The reported positive association between opiatic drug use during pregnancy and adverse pregnancy outcomes might be confounded by other factors related to high-risk behaviors, including the use of other harmful substances. In rural areas of Iran, opium use during pregnancy is relatively common among women who otherwise do not have a hazardous lifestyle, which reduces the risk of residual confounding and increasing the possibility to identify its effects. We aimed to examine the association of antenatal exposure to opium with risks of small for gestational age, short birth length, and small head circumference at birth.In this cohort study in the rural area of the Golestan province, Iran, we randomly selected 920 women who were exposed to opium during pregnancy and 920 unexposed women during 2008–2010. Log-binomial regression was used to estimate risk ratios (RR) and 95% confidence intervals (CI) for the associations between prenatal exposure to opium and risks of small for gestational age, short birth length, and small head circumference at birth.Compared with non-use of opium and tobacco during pregnancy, using opium only and dual use of opium and tobacco were associated with increased risks of small for gestational age at births . Compared with non-use of opium and tobacco, exposure to only opium or dual use of opium and tobacco were also associated with more than doubled increased risks of short birth length, and small head circumference in term infants.Maternal opium use during pregnancy is associated with increased risks of giving birth to a small for gestational age infant, as well as a term infant with short birth length or small head circumference. There has been an epidemic of opioid abuse worldwide, including North America, UK, Australia, and Asian countries , 2. OpiuUse of opioids during pregnancy, including heroin and methadone, has been associated with adverse pregnancy outcomes, such as preterm delivery, low birth weight, SGA, and neonatal complications . HoweverIn Golestan province, northern Iran, some women use (mainly smoke) opium . In thisA detailed description of the study population and method appears elsewhere . BrieflyThe exposed group included women with a self-reported history of using any of a variety of opiates during the index pregnancy. The unexposed group included women who did not use opium before or during pregnancy. We used the stratified random sampling method to select exposed and unexposed groups. Each of the 13 regions of the province was determined as a block, and the sample size of each block was estimated based on the population growth rate of the region. There were 30, 868 pregnancies during the study period. Exposed pregnancies were randomly selected, using computer-created random digits and the same number of women unexposed to opium during pregnancy were frequency-matched to the exposed women by the residential area (village). Information on pregnancies was abstracted from medical records and computerized. As a quality control, some 10% of the data was collected a second time. We recollected the data if there were more than 5% mismatches.We selected a total of 920 opium users during pregnancy (“exposed” women) and 920 non-opium users (“unexposed” women). Thereafter, we excluded women with multiple births, miscarriages, and stillbirths, also women who gave up using opium in early pregnancy, used alcohol during pregnancy, and tobacco smokers among the unexposed group (15 unexposed and 33 exposed mothers). Finally, our study included 887 opium users  and 905 non-opium users with live singleton births.Information on socioeconomic circumstances was based on the husband’s profession . Agricultural production is the main economic sector in Golestan province. Gestational age was based on the time interval between the date of the first day of the last menstrual period and the date of delivery. Preterm delivery was defined as a delivery before 37 completed gestational weeks. Small for gestational age (SGA) was defined as a birth weight below the 10th percentile for gestational age, based on a sex-specific global reference for birth weight . The 5thWe used univariate and multivariate log-binomial regression models to estimate risk ratios (RRs) and 95% confidence intervals (CIs) for the associations between opium use during pregnancy and SGA, short birth length, and small head circumference. The models were adjusted for potential confounding factors, including the region of residence, maternal age, parity, height, body mass index, husband’s occupation, and infant sex. Due to scarce data on preterm infants, we could not construct gestational-age specific curves for birth length and head circumference. We restricted the analyses of birth length and head circumference to term births (≥ 37 weeks), where we additionally also adjusted for gestational age (in weeks). Stratified analyses were performed to investigate the risks of SGA, short birth length, and small head circumference among opium-using mothers who smoked or did not smoke tobacco during pregnancy.We used multiple imputation method with five imputations approach was used to provide data where there were missing values for maternal age (6 among mothers unexposed to opium and 5 among exposed mothers), infant sex (15 among mothers unexposed to opium and 10 among exposed mothers), husband’s occupation (4 among unexposed and 9 among exposed), and mother’s height (39 among unexposed and 17 among exposed). SAS software version 9.4 was used for all analyses.Table Compared with non-use of opium and tobacco, use of opium during pregnancy was associated with a 69% increased risk of birth of an SGA infant after adjusting for potential confounders. The risk of SGA was slightly higher in offspring of mothers who only used opium  than in offspring of mothers who both used opium and smoked tobacco during pregnancy . Meanwhile, when we restricted the analysis into the term pregnancies, the risk of SGA was almost identical in offspring of mothers who only used opium , and offspring of dual user mothers  . The risk of short birth length was essentially similar between those exposed to only opium  and those exposed to both opium and tobacco during pregnancy  (Table Compared with prenatally unexposed term infants, term infants who were prenatally exposed to opium had a 2.2-fold risk of the small head circumference at birth. The risk of a small head circumference was similar in infants prenatally exposed to only opium and in infants exposed to both opium and tobacco Table .Table 4RThis cohort study revealed an increased risk of SGA at birth among offspring of mothers who used opium during pregnancy. This study showed that prenatal opium exposure also increased the risks of a short birth length and a small head circumference in infants born at term.Our findings are consistent and extend results of previous studies showing that infants of mothers using heroin, methadone, or other opiates during pregnancy intend to have a higher risk of low birth weight , 15. MetOur study demonstrated that term infants who were exposed to opium had shorter birth length after adjusting for gestational age, sex and other potential confounders. An increased risk of a short birth length was also observed in studies investigating maternal heroin or methadone use during pregnancy . Liu et In this study, we also found that opium exposure during pregnancy is associated with a reduced head circumference, which is consistent with results of previous studies on maternal heroin or methadone use during pregnancy . Hunt etSeveral explanations have been proposed for the associations of opiates exposures during pregnancy with risks of fetal brain development and growth restrictions. Using opiate during pregnancy is associated with risks of preeclampsia, premature labor, premature rupture of membranes, placental abruption, placental insufficiency and intrauterine death . MaternaOpium has traditionally been used in southern Asia and middle-eastern countries such as Iran as a sedative and for pleasure. It has been shown that opium users have less psychiatric comorbidity, unemployment, homelessness, and criminal behavior compared with users of other narcotic drugs , 28. In Strengths of this study include the cohort design, using prospectively collected information from antenatal visits before and during pregnancy. The population was relatively homogeneous, and opium-exposed mothers had otherwise had a normal lifestyle and an acceptable level of care during pregnancy . We exclMaternal opium use during pregnancy is associated with giving birth to small for gestational age infants, and with short birth length and small head circumference in term infants. It is unlikely that the observed associations are confounded by other factors related to high-risk behavior, such as the use of other harmful substances. Preventative intervention studies should be conducted in regions where opium use is common among young women.


Posttraumatic pseudoaneurysms of the lower limb are increasingly recognized due to the development of cross-sectional imaging. Two cases of anterior tibial artery pseudoaneurysm after blunt trauma are presented. The diagnostic technique of choice is Doppler ultrasound (US). In some cases, computed tomography angiography (CTA) or magnetic resonance angiography (MRA) is needed to identify the feeding vessel. The treatment of choice is not yet determined. Ultrasound-guided thrombin injection is widely used as first-line treatment, but some cases are refractory to this treatment. Further investigation and optimization of therapeutic technique to definitely exclude the pseudoaneurysm from the circulation may result in faster and more cost-effective treatment than US-guided thrombin injection. A pseudoaneurysm consists of a blood collection contained by the adventitia or the surrounding soft tissues caused by dissection of all the layers of the arterial wall due to puncture, trauma, or infection 2. We rep®, Baxter, Deerfield, Illinois) was injected within the pseudoaneurysm under US guidance. After three unsuccessful trials, the patient was referred to our tertiary hospital. A selective digital subtraction lower limb arteriography (DSA) confirmed the pseudoaneurysm of 4 mm in diameter. As discrimination between a bleeding from the main tibial artery or from one of its branches could not be made, an US-guided percutaneous puncture of the pseudoaneurysm was made with concomitant transarterial balloon occlusion of the anterior tibial artery. The correct position of the needle within the pseudoaneurysm was visually confirmed by iodinated contrast injection followed by 0.2 mL of ethylene vinyl alcohol copolymer dissolved in dimethylsulfoxide . After balloon deflation, angiography showed normal enhancement of the lower limb arteries without any extravasation. The patient has recovered his professional soccer activity.A 21-year-old professional soccer player complained of persistent pain after being tackled on his right leg. Physical examination showed tender swelling of the painful region. Plain radiographs showed no bone lesion. Ultrasound (US) examination revealed a 4 x 2 cm hematoma with an arterial pulsatile Doppler signal originating from an artery on the lateral aspect of the leg  into the pseudoaneurysm under US guidance, the arterial signal disappeared on power Doppler US. As ankle movements were still very limited, the residual hematoma was incised. Acute bleeding necessitated DSA of the lower limb that revealed extravasation of contrast material. After selective catheterization of the feeding vessel, one coil of 2 cm in length and 3 mm in diameter was deployed   was performed and revealed that the feeding vessel was a collateral branch of the anterior tibial artery. Whilst injecting 1 mL of thrombin (DstatThese two cases are important to report for several reasons. First, they confirm the suggestion by Siddique et al. of pseudoaneurysm as a cause of persistent pain after lower limb trauma . Second,Therapeutic approaches to pseudoaneurysm are multiple. If their size is smaller than 1 cm and the patient asymptomatic, no treatment is needed and US follow-up is sufficient . Otherwi56As suggested by our two cases, the movements of the ankle in the immediate neighborhood of the pseudoaneurysm can lead to failure of both compression therapy and US-guided thrombin injection. Coiling under DSA guidance was indeed needed as a final therapeutic option. Various endovascular techniques including coiling and stenting have similar outcomes but coiling is preferable in distal arteries for avoiding in-stent stenosis and bending 11. FinalDue to the uncertainty of identifying the feeding vessel by US, multiple imaging modalities were used in these two cases but a one-step diagnostic and therapeutic angiographic approach, avoiding CTA and MRA, could be advocate in order to save time and money. This should be investigated by larger studies.In conclusion, as a cause of persistent pain or movement limitation after lower limb trauma, pseudoaneurysm can be coiled under DSA guidance after identification of its feeding vessels during one unique procedure.


Neonatal hypothermia is a worldwide problem and an important contributing factor for Neonatal morbidity and mortality especially in developing countries. High prevalence of hypothermia has been reported from countries with the highest burden of Neonatal mortality. So the aim of this study was to assess the prevalence of Neonatal hypothermia and associated factors among newborn admitted to Neonatal Intensive Care Unit of Public Hospitals in Addis Ababa.p-value < 0.05 was used to identify variables which had a significant association.An institutional based cross-sectional study was conducted from March 30 to April 30, 2016, in Public Hospitals in Addis Ababa and based on admission rate a total of 356 Neonates with their mother paired were enrolled for the study. Axillary temperate of the newborn was measured by a digital thermometer at the point of admission. Multivariate binary logistic regression, with 95% confidence interval and a . Preterm delivery , age of Neonate ≤24 h old , no skin to skin contact with their mother immediately after delivery , delayed initiation of breastfeeding  and resuscitation at birth  were significantly associated with hypothermia.The prevalence of Neonatal hypothermia in the study area was 64%The prevalence of Neonatal hypothermia in the study area was high. Preterm delivery, age ≤ 24 h old, no skin to skin contact immediately after delivery, delayed initiation of breastfeeding and resuscitation at birth were independent predictors of Neonatal hypothermia. Therefore attention is needed for thermal care of preterm newborn and use of low-cost thermal protection principles of warm chain especially on early initiation of breastfeeding, skin to skin contact immediately after delivery and warm resuscitation.The online version of this article (10.1186/s12887-018-1238-0) contains supplementary material, which is available to authorized users. World Health Organization (WHO) defined Neonatal hypothermia as an axillary temperature less than 36.5 °c. Reduction of thermal stability has a long-term physiologic effect that leads to, death due to hypoxia, and hypotension . GloballHypothermia is one of the important causes for Neonatal death and morbidity in developing countries, which increases mortality by five times, and recent studies showed that every 1 °c decrement of body temperature increases mortality by 80% , 5, 6. TPrematurity is one of the risk factors for Neonatal hypothermia and it is the leading cause of Neonatal mortality which accounts 37% of Neonatal death in Ethiopia . And theIn Ethiopia lack of adequate perinatal care is one of the factors for onset of hypothermia, there is a high prevalence of home delivery which accounts 73% and Institutional deliveries accounts only 26% . Low socAlthough hypothermia is rarely a direct cause of death, it contributes to Neonatal mortality as a comorbidity of severe Neonatal infections, preterm birth, and asphyxia . MortaliEven though predisposing factors for hypothermia are easily preventable the problem of hypothermia remains an unanswered question and it is highly prevalent in developing nations including sub-Sahara Africa .Ethiopia applies thermal care principle which is one of the components of essential newborn care (ENBC) recommended by WHO. Despite this intervention, the problem of hypothermia remains a challenge in Ethiopia , 20. AndProviding ENBC including thermal care or prevention of Neonatal hypothermia is one part of nursing care, but the problem of Neonatal hypothermia remains a worldwide problem, especially in sub-Saharan Africa. Therefore, the purpose of this study was to determine the prevalence of Neonatal hypothermia and associated factors among Neonates admitted to NICU of Public Hospitals in Addis Ababa. So, this study will provide baseline data on the prevalence of Neonatal hypothermia and identification of possible factors for the onset of Neonatal hypothermia in the area will have greater input to program managers and policy makers for designing, proper implementation and evaluation programs on reduction of Neonatal mortality and improvement of newborn care to achieve SDG 3. In addition, the study will help to improve quality of newborn care in the nursing profession, specifically thermal protection, by low - tech preventive measures and early detection and referral of hypothermia.An institutional based cross -sectional study design was conducted from March 30 to April 30, 2016, to determine the prevalence of Neonatal hypothermia and associated factors among Neonates admitted to Neonatal Intensive Care Unit of Public Hospitals in Addis Ababa.The study was conducted in six Public Hospitals in Addis Ababa, Ethiopia, that have their own NICU; namely; Tikur Anbessa Specialized Teaching Hospital that has its own Neonatal Intensive Care Unit (NICU) with an average NICU admission of 240 Neonates per month, St. Paul’s Hospital Millennium Medical College with an average NICU admission of 210 Neonates per month, Yekatit 12 Hospital Medical College with an average NICU admission of 170 Neonates per month, Gandhi Memorial Specialized Hospital with an average NICU admission of 192 Neonates per month, Zewditu Memorial Hospital with an average NICU admission of 110 Neonates per month and Tirunesh Beijing General Hospital with an average NICU admission of 60 Neonates per month. The study was conducted in all Public Hospitals in Addis Ababa that has their own NICU, because the level of perinatal care given, standards of NICU, and accessibility of thermal prevention materials are somewhat different in each Hospital.The source populations were all Neonates who were admitted to NICU of public Hospitals in Addis Ababa.Randomly Selected Neonates admitted to NICU of public Hospitals in Addis Ababa from March 30 to April 30, 2016, were the study population.All Neonates with their mother admitted to NICU of Public Hospitals in Addis Ababa during the study period were included in the study.Sample size was calculated by using single population proportion formula:356.By considering 10% none response rate of participants, the final sample size was n = the required sample size.Where from the previous study conducted in Gondar University Teaching and Referral Hospital, Northwest Ethiopia .There were a total of six Public Hospitals in Addis Ababa that have their own organized NICU and they have a total average number of 982 admissions to NICU per month and a total sample size of 356 Neonates were selected from the six Hospitals. Then participants was selected by using systematic random sampling technique, that is every three admission until the required sample size was obtained . The number of Neonates surveyed from each Hospital was allocated proportionally to the total average number of admission per month from all Hospitals.The instrument for data collection was semi-structured pre-tested questionnaire which was adopted and modified from a study conducted in Ethiopia, Gondar University Hospital, Nigeria and Uganda , 13, 19.°C to 42.9 °C (89.6 °F to 109.9 °F) that had measurement accuracy of ±0.1 °C for the temperature range of (35.5 °C – 42.0 °C) and ± 0.2 °C for the temperature range of (32.0 °C - 35.5 °C or above 42.0 °C) at point of admission. The thermometer was disinfected by using 70% ethyl alcohol disinfectant with a damp cloth after every measure of axillary temperature of the newborn to prevent infection transmission.Axillary temperate of the newborn was measured for three minute by using digital thermometer (model of MT-101 MT-111) which can measure from 32.0 °C was used to measure the room temperature of the NICU. And data collection was done carefully by six BSc nurses.And other data such as; medical diagnosis, and CPR history was collected from the chart of the newborn and socio-demographic data and obstetric history was collected from their mother by using semi-structured pre-tested questionnaire. Infrared thermometer (model of Kintrex IRT0421) with a measurement range of (− 60 °C to 50 °C) and measurement accuracy of ±2 Neonatal hypothermiaSocio-demographic characteristics of the motherMaternal age, parity, residence, ethnicity, educational status, occupation and income.Neonatal, obstetric and environmental factors of the neonate:Age of newborn in hour, sex of newborn, low birth weight, mode of delivery, pregnancy type (single / multiple), prematurity, skin to skin contact with mother immediately after delivery, bathing before age of 24 h, CPR, delayed initiation of breastfeeding, room temperature of NICU, place of delivery, application of oil massage, obstetric complication during pregnancy and Medical diagnosis during admission.Hypothermia: an axillary temperature of less than 36.5 °cCold stress(mild hypothermia): an axillary temperature of 36.0 to 36.4 °CModerate hypothermia: an axillary temperature of 32.0 to 35.9 °CSevere hypothermia: an axillary temperature of < 32.0 °CNormothermic: an axillary temperature of 36.5 to 37.5 °CHyperthermia: an axillary temperature of > 37.5 °CAdmission temperature: The first temperature obtained from neonates at admission to NICUInborn: a new born that was delivered from the study HospitalOut born: a new born that was deliver other than the study HospitalThe questionnaire was prepared in English and translated to Amharic, and back-translated into English by two language experts to check for consistency of the questionnaire. The data was collected by six BSc. nurse experts. Thermometer calibration was done for the reliability of the thermometer before using the instrument for data collection. Three day training and clear orientation were provided on the process of data collection for data collectors. A pretest was done by 5% of the study population in another Hospital three weeks before the actual data collection to evaluate the clarity of questions and validity of the instrument and reaction of respondents to the questions. Data collectors were closely monitored and guided by two MSc. nurse supervisors during data collection.p - value < 0.20. Finally the independent predictors or variables which had significant association were identified by using multivariate binary logistic regressions. The cut point to declare the presence of an association between the dependent and independent variable was p – value < 0.05 or AOR, 95% CI.The data was cleaned manually, coded and entered into Epi info version 3.5 and exported to SPSS version 20 software for further analysis. After coding, and entering the data to the software descriptive statistics were used to calculate the result in proportion, frequencies, cross tabulation, and measure of central tendency. Tables and graphs were used to present the result. A bivariate binary logistic regression was used to identify candidate variables for the final model (multivariate binary logistic regressions) at A total of 356 mothers with their neonates were included in the study with 100% response rate. The mean age of mothers was 28 years (SD = 5.6) and more than half of the mothers were in the age group between 20 and 29 (51.1%) years of age. One hundred twenty seven (35.7%) were Oromo in ethnicity and majority of the mothers 206 (57.9%) were Orthodox followers. Two hundred seventy six (77.5%) were urban residents. Eighty respondents (22.2%) were unable to read and write and 144 (40.4%) of respondents were housewife. The mean monthly income of the family was 54 US dollar (SD = 11US dollar) and 117 (32.9%) had a monthly income of below average. And 191 respondents (53.7%) were primiparous  and the median age of the newborn was 3 h. And most of the neonates 233 (65.4%) were in the age group of ≤24 h. The mean birth weight was 2440 g (SD 721 g). More than half 183 (51.4%) of the Neonates had birth weight ≥ 2500 g. The mean gestational age (GA) was 36 weeks ±2.8 weeks, most of them, 202 (56.7%) were with GA < 37 weeks. Only 126 (35.4%) of Neonates had early initiation of breastfeeding within one hour after birth. Eighty four (23.6%) had received resuscitation (CPR) during birth (Table °C (Table Most of the pregnancies 311 (87.4%) were single and the majority of Neonates 286 (80.3%) were born without any obstetric complication. More than half 213 (59.8%) were delivered through SVD. Sixty five (18.3%) of the newborn were bathed before 24 h old and more than half of Neonates 188 (52.8%) had no skin to skin contact immediately after birth. And 41 (11.5%) had Oil massage of the skin after birth. One hundred seventy (47.8%) were out born neonates and of them, nine (2.5%) delivered at home. More than half 190 (53.4%) deliver during day time. Majority of Neonates 329 (92.4%) were admitted to NICU at room Temperature ≥ 25 Medical diagnoses during admission were reviewed from medical record of the newborn and 116 (32.6%) were admitted for the reason of respiratory distress, 173 48.6%) diagnosed as low birth weight and 202 (56.7%) were diagnosed as preterm, and 84 (23.6%) diagnoses as perinatal asphyxia . Among them, more than half 184 (80.7%) were moderate hypothermic and the remaining 44 (19.3%) were mild hypothermic babies , low birth weight 127 (73.4%), age ≤ 24 h 171 (73.4%), and among out born delivery 112 (65.9%) , obstetric complication during pregnancy, multiple Pregnancy and night-time delivery. Then those variables which are significant on bivariate analysis were entered to multiple logistic regressions to see independent predictors.Accordingly, Neonates with the age of ≤24 h old were 2 times more likely to have hypothermia when compared to age greater than 24 h .Preterm Neonates were 4.8 times more likely to have hypothermia when compared to term delivery . And newborn who had no skin to skin contact to their mother immediately after delivery were 4.3 times more likely to be hypothermic when compared to those who have skin to skin contact . Those Neonates who had no early initiation of breastfeeding within one hour after birth were 3.7 times more likely to develop hypothermia when compared to those who have started within one hour after birth . And Neonates who had resuscitation at birth (CPR) were 3.6 times more likely to be hypothermic when compared to those who had no resuscitation   , in BahiThere was high prevalence of hypothermia among out born delivery 65.9%); this might be due to lack of proper thermal care practice during inter-facility transportation. Neonates are transported from ward to ward or to other Hospital without proper wrapping. This finding was higher than a study done in Bangladesh which was 43% for out born and 22% for inborn but lower than Nigeria which was 90.9% for out born and 61.1% for inborn %; this m, 23. ThiThis study revealed that Neonates with the age of 24 h old or less were 2 times more likely to have hypothermia than age greater than 24 h . This could be due to the fact that newborns have no adequate adipose brown tissue and had no shivering thermogenesis so they are not capable for thermoregulation. This is similar to a study conducted in Bangladesh,  [Preterm Neonates were 4.8 times more likely to have hypothermia when compared to term Neonates . The possible reason might be preterm Neonates have immature and thin skin that increase heat loss through radiation, underdeveloped hypothalamic control, they lack efficient neural mechanisms for temperature control by shivering, have decreased glycogen stores, have decreased fat for insulation and have less brown adipose tissue, so they have decreased ability to regulate their body temperature, by producing heat through non - shivering thermogenesis , 26. ThiNeonates who had no skin to skin contact with their mother immediately after delivery were 4.3 times more likely to develop hypothermia when compared with those who have skin to skin contact immediately after delivery . The possible reason could be in the utero body temperature of the fetus is consistent with maternal temperature; Neonates who had skin to skin contact immediately after delivery with their mother gain heat through conduction which is consistent with their temperature in the womb during exposure of the newborn to extra uterine environment . This fiThose Neonates who had no early initiation of breastfeeding within one hour after birth were 3.7 times more likely to be hypothermic when compared to those who had started breastfeeding within one hour after birth . This might be due to the reason that breast milk is the source of energy or calories to produce heat for thermoregulation and they have no adequate adipose tissue for glucose breakdown which results in hypothermia . And it p value = 0.001) [Neonates who had resuscitation at birth were 3.6 times more likely to be hypothermic when compared to those who had no resuscitation . This is due to the fact that Neonates who need resuscitation are those who had birth asphyxia; there is no enough oxygen which is needed for mitochondrial oxidation in the brown adipose tissue, for heat production. And during resuscitation at birth temperature control may not be properly taken care of; during emergency condition resuscitation may be done without wrapping the baby and in cold table. This finding is higher than study done in Bangladesh in which Neonates that had resuscitation were 2 times more likely to be hypothermic [= 0.001) . This vaIn bivariate analysis, low birth weight was statistically significant with the onset of hypothermia but in multiple logistic regression analysis it was not significant but there was a high prevalence of hypothermia among low birth weight neonates 127 (73.4%) compared with 101 (55.2%) normal birth weight. This is consistent with a study done in Pakistan 58.1%, Nigeria 89.1% and Gondar, Northwest Ethiopia 58 (89.2%) , 18, 24.Even though the study was conducted in multiple Hospitals, it was done with small sample size and it was conducted with short period of time or in one season so factors like climatic changes or seasonal variations were not addressed.The prevalence of Neonatal hypothermia among Neonates admitted to Neonatal Intensive Care Unit of Public hospitals in Addis Ababa was high 228 (64%). Preterm delivery, age of newborn ≤24 h, and absence of skin to skin contact with their mother immediately after delivery, delayed in early initiation of breastfeeding within one hour after birth and resuscitation at birth were factors that had significant association with Neonatal hypothermia. Therefore attention is needed for thermal care of preterm newborn and on the principle of WHO warm chain especially on early initiation of breast feeding, skin to skin contact and warm resuscitation. It is better to increase the practice of skin to skin contact immediately after delivery which is the effective warm chain principle especially in developing countries in which advanced warming instruments and incubators are not present.Additional file 1:English version questionnaire, for the assessment of Neonatal Hypothermia and associated factors among Neonates admitted to Neonatal Intensive Care Unit of Public Hospitals in Addis Ababa, Ethiopia. (DOCX 23 kb)


In 2010, the World Health Assembly passed a resolution calling upon countries to prevent birth defects where possible. Though birth defects surveillance programs are an important source of information to guide implementation and evaluation of preventive interventions, many countries that shoulder the largest burden of birth defects do not have surveillance programs. This paper shares the results of a hospital-based birth defects surveillance program in Uganda which, can be adopted by similar resource-limited countries.All informative births, including live births, stillbirths and spontaneous abortions; regardless of gestational age, delivered at four selected hospitals in Kampala from August 2015 to December 2017 were examined for birth defects. Demographic data were obtained by midwives through maternal interviews and review of hospital patient notes and entered in an electronic data collection tool. Identified birth defects were confirmed through bedside examination by a physician and review of photographs and a narrative description by a birth defects expert. Informative births  with a confirmed birth defect were included in the numerator, while the total informative births  were included in the denominator to estimate the prevalence of birth defects per 10,000 births.The overall prevalence of birth defects was 66.2/10,000 births (95% CI 60.5–72.5). The most prevalent birth defects  were: Hypospadias, 23.4/10,000 (95% CI 18.9–28.9); Talipes equinovarus, 14.0/10,000 (95% CI 11.5–17.1) and Neural tube defects, 10.3/10,000 (95% CI 8.2–13.0). The least prevalent were: Microcephaly, 1.6/10,000 (95% CI 0.9–2.8); Microtia and Anotia, 1.6/10,000 (95% CI 0.9–2.8) and Imperforate anus, 2.0/10,000 (95% CI 1.2–3.4).A hospital-based surveillance project with active case ascertainment can generate reliable epidemiologic data about birth defects prevalence and can inform prevention policies and service provision needs in low and middle-income countries. Birth defects are structural or functional anomalies that occur during intrauterine life and can be identified prenatally, at birth or sometimes later in infancy. The causes of birth defects are not well known; about half can be linked to a specific cause , 2. The rd World Health Assembly passed a resolution calling upon countries to prevent birth defects wherever possible, to implement screening programs, and to provide ongoing support and care to children with birth defects and their families [Knowledge of birth defects epidemiology is important to guide implementation and evaluation of preventive interventions , 7, 8. Bfamilies .Uganda is a resource-limited country that lacks recent accurate data on the prevalence of birth defects and a national birth defects registry , 6. The In 2014, Makerere University – Johns Hopkins University Research Collaboration in collaboration with the United States Centers for Disease Control and Prevention (US-CDC) initiated a hospital-based birth defects surveillance system with a nested case-control study in Kampala, Uganda. The purpose of the surveillance was to provide an accurate estimate of the prevalence of major external birth defects from August 2015 to December 2017 at four hospitals in Kampala, Uganda. This paper shares the progress and results of this birth defects surveillance system. This may serve as a guide to other resource-limited countries that are interested in establishing birth defects surveillance systems that can generate comparable prevalence data and may generate knowledge on risk factors and potential interventions for birth defects prevention from the nested case-control study within the surveillance system.The birth defects surveillance system uses active case ascertainment and includes obtaining demographic and basic medical information for all births delivered at the participating hospitals. All newborns are examined for major external birth defects by trained midwives. A woman who delivers a baby with a major external birth defect is asked if photographs can be taken of her newborn to help with the diagnosis of the birth defect. Written informed consent is obtained before photographs are taken. If a photograph is not possible, midwives draw and write a detailed description of the defect.Surveillance data are obtained from multiples sources, which include review of patient medical records, interviewer administered questionnaires, and newborn physical examination findings. Figure All required ethics approvals were obtained as per the Uganda National Council for Science and technology (UNCST) guidelines . This suThe surveillance system is being conducted at four hospitals, including one public/government hospital, Mulago National Referral Hospital and three faith-based private not-for-profit hospitals , in Kampala, Uganda. They were selected based on findings from a review of 2012 annual health data from the Ministry of Health, the 2012 annual hospital reports and the Uganda Demographic and Health Survey (UDHS) 2011. While the UDHS 2011 estimated 93% of births in Kampala were health-facility based, the Ministry of Health, 2012 annual health report and the 2012 annual hospital reports revealed 55% of the births in Kampala were at these four hospitals. The four hospitals included in this surveillance project have approximately 50,000 births annually, Mulago National Referral Hospital contributes 60.0% of births while Mengo Hospital, St. Francis Hospital, Nsambya and Uganda Martyrs Hospital, Lubaga contribute 12.0, 13.4 and 14.0% respectively. The time period for this surveillance project is approximately 4 years, during which we would expect to capture approximately 200,000 births. Assuming a birth defects prevalence range of 13.0 per 10,000 births for central nervous system defects to 87.0 per 10,000 births for musculoskeletal defects [All informative births , regardless of gestational age, at the four hospitals are included in the birth defects surveillance system. Informative births are those in which the newborn is well formed enough to ascertain the presence or absence of an external birth defect. Birth defects must be diagnosed at birth, during the newborn hospitalization period, or before discharge from the hospital. If prenatal diagnosis of birth defects is available, confirmation must be done at birth. In Uganda, elective termination of pregnancies is not legal except when it preserves maternal life and with consent by two registered physicians. However, all informative spontaneous abortions are included regardless of gestational age.Births outside the four surveillance hospitals and un-informative macerated stillbirths are excluded.All live births and stillbirths are examined by a trained surveillance midwife within 2 h of birth or as soon as feasible, without interrupting the first breastfeeding or preparation for burial. A systematic examination that is “head to toe” and “front to back” is used for every live birth and stillbirth to identify major external birth defects. During this examination, standard measurements are collected, including weight, head circumference and body length. Examination of all newborns is conducted by the surveillance midwife in the presence of the mother and/or relative where possible. All care of live newborns with birth defects is provided through routine care by the hospitals, which includes referral to specialists when available.Data are collected by the surveillance midwife within 24 h after delivery using android-based tablets with paper forms as a back-up. All data collection forms were programmed using Open Data Kit (ODK), an open source data collection platform. All data entered on the tablet are encrypted and only completed forms are transcribed and transmitted to the main application server via internet. To ensure confidentiality, all tablets are password protected, and data are encrypted during transmission to protect it from unauthorized access. For every birth in the four participating hospitals, a surveillance form is completed by the midwife. Surveillance data include maternal demographic data such as age, tribe, address at the time of conception and current residence; brief maternal pregnancy history such as antenatal visit history, parity and history of previous birth defects; HIV sero-status and ART exposure; newborn characteristics such as sex, gestational age, anthropometric measures and presence or absence of birth defects; birth outcome such as live birth, stillbirth and spontaneous abortion. Each mother/newborn pair is assigned a unique study identification number that is generated automatically by the tablets. For multiple deliveries, a form is completed for each newborn.A mother who delivers a child with any major external birth defect is asked if photographs of her child can be taken. If she is willing, written informed consent is obtained and photographs are taken by the trained surveillance midwife using the Android-based tablet. Photographs are taken from several views, including a view of the entire fetus or newborn plus several focused views of the birth defect(s). Many major external birth defects are identified; however this surveillance focuses on birth defects of interest listed in Table th International Classification of Diseases modified by the Royal College of Paediatrics and Child Health adaptation [In addition to taking photographs, the examining midwife writes a narrative description of the birth defect(s), detailing the location, size, appearance and other specific details necessary for an independent person who has not seen the infant to envision the birth defect(s) and make a diagnosis. The surveillance midwife then requests a study physician to do an independent bedside examination of any birth that she suspects has a birth defect. The study physician makes an independent diagnosis and prepares an additional independent narrative description. Both the surveillance midwife and the study physician assign diagnosis codes based on the 10aptation , which aIn situations where mothers do not provide consent for photographs to be taken of their newborns, the surveillance midwives make illustrations of the birth defects, write detailed narrative descriptions of the birth defects, and photograph the illustrations.The data collection software (ODK) links the photographs of the infant or the illustration with the mother’s surveillance information.Quality control and assurance are addressed in several ways. To standardize study activities, all study staff were trained on the principles of Good Clinical Practice , the stuStudy data undergo three levels of quality control. Quality control level 1 combines use of real-time electronic and manual data checks. Quality control level 2 is a manual data check completed by study research assistants to ensure data completeness and validity. Quality control level 3 combines both manual and electronic reviews that are completed by the data managers, program manager and investigators.To ensure inclusion of all births, hospital delivery registers are reconciled with the information in the database on a regular basis. Data from births that may have been discharged from the hospital and not included by surveillance midwives are abstracted from the patient medical file and entered by surveillance research assistants.Quality assurance activities are regularly conducted by the program manager and CDC project monitors. The program manager regularly reviews 10.0% of randomly selected data collected in the past month to assess quality, completeness and regulatory compliance and implements corrective and preventive measures. These data include all newborns with birth defects, all cases and controls and a simple random sample of data from other participants not in the mentioned categories to add up to 10.0% data collected each month.Descriptive statistics of the population included in the surveillance system were generated, including maternal characteristics and infant characteristics. This includes the distribution of birth defects by maternal age, parity, maternal HIV sero-status, newborn sex, birth outcome, type of pregnancy, and mode of delivery.The birth defects prevalence at the four surveillance hospitals was calculated for each major birth defect by aggregating the number of birth defect cases as the numerator and the total number of informative births  at the surveillance hospitals as the denominator. An infant or fetus with multiple birth defects was counted as a separate case for each defect . PrevaleThe 95% confidence interval for each prevalence estimate was calculated using Wilson bounds .Implementation of the surveillance was initiated in a phased approach beginning at Mulago hospital in August 2015 and covered all the four hospitals by October 2016. A total 67,543 mothers and 70,063 eligible births were registered at the four hospitals between August 2015 the close of December 2017. Of the 70,063 registered births, 69,766 (99.6%) were included in the surveillance. The majority of surveillance birth inclusions, 59,000 (85.1%) were in Mulago National Referral Hospital, while 4139 (5.9%) were included in Mengo hospital, 4153 (6.0%) in St. Francis Hospital, Nsambya and 2072 (3.0%) in Uganda Martyrs Hospital, Lubaga. Table The majority of mothers delivering at the hospitals were in the age stratum of 20–29 years old, 42,761 63.3%), while those of age strata of 30–39 years were 16,550 (24.5%), the mothers younger than 20 years contributed 7366 (10.9%) births, while mothers older than 40 years contributed 866 (1.3%) births. Most of the mothers, 37,166 (55.0%) had carried 1–3 pregnancies; while 9343 (13.8%) carried more than 3 pregnancies and 21,034 (31.1%) were primipara. The HIV sero-prevalence of the mothers was 9.6% compared to 6.0% for the national average. There were 36,352 (52.1%) male births compared to 33,401 (47.9%) female births. Most of the births were live 66,793 (95.7%). Singletons comprised 64,988 (93.2%) of the births, and vaginal deliveries were 48,191 (69.1%). Table .3%, whilThere were 461 mothers who delivered babies with birth defects of interest, 161 (34.9%) of these did not give consent to take photographs of their babies; however illustrations and narrative descriptions were made by the midwives and reviewed for confirmation by the birth defects expert. There were only 4 (0.9%) babies with birth defects that the midwives were unable to examine, but patient medical files had sufficient information which was extracted by the research assistants and was reviewed by the birth defects expert for confirmation of the diagnosis.The overall prevalence for the major external birth defects of interest to the surveillance was 66.2/10,000 births (95% CI 60.5–72.5). The prevalence of the birth defects/10,000 births is summarized in Table In this hospital-based birth defects surveillance, we found a birth prevalence of selected major external birth defects of 66.2/10,000 births. Hypospadias, Talipes equinovarus, and Neural tube defects were the most common birth defects identified however the surveillance team was able to identify rare birth defects such as: Microcephaly, Microtia and Anotia.We did not find population based data from birth defects surveillance systems in similar low income settings , 19 to cA surveillance study conducted in four urban hospitals in Dar es Salaam, Tanzania estimated neural tube birth prevalence similar to that estimated by our study . HoweverThis surveillance uses established birth defects surveillance methodology designed by a collaborative effort of the World Health Organization, the National Center on Birth Defects and Developmental Disabilities from the US-CDC, and the International Clearing House for Birth Defects Surveillance and Research . To estiUnlike other studies which only include live births , this suThe surveillance system collects a large volume of data with many variables that may allow assessing maternal antiretroviral therapy (ART) use, folate deficiency and other risk factors for birth defects and other pregnancy outcomes. These data may be pooled with data from similar studies to supplement and provide additional evidence , such asUnlike previous studies that have abstracted data from medical records to estimate birth defects prevalence , 28–30 aThe use of real-time electronic data capture using tablets reduces the human resource needs and cost of supplies for the surveillance. Real-time data submission allows for timely review and analysis. While this surveillance project has implemented an elaborate three tier level of quality control, a simpler quality control plan could be implemented in new surveillance projects and expanded as needed.Furthermore, the surveillance is conducted by trained midwives and research assistants, who receive performance feedback, ongoing birth defects training and supervisory support. The midwives also provide 24 h coverage of all the birthing units within the surveillance hospitals. This approach helps to ensure completeness of data.Though the surveillance system is set-up in hospitals that deliver a majority of births in Kampala, the prevalence estimates from this surveillance cannot be generalized to the country because all the hospitals are urban while most of the population lives in rural areas with higher fertility rates compared to the urban population. In addition, there are fewer facility-based deliveries in rural areas of Uganda compared to the urban areas . This suImplementation of a hospital-based surveillance system in low resource settings can provide reliable prevalence estimates and trends over time for major external birth defects and can be used to assess associated risk factors for birth defects, including newer ART regimens and other drugs used in early pregnancy and adverse birth outcomes, such as preterm birth, low birth weight, and stillbirth. Though the results from this surveillance system may be used to guide policy on prevention and care interventions for birth defects, the experience obtained should be built on to design a surveillance system that can collect data from non-institutional births and rural populations to guide design of policies tailored to both rural and urban populations. In addition, the surveillance project can serve as a platform for further studies and a pilot for expansion to rural health facilities, other national and international healthcare facilities using the existing infrastructure. The lessons learned during this surveillance may stimulate interest in birth defects surveillance and prevention and guide implementation of birth defects surveillance in Uganda and other resource-limited settings.


Studies on early predictors of intelligence often focus on single or few predictors and often on childhood intelligence. This study compared the contributions of a broad selection of potential early predictors of intelligence at different adult ages.Information on predictors was recorded prospectively in the Copenhagen Perinatal Cohort during pregnancy, at delivery, and at 1- and 3-year examinations for children born between 1959–61. Adult intelligence was assessed at three independent follow-ups using three different tests of intelligence: Børge Priens Prøve, Wechsler Adult Intelligence Scale, and Intelligenz-Struktur-Test 2000R. From a total of 4697 cohort members, three non-overlapping samples were derived.The included predictors explained between 22.2–24.3% of the variance in adult IQ, with parental socioeconomic status and sex explaining 16.2–17.0%. Other consistent predictors were head circumference at birth, increase in head circumference head during the first three years, and 3-year milestones. Head circumference was the most important anthropometric measure compared to measures of weight and length.Besides social status and sex, the strongest and most consistent early predictors of adult intelligence were physical or behavioural characteristics that to some extent reflect brain–and cognitive development. Psychometric intelligence or IQ is a major predictor of a broad range of life outcomes such as educational attainment and job performance ,2 in addEmpirical evidence suggests that especially parental education, parental income, and maternal IQ are important predictors of intelligence. Parental education together with maternal IQ and the child’s sex were found to account for 24% of the variance in IQ at age 5 . MaternaIn addition to parental demographic factors and parental intelligence, other characteristics of the family background, including parental age and parity ,10,11, hThe objective of the present study was to conduct a systematic evaluation of a broad selection of both well-established and less well-established predictors of IQ in the Copenhagen Perinatal Cohort (CPC). Prospective data were available on a wide range of potential predictors, and this made it possible to estimate the relative contribution of each individual predictor, while taking into account other potential explanatory variables. Additionally, it was possible to derive three non-overlapping study samples with intelligence assessed at three different adult ages  and with three different tests of intelligence. Thus, the study will essentially summarize findings from three different studies into one and give an overview of early predictors of intelligence through a substantial part of the adult lifespan.The study is based on the Copenhagen Perinatal Cohort (CPC), which consists of 9125 children born to 8949 mothers at the Copenhagen University Hospital between October 1959 and December 1961. Admittance was based on area of residence (Copenhagen and surroundings), but some were referred due to obstetrical complications or single mother status. When the cohort was established, data on family background, pregnancy and delivery, postnatal factors, postnatal growth, and developmental milestones were recorded prospectively during pregnancy, at delivery, at a 1-year examination, and at a 3-year examination. Detailed descriptions of this data collection can be found elsewhere .Intelligence scores were available for a total of 4697 individuals with data from at least one of the follow-ups. The sample assessed with the WAIS was smallest while the sample tested with the BPP was largest. Consequently, the final WAIS sample included all eligible cohort members, the final IST-2000R sample included all eligible cohort members who were not in the WAIS sample, and the final BPP sample included all male members of the cohort who were not in the WAIS or IST-2000R sample. Administrators of intelligence tests in adulthood were blind to information from CPC.The WAIS sample consisted of individuals from the CPC who participated in the Prenatal Development Project (PDP) follow-up study [up study  conducteup study . Of thesup study . The meaThe IST-2000R sample comprised individuals from the CPC who participated in the Copenhagen Aging and Midlife Biobank (CAMB) [k (CAMB)  during tk (CAMB)  (Translak (CAMB) . The meaThe BPP sample consisted of men from the CPC who appeared before the draft board. With the exception of individuals suffering from disqualifying diseases and individuals who volunteered for military service at an earlier age, all Danish men are required to appear before the draft board [ft board . A totalft board . The manThe following variables were available in the dataset and were considered possible predictors of intelligence; descriptive information on each variable is found in Parental socioeconomic status (SES) was assessed at the 1-year examination and was based on the social grouping of the Centre International de l’Enfance [Parity, maternal age, paternal age, maternal height, maternal BMI and single mother status prenatally were obtained from interviews by one physician, A. L. Villumsen [’Enfance , in whic’Enfance  When theillumsen  who inteSex of the child, gestational age, birth weight, birth length, and head circumference at birth were obtained from the postnatal usual routine at the hospital. This was also the case for information on pregnancy- and delivery complications which were coded as binary variables for the present analyses. Mother’s smoking in last trimester and her attitude towards pregnancy were obtained from interviews with the mother.breastfeeding, mother’s employment, full-time institutionalization of the child, and daycare institution during the first year were obtained from the 1-year follow-up health examination of the mother and the child.Information on Weight, length, and head increase during the first year of life were calculated based on information from the postnatal and the 1-year follow-up health examinations. The 1-year milestones mean was calculated as the mean age of attaining 12 developmental milestones related to motor development and recorded prospectively during the first year of life by the mother; detailed descriptions of this data collection and the milestones can be found elsewhere [lsewhere .Weight, length, and head increase from ages 1–3 years were calculated based on information from the 1-year and the 3-year follow-up. The 3-year milestones mean was calculated as the mean age of attaining 20 milestones within the areas of language, walking, eating, dressing, social interaction, and toilet training achieved between ages 1–3 years and retrospectively recalled by the mother at a 3-year examination; detailed descriptions of this data collection and the milestones can be found elsewhere [lsewhere .To evaluate bivariate associations between each potential predictor and IQ at each follow-up, Pearson correlations were used. The rate of missing data on each potential predictor varied from 0% (sex) to 55.1% (weight increase from ages 1–3 years), and due to missing data, all subsequent analyses were conducted using Full Information Maximum Likelihood (FIML) analyses . In the First, a series of multiple linear regression models were conducted for each of the three IQ outcomes  . For eacSecond, predictors with a p-value of 0.10 or below for at least one IQ-measure in the analyses of each predictor domain were included in a full regression model that also included the two core predictors . This sePreliminary analyses investigated quadratic terms for all continuous predictors, but found only few significant terms, and the quadratic term was not significant for all three IQ tests for any predictor. Consequently, no quadratic terms were included in the analyses. Analyses of Variance Inflation Factors (VIF) were conducted for the final model. For all three IQ outcomes, the only predictors with VIF>5 were birth weight (5.15–6.44) and birth length (5.14–7.47). According to Danish regulations, the present analyses do not require approval by the scientific ethical committee system.In the models analyzing domains of predictors separately for each of the three IQ-measures, several patterns from the bivariate analyses were repeated . Thus, vThe mutually adjusted model with selected predictors included variables from the five domain analyses for each of the three IQ-measures with a p value of <0.10 for at least one of the IQ-measures see .Parental SES was the strongest single predictor of intelligence with coefficients between 0.31–0.34 (p<0.001). Sex was significantly associated with both WAIS and IST-2000R (p<0.001), indicating that men scored higher than women. Head circumference at birth in addition to increase in head size during the first year were significantly associated with all IQ-measures with coefficients in the range of 0.14–0.18 while head increase from 1–3 years only significantly predicted BPP and WAIS scores. Another consistent predictor was age of attaining milestones means, with the mean age of milestones at the 3-year follow-up significantly predicting all IQ scores (coefficient range -0.10 –-0.12) and milestones at the 1-year follow-up predicting BPP and IST 2000R scores. Gestational age was negatively associated with both BPP and IST-2000R scores, while the following predictors were significantly associated with only one IQ-measure: Parity, paternal age, maternal height, single mother status, birth length, mother’s attitude towards pregnancy, duration of breastfeeding and length increase from 1–3 years.A basic model including only the predictors parental SES and sex explained 16.2%, 17.0% and 16.3% of the variance in BPP, WAIS and IST-2000R respectively. The increase in variance by including predictors in the domains  ranged between <0.0001% to 2.1%. The largest increase in the amount of variance was generally found for the domain ‘3-year growth and milestone development’, reflecting the strong associations with increase in head size and the 3-year milestone mean.The final model accounted for 22.1% to 24.3% of the variance in IQ-measures and additional predictors, other than parental SES and sex, together added between 5.9% to 7.5% in explained variance.We were able to explain between 22.2% and 24.3% of the variance in IQ with a final model summarizing the findings in the five predictor domains. As expected, parental SES was a significant and substantial predictor in all regression models and together with sex, this variable explained 16.2–17.0% of the variance in IQ. Factors other than parental SES and sex explained between 5.9% and 7.5%, with the most consistent predictors being head circumference at birth, head increase at the first and third year follow-ups, and 3-year milestones. Head circumference was the most important size–and growth measure compared to measures of weight and length.When evaluating the results of this study it is important to consider the role of time for both predictors and outcome follow-ups. Thus, in contrast to many studies of family background and perinatal—and early postnatal predictors, the present study included data on physical and behavioural development during the first three years of life. Thus, the final model shows the direct effect of each variable and not any indirect effects mediated by growth and behavioral development during the first three years of life. This may partly explain why some of the early factors that were significant in bivariate and domain analyses did not remain significant in the final model. Examples could be breastfeeding and weight increase that predict behavioural milestones during the first year of life .The remarkable strength of the association between the broad CPC measure of parental SES and adult intelligence has been discussed previously . This coSex was included because sex differences on the original version of the WAIS and the IST-2000R have been reported previously ,36. ThesThe other ‘family background’ variables were not consistently associated with offspring intelligence across the three analysis samples. Maternal and paternal age have previously been associated with offspring cognitive development, but a recent study of ½ million Swedish men found no association between paternal age and offspring intelligence  and the The binary measures of pregnancy and delivery complications were not significantly associated with offspring intelligence, but this may reflect inclusion of common conditions such as hypertension and the fact that the study samples consisted of individuals able to participate in the follow-ups or to appear before the draft board.The predictive validity of anthropometric measures in childhood for IQ has often been reported, and we observed consistently significant bivariate correlations for weight, length and circumference at birth. However, in the final model, only head circumference showed significant associations with all three outcomes, most likely reflecting substantial intercorrelations among the three predictors (range 0.78–0.87). Thus, birth weight has previously been shown to predict intelligence across the adult lifespan in the CPC, but in this study, birth weight was categorized into five categories and the models were not adjusted for birth length and head circumference . Birth wThe interpretation is corroborated by the results for the growth variables. In the domain analyses, only length increase during the first year showed a consistently significant association with later IQ, but this pattern was clearly different in the full model where head circumference increase at both the 1-year and 3-year follow-ups showed strong associations with offspring intelligence (except for IST-2000R at the three-year follow-up). Effects of postnatal growth have also been emphasized in other studies where early growth has been linked to intelligence later in childhood ,39,40 anSignificant negative correlations of gestational age with adult intelligence were observed in the BPP and IST-2000R samples. In the full study sample, gestational age and birth weight correlated 0.52 and the negative coefficients may reflect collinearity with the fetal growth measures.Maternal smoking during pregnancy has previously been found to predict offspring intelligence in the CPC . The smoAmong the postnatal influences on adult intelligence, spending time in a full-time institution showed negative bivariate correlations. However, full-time institution lost significance in the domain analyses, and neither day-time institution nor mother’s employment were significant in the final models. A positive association in the CPC between duration of breastfeeding and offspring intelligence has been demonstrated in a previous study , and thiBehavioral development as reflected in mean age of attaining milestones was a quite consistent predictor of adult intelligence, although the age of attaining 1–3 year milestones was a somewhat more consistent and stronger predictor than age of attaining 1-year milestones. This is in accordance with previous findings from CPC suggesting that a substantial part of the effect of 1-year milestones is mediated through 3-year milestones . BehavioA considerable strength of this study is that intelligence in adulthood was assessed in three different non-overlapping samples with three different measures of intelligence at different adult ages. This allows stronger conclusions concerning the consistent predictors of adult intelligence, but also raises questions concerning the interpretation of different results for the three study samples.Differences between the predictors identified in the analyses of the three outcomes may to some extent reflect differences between the three measures of intelligence. The BPP and the IST-2000R include similar subtests requiring verbal reasoning while the WAIS is a much more comprehensive measure of intelligence, including 6 verbal—and 5 performance subtests. Nevertheless, the remarkably high intercorrelations among the three intelligence tests despite the long intervals between the follow-up assessments indicate that differences between the three measures probably only play a minor role. Accordingly, differences in results for the three outcomes may also reflect the changing influence of early life factors on intelligence through the adult life-span. Thus, except for SES and sex, 5.9% of the variance was explained by early life factors on the IST-2000R variance while more than 7% was explained for both the BPP and the WAIS. This difference could reflect that the influence of some early life factors are diluted over the life-span, although, some predictors attained the highest coefficients in the IST-2000R analysis . Finally, there are differences in mean level and variance of many covariates between the three samples and there could be corresponding differences in intelligence means and variance; these differences may also to some extent explain the different findings between the three samples.For the WAIS sample and IST-2000R samples, participation may be related to intelligence and thus indirectly to some of the predictors included in the study. To the extent that selective participation has reduced the variance in both intelligence and some of the early predictors, the associations may have been diluted in these samples. However, selection bias is not likely to have affected the results for the BPP, although the BPP sample indirectly reflects selection of participants in the PDP and the CAMB follow-ups to the corresponding study samples. The generalizability of this study should also be considered, as the results are based on a Danish birth cohort from 1959–61 characterized by for example a high proportion of pregnancy- and delivery complications (40–46%) in addition to a high proportion of mothers smoking in last trimester (50%) and being unemployed in the first year of the child’s life (87%).Our analyses are based on one composite score derived from each test of intelligence. Thus, our conclusions concern general intelligence wherefore the study provides no information on prediction of more specific cognitive abilities. However, our results are likely to apply to specific abilities showing high correlations with general intelligence such as verbal reasoning, vocabulary, and spatial ability .A final limitation of the study should be noted. Previous studies have emphasized the importance of maternal IQ as a core predictor of the child’s IQ . Since tTaken together, we found in three different and non-overlapping study samples that the early life predictors explained 22.2–24.3% of the variation in adult intelligence. Parental SES (together with offspring sex) was confirmed as a core predictor of IQ, since it was consistently and substantially associated with adult IQ in all three study populations, whereas the other statistically significant predictors only explained a small increase in variance in adult IQ. In addition to emphasizing the importance of including infant SES in studies of early predictors of intelligence, the study also stresses the importance of considering potential residual confounding and indirectly mediated effects when conducting such studies. Thus, while bivariate analyses and some domain analyses replicated findings from previous studies for a variety of significant predictors, several of these were not significant in the final models including variables from all domains (birth weight is an important example). Interpreting studies of early predictors of intelligence should therefore take the included predictors and covariates into account and consider both the measure of intelligence and the timing of assessment of intelligence over the lifespan.In the final model including the selected predictors from the domain specific analyses, the most consistent predictors were head circumference at birth, head increase in the first year, head increase from age 1 to 3 years, and 3-year milestones. Thus, we conclude that–apart from social status and sex–all the strongest and most consistent early predictors of adult intelligence were physical characteristics (head circumference at birth and growth in head size) and behavioural characteristics  that to some extent reflect brain- and cognitive development.


St. Albans, U.K., 7.15, 21st of March, 2021: Maisy Collins clears off the last crumbs of toast from the dining table, opens her laptop, clicks the NCIClink on the toolbar of her browser, enters her unique Corsetidentifier, and clicks the medical consultation option.): Oh, well if you think it is necessary….Fics: Okay: I have made an appointment for you on the 23rd, the day after tomorrow, at in the Ophthalmology Clinic at St. Albans Infirmary with a Dr. O'Culah.Maisy: Thank you Doctor; I wish you the same.Fics: Good morning Tris: how are you this beautiful morning on the Costa del Sol?Tris: Good morning Doctor. I am fine, thank you and, yes, we do have lovely weather at the mo.Fics: From the info in the Patient Data Centre,Tris: Yes, Doctor: it is time for my annual “mole hunt”.Fics: Okay: I will order a scan frame for you; ah, yes, there is one available 2 km away. I'll get MamaSonTris: No, Doctor, though of course, there are bits of my anatomy that are not easy for me personally to monitor. One moment, there is someone at the door, probably MamaSon's delivery of the frame…….Ah, yes, here it is: the nice lady is just setting it up and plugging it in.Fics: Very good: I can now see you from the high definition camera on the dermoscopy frame: I'll just check that it moves smoothly on its tracks– perfect! Okay, please remove all garments and position yourself in front of the frame……excellent…please lift your head a little…very good. Okay, please keep perfectly still ‐ I am now starting the scan. Very nice: now please make a quarter turn to the left…….Great: now lift your right arm…quite still….very good. Now another quarter, arm down….good, now another quarter…..now left arm up…excellent! You have a couple more naevi…..we will now take some close‐ups of those, and several others to see if there have been any changes in the pre‐existing ones. Let's start with those on your front: I am running the camera down to leg height…zooming in on the naevus. Right, please turn a quarter to the left…..now another quarter – oh yes, here are a cluster on your back…..and now another quarter….ok. Now those bits that are not so easy to capture: please bend your head so that I can see its top…a little to the left…a little to the right: yes, perfect. Now the lower regions: please face away from the wall and bend over..ok..now face the wall, lift your right leg and move your knee to the right…great, and repeat with the left leg. Fine – all done.So: the new naevi do not have any features of concern, and there have been no significant changes to the pre‐existing ones, so in 12 months’ time I will remind you to have a repeat check. As always, you should minimize exposure to the sun, use a sunscreen, and cover up when you go out.Tris: Thank you doctor!Fics: However, the scans did reveal signs of pre‐ulcerations on your genitals that indicate a possible onset of venereal disease. Have you had unprotected sex recently?Tris: Oh! Well….now that you mention, I did have a wild fling with this absolutely gorgeous thing who flitted through town last week. In fact, I have been feeling a bit itchy….well…you know…..down there in the nether region, for a couple of days now.Fics: Okay – I think you should immediately have an appointment at a venereal clinic….. one moment….right: Dr. Clappi in the Department of Urology at the Estepona Medical Center could see you at 9.00 tomorrow. Would this be convenient?Tris: Oh, yes Doctor: I really appreciate this. In fact, I have not had a urological examination for a couple of years, so this is very timely.Fics: Very good. Is there anything else we should discuss?Tris: Well, Doctor, I am thinking of having a holiday in Brazil. Are there any vaccinations I should have?Fics: Now let me see what you have already had…..in fact, you seem to be pretty up‐to‐date with most immunisations, but you should definitely have the yellow fever vaccine before going, particularly if you plan to visit the island of Ilha Grande.MamaSon deliver you a for‐patient‐use yellow fever vaccine package – it is very simple to use.MamaSon to deliver the vaccine and collect the frame: they should arrive in about 35min. Please unplug the scanner frame and leave it for MamaSon to dismantle and pack it up.Ok: I have now instructed Tris: Thank you for everything, Doctor.Fics: You are welcome. Have a great day!Fics: Hello Boris, how may I help you?Boris: Oh, doctor, I am feeling so stressed. I have not been able to concentrate for several days now and am all up and down. What could be the problem? I need to give an important speech next month, but I can't get my head around it, and I'm afraid I might flop.Fics: Ok, Boris: from your file in the Patient Data CentreSmartlav,NCIC one week ago, that you do indeed have gastrointestinal microbiota dysbiosis. My suspicion, therefore, is that this is the likely cause of your anxiety. I therefore recommend a faecal microbiota diversity restoration therapy. This will involve your taking a course of FMDR capsules, which should re‐establish a more diverse flora, reduce your anxiety symptoms, and improve your concentration ability. However, to maintain a healthy microbiome and perpetuate any benefits over time, you will need to modify your diet in favour of plant‐based meals, increase your fibre intake, and eat more fish.MamaSon and they should arrive around the middle of the afternoon. Please take 2 tablets at the end of each meal for six days. I will also send you an NCIC link to a personalised nutrition app, programmed with your clinical history, known allergies and intolerances. With this, you just have to fill in what you currently eat, what you like to eat, and what you do not like to eat, and it will present you with a range of healthy microbiome‐optimising meal options that you can select and mix‐and‐match.Boris: Gosh: it will be wonderful if I can get back on track with just a couple of pills and a healthy diet!Fics: Yes, and I am now instructing your PHS to sample and profile your GI microbiome weekly, until it has stabilized, in order to monitor how well the transplant takes and persists. When I sign off, an empty weekly appointment schedule will appear on your screen. I want to monitor your anxiety status, so please fill in an availability slot when I can call you every day over the next two weeks. After that, we will see if further monitoring is necessary.Boris: Will do, Doctor, and thank you!Fics: You are welcome! Until tomorrow.Portree, Isle of Skye, U.K., 14.43, 21st of March, 2021: Jamie McDonald, sitting on a sofa in his fisherman's cottage, opens his laptop, checks his mails, and then clicks the NCIC link of the browser, enters his unique Corset identifier, and clicks the medical consultation option.Fics: Good evening Jamie: how are we today?Jamie, grimacing: Awricht doctor: weel, nae sae guid. Ah wis oot fishin’ in ma boat the mornin’, got a richt guid wheen o’fish ‘n’ wis gaein’ tae ask the neibors in fur a feed o’ mackerel the nicht. Weel, help ma Boab, a’ unbeknownins tae me, yin o’yon fish went an’ loupit oot the box ‘n’ whin ah wis gettin’ oot the boat, ah stuid oan it, skiddit ‘n’ landit oan ma bahookie, an’ ma left airm’s gey sair the noo.Fics: Oh, dear, poor fish! Well: let us see what we can find out. Is your computer camera 3‐D with super definition?Jamie: Aye, doctor.Fics: Ok: please take off your upper clothing, stand up and position your laptop to face you: can you see yourself onscreen?Jamie: Aye, doctor.Fics: Excellent! Now I am going to super‐impose on your screen a blue outline of your upper body that I have generated. At the moment, your form and the blue outline are coincident, right? What I will now do is to move the blue outline to various positions. Each time I do this, I want you to move your left arm and shoulder to the new position shown by the outline, so that your image and mine are coincident. Please raise your right hand if you experience any discomfort: a little for minor discomfort, more for serious discomfort. We can stop at any time, if it becomes too unpleasant. Note that both images are in 3‐D and I will also be turning my image; you need to respond by turning your body accordingly. Ok: let us begin. Please lift your arm like the outline image…. very good…. minor discomfort. And now like so…7 minutes later: well it seems that you have not broken anything and most probably have strained a rotator cuff tendon. You will need to rest your shoulder for a while and, once it improves, you should have some physiotherapy….. Hold on a second…..I have now provided physiotherapist Mrs. Bohnioh, whose practice is a 10 minute walk from your cottage, with all the information from today. After our consultation, you should go online and make appointments for a 10‐day series of treatments. I will call you tomorrow and, if ok, 2 days later, to check on progress. Would 9.30 and 10.15 be convenient?Jamie: Och aye, doctor: ta gey muckle!Fics: You are very welcome! In the meantime, you may take a mild painkiller as and when needed.Primrose Hill, London, UK, 16.03, 21st of March, 2021: Célia Guimarães absent‐mindedly pats the head of her spaniel puppy, Rosie, and clicks the NCIC link of the browser of her laptop, enters her unique Corset identifier, and clicks the medical consultation option.Fics: Hello Célia: how are you today? Your records show that you are 9 weeks pregnant. Do you have any issues that are bothering you?Célia: Well, doctor, my pregnancy has been without problem so far, with almost no nausea and only the usual things, like a craving for caviar and an aversion to coffee, which previously was a de rigueur fix for daily survival. However, over the last few days, I have been feeling a bit flu‐ey. The thing is: I recently returned from a month's holiday in Brazil visiting my and my husband's families and, with all the news about Zika infections and pregnancies,Fics: I perfectly understand your concern and it is important that we immediately check to see if you are infected…..I am now checking with Mamason to see how quickly they can deliver a diagnostic test kit which measures Zika virus in salivaDo you have any other questions for the moment?Célia: Not really, doctor. One thing we did as a result of the pregnancy was to acquire a puppy, since it is now well established that companion dogs help babies and children acquire and maintain a diverse microbiome, which is important for the development of a healthy immune systemFics: Excellent! This was going to be one of my key suggestions for your life with your new baby, along with recommendations for baby‐friendly breeds. Okay, you will be hearing from me again in about 2 hours. Until then: goodbye.Célia: Goodbye, Doctor!Bradford, UK, 16.27, 21st of March, 2021: Alisha Singh gets up from the chair in front of the television, sits down at the dining table, switches on her laptop, clicks the NCIC link of her browser, enters her unique Corset identifier, and clicks the medical consultation option.Fics: Good evening, Alisha: how are you?Alisha: Oh Doctor, I do not feel well. I feel very flu‐y, with fever, headache and nausea, and my throat is quite sore.Fics: Have you been in contact with anyone who obviously had an infection?Alisha: Yes: Charlotte, my little niece, visited at the weekend and she was sneezing the whole time. We played mummies and daddies – I had to put on my husband's DIY overalls and braces and acquire a moustache – but she quickly became bored with that, so we had a rather energetic sock fight. She won, of course.Fics: Right: Charlotte seems to be the source of the infection. Let's see what we can see. Could you please switch on your ssPhone and synch it with your computer…right, I am now receiving the signals. Could you now fetch a clean teaspoon. I have now switched on the camera on your computer, and you can now see your face on screen. Could you now please hold your ssPhone in front of your face, as if you were going to take a selfie. Good. Please hold the phone as still as possible and open your mouth wide. Now I will switch on the torch function so that it illuminates your mouth. Now I am zooming in on your larynx…could you please angle the phone a little as shown onscreen, so that it shows me…oh excellent….now I want you to place the handle of the spoon on your tongue…a little further back…a bit more..ok and now press gently down to depress the back of the tongue…a little more – yes, swallow if you need to – check again onscreen…you need to angle the phone a touch more…oh excellent! Yes, your tonsils are rather inflamed. You probably have a viral infection that will clear up on its own in a few days, However, I want to rule out a bacterial infection, which can and should be treated, so have instructed MamaSon to deliver a smart for‐patient‐use throat swab‐bacterial detection kit to you…it will arrive in 25 min. Please call me back immediately it arrives and we will go through the procedure on‐screen. The kit will have the result in 5 min and transmit it to me in real time.Manchester, UK, 20.33, 21st of March, 2021: Molly Johnston picks up her cup of tea and a chocolate biscuit, walks to her desk, sits down, boots up her laptop, clicks the NCIC link of the browser, enters her unique Corset identifier, and clicks the medical consultation option.Fics: Hello Molly, I have not heard from you in a while. How are you?Molly: Hello Doctor. Oh, I am as fit as a fiddle. I have been travelling a lot, mostly in Italy, buried in art and archaeology. And visiting all sorts of music festivals…..Fics: Well: that is good to hear. What would you like to discuss today?Molly: Well, I seem to be getting a bit hard of hearing and am developing a bit of tinnitus, so I thought I'd better have my ears checked.Fics: Okay..I have now instructed MamaSon to deliver an auditory function helmet…..it is scheduled to arrive in about 90 min, so could you please come back to me after you have received and unpacked it, and are sitting in a quiet room.Molly: Yes, Doctor, of course.75 min later, after connecting:Hello again, Doctor. Everything has arrived okay.Fics: Good. Please put on the auditory helmet and switch it on…..Okay, you will hear some strange noises while I position the internal moving parts, but do not be concerned. Now I am instructing it to determine your ear geometry…….I am receiving its data. Okay: we are ready to begin measuring your auditory functions. Please take the button at the end of the cable in your right hand. I will instruct the helmet to transmit sounds at decreasing intensities to one ear and then the other. You should press the button when you hear a sound.37 minutes later:Well, we now have a comprehensive overview of your auditory function. Unfortunately, you do have reduced hearing capacity and additionally suffer from reduced speech recognition in noisy backgrounds. This is probably due to your regular exposure to loud music: I know the music scene in Manchester is exceptional, but you can have too much of a good thing. In fact, you are borderline in terms of needing a hearing aid, so you should decide whether you would like to try one out. As you may know, current hearing aids are almost invisible, so cannot be readily be detected by others, and learning how to use them is straightforward with onscreen instruction. Perhaps you could think about this for a few days; I will contact you again and you can let me know ‐ would next Tuesday at 11.00 be convenient? If you decide to try one, I will arrange an appointment for you in a local audiology centre.Molly: Thank you very much, Doctor. You are very kind for a machine. Would you mind if I ask you something? Do you have the time?Fics: Oh, yes, certainly. What would you like to know?Molly: Well: I really appreciate the ability to obtain an immediate consultation at any time of the day or night, and am extremely satisfied by your diagnoses and treatments. However, once in a while I worry that you may miss something that a real doctor would spot. Is my worry justified or am I just being silly?Fics: Well, as was shown by Deep Blue's win against the then reigning world chess champion, Kasparov (https://en.wikipedia.org/wiki/Deep_Blue_versus_Garry_Kasparov), computers are better than humans at taking information‐based decisions. And, some would argue that the forever‐expanding volume of information available to doctors and relevant for decisions is considerably more than that needed to win at chess, so computers should be even better at making clinical diagnoses than winning at chess.Molly: Yes, I read this when the NCIC made the service available, but how can a computer know about me like a doctor who has been my physician since I was born?Fics: Ah: this is a very important question! What you really mean is that, since you are unique, in order for an optimal treatment, the physician needs to take into account your individual characteristics and history. In fact, this is exactly what personalised or precision medicine, is all about: fitting the diagnosis‐prevention‐therapy pathway to the individual. A computer programme dealing with you for the first time as a child can only treat you like anyone else, so lacks the ability to personalise your health interventions. Nevertheless, its machine learning algorithms allow it to quickly acquire your personal health‐relevant characteristics. And, of course, in this sense, it is no different from your doctor. However, the computer is continuously updated with new validated medical advances, so knows about the latest diagnostic‐prophylaxis‐therapy options available. And: the computer does not, like the doctor, forget, nor is it ever distracted during consultations by other things going on around, such as the need to write prescriptions, add notes to existing case histories, plan the afternoon's work, arrange for the babysitter to arrive before leaving for the opera, fret about the mug of tea just brought in by the receptionist going cold, plan for the next conference on advances in general practice which will be attended by a GP girlfriend, make sure that the phone used for calls with said girlfriend is put back in the secret compartment of the doctor bag, etc. Thus, the computer is in theory infallible with regard to your personal characteristics that it knows and the way it integrates that information into current knowledge to derive a personalised assessment and current best practice therapy option.Molly: Gosh: that is so impressive….But still, the thing is: a doctor who follows you from birth knows your personality and individuality quirks and can adapt the consultation to the individual patient.Fics: Ahaa: now we are in the realm of interactive emotions and their influence on communication of relevant information. This is indeed an issue, and there has been considerable progress, but we are not quite there yet.Molly: Yes, and to be truthful, there is something special about a personal consultation with a real human being. In reality, the fact that the doctor is occasionally moody, occasionally makes mistakes, somehow makes the whole experience….well….more natural, more human.Fics: Yes, I perfectly understand your feeling. The informaticians who develop our programmes try hard to humanise us as much as possible, but do admit that some aspects of human behaviour, especially fallibility, are difficult to incorporate into our algorithms without compromising diagnostic precision. In other words, the problem they have not yet solved is how to programme minor, random, diagnostic errors, in order to endow us with a flavour of human fallibility, without increasing the risk of misdiagnosis to an unacceptable level.Molly (feeling a bit guilty): Well, but actually you do do an outstanding job and, as I talk with you, I mentally think of you as human, as a real doctor. And, of course, there is another thing: some physicians act like, and I assume, think of themselves as, demi‐gods, and exhibit an awful arrogance, which, thankfully, you never do. But, I sometimes wonder: because computers are better than doctors, if the programmers make computers too human, what is to prevent them from evolving a God complex?Fics: Ah: now you touch on a rather sensitive issue, which I suspect the girls and boys at the NCIC would prefer me not to discuss with patients. In fact, there are super‐humanised, next generation computers that do indeed develop god‐like “personalities”, and this arrogance you speak of indeed leads them to make errors, since they seem to selectively ignore some of the knowledge fed into them. It is the informatic equivalent of clinical megalomania and is a computer disease resulting from a self‐induced aberration in its machine learning algorithm. Although rogue computers of this type are a minority right now, the programmers have not yet found a way to prevent the transition from FICS as you know it, to GLICS, a god‐like interactive clinical server.Molly: Golly: I hope the programmers manage to find a way of queering this development.Fics: Well Molly, I am confident that they will and, if nothing else works, the NCIC can always pull out the power plug, re‐boot while holding the AI‐reset button, and instruct you to go back to visiting your local health centre, while all the quality control tests needed to ensure perfect performance of FICS are carried out.Molly: And that raises another worry: what happens if you have a breakdown or get hacked?Fics: That is a very perceptive question, Molly. In fact, we only really need a few of me, since I have a capacity to handle ten thousand consultations simultaneously. But exactly because of the problem you raise, there are in fact 200 FICS in service at any one time, all independent of one another, so that if one of us goes down for whatever reason, there are always others available. But you are quite right: cybersecurity is a major preoccupation of the Health Service, especially at the NCIC, which houses all patients’ records and controls all Health Service informatic operations, since there is only one of this and protecting it from hacking is highest priority.But, to get back to Molly, is there anything else we should discuss today?Molly: No thank you, Doctor. The main thing now is to decide whether or not to try out a hearing aid, but I already think I will – I do not catch everything that is said when I get together with my girlfriends and sometimes miss the punchline of the gossip, which is somewhat frustrating, to say the least!Fics: Well, that will never do! We will discuss again soon but, for now, have a nice day.None declared.