is maximal at a postmenstrual age of 28–38 weeks, reflecting a particularly high iron need. The risk of IDA in preterm infants is further increased by frequent uncompensated iatrogenic phlebotomy losses . Initial evaluation should include a clinical history, to assess prematurity, low birth weight, diet, chronic diseases, family history of anemia, and ethnic background. A complete blood count is the most common initial diagnostic test used to evaluate for IDA, and this count allows for differentiating microcytic, normocytic, and macrocytic anemia based on the mean corpuscular volume (MCV). Ferritin measurement is the most sensitive test for diagnosing IDA, being a good reflection of total iron storage and is also the first laboratory index to decline with ID .Developmental outcomes are positively influenced by adequate nutrition early in life, revealing an improvement in neurodevelopment and cognitive abilities in preterm infants, for instance. During development, ID negatively affects the growth and functioning of multiple organ systems particularly the brain, skeletal muscle, heart and the gastrointestinal tract ,9,10,11.An excess of iron can affect organ development during the postnatal period. There is an association between iron overload, retinopathy of prematurity, and bronchopulmonary dysplasia. Non-protein bound iron mediates oxidative stress in the presence of poor antioxidant capabilities, and this condition initiates or potentiates the progression of these pathologies through generation of free radicals . ID and There is a high prevalence of preterm babies who have an increased risk of IDA, and there are no clinical consensuses or guidelines about the clinical benefits of iron supplementation and/or fortification that the hospitals should follow with this extremely vulnerable population. Therefore, it is relevant to evaluate the effects of ID or iron toxicity on the optimal development and function of the organs such as the brain in premature infants, ID and iron overload disorders during this period .Consequently, this systematic review performed an analysis of the influence and effects of iron on the hematological parameters, growth, and development of premature infants. Thus, the review also revealed a systematic literature evidence on the beneficial effects of iron supplementation in premature infants.Descriptive study of the articles retrieved using a systematic technique.The data used in this study were obtained from the consultation and direct access to the scientific literature collection in the following databases: Medlars International Literature Online (MEDLINE) via PubMed , The Cochrane Library Plus, SCOPUS (World’s largest abstract and citation database of peer-reviewed literature), Web of Science and Literatura Latinoamericana y del Caribe en Ciencias de la Salud (LILACS).A search strategy for electronic databases was developed with regard to information processing, from the Thesaurus study, Medical Subject Headings (MeSH) developed by the U.S. National Library of Medicine.The use of the following terms was considered appropriate: “Iron”, “Growth and Development”, and “Infant, Premature”, as well as descriptors as text in the title and summary record fields. A search equation was developed for use in the MEDLINE database, via PubMed, using boolean connectors, and then adapted to the rest of aforementioned data bases: (“Iron”(Mesh) AND “Growth and Development”(Mesh) AND “Infant, Premature”(Mesh)). The search was expanded to include “Anemia, Iron-Deficiency” (Mesh).The proposed criteria for the studies included in the review, considering the PICO  model  were: , as the calculated period of obsolescence  should be greater than 80% . In the Two independent reviewers extracted the data. To increase uniformity and reduce bias, a standardized data collection form was used by each reviewer. The reviewers made a consensus decision when there was a discrepancy. From each eligible study, the following information/data were extracted: first author, year of publication, journal, eligibility criteria, definition of premature or low-birth weight, number of participants, hematological data (hemoglobin (Hb), hematocrit (Ht), serum iron) before iron supplementation, iron supplement dose, duration of iron supplementation, hematological data after iron supplementation, growth status after iron supplementation, adverse effects of iron supplementation, and effects of iron on cognitive status.The final sample of studies for review was subsequently assessed by the authors  separatehttp://www.TREND-statement.org) that must be described in the publication. The TREND statement is specifically developed to guide standardized reporting of nonrandomized controlled trials. The TREND statement complements the widely adopted CONsolidated Standards Of Reporting Trials (CONSORT) statement developed for randomized controlled trials. This is a collective effort in promoting the idea that transparent reporting is valuable to improve research synthesis and advance evidence-based recommendations for best practices and policies. A point was assigned for each item featured. Meanwhile, if an item was not applicable, it did not score (a maximum score of 22). When an item was composed of several points, these points were evaluated independently, giving the same value to each of them, and subsequently an average was made , in such a way that in no case the score of one point per item could be exceeded. For each included study, a consensus was achieved after discussion of discrepancies. Non-randomized studies were assessed using the Transparent Reporting of Evaluations with Non-randomized Designs (TREND) statement , which cUpon thoroughly reviewing the data collection and statistical analysis of each included article, it became apparent that there was too much variation between these studies, and, therefore, it was not considered appropriate to conduct additional analysis such as a meta-analysis using the reported findings.J.M-F and J.D-C reviewed the articles independently, extracted relevant data from each included article, and assessed risk of bias. The Delphi List was carried out in the 5 randomized studies selected, while the TREND Checklist was used in the 9 non-randomized selected manuscripts. Any discrepancies in scoring were resolved by mutual discussion or through discussion with J.J.O. An overall risk of bias rating was assigned to each study based on the quality score.The Delphi List carried out in the selected manuscripts is provided in The above methodology resulted in a total description of search criteria and provided a total of 108 citations. The title and abstract of each report were reviewed by J.M.-F. and J.D.-C. to assess eligibility, and if the abstract or title was not described in enough detail, full reports were assessed to consider their inclusion. In this sense, all full-text reports for inclusion eligibility were independently assessed by J.M.-F. and J.D.-C. The level of agreement between the two researchers was high enough. The researchers resolved minor differences related to the inclusion of the reports by discussion. The potential lists of each relevant recovered article were scanned and four more articles were identified as possibly relevant. Duplicate manuscripts and those based on exclusion criteria and limits were removed. Thirty studies remained potentially eligible . SixteenThe summary of the most relevant characteristics of the randomized and non-randomized studies included are shown in Regarding the randomization, this review includes six randomized studies ,27,28,29Sample sizes ranged from 46 to 401 subjects. Among the total of 1743 participants included in the trials, 50.95% were men and 49.05% were women. One study did not specify the number of male and female participants . The ageAmong all included studies, a variety of different iron status parameters were investigated. Most of the studies analyzed Hb, serum ferritin (SF), serum iron, and total iron-binding capacity (TIBC). Neurological development was also studied to measure the developmental assessment scale and child behavior checklist questionnaire. Berglund et al.  followedAll of the studies included in the review assessed the iron status in preterm neonates to evaluate iron metabolism and requirements, supplying a large number of different measures. SF levels were reported in all the studies except Amin et al. , who meaTwo studies measured reticulocytes counts ,34, one p < 0.001) at an expected rate for the age of the population during the postnatal development of all infants, although these measurements were lower in preterm infants. The infants with iron depletion showed a lower birth weight than those without iron depletion. Lower birth weight and SF concentrations are independently associated with iron depletion at 6 weeks of age [In all studies that analyzed the weight, the length and head circumference of premature infants was lower than that of the term infants. As suspected, these measurements were lower in preterm infants when compared to term infants, although Yamada and Leone  showed as of age . A studys of age  showed as of age . On the s of age .In addition, a lower birth weight was found in infants born with iron overload compared to those with normal iron status and latent ID status (serum ferritin ˂76 ng/mL or ˂170.7 pmol/L) . Other sID was evaluated despite the different iron status indices used in the studies consulted. Most of the studies revealed that preterm or late preterm neonates usually featured a high risk of ID. p = 0.01). Moreover, ferritin levels were significantly lower (p = 0.006) those in the preterm appropriate for gestation group compared to the term appropriate for gestation infants. Serum iron also decreased in the preterm small for gestational age group compared to the preterm appropriate for gestation group (p = 0.002). Amin et al. [In this sense, indicators of iron status, including Hb levels and the prevalence rates of ID and IDA at 6 months, differed significantly in a dose-dependent manner between the intervention groups. Berglund et al.  found thn et al.  showed tp = 0.02). Hb and Ht decreased mainly in the late-preterm newborns (p < 0.001). At birth, differences between the two groups were observed only for Ht (p = 0.010), whereas at two months, more dramatic differences in both Hb (p = 0.020) and Ht (p < 0.001) were reported. Iron concentrations in the late-preterm infants were lower at term (p = 0.0034) and at one month post term (p < 0.001). TIBC showed lower levels when measured in the late-preterm infants at birth (p < 0.001). Transferrin saturation presented a similar pattern and was lower in late-preterm newborns at birth (p < 0.001). In a study conducted by de Waal et al. [p < 0.001). At the same time point, infant with ID did not show any differences in ZnPP/H levels compared with normal infants p = 0.223). In addition, a study conducted by Amin et al. [In this sense, Yamada and Leone , indicatl et al.  with pren et al.  revealedp = 0.005, p = 0.018, respectively). At the birth of preterm infants, SF levels were lower than those of full-term infants (p = 0.011), and the transferrin saturation of preterm infants did not differ between birth and 1 month after birth levels. Preterm infants showed a significant increase in prohepcidin levels (p = 0.011), a finding not shown in full-term infants.Conversely, others studies reported that there were no significant differences between preterm or term infants with regards to ID. Therefore, Molloy et al.  observedIron supplementation was established in 9 studies. Most of the studies supplied iron as elemental iron in ferrous sulfate chemical form, colloidal iron and ferrous succinate mixture while 3 studies did not specify the chemical form of iron supplement ,34,38. TIn the scientific literature, controversies abound on the necessity of t iron supplementation. Some studies showed a positive effect of iron supplementation and its role in preventing or treating ID. In a study with marginally low birth weight preterm or term infants, Berglund et al.  showed tIn contrast, other studies did not show any effect of iron supplementation. Saha et al.  suggesteUijterschout et al.  proposedOf the studies included in this systematic review, three directly investigated the effects of iron status on cognitive performance in premature infants. The techniques used to assess cognitive performance were noninvasive. p = 0.08).Berglund et al.  tested tp = 0.040) and “attention problems” (p = 0.022) subscales and a similar trend in all subscales except “withdrawn.” This study was reported to be the first showing a positive effect of early iron supplementation on neurobehavioral development beyond 2 years of age. This is another surprising result because ID impairs brain development and interferes with neurotransmitter function and biosynthesis, particularly with regard to dopamine and other monoamines. Another study by Berglund et al. , used thBerglund et al.  tested aIn general the results observed in the different studies of Berglund ,27,28,29Gupta et al.  evaluateThis review investigated the literature available about the iron status of premature infants and their growth and development. Moreover, it evaluated the effect of iron supplementation in this age group. Premature infants revealed a lower weight, length, and head circumference compared to term infants. The preterm infants with iron depletion had a lower birth weight than those without iron depletion. Iron depletion at 6 weeks of age was independently associated with lower birth weights together with lower SF concentrations . Low iroRegarding iron status, results from the studies analyzed showed the absence of consensus on how to define iron stores in preterm infants. SF measurement is the most widely used index to evaluate iron status and it was measured in all studies. Even so, SF is not considered the best iron status parameter by all the authors, because it changes over time and increases during infection and inflammation ,34,42,43Mukhopadhyay et al.  used ferIron has a crucial role in many metabolic pathways, especially in erythropoiesis and neurodevelopment . SpecifiAlthough, some studies did not find lower iron parameters in premature infants after birth, Molloy et al.  found thTaking into account iron status in preterm infants, it seems highly relevant to study the presence or absence of ID or IDA in this population. Premature infants have increased risk to develop ID, which is the most common micronutrient deficiency in the world , and alsDewey et al.  reportedAccording to these results and taking into account the importance of avoiding ID in this population, iron supplementation of preterm neonates seems vital because rapidly depleting inadequate iron stores cannot meet the iron requirement of enhanced erythropoiesis during this period. However, previous studies on iron supplementation in preterm infants have yielded conflicting results ,58. AlthIn contrast, Sankar et al.  did not Regarding the effect of iron supplementation on cognitive performance, Berglund et al.  evaluateBerglund et al.  did not Iron plays a crucial role in myelination, dendritic growth, and synaptogenesis. Therefore, ID could interfere with neurotransmitter function, mainly with regard to dopamine and other monoamines, which interestingly are closely associated with behavior . NonetheFinally, Gupta et al.  evaluateIn summary, the findings of this systematic review provide evidence that premature infants require iron supplementation in most of the cases and are susceptible to the development of ID or IDA, which could affect postnatal cognitive development and behavior. However, although ID impairs brain development due to its essential role in myelination, dendritic growth, synaptogenesis, and neurotransmitter function, motor, mental, and development parameters were not affected by iron supplementation in preterm neonates. Infants with iron depletion and overload showed a lower birth weight than those with a normal iron status. Taking into account these considerations, future research with more robust experimental designs and lower RoB are needed to achieve more solid conclusions.


This study examined the association of anthropometric measures including height, leg length, trunk length and body mass index (BMI) at 11 and 18 years with systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol, low-density lipoprotein cholesterol (LDL-c) and high-density lipoprotein cholesterol (HDL-c) at 11 and 18 years. We analysed data from 661 participants from the Aboriginal Birth Cohort study–a longitudinal study based in the Northern Territory, Australia. Associations between anthropometric measures and cardiovascular risk factors were investigated in linear regression analyses adjusted for confounding, with imputation for missing data. In adjusted analyses, increasing leg length , trunk length  and height  at 11 years was associated with higher SBP at 11 years. When these exposures were measured at 18 years the effect on SBP at 18 years had attenuated, and only increased trunk length was associated with higher SBP at 18 years for both sexes . We observed little association between height, leg length and trunk length and DBP, total cholesterol, LDL-c and HDL-c. Increased BMI was associated with elevated SBP and DBP at 11 and 18 years. Our findings suggest that height, leg length, and trunk length measured at 11 and 18 years was generally not associated with cardiovascular risk factors at 11 and 18 years. However, greater childhood BMI was associated with higher blood pressure and this association persisted into adolescence. This study contributes to the limited body of evidence on the association between measures of early anthropometry and cardiovascular risk among the Australian Aboriginal population. Numerous studies in western countries have found that greater adult height and, in particular longer legs, has been associated with lower pulse pressure, systolic blood pressure, and decreased risk for cardiovascular disease (CVD) and death –6. HowevWhile a substantial body of research exists on the effects of small size at birth with later CVD and death –12, few Growth trajectories in children may vary by ethnicity, and be affected by the quality of the living environment during the years of physical development . ChildreUsing data from the Aboriginal Birth Cohort study (ABC study), the aim of the present study was to investigate whether anthropometric measurements including birth weight, birth length, height, leg length, trunk length and body mass index at 11 and 18 years was associated with measures of cardiovascular risk at 11 and 18 years. To our knowledge no previous study has examined associations between aspects of anthropometry and CVD risk factors in early life among Aboriginal peoples, whose childhoods were mainly spent in rural and remote regions of Northern Australia.The ABC study is a longitudinal examination of a birth cohort of Aboriginal Australians. Study design and sample information for the ABC study has been described elsewhere . In brieThe main exposure variables included birth weight, birth length, leg length, trunk length, leg-to-trunk ratio, height, and body mass index. Birth weights were recorded to the nearest gram using a balance scale, crown-heel lengths were measured with a length board using standard anthropometric methods , 19. At The outcomes measured at the 11 and 18 year follow-up included systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol, low-density lipoprotein cholesterol (LDL-c) and high-density lipoprotein cholesterol (HDL-c). Blood pressure was obtained three times, on the right arm, whilst sitting, and post resting using an automatic oscillatory unit . Mid upper arm circumference was measured at both times and the appropriate cuff size was used. Blood samples were collected, and participants were defined as fasting if they had fasted for 8 hours or more prior to the sample collection (330 and 453 were defined as fasting at the 11 year and 18 year follow-up respectively). Blood samples were centrifuged post clotting as soon as possible, within two hours after collection, frozen, and transported in cold-boxes to the laboratory. Cholesterol concentrations were measured enzymatically .Potential confounding factors included: child age; gestational age; birth weight for gestational age z score ; place oLinear regression models were used to estimate associations between anthropometric measures at birth, 11 and 18 years and cardiovascular risk factors at 11 and 18 years. Three sequential models were used to estimate the effect of anthropometric measures on outcomes. The first model included only age. The second model added place of residence, birth length, birth weight for gestational age z score, and gestational age. When examining associations between leg length or trunk length on outcomes the third model included all variables in models 1 and 2, and the other component of height (leg or trunk length). The decision to adjust for the other component of height was based on previous studies , 14, 27 A total of 661 children  were included in the analysis. Characteristics of the study participants at birth, at the childhood and adolescent visit are shown in Tables Birth weight or birth length was not associated with SBP or DBP at 11 or 18 years. In fully adjusted models, increasing leg length [males: 0.47mmHg/cm ], trunk length  and height [males: 0.32mmHg/cm ] at 11 years was associated with higher levels of SBP at 11 years. Leg length at 11 years was also associated with higher levels of SBP and DBP at 18 years for males and for females, whereas increasing height was only associated with higher SBP for males at 18 years  . Second,We found that birth weight, birth length, height, leg length, and trunk length measured at 11 and 18 years was generally not associated with cardiovascular risk factors at 11 and 18 years among an Aboriginal population. However, increased BMI was consistently associated with increased SBP and DBP in both sexes. This study contributes to the limited body of evidence on the association between measures of early anthropometry and cardiovascular risk among the Australian Aboriginal population.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.


In boys only, average prenatal CO was inversely associated with mtDNAcn . When examined by study arm, LPG cookstove had the opposite effect in all children , but especially boys , as compared to Control. Increased mtDNAcn was associated with improved birth outcomes. Conclusions: Increased prenatal HAP exposure reduces CBMC mtDNAcn, suggesting cumulative prenatal oxidative stress injury. An LPG stove intervention may reverse this effect. Boys appear most susceptible.Background: Associations between prenatal household air pollution (HAP) exposure or cookstove intervention to reduce HAP and cord blood mononuclear cell (CBMC) mitochondrial deoxyribonucleic acid copy number (mtDNAcn), an oxidative stress biomarker, are unknown. Materials and Methods: Pregnant women were recruited and randomized to one of two cookstove interventions, including a clean-burning liquefied petroleum gas (LPG) stove, or control. Prenatal HAP exposure was determined by serial, personal carbon monoxide (CO) measurements. CBMC mtDNAcn was measured by quantitative polymerase chain reaction. Multivariable linear regression determined associations between prenatal CO and cookstove arm on mtDNAcn. Associations between mtDNAcn and birth outcomes and effect modification by infant sex were explored. Results: LPG users had the lowest CO exposures ( Approximately 3 billion people worldwide are exposed daily to household air pollution (HAP) secondary to the burning of solid fuels in combustion-inefficient cookstoves . HAP is Alterations in fetal development, including those resulting from environmental exposures, may program the fetus for future disease ,9. EmergEnvironmental pollutants such as air pollution induce oxidative stress ,19. HumaFetal development requires the careful orchestration of complex processes across gestation and sex-specific differences in development have been demonstrated . The impUsing a prospective, rural Ghanaian birth cohort derived from a cookstove intervention trial, we examined the impact of prenatal HAP exposure, as indexed by personal prenatal CO exposure assessments, and a cookstove intervention on CBMC mtDNAcn. Sex-specific differences were examined. Finally, we examined the associations between CBMC mtDNAcn and birth anthropometrics.n = 1414 pregnant, nonsmoking women were recruited from the Kintampo North Municipality and Kintampo South District of Ghana and randomized prior to the second trimester of pregnancy to a clean-burning stove, the liquefied petroleum gas (LPG) cookstoves; an improved combustion-efficiency biomass burning stove, the BioLite cookstove; or control, the traditional 3-Stone Fire. The analyses in this manuscript include n = 120 mother–infant dyads who delivered between October 2014 and August 2015 at gestational age > 37 weeks and had successful cord blood collection at delivery with subsequent CBMC mtDNAcn analyses.Participants were mother–infant dyads participating in the Ghana Randomized Air Pollution and Health Study (GRAPHS), a cluster randomized cookstove intervention trial described in detail elsewhere . BrieflyThe study was approved by the Kintampo Health Research Centre (KHRC) Institutional Ethics Committee  and the Institutional Review Boards of the Icahn School of Medicine at Mount Sinai and the Columbia University Mailman School of Public Health. Written informed consent was obtained from all pregnant women prior to commencement of study activities.Pregnant women performed four 72-hour prenatal personal CO monitoring sessions . Participants wore the personal monitor in their breathing zone except while sleeping or bathing, during which times they were asked to keep the monitor off the floor and nearby. A custom plastic case with a tea bag filter protected the CO monitor from rain and dust.The CO monitors recorded readings in parts per million (ppm) every 10 seconds. Laboratory technicians exposed the monitors to certified span gas (50 ppm CO in zero air) every 6 weeks to quantify response and adjust field values. Additional visual inspection and run time checks of all exposure data were made following GRAPHS protocols. Data used in this analysis passed all Quality assurance and quality control checks.CO exposure at each of the four prenatal sessions was based on the first 48 hours of each deployment, to avoid cases where field pick up schedules or battery issues could have resulted in incomplete data . The mean of each 48-hour personal CO monitoring session was averaged to determine the average prenatal CO exposure for each dyad. Forty-eight-hour data completeness for the GRAPHS cohort was high (93.6%); this was evenly distributed among intervention groups. TM) was performed to isolate CBMCs, which were immediately stored at −80 °C until analyses. DNA was extracted using the Qiagen DNA Midi Kit. DNA samples were normalized to 2 ng/µL and concentrations were confirmed using PicoGreen quantification prior to amplification. Samples failing to meet the minimum concentration were excluded from analysis.Immediately following delivery, cord blood was collected into BD vacutainer heparinized tubes and transported to the KHRC Clinical Laboratory on IsoRack cool packs . Density gradient centrifugation (Ficoll-PaqueMtDNAcn was assayed using duplex quantitative PCR (qPCR) to compare the relative amplification of nuclear and mitochondrial segments of DNA as previously described . The priBirth anthropometrics were measured within 24 hours of delivery. Birth weight was measured to the nearest gram using the Tanita BD 585 digital baby scale  after standardizing with a 1-kilogram (kg) weight. Birth length and head circumference were also measured to the nearest 0.1 centimeter (cm) using a standard measuring board and a Lasso-o™ respectively. Gestational age at delivery was determined using ultrasound-established dates at GRAPHS enrollment; all ultrasounds were performed prior to 24 weeks gestation .Maternal age and ethnicity were obtained through questionnaires at enrollment. Maternal height to the nearest 0.1 cm and weight to the nearest 0.1 kg were measured at enrollment. Household assets were surveyed and used to assign a wealth index relative to others in the study. Date of birth was recorded at delivery and, in combination with the ultrasound-confirmed gestational age at delivery, used to calculate gestational age at delivery. Child sex was recorded at delivery.First, we employed multivariable linear regression models to determine the impact of prenatal HAP exposure, as indexed by CO and the GRAPHS cookstove intervention arm, independently, on CBMC mtDNAcn percentage relative to the study average. For analyses of cookstove intervention arm, the 3-Stone Fire was considered the referent. Models were adjusted for the following covariates, maternal height and weight, ethnicity, and age; household wealth index; gestational age at delivery; child sex; and plate number. Effect modification by child sex was then examined in stratified analyses and by fitting interaction terms. As the LPG stoves were considered the least polluting, we repeated analyses with the LPG arm as the referent.We then employed multivariable linear regression models to determine associations between CBMC mtDNAcn and birth outcomes, including birth weight, head circumference, and gestational age at delivery, considered independently. We again adjusted for maternal height and weight, ethnicity, age; household wealth index; gestational age at delivery; child sex; and plate number. Effect modification by child sex was then examined in stratified analyses and by fitting interaction terms.p-value was less than 0.05. Interaction terms were considered suggestive of an interaction if the p-value was less than 0.10. Analyses were implemented in R version 3.3.3, .Main effects were considered statistically significant if the n = 162 samples. The MtDNAcn assay was successfully performed in n = 120 samples  (Cord blood mononuclear cell DNA was extracted in .6–87.8) . There w.6–87.8) .n = 63, 53%). Wealth index was evenly distributed amongst the group. The majority of mothers had no  or primary school only  education. The median age of participating mothers was 25 years at enrollment (IQR 22–33) and the median gestational age at delivery was 39.7 (IQR 39.3–40.9).Participant characteristics are summarized in p = 0.02 by ANOVA), with participants randomized to the Control arm having a median average prenatal CO exposure of 1.07 ppm (IQR 0.75–1.60), BioLite arm median 1.15 ppm (IQR 0.53–1.41), and LPG arm 0.60 ppm (IQR 0.39–0.97).Average prenatal CO exposure varied by arm (p = 0.43 per 1 ppm increase in CO) following adjustment for maternal height and weight, ethnicity, age, and marital status; household wealth index; gestational age at delivery; child sex; and PCR plate number  following adjustment for maternal height and weight, ethnicity, age, and marital status; household wealth index; gestational age at delivery; child sex; and PCR plate number. No statistically significant association was seen in girls.Median CBMC mtDNAcn in the LPG arm was 79.54% (IQR 63.23–98.53), BioLite 63.89% (IQR 50.85–78.39), and Control 58.30% (IQR 9.03–84.45).p = 0.049) compared to the 3-stone fire arm, following adjustment for maternal height and weight, ethnicity, age, and marital status; household wealth index; gestational age at delivery; child sex; and PCR plate number (p = 0.51). When LPG was considered the referent, infants born to mothers randomized to the BioLite arm demonstrated a trend toward reduced CBMC mtDNAcn , Multivariable models identified a statistically significant association between cookstove intervention arm and CBMC mtDNAcn. Specifically, infants born to mothers randomized to the LPG arm had 19% higher relative CBMC mtDNAcn  as compared to 3-stone fire (p = 0.51) as compared to 3-stone fire. No statistically significant associations were seen in girls. P-interaction was not statistically significant (p > 0.10). When LPG was considered the referent, boys born to mothers randomized to the BioLite arm demonstrated reduced CBMC mtDNAcn , Sex-stratified analyses demonstrated that boys born to mothers randomized to the LPG stove had a 31% increase in mtDNAcn  and 0.042 day increase in gestational age at birth  following adjustment for maternal height and weight, ethnicity, age, and marital status; household wealth index; gestational age at delivery; child sex; and PCR plate number  and traditional 3-stone fire stove, suggesting that the cookstove intervention may reverse the effects of HAP exposure. Boys appear to especially benefit from the clean fuel intervention. Further, our data suggest that in all children increased CBMC mtDNAcn, such as that resulting from the clean cookstove intervention, is associated with improved birth outcomes including head circumference and gestational age.2.5 exposure, demonstrating an approximate 5% reduction in cord blood mtDNAcn per 10 3 increase in PM2.5 [10 and NO2 exposures, have also been demonstrated [The findings from previous studies on the relationship between prenatal air pollution and mtDNAcn in a variety of tissues are inconclusive. In cord blood, only one study has shown a significant and inverse relationship with prenatal ambient PMin PM2.5 ; other snstrated ,29. Nonhnstrated . To our nstrated  and no sIn the current study, the clean cookstove intervention (LPG) arm had the lowest prenatal CO exposures. Further, this cookstove intervention was associated with increased CBMC mtDNAcn as compared to both the BioLite stove or 3-Stone Fire. These findings suggest that a clean cookstove intervention may be able to reverse the cumulative oxidative stress injury induced by prenatal HAP exposure. Oxidative stress secondary to increased air pollution exposure leads to an initial increase in mtDNAcn as a compensatory mechanism to increase the number of mitochondria in a cell in order to meet the cell’s energy demands. However, with continued or higher exposures, these compensatory mechanisms become overwhelmed resulting in a decreased synthesis of mtDNA . This le2.5, that were not measured. Alternatively, it is plausible that other exposures, including other chemical [Our data suggest the effect of LPG cookstove intervention on mtDNAcn is not proportional to the effect of prenatal HAP exposure. In this GRAPHS subset, the median CO average prenatal exposure in the control arm was 1.07 ppm as compared to 0.60 ppm in the LPG arm, or a difference of 0.47 ppm between arms. Yet, among boys, the impact of the LPG cookstove on mtDNAcn was more than twice that seen with a 1 ppm increase in prenatal CO. It is possible that these differences are attributed to other air pollutants, such as PMchemical  and noncchemical ,40.2-induced changes in mtDNAcn were associated with decreased birthweight [2 and birthweight was due to changes in mtDNAcn [Alterations in mtDNAcn with subsequent mtDNA dysfunction has been implicated in fetal programming thus changes in mtDNAcn may represent an intermediary pathway that links HAP exposure to adverse health outcomes ,22,41. OSex-stratified analysis suggest that male infants are more sensitive to the effect of prenatal HAP exposure on CBMC mtDNAcn. A clean cookstove intervention may prevent these changes. These findings are in line with previous studies that similarly pointed to sex-specific differences in the effect of air pollution on cord blood mtDNAcn, with boys most affected . Male feOur study demonstrates many strengths. This is the first study to exclusively randomize pregnant women to a cookstove intervention, including a clean-burning fuel stove, and perform extensive prenatal HAP exposure assessments. In a subset of children, cord blood was collected and CBMC mtDNAcn analyzed. We used the gold standard technique—duplex quantitative PCR—to measure mtDNAcn. Gestational age was determined at enrollment via ultrasound and birth anthropometrics measured within 24 hours of birth, allowing exploration of associations between mtDNAcn and birth outcomes. Our well-characterized cohort allowed for adjustment for a number of known confounders and covariates.However, there are important limitations of note. While we adjusted for many important confounders, we did not have data for diet and other environmental exposures, both chemical and nonchemical, that may covary with air pollution. It is plausible that other environmental exposures, such as a history of maternal trauma, may have been unevenly distributed between study groups. Diet affects mitochondrial function  and dietIn conclusion, we have demonstrated that increased prenatal CO exposure is associated with reductions in CBMC mtDNAcn in male infants. Among all infants, but especially males, a prenatally-delivered LPG cookstove intervention to reduce HAP exposure attenuated these effects. Finally, CBMC mtDNAcn was associated with improved birth outcomes. Future studies are needed to confirm these findings and determine associations between CBMC mtDNAcn in disease processes across the lifecourse.


Melatonin stimulates the production of progesterone, which is essential for the maintenance of pregnancy. Since melatonin in blood is reduced due to work under illuminated conditions during night work, it has been hypothesized that night work may increase the risk of preterm birth. Previous meta-analyses have not revealed increased risk of preterm birth in women working night shifts during pregnancy. Still, these studies might have been limited by inaccurate self-reports of timing, intensity and duration of night work most likely causing bias towards the null. The aim of this is study was to investigate if the frequency and duration of night work during the first (week 1–12) and second (week 13–22) trimester of pregnancy were associated with risk of preterm birth when objective and prospective data on night work are used.In a register-based prospective cohort study, we obtained individual day-to-day information on working hours from The Danish Working Hour Database  and information on preterm birth from the Danish Medical Birth Registry. Night-shift was defined as at least three working hours between 23:00 and 06:00. Preterm birth was defined as giving birth during gestational weeks 23–37. Odds of preterm birth according to working night shifts were analysed by logistic regression.We identified 16,501 pregnant women eligible for the study, of which 10,202 women (61.8%) had at least one night-shift during the first 22 gestational weeks. The risk of preterm birth was not elevated among women working night shifts compared to women working only day shifts during either the first or second trimester. Within night-shift workers, the risk was not related to the number of night shifts, the duration of night shifts, consecutive night shifts or quick returns defined as short intervals between shifts. Odds of preterm birth was not related to change of working schedule from the first to second trimester, although women changing from night shifts in the first trimester to day work only in the second trimester displayed a weak increased odds of preterm birth  compared to women working night shifts in both trimesters.Our results, which are without bias from self-report of either exposure or outcome, are in line with the results of previous meta-analyses. Due to the detailed information on hours worked during pregnancy, we were able to investigate several dimensions of night work not previously investigated, of which none were associated with elevated risk of preterm birth. Working hours outside what is regarded as regular working hours, including night work, are highly prevalent in both industrialized and post-industrialized societies , particuDuring work at night the circadian rhythm is disturbed due to the exposure to light. The circadian rhythm aligns physiological and behavioural rhythms like sleep, appetite, hormone levels, blood pressure, temperature and alertness to the 24-hours day-night cycle. Repeated changes of the 24-hour maternal circadian rhythm may influence fetal development  by interMeta-analyses of the association between night work and preterm birth have not shown an increased risk for preterm birth, e.g. in the meta-analysis by van Melick et al 2013) 11 studies of shift or night work during pregnancy and risk of preterm birth were pooled and yielded a odds ratio of 1.04 (95% CI 0.90–1.20)  11 studi,15. HoweThe potential healthy worker effect presents another important methodological limitation . If emplThe aim of this study was to examine whether the number of night shifts, the duration of night shifts, consecutive night shifts or quick returns defined as intervals between shifts during the first (week 1–12) and second (week 13–22) trimester of pregnancy were associated with an increased risk of preterm birth. Further, to examine whether associations were modified by change of work schedule during pregnancy. This was done using objectively and prospectively collected data on daily working hours and preterm birth.We used the Danish Working Hour Database (DWHD), which is a national database created from administrative payroll data . DWHD inWe identified all pregnant women from the DWHD who gave birth at least once between January 2007 and December 2013 . We excluded women ≤18 or ≥50 years of age (n = 15), multiple pregnancies , pregnancies conceived in 2006 , women without registrations of night or day work during the first 22 weeks of gestation , as well as women working both day, early morning and evening shifts without night shifts  . The perData on exposure was retrieved from the information on individual daily worked shifts including on-call night work in DWHD. For each shift a participant worked, a precise time for the beginning and the end of the shift was given. Shifts lasting at least three hours were defined as day (start time after 06:00 and end time before 21:00), evening (end time after 21:00 and before 02:00), night (any start and end time including working hours between 23:00 and 06:00) and early morning (start time between 03:00 and 06:00) . Night wWe investigated whether change of work schedule from the first (week 1–12) to the second (week 12–22) trimester, was associated with risk of preterm birth compared to working schedules including at least one night shift in both time periods.Concerning the effect of the different dimensions of night work we used the exposure variables as described in our previous study, on night work and hypertensive disorders of pregnancy . We grouThe observational unit was the first pregnancy in the study period, defined as a child birth reported in the Danish Medical Birth Registry . The stu2), parity  and smoking  registered by the midwife or family doctor at the first antenatal visit were retrieved from the Danish Medical Birth Registry [Age (continuous), body mass index (BMI)  with 95% confidence intervals (CI) for preterm birth according to the different dimensions of night work  during gestational weeks 1–12 (first trimester) and 13–22 (second trimester). Analyses were first performed crudely and subsequently adjusted for age, BMI, parity, smoking, SES and sickness absence during the three months prior pregnancy. Analyses investigating duration of night shifts, consecutive night shifts and quick returns were in addition adjusted for the number of night shifts. We performed comparisons of night workers with day workers, and comparisons within night workers, where the night workers with the lowest exposure category served as reference group.Additionally, we investigated preterm birth according to changes of work schedule from the first to the second trimester using logistic regression, with women working night shifts in both trimesters as the reference, and in a second analysis with women working only days in both trimesters as the reference group.In sensitivity analyses, the main analyses were repeated for first time pregnant women, to take into account that later pregnancies may be conditional on the outcome of previous pregnancies.The statistical analyses were conducted on a secure platform at Statistics Denmark using SAS software, version 5.1 for Windows . We used a two-sided p-value with a level of significance of 0.05.Through the notification system in the Capital Region of Denmark, the study was approved by the Danish Data Protection Agency (j.nr.: 2012-58-0004). By Danish law, no informed consent is required for a register-based study.The study population consisted of 16,501 pregnant women, of which 10,202 women (61.8%) worked at least one night shift during the first 22 gestational weeks . CompareAs presented in The odds of preterm birth was lower during the second trimester in women working night shifts of ≥8 hours compared to women working day shifts only  yielded similar results as the main analyses .In this study, the potential association of night work with risk of preterm birth was investigated with the use of objective and precise daily exposure information on working time. Based on these data we were also able to investigate selection in and out of night work during pregnancy. We found no associations of the number of night shifts, consecutive night shifts or quick returns with preterm birth, after adjustment for covariates. However, women working at least one night shift lasting >8 hours during the second trimester had lower odds of preterm birth compared to day workers.A reason for our null-findings could be primary selection, i.e. ‘the Healthy Hire Effect’ which proposes that healthier employees are more likely to be selected or self-selected into night work before becoming pregnant ,19, whicThe odds of preterm birth was not related to change of work schedule from the first to the second trimester, i.e. secondary selection which is selection during pregnancy, although data indicated a potential, non-significant increased odds of preterm birth among women changing from night work in the first trimester to day work only in the second trimester compared to women working night shifts in both trimesters. We made two analyses, one with the reference group being women working night shifts in both trimesters and one with a reference group of women working days only in both trimesters with similar results. The argument for using night shift workers as the reference group was that these women might be ‘healthier’, according to the healthy worker survivor effect . In the Our study had some limitations. We did not have information on sleeping conditions during on-call night shifts, work load or lighting conditions, nor did we investigate different occupational groups or have the power to specifically investigate permanent night work. However, a prospective study of 40,237 pregnant Danish women within different occupational groups, where 400 had permanent night work, did not find an increased risk of preterm delivery  . A smallStrengths of the study include detailed register information on daily, individual working hours. This enabled the examination of risk according to different dimensions of night work within each gestational trimester as well as the effect of cumulative night work. As the gestational age at delivery was registered in the Danish Medical Birth Registry by the midwives assisting the respective birth, the preterm birth variable was not collected retrospectively or self-reported, hence precluding recall-bias. Even though the study was entirely register-based with limited personal data on health behaviour and lifestyle, though we did include smoking and BMI, we are not aware of important determinants of preterm birth that we were not able to address. Finally, our study was large enough to investigate first time pregnancies.In conclusion, our results of no increased risk of preterm birth among women working night shifts, are in line with the results of existing meta-analyses, although these might have been biased by self-report and were not able to address selection ,30. Due S1 Fig(TIF)Click here for additional data file.S1 TableAdjusted analysesa. a Adjusted for parity, age of mother, pre-pregnancy BMI, smoking during pregnancy and sick leave 3 months before during pregnancy.(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.


Unexplained deaths in infancy comprise ‘sudden infant death syndrome’ (SIDS) and deaths without ascertained cause. They are typically sleep-related, perhaps triggered by unsafe sleep environments. Preterm birth may increase risk, and varies with ethnicity. We aimed to compare ethnic-specific rates of unexplained infant death, explore sociodemographic explanations for ethnic variation, and examine the role of preterm birth.2 for ethnic variation.We analysed routine data for 4.6 million live singleton births in England and Wales 2006–2012, including seven non-White ethnic groups ranging in size from 29 313 (Mixed Black-African-White) to 180 265 (Pakistani). We calculated rates, birth-year-adjusted ORs, and effects of further adjustments on the χ2(10 df)=113.6, p<0.0005). Combined adjustment for parents’ marital/registration status and mother’s country of birth (UK/non-UK) attenuated the ethnic variation. Adjustments for gestational age at birth, maternal age and area deprivation made little difference.There were 1559 unexplained infant deaths. Crude rates per 1000 live singleton births were as follows: 0.1–0.2 for Indian, Bangladeshi, Pakistani, White Non-British, Black African; 0.4 for White British; 0.6–0.7 for Mixed Black-African-White, Mixed Black-Caribbean-White, Black Caribbean. Birth-year-adjusted ORs relative to White British ranged from 0.38 (95% CI 0.24 to 0.60) for Indian babies to 1.73 (1.21 to 2.47) for Black Caribbean (χSubstantial ethnic disparity in risk of unexplained infant death exists in England and Wales. Apparently not attributable to preterm birth or area deprivation, this may reflect cultural differences in infant care. Further research into infant-care practices in low-risk ethnic groups might enable more effective prevention of such deaths in the general population. In 2015, about 7% of approximately 2500 annual deaths under the age of 1 year in England and Wales were ‘unexplained deaths in infancy’ (UDI),7Striking ethnic disparities in rates of SUID or SUDI are reported in the USA6We aimed to compare ethnic-specific national rates of UDI during 2006–2012, explore the extent to which sociodemographic factors might account for any ethnic disparities, and test the prior hypothesis that preterm birth might contribute to the relationship between ethnicity and risk of UDI.We conducted a national birth cohort study, using routinely-collected administrative data. The study hypothesis arose before inspection of the data. The cohort consisted of all singletons born alive at 22+weeks’ gestation in England and Wales from 1 January 2006 to 31 December 2012. The Office for National Statistics provided an anonymised data extract linking birth and death registrations with the NHS Numbers for Babies dataset.Following methodology used by the Office for National Statistics,Ethnicity coding in the NHS Numbers for Babies dataset is compatible with the categories used in the 2001 Census.Covariates were categorised as follows: gestational age at birth ; baby’s sex ; mother’s country of birth ; mother’s age at delivery ; parents’ marital/registration status based on the registration of the baby’s birth 162 for ethnic variation, based on the likelihood ratio test statistic. To avoid potential problems with sparse data, we did not include interaction terms. All analyses were done in Stata V.13, using two-sided tests with a 5% significance level.We calculated crude rates of UDI per 1000 live singleton births by ethnic group over the full study period 2006–2012. We estimated risk for each ethnic group relative to White British by using a logistic regression model to calculate ORs with adjustment for potential confounding by birth year, which was fitted as a linear trend. To assess the extent to which other factors in our dataset might account for the ethnic variation in risk, we fitted exploratory models that were adjusted for these covariates first individually and then cumulatively, choosing each additional factor to minimise the χWe treated quintile categories of area deprivation for England and for Wales as equivalent in the main analysis; to check sensitivity to this simplification, the analysis was repeated using a 10-level factor representing each combination of England/Wales and the five quintiles. We did not include birth weight in the main analysis because it is very closely associated with gestation; for completeness, the analysis was repeated replacing gestation with birth weight, expressed either as categories  or as ‘small for gestational age’ (10% cut-off within sex–gestation strata of the dataset). To check for sparse-data bias, which can occur in maximum likelihood estimation where there are small numbers of events in some categories,18Representatives of organisations concerned with infant health and/or work related to ethnic minority groups were consulted at the design and interpretation phases of the study and will be involved in dissemination of the results.The source file contained data on 4 744 666 babies. We successively excluded 16 695 for implausible gestation, 20 999 for missing birth weight, and 72 040 for implausible birth weight, leaving 4 634 932. Of the 15 001 infant deaths in the reduced dataset, 75 deaths had no ICD-10 cause codes. For these deaths, we used the ‘underlying cause’ if supplied .10.1136/jech-2018-210453.supp1Supplementary file 1There were 1559 UDIs, with highest frequency in the second month after birth . The pro2(10 df)=113.6, p<0.0005), with ORs relative to White British ranging from 0.38 (95% CI 0.24 to 0.60) for Indian babies to 1.73 (95% CI 1.21 to 2.47) for Black Caribbean  and greatest after adjustment for deprivation  ; with these two adjustments none of the ethnic groups was significantly different from White British  . When th British . Further2 for ethnic variation. Modelling the deprivation scores for England and Wales separately, or using penalised rather than maximum likelihood methods, made little difference.Sensitivity analyses did not materially affect the findings. Adjustments for birth weight or ‘small for gestational age’ were less effective than adjustment for gestation in reducing the χThere is nearly fivefold variation in risk of UDI between ethnic groups in England and Wales. This variation does not appear to be explained by preterm birth, baby’s sex, maternal age, or area deprivation. The risk is lowest for Indian, Bangladeshi, Pakistani, White Non-British and Black African babies; intermediate for White British; and highest for Mixed Black-African-White, Mixed Black-Caribbean-White and Black Caribbean. Parents’ marital/registration status  and mother’s country of birth (non-UK/UK) seem to jointly account for much of the ethnic disparity in risk, perhaps representing cultural variation in causal factors.This is the first study to quantify risk of UDI in England and Wales by ethnicity. Based on a recent 7-year national birth cohort, the study is large enough to yield precise estimates for relatively homogeneous minority ethnic groups. Some of the findings may be generalisable to similar groups resident in other countries. Several potentially important covariates are examined. Studies based on routine data are particularly valuable for this research area, because much of the existing evidence for risk factors derives from interview-based case–control studies, with potential for participation and reporting bias. The study is limited by a lack of information for individual families on infant sleep practices and other lifestyle factors, although prior national surveys provide some relevant data at the subpopulation level, as discussed below. The proportion of births with unstated ethnicity is 6% overall, decreasing with time, but the characteristics of such births have been shown to resemble those of White British,To our knowledge, there are only three comparable UK studies.8In the UK from the mid-1950s to the late 1980s, professional advice to use the front or side sleeping position influenced a whole generation of mothers, and was almost certainly a real cause of SIDS.27Protective infant-care traditions may tend to persist in ethnic groups with high proportions of mothers born outside the UK. In 1991, shortly before the start of the national ‘Back to Sleep’ campaign, a survey of multiparous mothers attending an antenatal clinic in Birmingham found that 34% of 172 South Asian mothers, or 47% of those who had lived in the UK for less than 5 years, reported placing their babies on their backs for sleep, compared with 6% of 202 White mothers.7 33 34Although maternal smoking and not breast feeding are associated with increased risk of sleep-related infant death in the general population,Safe-sleep campaigns in the UK should target the ethnic groups that experience higher rates of UDI. Further research into ethnic-specific infant care practices is required, and potentially protective customs in South Asian families should be evaluated. Implications for the aetiology of UDI should be considered.Striking ethnic disparities in risks of ‘sudden unexpected infant death’ (SUID) or ‘sudden unexpected death in infancy’ (SUDI) exist in the USA and New Zealand.Comparable UK data have recently become available, making it possible for the first time to investigate national variation in risk among the ethnic groups resident in England and Wales.There is nearly fivefold variation between ethnic groups in England and Wales in risk of ‘sudden infant death syndrome’ (SIDS) and other unexplained death in infancy.The variation does not seem to be attributable to preterm birth, maternal age or area deprivation, and may reflect cultural differences in infant care.Further research into infant-care practices in low-risk ethnic groups might enable more effective prevention of these deaths in the general population.


However, at current levels, NOX and particle emissions are still mitigated at lower costs by state-of-the-art after-treatment systems than through the electrification of powertrains. Overall, the observation of robust technological learning suggests policy makers should focus their support on non-cost market barriers for the electrification of road transport, addressing specifically the availability of recharging infrastructure.This article presents experience curves and cost-benefit analyses for electric and plug-in hybrid cars sold in Germany. We find that between 2010 and 2016, the prices and price differentials relative to conventional cars declined at learning rates of 23 ± 2% and 32 ± 2% for electric cars and 6 ± 1% and 37 ± 2% for plug-in hybrids. If trends persist, price beak-even with conventional cars may be reached after another 7 ± 1 million electric cars and 5 ± 1 million plug-in hybrids are produced. The user costs of electric and plug-in hybrid cars relative to their conventional counterparts are declining annually by 14% and 26%. Also the costs for mitigating CO •Since 2010, the price of electric and plug-in hybrid cars has been decreasing by 63% and 24%.•Electric and plug-in hybrid cars show learning rates of 23 ± 2% and 6 ± 1%.•Additional production of 7 ± 1 million electric cars and 5 ± 1 million plug-in hybrids before price break-even with conventional cars.•Non-cost market barriers for electric vehicles such as recharging infrastructure demand more attention. However2) and air pollutant emissions through electrified vehicles may follow alongside. If so, electric and plug-in hybrid cars are not just becoming financially more attractive to consumers but also economically more efficient in mitigating the negative impacts of road transport  and series plug-in hybrids that are propelled by the electric motor(s) only. Our choice ensures a sufficiently large vehicle sample for the years 2011 and 2012 when only few plug-in hybrid car models were offered on the market. The terms ‘conventional car’ and ‘conventional vehicle (CV)’ are used for passenger cars propelled exclusively by an internal combustion engine that draws its energy from gasoline or diesel.Throughout this paper, we use the terms ‘electric car’, ‘fully electric car’, and ‘battery-electric vehicle (BEV)’ for passenger cars that are propelled by one or multiple electric motors and draw their propulsion energy solely from an electric battery. The terms ‘plug-in hybrids’, ‘plug-in hybrid car’, and ‘plug-in hybrid vehicle (PHEV)’ are used for passenger cars that: (i) are equipped with an internal combustion engine (ICE) and one or multiple electric motors, (ii) draw their propulsion energy from combustible fuels and/or electricity, and (iii) can be charged from an external electricity source. No distinction is made between 2.2We start by identifying through an extended web search all mass-produced electric and plug-in hybrid car models sold in Germany between 2010 and 2016, covering the period from their introduction into the mass-vehicle market to the point of writing. Electric cars whose traction battery is offered through a separate lease contract are excluded as these cars are cheaper than those sold with a traction battery  per vehicle .2 emissions [gCO2/km], and the certified emissions standard as published by car manufacturers or third-parties in print or online  and particle number (PN) emissions as both pollutants cause major concerns for public health  the rated power [kW] and (ii) the battery capacity [kWh] in the case of electric cars.The deflated and tax corrected real price Pit [EUR2015/kW] between each electric and plug-in hybrid car model i in year t and its conventional counterpart was calculated as:PEit represents the specific price of the electric and plug-in hybrid car model [EUR2015/kW] and PCit represents the specific price of the comparable conventional car model [EUR2015/kW]. The real specific prices and price differentials were used in the first part of our analysis to explore price trends and establish experience curves.In a final step, the price differential Δ2.3.2® by plotting the yearly mean price  and price differential [EUR2015/kW], denoted here as Pt(xt), for electric and plug-in hybrid cars as a function of cumulative vehicle production. Plotting the mean values instead of the individual prices and price differentials of all electric and plug-in hybrid cars ensures each year receives the same weight in the experience curve analysis. Then, a non-linear regression analysis was conducted by fitting the following power-law function to the data:P0(x0) represents the mean price or price differential of electric and plug-in hybrid cars in the base years 2010 (electric cars) and 2011 (plug-in hybrids); x0 and xt represent the cumulative production in the base year and in year t of the analysis; b represents the experience index, depicting the rate at which prices and price differentials of electric and plug-in hybrid cars decline. Depicting the resulting experience curve on a double-logarithmic scale yields a linear regression line with slope b. From this slope, the learning rate LR [%] was deduced as the rate at which prices and price differentials decline with each doubling of cumulative vehicle production:Experience curves were established with SigmaPlotb obtained from Equation xBE of electric and plug-in hybrid cars that is necessary to achieve a price break-even with conventional cars:x2016 represents the cumulative production in year 2016, PBE the break-even price equal to the average price [EUR2015/kW] of conventional cars in 2016, and P2016 the average price of electric and plug-in hybrid cars, respectively in 2016. The error interval of the marginal cumulative production was estimated from the standard error of the experience index b.The standard error of the slope parameter 2.3.3Ci,t [EUR2015/km] of each electric, plug-in hybrid, and conventional car model i sold in year t were calculated as:Pit represents the real absolute vehicle price [EUR2015], CMi the yearly maintenance costs [EUR2015] comprising vehicle maintenance, registration, and insurance, Mi the yearly driving distance [km], Fi the distance-specific electricity or fuel consumption  under real-world conditions, CF the price of electricity or fuel , and Li the lifetime [a] of each respective model i. For each year, the mean and standard deviation of user costs for all car models were calculated.In the second part of the analysis, user costs 2.3.42 and air pollutant emissions CEit  of each electric and plug-in hybrid car model i sold in year t were calculated as:Cit(BEV-PHEV) represents the user costs of each electric and plug-in hybrid car model [EUR2015/km], respectively, Cit(CV) stands for the user costs of the equivalent conventional car model [EUR2015/km], Eit(BEV-PHEV) represents the distance-specific emissions of each electric and plug-in hybrid car model, and Eit(CV) represents the distance-specific emissions of each conventional car model, respectively. The so-calculated costs CEit represent the marginal costs of mitigating CO2 and air pollutant emissions below the emission levels of conventional cars. Costs can assume extremely large positive or negative values depending on the differences in user costs and emissions between electric and plug-in hybrid cars on one hand and their conventional counterparts on the other hand. Therefore, the calculated costs CEit have to be interpreted with caution and after careful inspection of the underlying data. To avoid that outliers bias the cost estimates, we chose the median and half of the interquartile range to represent the general trend and variability in the costs for mitigating emissions through electric and plug-in hybrid cars.In the third part of the analysis, the costs for mitigating CO2 emissions were calculated for four scenarios that consider: (i) the distance-specific tailpipe CO2 emissions as certified during type approval, (ii) the distance-specific tailpipe CO2 emissions under real-word driving conditions based on 2 emissions along the entire well-to-wheel (WTW) electricity and fuel supply chain  the distance specific tailpipe NOX and particle number emission under real-world driving conditions and (ii) in the case of NOX additionally the emissions from electricity generation, accounting thereby for the indirect NOX emissions caused by electric and plug-in hybrid cars  the learning rates for electric cars and plug-in hybrids apply in the future and (ii) the prices of conventional cars remain as in 2016, an additional 7 ± 1 million electric cars and 5 ± 1 plug-in hybrids have to be produced before reaching price break-even with conventional cars. These numbers are remarkably low and account for less than 10% of the annual global production of passenger cars .3.22015/km, 1.06 ± 0.41 EUR2015/km, and 0.71 ± 0.44 EUR2015/km, respectively. The latter number represents the user costs of all conventional cars contained in our analysis.User costs do not follow the trend of vehicle prices but tend to remain constant (electric cars) or increase  between 2010 and 2016 a. This o2015/km, 0.75 ± 0.27 EUR2015/km, and 0.52 ± 0.29 EUR2015/km in 2016 when considering an extended vehicle life time of 11 years and 150,000 km  instead of 6 years cuts the CO2 mitigation costs by a similar margin. For example, the costs of mitigating real-world CO2 tailpipe emissions by electric vehicles decrease from 703 ± 219 EUR2015/t CO2 to 292 ± 203 EUR2015/t CO2 under the assumption of an 11 years vehicle lifetime  and 48% (to 3.0 × 105 EUR/t NOX) between 2010 and 2016 for mitigating the tailpipe NOX emissions of gasoline and diesel vehicles, respectively. The costs roughly halve to 6.8 × 105 EUR/t NOX and 1.6 × 105 EUR/t NOX when assuming an extended vehicle lifetime of 11 years  and plug-in hybrids  emit on average more NOX than their conventional counterparts  when considering gasoline cars and 58% (from 3.5 × 105 EUR/1017 particles to 1.5 × 105 EUR/1017 particles) when considering diesel cars, respectively. The higher costs of electric cars to mitigate particle emissions of diesel cars compared to those of gasoline cars stem from the high emissions factor for gasoline cars without particulate filters  and price differential of electric cars (32 ± 2%) exceed: (i) the those identified by The high learning rates for electric cars suggest rapid technological learning in the manufacturing of traction batteries, which constitute the largest individual cost component of an electric powertrain . Togetheper se prohibit the market penetration of status revealing commodities such as passenger cars. We think the deployment of electric and plug-in hybrid cars could benefit greatly from branding, marketing, and clever product positioning that exploits status competition and social frames of consumers  captures the average situation in Germany as of 2013 and includes own consumption of power plants and well-to-plug losses in the electricity system  and 1400 ± 1600 EUR/t CO2 (plug-in hybrids). The median costs for electric cars level at 1700 ± 1000 EUR/t CO2 if indirect emissions of electricity generation are accounted for; these costs could decrease to 680 ± 220 EUR/t CO2 if electricity was generated by renewables. The cost levels in all scenarios could decrease by more than 50% when assuming a vehicle lifetime of eleven years instead of six years  high when assuming a 6-year vehicle lifetime and (ii) comparable when assuming an 11-year lifetime with renewable energies like wind and photovoltaics that can to date already save CO2 emissions at no additional costs compared to fossil energy resources  after-treatment technologies of conventional cars  drive range, recharging times, and availability of recharging infrastructure, (ii) warranty, maintenance, and take-back plans, (iii) branding, marketing, and product positioning that capitalizes on the status competition and social frames of consumers.•2 and air pollutant emissions by electric and plug-in hybrid cars scatter over wide ranges and are specific to the set of assumptions applied here.The costs for mitigating CO•2 and pollutant emissions, even when considering the indirect emissions from electricity generation and battery production. The CO2 mitigation costs will likely continue to decrease in the future through technological learning and a growing share of renewables in the electricity mix.Electric cars can mitigate CO•X and particle emissions by electric and plug-in hybrid cars decline but are comparatively high. At current levels, NOX and particle emissions are mitigated less costly through state-of-the-art after-treatment systems than through the electrification of powertrains.The costs for mitigating NOWe draw the following conclusions:None.


Poor pregnancy outcomes are associated with exposure to air pollution. Therefore, this study aims to investigate the association between air pollutions (PMn = 687) or low birth weight information (n = 1097) were used as experimental group. Control group included 1766 women with healthy birth information. Logistic regression models were employed to evaluate the effects of air pollutants on the risk of preterm birth and low birth weight.All maternal data and birth data from January 1, 2014 to December 31, 2015 were selected from a National Free Pre-pregnancy Check-ups system, and the daily air quality data of Guangdong Province was collected from China National Environmental Monitoring Center. 1784 women with either preterm birth information  and October (84.18 μg/m3) were relatively higher. After controlling for the impact of confounding factors, the increases in the risk of preterm birth were associated with each 10 μg/m3 increase in PM2.5  and PM10  during the first trimester and in PM2.5 , PM10 , SO2 , and O3  during the third trimester. The increase in the risk of low birth weight was associated with PM2.5, PM10, NO2, and O3 in the first month and the last month.The pollution levels of PMThis study provides further evidence for the relationships between air pollutions and preterm birth/low birth weight. Pregnant women are recommended to reduce or avoid exposure to air pollutions during pregnancy, especially in the early and late stages of pregnancy. More ide (CO) . Xu et ale (TSP) . The risle (TSP) . Anotherrimester  and exporegnancy  had a strm birth . Althougrm birth .3) are included in the “National Ambient Air Quality Standards” implemented by the Chinese Ministry of Environmental Protection in 2016, and the concentration limits of PM and NO2 are also adjusted. Data from the National Monitoring Center shown that the annual average concentrations of PM10, PM2.5, and SO2 have decreased, whereas the pollution levels of NO2 and O3 have increased. [3 is increasing year by year, and O3 pollution has gradually replaced PM2.5 as the primary air pollutant in major Chinese cities [3 is a risk factor for preterm birth [3 and low birth weight.In China, the air quality in urban and rural areas has deteriorated in recent years. The Chinese government has paid great attention to the environmental protection issues, such as the average concentration limits of PM and ozone  and preterm birth/low birth weight in Guangdong province, a main province located in the southern China. The expected results are that there are significant correlations between the incidence of preterm birth/low birth weight and maternal exposure to air pollutions, respectively.Therefore, the objective of this study is to respectively investigate the relationships between atmospheric pollutants  or low birth weight information (n = 1097), and 84,095 had healthy birth information excluding the data of duplicate records. The control group was selected from the 84,095 reproductive-aged women with healthy birth information using the simple random sampling method. To match the number of women in the experimental group, 2.1% of the data with healthy birth information were randomly selected as the control group (N = 1766). The participants were from 20 to 49 years old with the average age of 28.45 ± 4.53 years. The majority of the participants belonged to Han ethnic group (96.19%). There were no significant differences in demographic variables between the experimental group and the control group (p > 0.05). The descriptive statistics regarding the participants were showed in Table All maternal data and birth data from January 1, 2014 to December 31, 2015 were selected from a National Free Pre-pregnancy Check-ups (NFPC) system. The NFPC has been supported by the National Health and Family Planning Commission of the People’s Republic of China since 2010, a population-based health survey for couples of reproductive-aged who wish to conceive. In this study, maternal demographic information , pregnancy outcomes (e.g. preterm birth weight and low birth weight) and infant information  were collected. A total of 86,139 reproductive-aged women with fetal information were selected. Of these women, 1784 had either preterm birth information  average concentration of O3 were collected. There must be at least 75% of the one-hour values on a particular day in order to calculate the 24-h average concentration of PM2.5, PM10, SO2, NO2, and CO. It is required to have at least six hourly values from 10 AM to 6 PM to calculate the 8-h average of O3 [On the other hand, the air pollution data was collected from China National Environmental Monitoring Center which provides daily concentrations of pollutants from 111 monitoring site stations in Guangdong province, including 102 National Ambient Air Quality Monitoring Sites in 21 prefecture-level cities and Shunde District, 8 regional stations and 1 superstation. There are typically multiple monitors located within a city, some of which provide integrated daily measurements. Therefore, city-specific exposure analysis can be used to reduce exposure misclassification. The routine detections of pollutants mainly include the detections of PMge of O3 .Excel 2010, SPSS 20.0, and some packages in R 3.5.1  were used for data analysis. The mean values of the concentrations of air pollutants measured at all monitors in each city were used as the daily air pollution levels. Logistic regression models were employed to evaluate the effects of air pollutants on the risks of preterm birth and low birth weight, which effectively controlled for the impact of other variables such as maternal age, education level, occupation, registered residence, gestational age, infant sex, childbirth time, month of conception and parity. According to the division of seasons by meteorological department, this study divided the whole year into four seasons: spring (from March to May), summer (from June to August), autumn (from September to November) and winter (from December to February).df) was selected by assessing the model fitting on the basis of the Akaike Information Criterion (AIC). If the relationships between air pollution and preterm birth/low birth weight were linear, then the odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) of preterm birth/low birth weight for a 10 μg/m3 increase in PM2.5, PM10, SO2, NO2, O3 and for a 100 μg/m3 increase in CO were calculated; Each air pollutant was added into the single-pollutant model separately. Otherwise, the ORs and the 95% CIs of preterm birth/low birth weight comparing the 75th and 95th percentiles of air pollution versus the minimum preterm birth/low birth weight concentration of air pollution (threshold) were computed. To determine the threshold of air pollutant, we plotted the relationships between air pollutants and preterm birth/low birth weight, and then visually examined the possible range of the threshold. The concentrations of air pollutants corresponding to the lowest AIC values were selected as the thresholds (minimum preterm birth/low birth weight concentrations of air pollutants) [p < 0.05).Natural cubic splines were employed for air pollutants in single-pollutant model to check whether the associations between air pollutants and preterm birth/low birth weight were linear or nonlinear. The degree of freedom (lutants) . In addi2.5, PM10, SO2, NO2, CO, and O3 were 36.45 μg/m3, 55.45 μg/m3, 14.90 μg/m3, 26.37 μg/m3, 1.02 mg/m3, and 56.40 μg/m3, respectively. According to the reference values of National Ambient Air Quality Standard (GB 3095–2012), the pollution levels of PM2.5, PM10, SO2, NO2, CO, and O3 in Guangdong province were all lower than the national air pollution concentrations.Table 2.5, PM10, SO2, NO2 and CO had obvious seasonal trends with the highest in winter and the lowest in summer. However, the concentration of O3 mainly concentrated in autumn, especially on October (84.18 μg/m3) and September (65.72 μg/m3).Table 2.5, PM10, NO2, CO, and O3 above the knot locations increased, the risks of preterm birth/low birth weight increased as a whole. As shown in Table 3 increment in the exposure to PM2.5  and PM10  during the first trimester, indicating 4.3 and 3.9% increased risk of preterm birth, respectively. In addition, significant associations were found for preterm birth with PM2.5 , PM10 , SO2 , and O3  during the third trimester. Moreover, the increase in the risks of low birth weight was associated with each 10 μg/m3 increment in NO2  during the second trimester and with each 100 μg/m3 increment in CO  during the first trimester. For the entire pregnancy, the odds ratios of preterm birth for a 10 μg/m3 increase in PM2.5 and PM10 were 1.007 (95% CI 1.01–1.08) and 1.038 (95% CI 1.01–1.07), respectively. And the odds ratios of low birth weight for a 10 μg/m3 increase in PM2.5 and PM10 were 1.028 (95% CI 1.00–1.06), 1.018 (95% CI 1.01–1.04) and for a 100 μg/m3 increase in CO was 1.340 (95% CI 1.04–1.73), respectively.Figures 2.5, PM10, NO2, CO and preterm birth in the second month were found, and the ORs were 1.038 (95% CI 1.01–1.07), 1.021 (95% CI 1.01–1.04), 1.043 (95% CI 1.01–1.08) and 1.069 (95% CI 1.001–1.14), respectively . The ORs of premature birth for a 10 μg/m3 increase in NO2 in the last month was 1.034 (95% CI 1.00–1.07). Additionally, for each 10 μg/m3 increase, the resulting ORs of low birth weight were 1.059 (95% CI 1.02–1.10) for PM2.5, 1.090 (95% CI 1.03–1.15) for PM10, 1.328 (95% CI 1.01–1.75) for SO2, 1.185 (95% CI 1.06–1.32) for NO2, and 1.108 (95% CI 1.03–1.19) for O3 in the first month; and for each 100 μg/m3 increase, the ORs was 1.117 (95% CI 1.05–1.19) for CO. And the associations between PM2.5, PM10, NO2, O3 and low birth weight in the last month were also found   and preterm birth/low birth weight. The results showed that after controlling for the impact of confounding factors, there were significant associations between preterm birth and PM2.5, PM10, SO2, NO2, and O3, especially during the first trimester and the third trimester, which were consistent with the previous studies [3 concentration during the early pregnancy [2.5 in the third trimester might increase the risk of preterm birth, especially in the half a month before delivery [10 also affected on preterm birth in the late pregnancy, especially in the seventh and ninth month of pregnancy [2 in the late pregnancy was statistically significant for Percutaneous Transluminal Dilatation (PTD) patients [2 during the third trimester increased the risk of preterm birth, and this relationship was statistically significant [This study investigated the association between air pollutions (PM studies –20. Olssregnancy . Cheng eregnancy . Additiopatients . Moreovenificant .2.5, PM10, SO2, NO2, O3, CO in the first month and with PM2.5, PM10, NO2, O3 in the last month. The effects of air pollution on low birth weight also were found from the previous studies [10 in the late pregnancy could predict the neonatal weight after controlling for baby gender, the pregnant women’s age, living area, ethnic, education, drugs and alcohol use. For every 10 μg/m3 increase in PM10 concentration of 24 h during the late trimester, the weight of newborn was reduced by 11 g [2 and PM10 concentrations during early pregnancy on low birth weight using the multiple regression models [2, CO, PM10 and PM2.5 during pregnancy increased, and birth weight decreased by 8.9 g, 16.2 g, 8.2 g and 14.7 g, respectively [3 increase in concentration, the OR value of the effect of exposure to SO2 at early pregnancy on low birth weight was 1.20, and the corresponding 95% CI was from 1.11 to 1.30 [On the other hand, the significant associations were found for low birth weight with PM studies . For exan models . Gouveian models . An inteectively . Additio to 1.30 .2.5, PM10, NO2, SO2, CO, O3 pollutions, and further confirmed the previous reports on the adverse effect of air pollution on preterm birth and low birth weight. These results suggested that pregnant women should reduce or avoid exposure to air pollutants during pregnancy, especially in the early and late stages of pregnancy.Therefore, the present study demonstrated that the early and late pregnancy might be the critical period of preterm birth and low birth weight caused by PMThis study has some limitations that merit future improvements. First, the number of monitoring sites in each city of Guangdong province is different and individual cities has only four or five monitoring sites, which might lead to incomplete monitoring data. Second, we assumed that the pollution levels were homogeneous for every resident in the present study. People in some areas inevitably expose to high pollution, while others are relatively low. Therefore, it is valuable to investigate the differences in adverse pregnancy outcomes between the areas with the highest levels and the lowest levels of air pollution in the future. Third, some other important factors associate with pregnancy outcomes are not considered in this study, such as social economic status, smoking, altitude, etc. Last, but not least, another line of research worth considering is to explore the interactions of various pollutants and other influencing factors, and the influence of two or more pollutants on preterm birth and low birth weight.3 increase in PM2.5, PM10 during the first trimester and in PM2.5, PM10, SO2, O3 during the third trimester. The increase in the risk of low birth weight is associated with PM2.5, PM10, NO2, and O3 in the first month and the last month. Additionally, the current study has found that the concentrations of O3 in September and October are relatively high, thus it is strongly recommended that pregnant women in Guangdong should avoid pregnancy during the two months with high O3 concentrations. Finally, public policies and guidelines for maternal health should be improved to protect women from the risks of preterm birth and low birth weight due to air pollution.This study provides further evidence for the relationships between air pollutions and preterm birth/low birth weight. The risks of preterm birth increase for each 10 μg/m