Gender and sex differences in the development of children and adolescents are commonly found in the psychiatric examination. Family and developmental history is an important part of the clinical diagnostic interview, the basic examination technique. Attention-deficit/hyperactivity disorder (ADHD) is associated with diagnosis-specific markers in family and development history. However, it is unclear to what extent ADHD-specific signs and narratives differ between females and males. The aim of this study was to assess and to compare the family and developmental history profiles of female versus male adolescents with ADHD.n = 92). The two groups were matched in pairs for sex, IQ and ICD-10 diagnosis . Interview data were operationalized in three categories: 0 - physiological marker, 1 - subclinical marker, 2 - clinical marker. The two groups were compared with two-way ANOVA.Data were collected using the clinical diagnostic interview technique from parents of female and male patients diagnosed with ADHD  between the ages of 12 and 17  years (Information about female in comparison to male adolescents were reported in the parental interview with few differences.Our study suggests that family and developmental history of the neurodevelopmental disorder ADHD is only poorly influenced by gender or sex. Attention-deficit hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder, characterized by developmentally inappropriate and impairing inattention, motor hyperactivity and impulsivity, with difficulties often continuing into adulthood . Sex is Gender refers to the socially constructed norms that impose and influence social roles and relationships, whereas sex refers to the neurobiological differences between boys and girls. The development as a girl or a boy in a family influences the children’s way they are parented and people’s reactions to certain behaviors, play styles or interests. Besides underlying genetic and endocrine processes, these social expectations lead to differing behaviors in female adolescents and male adolescents . The comGender-related behaviors in adolescents do not influence only parents’ view on children, but also mental health professionals’ (MHP) view on patients with ADHD in daily clinical routine. There is an increasing awareness among experts that females with ADHD show a somewhat different set of behaviors in school, in family life and in clinical settings in comparison to male adolescents with ADHD. These common and typical perceptions in daily clinical routine lead to social expectations on gender-specific behaviors by MHPs. The use of these stereotypes like “the disruptive boy” or “the dreamy girl” among parents and among experts in working with children and adolescents with ADHD takes part in our daily social life .Social gender expectations as well as underlying neurobiological sex-related processes could therefore be linked to the diagnostic process and outcome during the first consultation of an MHP service. Working with females with ADHD highlights special challenges in recognition of this group, possibly caused by female compensatory strategies or developmental differences on the one hand and stereotypical expectations of MHPs, parents or teachers on the other hand . A betteFollowing clinical guidelines, the diagnostic process of potentially psychopathological behavior is typically tripartite including a clinical diagnostic interview, the use of rating scales and the assessment of setting-specific information like school reports . The cliIn contrast to the clinical diagnostic interview technique, rating scales are a useful and time-saving tool in the process of forming diagnostic hypotheses and treatment monitoring. In everyday clinical practice, however, rating scales should be carefully included in first diagnostic decisions, because rigid adherence to cut-offs can lead to a high number of false-positive and false-negative results. In particular, norms for female patients are often not available or females are in some cases underrepresented . When diIn non-referred population samples of children and adolescents, ADHD is more common in males than in females with a sex ratio of around 3:1 . By contADHD is associated with a specific profile in family and developmental history, but the empirical literature is still scarce in this domain. In a previous work by our group , a profiIn our previous study, we have investigated family and developmental history of male adolescents with ADHD compared to typically developing male adolescents . As we hThe aim of this study was to assess and compare family and developmental history profiles of female and male adolescents with ADHD. Here, we investigated 46 pairs of female and male adolescents with ADHD matched for IQ, ICD-10-diagnosis and sex, using a typical set of questions regarding family and developmental history assessed with the clinical diagnostic interview technique.In this study, we investigated family and developmental history of male adolescents and female adolescents with ADHD, by interviewing the parents. In the following, the participants are the parents.n = 92). The first one are parents of a female adolescent diagnosed with ADHD (n = 46) and the second one are parents of a male adolescent with ADHD (n = 46), matched in pairs for IQ, diagnosis and sex of their children to reduce relevant confounding factors. As female patients with ADHD are rarer than male patients with ADHD in the general population, for each female that could be included in the study population, a male matching partner was searched from the larger pool of potential males with ADHD. If no male matching partner was found, the female was not induced in the study. Twenty-four data sets out of these 46 sets of parents of male adolescents with ADHD used for this study were identical with 24 out of 56 data sets from our previous study comparing male adolescents with ADHD versus neurotypical controls  below 70. Although many patients with ADHD suffer from psychiatric comorbidities, which are important in clinical practice, these were excluded here to provide an unbiased study population. Additional sample characteristics can be found in The study has been approved by the Ethics Committee of the University Hospital Carl Gustav Carus (reference number EK 295072016) and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Participants and also their children gave written informed consent to participate.Parents of adolescents with an ADHD diagnosis were recruited as participants. Adolescents with ADHD were current or former patients at the University Hospital Carl Gustav Carus, Dresden. All participants were contacted by telephone and invited for an interview. Both parents and children provided written informed consent before being included in the study.Medical students from the Medical Faculty Carl Gustav Carus, Technical University Dresden, in their final year of Medical Studies, participated in the project as part of a voluntarily scientific program and without financial benefit. They were thoroughly trained in the “family and developmental history questionnaire” and were always supervised by a board-certified specialist in child and adolescent psychiatry. Interviewers were not blinded for the diagnosis of the patients and were equally distributed among both groups.Parents and interviewer met for an agreed appointment. Family and developmental history data were collected from at least one parent. Modeling a clinical setting, participant and interviewer were seated face-to-face in an examination room of the hospital. The interviewer asked the questions in the same order as they can be found in The “family and developmental history questionnaire” to study family and developmental history in the clinical examination scenario of the MHP has been described  and consOperationalization was oriented on the CASCAP-D system, which in turn was developed for children and adolescents on basis of the AMDP System for adult patients in psychiatry and the WHO ICD-10 diagnostic inventory . The CASMetric data like birth weight and APGAR scores were obtained from patient files.Intelligence quotient (IQ) was assessed with HAWIK (Hamburg-Wechsler-Intelligenztest für Kinder), the German version of the Wechsler Intelligence Scale for Children  .p < 0.05. As there were already few differences between the two groups, we did not correct for multiple comparisons, in order to prevent a false-negative interpretation of overall absent sex differences. Graphical artwork was created with Prism software. Graphs show mean, standard error of the mean (SEM) and significant results from two-way ANOVA. Tables show mean and significant results from two-way ANOVA. One asterisk in a graph represents p < 0.05, two asterisks p < 0.01, three asterisks p < 0.001. “ns” indicates a non-significant result (p > 0.05). Detailed statistical results are deposited in the The statistical method to analyze variances, two-way ANOVA, was performed with Prism software . Differences were considered statistically significant if In the first part of the interview, regarding family history, we collected information using parental memories about themselves and about the grandparents of the child. We assessed socioeconomic, psychosocial, psychiatric and medical history of parents and grandparents. For almost all items, parents of female adolescents and male adolescents remembered similar information about themselves and grandparents, irrespective of the sex of their child . As the We then assessed family relationships, conflicts in the family, maternal stress and medical history of the child. Parents of female adolescents and male adolescents with ADHD had for most items similar memories, irrespective of the sex of their child . For femFor prenatal and perinatal history, birth parameters and early childhood we observed again strong similarities in the memory of parents of both female adolescents and male adolescents . There wFinally, we assessed the socio-emotional and cognitive behaviors of female and male patients with ADHD during preschool, primary school and secondary school. Again, most items were not remembered with a sex difference . MothersWe investigated here family and developmental history in matched pairs of female adolescents with ADHD compared to male adolescents with ADHD. Remarkably, we found here a large overlap in family and developmental history among female and male adolescents with ADHD. There were only few differences, most of them appearing not to be related to the disorder, but to the development as a boy or a girl.Family history is important for the diagnostic process of ADHD as it reveals information about the burden of mental disorders and other diseases in the family, conflicts and relationships in the family and socioeconomic background. Developmental history reveals information about the development of psychopathological markers and finally about symptoms of ADHD. In our previous study of family and developmental history, male adolescents with ADHD were compared to neurotypical adolescents without psychiatric diagnosis . In thisThe main finding of the present study is that family and developmental history in ADHD is only poorly influenced by gender or sex. In turn, the picture of female adolescents with ADHD is very typical for the disorder. This is on first glance surprising, because the current scientific view emphasizes the importance of gender/sex issues, in particular here in mental healthcare of children and adolescents . An explThere were still a couple of gender/sex differences in our findings. As we did not correct here for multiple comparisons – in order not to mask remaining differences, besides the main finding of great overlap in the data set – most of these few differences would disappear when applying alpha correction.For mothers of females with ADHD, more dysfunctional interaction in the mother’s family was reported . This haLower Apgar scores were reported in our sample for males with ADHD . BetweenFor middle childhood, parents reported more impaired fine motor skills for males with ADHD during preschool and primary school ages ,C. FemalFor family and developmental history, we found in our sample an overlap between females and males in the disorder-specific clinical presentation of parentally reported concerns, observations and experiences with their children with ADHD. Our findings support the recommendation that the disorder-specific therapeutical approach should be for females with ADHD similar to males with ADHD , 31, notDespite the overall impression of great overlap, we still found a couple of tendencies of gender/sex differences in our sample, especially differences in mathematical competencies and fine motor skills, also well described for neurotypically developing females , 29, 33.Our study represents the number of matching male and female patients without comorbidity that could be recruited from about 10 years of treatment at a large child and adolescent psychiatry department. As our study excluded ADHD patients with comorbidities, which account for a large proportion of both males and females in clinical populations, statements for these patients are restricted. Other limitations of our study are non-blinded student interviewers. Further, applying correction for multiple comparisons would result in disappearing of most of the few differences found here. However, this does not contradict our main finding of strong overlap between boys and girls.Taken together, a relatively small clinical study population may in first line generate a new hypothesis – in ADHD without comorbidity there are few differences in family and developmental history between boys and girls. Our study was an exploratory study and equipped with limited resources. However, our study is to our knowledge the first one to investigate gender/sex differences in family and developmental history in ADHD.Our study suggests that the majority of ADHD-specific signs and narratives reported by parents in the exploration of the family and developmental history are stable diagnostic markers regardless of gender or sex.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of the University Hospital Carl Gustav Carus (reference number EK 295072016) and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Participants and also their children gave written informed consent to participate. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.JW collected the data, interpreted the results, and wrote the paper. JZ collected the data and wrote the paper. VR wrote the paper. RW designed the study, supervised the collection of data, interpreted the results, and wrote the paper. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Studies on neurodevelopmental outcomes in individuals with congenital anomalies who undergo neonatal surgery are scarce and have reported contradictory findings based on small study groups. The congenital condition VACTERL association includes at least three malformations: vertebral anomalies, anorectal malformations, cardiac defects, tracheoesophageal fistula with or without esophageal atresia, renal anomalies and limb deformities. Most of these patients undergo surgery during their first days of life. Neurodevelopmental disorders include a broad group of disabilities involving some form of disruption to brain development. Attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASD) and intellectual disability (ID) are diagnoses included in this group. The aim of the study was to investigate the risk of ADHD, ASD and ID in a cohort of individuals with VACTERL association.Data was obtained from four Swedish national health registers and analyzed using the Cox proportional hazards model. Patients born 1973–2018 in Sweden with the diagnosis of VACTERL association were included in the study. For each case five healthy controls matched for sex, gestational age at birth, birth year and birth county were obtained.The study included 136 individuals with VACTERL association and 680 controls. Individuals with VACTERL had significantly higher risk of ADHD, ASD and ID than the controls; 2.25 , 5.15  and 8.13  times respectively.A higher risk of ADHD, ASD and ID was found among individuals with VACTERL association compared to controls. These results are of importance to caregivers and to professionals participating in follow ups of these patients in providing early diagnosis and support, aiming to optimize the quality of life of these patients. The congenital condition VACTERL association includes at least three malformations: vertebral anomalies (V), anorectal malformations (A), cardiac defects (C), tracheoesophageal fistula (TE) with or without esophageal atresia, renal anomalies (R) and limb deformities (L) . Birth pToxic effects on the brain after exposure to anesthetics in new-born animals have been reported –7. AfterFrom systematic reviews an increased risk of impaired cognitive functions was reported in individuals with esophageal atresia (EA) , gastro-Population based studies have reported various neurodevelopmental outcomes such as increased risk of Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD) in individuals with anorectal malformations (ARM) , ADHD afOn the contrary no increased risk of neurodevelopmental impairment was found in a Randomized Controlled Trial where children were allocated to general or regional anesthesia for inguinal repair before the age of 60 weeks postmenstrual age  nor in pNeurodevelopmental disorders include a wide group of disabilities associated with disturbance in brain development. ADHD, ASD and Intellectual disability (ID) share common characteristics such as onset in childhood, being more common in males than in females, with high heritability and a high overlap between the disorders . GeneticStudies of neurodevelopmental outcomes after neonatal surgery in children with congenital anomalies are scarce and include small study populations with contradictory findings , 38–43. The aim of this study was to investigate the prevalence and risk of being diagnosed with ADHD, ASD and ID in a national population-based cohort of individuals with VACTERL association.The study was approved by the Swedish Ethical Review Authority, registration number 2019–06506, and amendments 2021–00777 and 2021–04067. Since all data were anonymized, the Swedish Ethical Review Authority approved that no informed consent was obtained from the study group.The Swedish National Board of Health and Welfare administers national registers enabling analysis and follow up of the Swedish healthcare services . Since tThe National Patient Register (NPR) contains information starting from 1964 and since 1987 includes information of all in-patient care in Sweden. From 2001, information about outpatient physician visits including day surgery and psychiatric care is also registered . High vaThe study group included patients born in 1973–2018 identified through the diagnosis codes from the Swedish versions of International Statistical Classification of Diseases and Related Health Problems (ICD) in ICD-8 for 1973–1986, ICD-9 for 1987–1996, and ICD-10 from 1997. Inclusion and exclusion criteria are presented in A control group of five healthy individuals per included case, matched for sex, gestational age at birth, birth year and birth county was collected from the MBR. Individuals with the exposure of the above mentioned inclusion diagnoses, were excluded through linkage with the NPR. As for the cohort, exclusion for chromosomal aberrations and syndromes were made.Data of all in-patient hospital visits up to and including the five-year birthday was collected from the NPR. From the age of five, outcome diagnoses from ICD-9 and ICD-10 including the age at first registration were obtained for ADHD ICD-10: F90.0-F90.1, F90.8-F90.9, ICD-9: 314, ASD ICD-10: F84, ICD-9: 299 and ID ICD-10: F70-F79, ICD-9: 317–319. Information on the Anatomical Therapeutic Chemical (ATC) codes of prescribed drugs for treatment of ADHD specified as N06B and C02AC02 was obtained from the PDR and information of deceased individuals from the CDR.Categorical variables are presented as numbers (n) and percentages (%) and continuous variables as median (M) and range. For significance testing of differences between cases and controls in numerical variables, Mann-Whitney U-test was applied. Chi-square test was used to test differences in proportions when expected values in each cell were higher than five, Fisher’s exact test was otherwise applied. The differences in outcome diagnoses were analyzed with time-to-event analysis using the Cox proportional hazards model where the hazard ratio (HR) indicates the relative difference in the risk of the outcome between case and control groups. The significance level was set to p<0.05. For the statistical analyses R version 4.1.1  was used.After applying the exclusion criteria, 716 individuals with an ICD-code of VACTERL association or ICD-codes of at least three of the malformations included in VACTERL association were identified. The application of the additional inclusion criteria resulted in 479 individuals. Among them 343 only had the diagnosis code of Q872, 759H or 759W and none or less than three of the listed malformations and were therefore not included. The final study group consisted of 136 individuals with at least three of the listed malformations whereof 22 with and 114 without the ICD code for VACTERL association. The matched control group consisted of 680 individuals. Flow chart of included cases and controls is displayed in Characteristics of the study groups are presented in In the study group, the median days of hospital stay until one year of age was 54 days (2–365) and during the first five years, 71 (2–645). The mortality rate in the VACTERL group was 12.5% compared to 0.7% in the control group (p < 0.0001) and age at death 0 (0–37) and 0 (0–46) respectively. When dividing the study cohort into two time periods according to the birth years, the mortality rate during 1973–1996 for VACTERL patients was 20.0% and for controls 1.5% (p<0.0001). During 1997–2018 the mortality rate was 5.6% for VACTERL patients and 0% for controls (p = 0.0007). In Among the nine individuals in the study group diagnosed with ADHD, six (66.7%) had been prescribed ADHD medication, ATC codes C02AC02 or N06B, since the start of PDR.After adjustment for sex, birth year and gestational age, the result of Cox regression indicated a significant Hazard Ratio of 2.25 (95% CI 1.03–4.91) times increased probability to be diagnosed with ADHD, 5.15 (95% CI 1.93–13.72) times with ASD, 8.13 (95% CI 2.66–24.87) times with ID and 5.11 (95% CI 1.28–20.44) times with mild ID compared to controls.When dividing the cohort into two time periods, before and from 1997 when ICD-10 was implemented, an increased HR was observed in the cohort born between 1973 and 1996 for ADHD, ASD and ID compared to controls but not in the cohort born 1997 and later. The HRs for the outcome of diagnoses in the whole cohort are presented in When adjusting also for SGA in the Cox regression model the results indicated significant Hazard Ratios for ADHD 2.56 (95% CI 1.12–5.84), ASD 3.52 (95% CI 1.16–10.66), ID 12.09 (95% CI 3.58–40.89) and Mild ID 7.27 (95% CI 1.55–34.05).To our knowledge this is the first population-based study investigating the risk of being diagnosed with neurodevelopmental impairments in a cohort of individuals with VACTERL association. We found a significantly increased probability of being diagnosed with ADHD, ASD and ID in patients with VACTERL association.In the literature there are conflicting data as to whether and how early surgery and anesthesia affect the brain during infancy , 28 and Reports in previous literature on associations between the diagnosis of VACTERL and neurodevelopmental disorders are rare. Among ten 5-7-year-old children with VACTERL association, intelligence was within normal range while attention difficulties were found in eight children whereof two were subsequently diagnosed with ADHD . In a stThe risk of ADHD in individuals with VACTERL association in our study was increased. Similarly, an increased risk of ADHD was reported in individuals with ARM  and withThe risk of ASD in the VACTERL cohort was increased and even higher than the risk of ADHD. An increased risk of ASD has also been found among individuals with ARM , congeniIn line with some other reports in children with major congenital anomalies, we found an increased risk of ID in the VACTERL cohort. ID has been reported in 4.8% of children with major non-cardiac congenital anomalies , 25% witAccording to the literature, there is a high overlap between the three diagnoses of ADHD, ASD and ID . High coIn the present study we found no increased risk of ADHD, ASD or ID in individuals with VACTERL association born 1997–2015. We speculate that the neurodevelopmental outcomes together with lower mortality in the later cohort may reflect some improvements achieved in anesthetic and surgical methods and neonatal intensive care during the latest decades . HoweverAdditionally, the general health outcomes in the VACTERL cohort may have improved during the latest decades due to progress in surgical and medical care. This is reflected in the decreasing mortality rate which was 20% among those born until 1996 compared to 5.6% among those born 1997 and later.A strength of the study was that it investigated neurodevelopmental outcome in patients with VACTERL association including the until now largest study population with the longest follow up. Another strength is the design of a population-based cohort study with data prospectively collected and with a control cohort matched on sex, gestational age at birth, birth year and birth county, thus reducing the risk of selection bias. Moreover, we included only individuals with at least three of the malformations VACTERL association encompasses, to avoid misclassification.A limitation of the study was the inherent design of a register study since the data was not confirmed through review of medical records. Furthermore, it was not possible to control for various prenatal, perinatal and neonatal cofounding factors such as heredity, complications and risk behavior during pregnancy and low birth weight. Our aim was to match the controls also for SGA but unfortunately it resulted in considerably fewer controls.To avoid misclassification we considered the ICD codes for VACTERL association not to be enough since there are other syndromes classified with the same ICD code. Thus, to make sure to obtain data on factual VACTERL patients we decided to include the ICD codes of at least three of the included malformations.A higher risk of ADHD, ASD and ID was found among individuals with VACTERL association compared to controls. These results are of importance to caregivers and professionals participating in the follow up of patients with VACTERL association in providing early diagnosis and support and thus optimizing the quality of life of these patients. Future prospective studies with long term follow up of individuals with VACTERL association are needed to increase knowledge of neurodevelopmental disorders in these patients.


Imagine going to your local pharmacy to pick up your repeat prescription and being told they are out of stock. You then hear the same news from every chemist in the area. That is the situation facing people with attention deficit hyperactivity disorder (ADHD), characterized by inattention, excessive impulsivity, and hyperactivity. Unlike other drug shortages caused primarily by supply chain problems, the reasons for this ongoing scarcity are more complicated.Total psychostimulant prescriptions jumped 10% among American adolescents and adults in 2020–2021, the first year of the COVID-19 pandemic. This rise in ADHD diagnoses was attributed to the January 2020 suspension of the 2008 Ryan Haight Act by the USA Department of Health and Human Services, which strictly regulated the prescription of controlled substances through telepsychiatry. This emergency policy allowed telehealth providers to prescribe controlled substances online without needing in-person examination. This Act’s suspension meant there had never been an easier time to obtain psychostimulant prescriptions in the USA. Partially better awareness and detection of ADHD may also have played a role in increased diagnoses, driven by advances in diagnostic criteria, public awareness, and less stigmatization.In October 2022, the Food and Drug Administration (FDA) announced a shortage of several Adderall formulations in the USA. Citing “intermittent manufacturing issues,” supply still cannot meet market demand 8 months on. Adderall, a mixed amphetamine salt, improves executive function and ameliorates ADHD symptoms. It is a medication essential for many to live normally and productively. Adderall withdrawal can severely affect one’s ability to function. Short supply has left many children and parents in turmoil, with reports citing significant school/work performance loss and turning to street drugs as illegal substitutes. Further deepening this problem, research indicates that the COVID-19 pandemic exacerbated the core symptoms of ADHD and comorbidities.Compounding the shortfall of available medicines, exploitative companies are targeting those who do not truly need ADHD medication. One recent BBC investigation exposed the issue of private clinics lacking regulation, which led to inappropriate psychostimulant prescriptions in the UK. An undercover reporter revealed that three online clinics diagnosed him with ADHD using unreliable mental health assessments. The reporter was subsequently prescribed Ritalin for long-term use, a psychostimulant used to treat the condition. But a subsequent in-person NHS assessment showed that the journalist did not fit the diagnostic criteria for ADHD. Another investigation by ABC News found that psychostimulant prescriptions for ADHD in Australia had more than doubled in 2018–2023, with private clinics capitalizing and charging $3000 for online consultations. Both reports illustrate potential ADHD misdiagnosis and prescription misuse in these countries.American telemedicine start-ups have been accused of prioritizing profit by former employees and government lawyers. Some are under federal investigation for violating the Controlled Substances Act. Most early pandemic prescribers have either ceased operations or had their licenses revoked, yet the number of Americans officially diagnosed with ADHD remains in the millions. Psychostimulant prescriptions are for life, meaning that they have no expiry date. In a letter to drugmakers last summer, the FDA pointed to “the sheer volume of ADHD medications on the market coupled with aggressive marketing practices” has led to a shortfall in the supply of ADHD medications.Government regulators have known about a potential shortfall of Adderall in the USA since 2021 but have been slow to act. The FDA announced plans to update box labels to curb misuse by guiding clinicians and patients, but this does nothing to address Adderall’s short supply. Psychostimulants used to treat ADHD are designated as schedule II controlled substances, so they possess therapeutic use but come with “a high potential for abuse.” The Drug Enforcement Administration (DEA) regulates production so that there is just enough supply while minimizing the risk of misuse or addiction. Monitoring rules on “suspicious orders” are proposed to become stricter under a proposed draft. Psychostimulant orders with unusual features would be flagged to the DEA so customers could be reported to this law enforcement agency for investigation. Though this move would prevent potentially inappropriate prescriptions, misuse of this proposed policy would add yet another barrier to legitimate patient access.The number of child and adolescent psychiatrists is low, and their patients are one of the most underserved populations—fewer than ten specialists per every 100,000 children in the USA. New prescriptions must be obtained if one’s existing pharmacy runs out of stock, disproportionately discriminating against those who do not own private health insurance. In the US Congress, Rep. Abigail Spanberger pressed the FDA to solve this public health emergency. However, the FDA’s outlook looks like one of law enforcement fearful of a repeat of another opioid epidemic.Pandemic changes shifted the pendulum on ADHD medication supply to trigger shortages in an already tightly restricted market. Lack of access to Adderall for patients is an unnecessary crisis that needs swift action. There is a need to tackle misuse by regulating private providers and increasing funding for child and adolescent psychiatrists in the medium to long term. Additionally, ensure that short-term production and distribution constraints are adjusted to guarantee appropriate providers are fully supplied with medication. A balance between prohibitive policies protecting patients and providing reasonable access to ADHD medication must be a public health priority.BMC Medicine recently announced a call for papers welcoming submissions on neurodevelopmental disorders. This article collection plans to focus on often underserved, misunderstood conditions and patients.This editorial highlights the vital issue of fair access to diagnosis/treatment during May’s mental health awareness month.


This study examined differences in ADHD symptoms and diagnosis between preterm and term-born adults (≥18 years), and tested if ADHD is related to gestational age, birth weight, multiple births, or neonatal complications in preterm borns.n = 1385 preterm, n = 1633 term; born 1978–1995) examined differences in self-reported ADHD symptoms[age 18–36 years]; and (3) a population-based register-linkage study of all live births in Finland  examined ADHD diagnosis risk in adulthood (≥18 years) until 31/12/2016.(1) A systematic review compared ADHD symptom self-reports and diagnosis between preterm and term-born adults published in PubMed, Web of Science, and PROQUEST until April 2021; (2) a one-stage Individual Participant Data(IPD) meta-analysis . Whereas in the register-linkage study, adults born preterm had a higher relative risk (RR) for ADHD diagnosis compared to term controls . Among preterms, as gestation length  and SD birth weight z-score  increased, ADHD risk decreased.Systematic review results were conflicting. In the IPD meta-analysis, ADHD symptoms levels were similar across groups (mean While preterm adults may not report higher levels of ADHD symptoms, their risk of ADHD diagnosis in adulthood is higher.Preterm-born adults do not self-report higher levels of ADHD symptoms, yet are more likely to receive an ADHD diagnosis in adulthood compared to term-borns.Previous evidence has consisted of limited sample sizes of adults and used different methods with inconsistent findings. This study assessed adult self-reported symptoms across 8 harmonized cohorts and contrasted the findings with diagnosed ADHD in a population-based register-linkage study.Preterm-born adults may not self-report increased ADHD symptoms. However, they have a higher risk of ADHD diagnosis, warranting preventive strategies and interventions to reduce the presentation of more severe ADHD symptomatology in adulthood. The conflicting information may reflect the varying quality of evidence, diverse methods, and small sample sizes of the cohort studies with adult follow-ups,6 Moreover, not all children with ADHD diagnosis in childhood continue to meet the diagnostic criteria in adulthood and the symptom profiles may change with inattention showing the highest and impulsivity-hyperactivity the lowest continuity.7 Also, the predominately male presentation of ADHD in childhood may no longer be present in adulthood.6Studying ADHD in preterm borns beyond childhood is important as the prevalence rate of ADHD diagnosis in the general population declines with age.We conducted a systematic review of existing studies examining associations between preterm birth and ADHD symptoms and diagnosis in adulthood, with evidence quality evaluation. We performed an Individual Participant Data (IPD) meta-analysis to determine if preterm and term-born adults differed in self-reported ADHD symptoms. In a population-based register-linkage study, we investigated the risk of receiving an ADHD diagnosis in preterm compared to term-born adults. We examined the effects of gestational age, birth weight, multiple birth status, bronchopulmonary dysplasia (BPD), and intraventricular hemorrhage (IVH) on later ADHD symptoms and diagnosis for preterm-born adults.8 The IPD meta-analysis and population-based register-linkage study were a part of the Research on European Children and Adults born Preterm (RECAP) consortium (https://recap-preterm.eu/) and Adults Born Preterm International Collaboration (APIC) (https://www.apic-preterm.org/members/). The analysis plan was submitted and approved by the RECAP consortium prior to the study (04/05/2019).The systematic review was performed in line with the preferred reporting items for Systematic Review and Meta-Analyses of Individual Participant Data (PRISMA IPD).We conducted systematic searches in PubMed, Web of Science, and PROQUEST from Inception to October 2019 and an updated search in April 2021 Fig. . DuplicaCohort, case–control, register-linkage studies, and meta-analyses assessing ADHD self-reported symptoms or diagnosis in adulthood (≥18 years) with data on gestational age, were included in the systematic review. Eligible symptom measures included a validated scale providing an overall ADHD symptom score, inattention, hyperactivity/impulsivity, attention problem scores, or clinical cutoffs. Eligible ADHD diagnosis included data from medical records, medical registers, or a structured diagnostic interview. Attention scales derived from neurocognitive tests were beyond the scope of this review. Due to profound changes in medicine during recent decades, we excluded studies with participants born before 1970. There were no language restrictions. Studies were excluded based on criteria presented in Supplemental Table Self-reported symptom studies, which met inclusion criteria for the systematic review and were a part of the RECAP project and APIC consortia were eligible to participate in the IPD meta-analysis. Cohorts with adult follow-up were invited to provide a data dictionary, which was checked for ADHD symptom scales. Studies that declined to provide a data dictionary, did not have an adult ADHD measure, or were not a part of the RECAP/APIC consortia were excluded.In the population-based register-linkage study, the participants were identified from the Finnish Medical Birth Register. We included all live births in Finland from 01/01/1987 to 31/12/1998.z-score and probable clinical ADHD based on clinical cutoffs. Secondary outcomes were inattention, hyperactivity/impulsivity, and attention problems z-scores.For the IPD, all included cohort studies utilized at least one ADHD self-reported symptom scale in adulthood  Revision (code: F90 for ADHD). The Care Register for Health Care is a validated research tool.z-scores were determined using the Intergrowth 21 reference10 and small-for-gestational age (SGA) was defined as birthweight z-score < −2 SD and those not born SGA as birthweight −2 SD or above.Gestational age was calculated from the mother’s last menstrual period or by ultrasound. Preterm birth was further categorized into extremely preterm , very preterm , moderate-to-late preterm  and term . Post term  births were excluded. Birthweight As cohort definitions varied, we harmonized BPD into “no BPD” versus “any BPD” and IVH as “no IVH” versus “any IVH” (Grades 1–4). In the register-linkage study, BPD was defined using the codes 770.7  and P27 (ICD-10) and IVH using codes 772.1 and 772.2 (ICD-9) and P52 (ICD-10). In both studies, multiple births were classified as a binary variable .11 Neurosensory impairment (NSI) was accounted for in sensitivity analyses. For the cohorts, NSI was defined as “no NSI” versus “any NSI” if the participant had severe visual (blind in both eyes) or hearing impairment (not corrected by hearing aids), cerebral palsy, or cognitive impairment (childhood IQ < 70). Missing NSI data in cohorts were treated as no evidence of NSI. In the register study, NSI was defined with the codes 369, 389, 343, and 318–319 (ICD-9) and F70-F79, H54, H90, H91, and G80 (ICD-10).Covariates included participant’s sex, age , and parental education (of either parent/of the mother), which was harmonized according to the International Standard Classification of Education (ISCED) into low (ISCED level 0–2), medium (3–5), and high (6–8).12 The assessment criteria for each NOS domain  were set out at the onset of the study  independently assessed the evidence quality of studies included in the systematic review using the Newcastle–Ottawa–Scale (NOS).IPD and data dictionaries were provided by each of the participating cohorts via the RECAP platform data nodes. The adult data were cleaned and harmonized by the University of Helsinki (RR and reviewed by PG). Perinatal data for the IPD were harmonized by the University of Warwick. The register data were cleaned and harmonized by the Finnish Institute for Health and Welfare. The data underlying this article cannot be shared publicly due to the personal and sensitive information of the participants. If requested, access to this data is subject to data sharing agreements.The IPD meta-analysis was conducted using a one-stage meta-analytic approach where IPD from all cohort studies were analyzed simultaneously. We applied linear mixed-effects models with the restricted maximum likelihood estimation method for the covariance parameters to examine mean differences with 95% confidence intervals (95% CI) in ADHD symptoms between preterm and term-born adults. We applied generalized linear mixed-effects models for binomial data to estimate the odds ratio (OR) with 95% CI of scoring above the clinical cutoff on any of the ADHD scales in preterm compared with term-born adults. In mixed-effects models, random intercepts were specified to account for the clustering of participants within cohorts.ADHD scores were normalized with rank normalization and standardized to the mean of 0 and SD of 1 within each of the studies. As all but two  of the included cohorts had two different ADHD measures, the mean of available standardized ADHD scores was taken.In the register study, we applied binomial logistic regression to estimate the relative risk (RR) with 95% CI of being registered with ADHD diagnosis in public specialized medical care in adulthood between preterm compared with term-born adults.z-score, multiple births, BPD, and IVH were associated with ADHD symptoms and diagnosis among adults born preterm. The effect sizes were estimated in adjusted models. Finally, in sensitivity analyses, we tested whether excluding participants with NSI affected the associations and in the register study, we also excluded participants who died or emigrated at 18 years of age or older.In both study designs, we tested whether the associations varied according to the degree of prematurity and SGA status. Then we tested whether gestational age, birth weight 13 had more than 50% attrition rate. Neither of the two cohorts  which utilized diagnostic interviews (MINI), observed differences between VLBW/ELBW preterms and normal birth weight (NBW) term controls.15 Of the 11 studies,21 with adult self-reported questionnaires, only three cohorts  found differences between preterms and term controls ,26 Germany ,28 UK & Ireland ,25 Norway ,27 Australia 29 and New Zealand .18 The POPs cohort 30 provided data but was subsequently excluded for lack of a control group. The two North American cohorts identified that did not provide data were the McMaster 13 and the Rainbow Babies and Children’s Hospital .16 For all three RECAP/APIC studies not included in the IPD meta-analysis,30 the previous findings are indicated in Table EightAll IPD data sets were checked for data integrity . Any questions were resolved with representatives of specific cohorts. Ultimately, no major issues were identified with the data sets.z-score and probability to score above the ADHD clinical cutoff  .Preterm birth was not associated with total ADHD symptoms off Fig. , hyperacz-score, multiple birth, and BPD were not associated with ADHD symptoms in adulthood .In preterm only analyses, gestational age, birth weight od Table . ADHD syVH Table . This diThe register study characteristics are described in Table Compared to term-born controls, the RR of ADHD diagnosis in adulthood was higher for preterms born SGA and not SGA and for those born VP  or emigrated (n = 90) after 18 years of age and those with NSI (n = 10157) . After excluding individuals with NSI, two associations became apparent: RR of ADHD diagnosis was higher for EP than term-born controls  and for preterms with any BPD compared with those with no BPD .The associations remained when we excluded those individuals who died  and the register-linkage studies (RR = 1.20) provided ample statistical power  to detect small effect sizes, the statistical power in the register-linkage study was superior. This is particularly true for comparisons of ADHD symptoms above the clinical cutoff in the IPD meta-analysis and ADHD diagnosis in the register-linkage study. The statistical power is, however, smaller in comparisons of different gestational age groups and in the comparisons limited to preterm borns only. Moreover, the discrepancies may also be attributable to the composition of the total IPD sample which comprised preterm and term-born individuals from seven different countries, while the register-linkage study comprised only Finns. It is also important to note, that due to differences in the study designs, the samples included in the IPD meta-analysis comprised different numbers of different gestational age groups. For instance, 36.7% of the total IPD EP participants came from the UK, 20.7% from Australia, 17.9% from New Zealand, 13.7% from Finland, 5.6% from Norway, and 5% from Germany., whereas over 70% of MLP and term-born gestational age groups came from Finland. That preterm and term-born adults self-reported equal levels of ADHD symptoms may also suggest that ADHD symptoms do not hinder the daily functioning of adults born preterm. While ADHD in the general population is associated with risk-taking behaviors and criminality,33 preterm-born individuals report less externalizing behaviors,22 less smoking and substance abuse,34 fewer contacts with police,34 have less often experienced sexual intercourse36 by young adulthood, and they also less often have substance use disorders and criminal convictions.4 The lack of differences may also reflect preterm personality: in comparison to term borns preterm borns report being more conscientious, shyer, and less impulsive, less excitement seeking, and less open to new experiences.38The discrepant IPD meta-analysis and the register-linkage study findings may relate to differences in the severity of ADHD symptoms detected by the meta-analysis of self-reports and register-linkage study of diagnosis. ADHD self-reports may have captured milder, sub-clinical symptoms, whereas the register-linkage study on ADHD diagnosis may have captured the most severe end of the ADHD spectrum, attending public specialized medical care. Only 0.64% of the individuals in our study had been registered with an ADHD diagnosis in adulthood. This is slightly lower than the estimated adult prevalence of 2.5–4.2% in high-income countries.22 which however included fewer cohorts with adult follow-up than included here. The register-linkage study findings indicating higher RR for ADHD diagnosis for preterms than term controls are consistent with another Finnish register study, which examined ADHD specialty care diagnosis between ages 3 and 19,1 and with a Swedish register study assessing specialty care diagnoses at ages 5–19 y.4 Our findings are also in agreement with Norwegian, and one Swedish, register studies assessing psychostimulant medication use at 18–38 y,39 30 y,40 and at 26–33 y.41The lack of differences between preterm and term adults in self-reported symptomatology in the IPD meta-analysis are consistent with a previous meta-analysis,6 This may be due to women with ADHD, at least in childhood, presenting less disruptive ADHD-like behaviors,42 and in parent and teacher reports girls with ADHD show lower levels of hyperactivity, impulsivity, inattention and externalizing symptoms and higher levels of internalizing symptoms.32 However, it has also been suggested that differences between men and women in ADHD may level off as the individuals’ age, and some studies have reported that in adulthood women report higher ADHD symptomatology than men.43Highlighting further differences between the results of the IPD meta-analysis and register-linkage study, women in the IPD meta-analysis of cohorts reported higher levels ADHD symptoms, while in the register-linkage study men had a higher RR for ADHD diagnosis in adulthood. The higher ratio of men to women, may suggest that women may be more likely to be underdiagnosed.The only agreed finding between the cohort IPD meta-analysis and the register-linkage study was that participants with lower parental/maternal education had higher ADHD symptoms and RR for diagnosis.44 This is important, as generally, the rates of ADHD tend to decline with age.5 Additionally, the limited range of birth years included allows contextualization of the medical practices or interventions, which may have played a role in the outcomes observed in this generation. Furthermore, performing an IPD meta-analysis allows for the same covariates and confounders to be accounted for across all studies, typically a weakness in standard aggregate data meta-analyses. Moreover, in both the IPD meta-analysis and the register-linkage study, we were able to uniquely parcel out gestational age and birth weight categories and study neonatal complications in relation to the risk of ADHD symptoms and diagnosis in those born preterms, for which individual cohort studies lack the power to do.A strength of our systematic review and IPD meta-analysis includes a focused age group, allowing for a better understanding of the symptomatology of adults, instead of summarizing the symptoms or diagnosis across the entire lifespan as done in other studies.44 Hence, the generalizability of the findings is limited. Additionally, although we had IPD data, we were not able to account for additional potential confounders, and residual confounding cannot be excluded. We were not able to obtain IPD from all cohorts with self-reported ADHD symptom scales in adulthood with preterm or VLBW participants. It is unlikely that the inclusion of these three studies would have changed the results, since these studies have previously reported no differences.21 Finally, while the Care Register for Health Care is a validated research tool, including ADHD diagnosis in childhood,9 comparable data on adulthood ADHD diagnosis is lacking.A common weakness, our results do not represent low- or middle-income countries, as the IPD meta-analysis included only high-income country populations.45 and with ADHD,46 understanding the intersections of the two is important. Pinpointing that preterms or specific preterm and birth weight groups are at a slightly increased risk of receiving ADHD diagnosis in adulthood than others may pave the way for targeted interventions to reduce the burden. On the other hand, the lack of differences in self-reported ADHD symptoms between preterm and term-born adults delivers a positive message for the preterm-born individuals themselves, their families, and healthcare professionals.Considering the substantial economic burdens associated with preterm birthSupplementary informationSupplementary Material


Understanding spatial variations in health outcomes is a fundamental component in the design of effective, efficient public health strategies. Here we analyse the spatial heterogeneity of low birthweight (LBW) hospital deliveries from a demographic surveillance site on the Kenyan coast.A secondary data analysis on singleton livebirths that occurred between 2011 and 2021 within the rural areas of the Kilifi Health and demographic surveillance system (KHDSS) was undertaken. Individual-level data was aggregated at enumeration zone (EZ) and sub-location level to estimate the incidence of LBW adjusted for accessibility index using the Gravity model. Finally, spatial variations in LBW were assessed using Martin Kulldorf’s spatial scan statistic under Discrete Poisson distribution.Access adjusted LBW incidence was estimated as 87 per 1,000 person years in the under 1 population  at the sub-location level similar to EZ. The adjusted incidence ranged from 35 to 159 per 1,000 person years in the under 1 population at sub-location level. There were six significant clusters identified at sub-location level and 17 at EZ level using the spatial scan statistic.LBW is a significant health risk on the Kenya coast, possibly under-estimated from previous health information systems, and the risk of LBW is not homogenously distributed across areas served by the County hospital.The online version contains supplementary material available at 10.1186/s12884-023-05586-6. The World Health Organization (WHO) defines newborns weighing less than 2500 g, irrespective of gestational age, as low birthweight (LBW) . LBW is The United Nations estimates that of the 20.5 million LBW babies , nearly 25% occurred in the African region in 2015 , 9. In 2Reducing LBW has long been recognized as a public health priority and it is now a global commitment with the adoption of the Global Nutrition Targets in 2012. The WHO has a goal to reduce LBW by 30% by 2025 . Some ofUnderstanding spatial variations in health outcomes is a fundamental component in the design of effective, efficient public health strategies. The identification of areas with high disease burden enables national and local health authorities to target limited resources to maximise impact and understand the finer scale epidemiology of poor health. There have been very few investigations of the national spatial variations in LBW burden –18 or fiGlobal maternal and newborn health initiatives, Every Newborn Action Plan and Ending Preventable Maternal Mortality, have identified priority indicators derived from facility-based data as important , 25. HowThis study was a retrospective analysis of routine data collected at Kilifi County Hospital , 27, matData was obtained from the maternity ward surveillance system at KCH established in January 2011 to standardize maternal admission procedures and improve standard of care under the Kilifi Perinatal and Maternity research (KIPMAT) study . RegistrThis study included deliveries between January 2011 and December 2021. Data were incomplete during health workers strikes which occurred in 2016, 2017, 2020 and 2021 –31 and mThe completeness of disease events described at health facilities will be dependent on a) accessibility to the recording facility, for example those with poorer access are less likely to be documented , 34, 35,i are the raw counts of LBW cases at EZi, PYOi is the weighted population for children under 1 year for EZi and LBWi is the LBW incidence per 1,000 person years in the under 1 population for EZi. These incidence estimates, however, represent a minimum measure of the true community burden of LBW, as LBW events may have occurred outside of Kilifi County hospital. This was repeated for a lower spatial resolution, sub-location, which represents a wider local administrative area sometimes used by county governments.The individual-level data was aggregated at EZ as the smallest unit of spatial analysis, equivalent to village, to estimate the incidence of LBW. The yearly population counts of children under 1 year for these EZs were derived from actual continuous population surveillance data in the KHDSS. The person years of observation (PYO) were adjusted to only include months when strikes did not affect admissions to the maternity ward, hereafter referred to as weighted population for children under 1 year. The overall incidence rate of LBW was computed using exact Poisson distribution with the 95% confidence intervals (CI) obtained from 1,000 bootstrap replicates, with resampling at the EZ level. For each EZ, the incidence rate of LBW was computed as follows:i are the raw counts of LBW cases at EZi, Ai is the access index for EZi and LBWadj,i is the adjusted LBW counts for EZi. The adjusted LBW counts at EZ level were aggregated to sub-location level to compute the incidence of LBW. Spearman’s rank correlation coefficient was used to assess whether the bias in the estimation of LBW incidence due to geographic access to KCH was present after adjustment.To account for variable geographical access to health care, an accessibility index was computed using the Gravity model , 38 whicTo assess whether there were spatial variations in LBW, spatial heterogeneity tests were performed on the adjusted LBW counts using Martin Kulldorf’s spatial scan statistic estimated in SaTScan software . SpatialTo detect hotspots of LBW cases, Kulldorf’s spatial scan statistic under the Discrete Poisson distribution was used. The goal was to investigate the existence of spatial heterogeneity at the EZ and sub-location levels, therefore, the maximum scanning radius was set to 1 km to allow each unit to be assessed as a possible standalone cluster. The inputs for SaTScan were adjusted LBW counts as cases, under 1 year population as the population at risk and EZ or sub-location centroids as the centres of the scanning windows. The most likely cluster was identified based on the maximum log likelihood ratio (LLR) and other clusters with statistically significant log likelihood values were defined as secondary clusters.Previously, socioeconomic factors have been shown to be associated with the odds of LBW . The smaMartin Kulldorf’s spatial scan statistic (SaTScan) was performed using R version 4.1.0 under the rsatscan package. The resulting cluster shapefiles were created using R version 4.1.0 under the rgdal package. Visualisation of maps and clusters was done using ArcGIS version 10.5.There were 49,827 births delivered at KCH between 2011 and 2021. Of these deliveries, we excluded 273 (0.5%) with missing residence details, 19,605 (39.3%) and 9,110 (18.3%) residing outside the KHDSS and in the KHDSS urban extent, respectively  were of LBW. The unadjusted LBW incidence at sub-location was estimated as 32 per 1,000 person years in the under 1 population ; a rate similar to that described at the EZ level. There were variations in the unadjusted LBW incidence across the study area with higher rates of LBW found in areas proximal to KCH  times higher risk of being LBW compared to areas outside the window. Within the other five significantly high incidence spatial clusters identified  times higher risk of being LBW compared to areas outside the window  reported LBW incidence of 45 per 1000 live births , howeverThe direct and indirect causes of LBW are manifold and there are spatial variations in maternal exposure to infectious diseases, notably malaria, nutrition and access to antenatal care (ANC) during pregnancy. Across Kenya, these risk factors all vary significantly , 43, potIt is important to note that 1) LBW incidence was only adjusted for spatial access to KCH as measures of spatial access are effective in understanding how likely a population is to utilize the available healthcare services as previously described . HoweverOur results suggest that birth outcomes are related to geographic location. Clustering of disease outcomes suggests a more intensive investigation of the spatial risks associated with LBW that may inform a more targeted approach to maternal and newborn interventions (policy/care package/services). As health systems and community care become more tailored and sophisticated at county levels, using local epidemiology data to guide intervention becomes more important. LBW is a significant health risk on the Kenya coast, possibly under-estimated from previous health information systems, and the risk of LBW is not homogenously distributed across areas served by the County hospital.Additional file 1: Fig. S1. The distribution of observed deliveries and weighted under 1 population. The darker the shade the higher the value. Panels A and B: shows the distribution at sub-location level. Panels C and D: shows the distribution at EZ level, respectively. The number of deliveries and weighted under 1 population on the maps are for the entire study period (2011 – 2021).Additional file 2: Table S1. Speeds assigned to different road classes and land covers. Figure S2. Distribution of travel time from EZ centroids to KCH; the darker the shade the longer the travel time. Figure S3. Distribution of access index from EZ centroids to KCH; the darker the shade the higher the access index.Additional file 3: Figure S4. The association between LBW incidence and travel time. Panels A and C: shows the distance decay for unadjusted LBW incidence at sub-location and EZ level, respectively. Panels B and D: shows the distance decay for adjusted LBW incidence at sub-location and EZ level, respectively.Additional file 4: Figure S5. Sensitivity analysis using varying maximum scanning radius for cluster identification using SaTScan software. Panel A and B: The radius was set at 2 km and 2.3 km at sub-location level, respectively. Panel C and D: The radius was set at 2 km and 2.3 km at EZ level, respectively. The clusters identified were in similar locations as those reported using the 1 km radius for the entire study period (2011 – 2021).


Escherichia coli (APEC) phage designated as PEC9 was isolated and purified from chicken farm feces samples. The morphology, genomic information, optimal multiplicity of infection (MOI), one-step growth curve, thermal stability, pH stability, in vitro antibacterial ability and biofilm formation inhibition ability of the phage were determined. Subsequently, the therapeutic effects of the phages were investigated in the mice model. The results showed that PEC9 was a member of the siphovirus-like by electron microscopy observation. Biological characterization revealed that it could lyse two serotypes of E. coli, including O1 (9/20) and O2 (6/20). The optimal multiplicity of infection (MOI) of phage PEC9 was 0.1. Phage PEC9 had a latent period of 20 min and a burst period of 40 min, with an average burst size of 68 plaque-forming units (PFUs)/cell. It maintained good lytic activity at pH 3-11 and 4-50°C and could efficiently inhibit the bacterial planktonic cell growth and biofilm formation, and reduce bacterial counts within the biofilm, when the MOI was 0.01, 0.1, and 1, respectively. Whole-genome sequencing showed that PEC9 was a dsDNA virus with a genome of 44379 bp and GC content of 54.39%. The genome contains 56 putative ORFs and no toxin, virulence, or resistance-related genes were detected. Phylogenetic tree analysis showed that PEC9 is closely related to E. coli phages vB_EcoS_Zar3M, vB_EcoS_PTXU06, SECphi18, ZCEC10, and ZCEC11, but most of these phages exhibit different gene arrangement. The phage PEC9 could successfully protect mice against APEC infection, including improved survival rate, reduced bacterial loads, and organ lesions. To conclude, our results suggest that phage PEC9 may be a promising candidate that can be used as an alternative to antibiotics in the control of APEC infection.The inappropriate use of antibiotics has led to the emergence of multidrug-resistant strains. Bacteriophages (phages) have gained renewed attention as promising alternatives or supplements to antibiotics. In this study, a lytic avian pathogenic  Escherichia coli (APEC) can cause colibacillosis in poultry, resulting in systemic infection and characteristic fibrous lesions such as perihepatitis, pericarditis, and airsacculitis (E. coli (ExPEC), posing a major threat to human health and public health  to remove bacteria. The filtrate was mixed with the host APEC AH50 (OD600 = 0.6 ~0.8) and incubated at 37°C 120 rpm to enrich phages. The enriched culture was then filtered. Equal volumes of the filtrate and the host AH50 were mixed with melted semisolid medium (0.7% agarose), spread onto LB plates according to the double-agar overlay method, and incubated overnight at 37°C. Purified phages were obtained by performing the plaque assay six times and stored at 4°C for further studies.A total of 50 chicken fecal samples were collected from chicken farms in Shanghai, China. Isolation and purification of phages were performed as previously described  with somPhage morphology was analyzed through transmission electron microscopy (TEM). The filtered high-titer phage particles were dropped on a copper grid and negatively stained with 2% phosphotungstic acid. Excess liquid was blotted off and then the grids were observed under a FEI T12 transmission electron microscope .The lytic spectrum of phage PEC9 was determined by the spot test and double-agar overlay method according to the previous study .5 - 1 × 1010 PFUs/mL) at the MOI as 0.001, 0.01, 0.1, 1, 10, and 100. Mixtures were cultured at 37°C 120 rpm for 4 h and then filtered through a 0.22-µm filter. The phage titers were determined using the double-agar overlay method. The experiments were performed independently three times.The optimal MOI of phage PEC9 was determined as previously described  with somThe adsorption ability of phage PEC9 was determined as previously described with someA one-step growth curve assay was performed as previously described with some modifications . Phage PFor thermal stability testing, the phage suspension was incubated at 4, 37, 50, 60, 70, and 80°C for 1 hour, respectively. The samples were taken after 20, 40, and 60 min and the phage titers were determined. In addition, the phage suspension was stored at room temperature (25°C) for 4 weeks and the phage titers were determined every week.For pH stability testing, the phage suspension was added to PBS buffer at pH ranging from 2 to 13  and incubated at 37°C for 1 h. The viable phages in each pH environment were detected by the double-agar overlay method.600nm of 0.2 and was infected with phage PEC9 at the MOI as 0.01, 0.1, and 1 respectively, followed by incubation at 37°C 120 rpm for 12 h. AH50 cultured without phage PEC9 was used as the control group. Bacterial growth was determined by monitoring the OD600nm at 2 h intervals. Meanwhile, the number of viable bacterial cells were determined by LB plate counting. The experiments were performed independently three times.The bacterial challenge test of the phage was performed as previously described with some modifications . The hosThe inhibition effect of phage PEC9 on biofilm formation by APEC AH50 was tested by referring to previous studies . The hosThe effect of phage PEC9 on the number of bacteria within the biofilm was tested as described previously  with somhttps://arxiv.org/abs/1401.5130) and SPAdes v3.12.0 , and the genome sequence was proofread using software MUMmer v3.1 (http://mummer.sourceforge.net/) (https://github.com/broadinstitute/pilon) (http://github.com/bbuchfink/diamond), HHpre (https://toolkit.tuebingen.mpg.de/#/tools/hhpred), BLAST and Conserved Domain Identify of NCBI. The Virulence Factor Database (http://www.mgc.ac.cn/VFs/main.htm) and Comprehensive Antibiotic Resistance Database (https://card.mcmaster.ca/) were queried to retrieve the toxic genes, virulence genes, and antibiotic resistance genes in the phage genome. tRNAscan-SE search program (https://lowelab.ucsc.edu/tRNAscan-SE/) was used to identify putative tRNAs (http://blast.ncbi.nlm.nih.gov).Phage DNA was extracted using the Phage Genome DNA Quick Extraction Kit . DNA concentration was determined using a spectrophotometer . The PEC9 genomic DNA was sequenced using an Illumina NovaSeq PE150 sequencer and reads were assembled into a whole genome using SOAPdenovov 2.04 software and GapCloserv1.12. High-quality paired-end reads were assembled using A5-MiSeq v20160825 (ge.net/)  and Piloe/pilon) . Potentie/pilon) . Genome ve tRNAs . A circuThe care and maintenance of all animals were performed following the guidelines of the Institutional Animal Care and Use Committee of Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS). The Ethics Committee of CAAS approved the use of mice for this study. The permit was documented under the number SHVRI-SV-20220812-G01.8 PFUs) (MOI=1) of phage PEC9 intraperitoneally at the 6 h post-infection. Treatment group 2 was injected intraperitoneally with 0.2 mL (1.0 × 108 PFUs) of phage PEC9 at 12 h post-infection. The control group was challenged with PBS buffer. The mice were monitored daily for 14 days to calculate the survival rate.To study the therapeutic effect of phage on APEC infection in mice. Twenty-four 6-week-old specific-pathogen-free (SPF) BALB/c mice were divided randomly into four groups , with six mice in each group for the experiment (n=6). The mice were housed in SPF mice isolator and had free access to food and water during the study period. All mice in four groups were infected with AH50 of 0.2 mL by intraperitoneal administration. The treatment group 1 was administered with 0.2 mL  with five mice in each group for the experiment (n=5). All mice in four groups were infected with AH50 of 0.2 mL by intraperitoneal administration. The treatment group 1 was administered 0.2 mL (1.0 × 107 PFUs) of phage PEC9 intraperitoneally at the 6 h post-infection. Treatment group 2 was injected intraperitoneally with 0.2 mL (1.0 × 107 PFUs) of phage PEC9 at 12 h post-infection. The control group was challenged with PBS. The live mice were euthanized at 24 h post-infection. Spleens were taken aseptically, weighed, homogenized in sterile PBS, and inoculated on LB agar plates to determine the bacteria counts. The livers and spleens were removed under sterile conditions and were fixed in 4% paraformaldehyde fix solution for pathological studies. The specimen was embedded and sliced, and then stained with hematoxylin and eosin (H&E).To evaluate the therapeutic efficacy of phage PEC9 P < 0.05 was considered statistically significant.Statistical analyses were conducted using the GraphPad Prism software (version 6.0) package. Multivariate comparisons were analyzed by using one-way or two-way analysis of variance (ANOVA). A value of Phage PEC9 was isolated and purified from chicken feces using APEC strain AH50 of serotype O1 as the host. It formed transparent round plaques on the double-layer agar plate with a diameter of 3.5-4 mm and clear boundaries  and O2 (6/20)  Table 1.The host bacteria APEC AH50 was infected with phage PEC9 at various ratios, and the phage titer was tested to determine the optimal MOI. When the MOI was 0.1, PEC9 obtained the highest titer, indicating that the optimal MOI was 0.1 Table 2.The results showed that phage PEC9 had an adsorption rate of 33.8% within 2 min, 52.7% within 6 min, 79.6% within 8 min, 84.1% within 10 min, and 81.9% within 12 min, indicating that adsorption reached saturation after about 10 min . And the bacterial viable counts within the biofilm were significantly reduced by phage PEC9 at different MOI (P < 0.001).The effect of phage PEC9 on inhibiting biofilm formation was determined by crystal violet staining in 96-well plates. Compared with the control group, the biofilm formation of APEC AH50 was significantly inhibited by phage PEC9 when MOI=0.01, 0.1, and 1 (http://www.mgc.ac.cn/VFs/main.htm) and Comprehensive Antibiotic Resistance Database (https://card.mcmaster.ca/), implying the safety of phage PEC9 in clinical application. The genome sequence of phage PEC9 has been deposited in the GenBank database under accession number ON548431.1.Whole-genome sequencing revealed that the double-stranded DNA (dsDNA) of phage PEC9 consisted of 44379 bp with a GC content of 54.39% , vB_EcoS_PTXU06 , SECphi18 , ZCEC10  and ZCEC11 . Comparisons of phage genomes were visualized using the Easyfig 2.2.5 tool. As shown in Shigella phage vB_SboD_StarDew, Shigella phage EP23, E. coli phage SSL-2009a, and E. coli phage vB_EcoS_011D5  is the most common phage group discovered to date. The optimal MOI of PEC9 was 0.1, suggesting that fewer phages can produce large numbers of phage progeny. The adsorption of phage particles to bacterial cells is the initial and key step in phage infection. About 84.1% of phage PEC9 adsorbed on the host within 10 min, indicating its high and rapid adsorption rate. The latent period, burst size, and stability of phage in different environments are key factors for the therapeutic applications of phages. Phages with larger burst sizes are beneficial for eliminating the targeted bacteria. PEC9 had a latent period of 20 min and an average burst size of 68 PFU/cell, which was different from phage JS09 with a latent period of 30 min and a burst size of 79 PFU/cell . The Ethics Committee of CAAS approved the use of mice for this study. The study was conducted in accordance with the local legislation and institutional requirements.LY: Data curation, Validation, Visualization, Writing – original draft. YLB: Data curation, Validation, Writing – review & editing. JH: Data curation, Validation, Writing – review & editing, Funding acquisition, Project administration. BZ: Validation, Writing – review & editing. ZW: Validation, Writing – review & editing. XW: Validation, Writing – review & editing. WG: Writing – review & editing, Formal Analysis. DW: Writing – review & editing, Validation. JQ: Writing – review & editing, Formal Analysis. MT: Formal Analysis, Writing – review & editing. YQB: Formal Analysis, Writing – review & editing. HL: Writing – review & editing, Funding acquisition, Methodology, Project administration, Supervision. SW: Funding acquisition, Methodology, Project administration, Writing – review & editing, Conceptualization.


Low birth weight  infants are at significant risk for death and disability. Improving outcomes for LBW infants requires access to advanced neonatal care, which is a limited resource in low- and middle-income countries (LMICs). Predictive modeling might be useful in LMICs to identify mothers at high-risk of delivering a LBW infant to facilitate referral to centers capable of treating these infants.We developed predictive models for LBW using the NICHD Global Network for Women’s and Children’s Health Research Maternal and Newborn Health Registry. This registry enrolled pregnant women from research sites in the Democratic Republic of the Congo, Zambia, Kenya, Guatemala, India , Pakistan, and Bangladesh between January 2017 – December 2020. We tested five predictive models: decision tree, random forest, logistic regression, K-nearest neighbor and support vector machine.We report a rate of LBW of 13.8% among the eight Global Network sites from 2017–2020, with a range of 3.8% (Kenya) and approximately 20% (in each Asian site). Of the five models tested, the logistic regression model performed best with an area under the curve of 0.72, an accuracy of 61% and a recall of 72%. All of the top performing models identified clinical site, maternal weight, hypertensive disorders, severe antepartum hemorrhage and antenatal care as key variables in predicting LBW.Predictive modeling can identify women at high risk for delivering a LBW infant with good sensitivity using clinical variables available prior to delivery in LMICs. Such modeling is the first step in the development of a clinical decision support tool to assist providers in decision-making regarding referral of these women prior to delivery. Consistent referral of women at high-risk for delivering a LBW infant could have extensive public health consequences in LMICs by directing limited resources for advanced neonatal care to the infants at highest risk.The online version contains supplementary material available at 10.1186/s12884-023-05866-1. More than 20 million low birth weight  infants are born annually . LBW infIdentification of pregnant women at risk for the delivery of LBW infants prior to birth could facilitate referral of these women to delivery centers with advanced neonatal care, thereby reducing neonatal mortality related to LBW. Machine learning, or predictive modeling, has been successful at identifying high-risk groups for certain health outcomes, –6 and thEunice Kennedy Shriver National Institute of Child Health and Human Development Global Network for Women’s and Children’s Health Research (GN) maintains a Maternal and Newborn Health Registry (MNHR) documenting pregnancy characteristics and outcomes for over 30,000 mother/infant dyads annually in seven LMICs. This high quality and large dataset is a unique resource to investigate predictive models for LBW in low-resource settings. For this study, our goal was to determine pregnancy characteristics associated with greater probability of delivering LBW infants using the GN MNHR dataset. We also aimed to develop and compare the performance of five predictive models to identify LBW infants using the MNHR data. Understanding these predictors may assist in identifying who will need additional care at delivery, facilitating timely advanced care for LBW infants and thereby reducing long-term morbidity and mortality. We hypothesized that predictive model analysis would identify previously-known prenatal predictors associated with LBW  and new predictors not previously considered or fully explored in prior analyses.The We used the GN MNHR dataset for this study, which includes data from eight GN research sites in seven LMICs  . The MNHFor this study, we included singleton livebirths and fresh stillbirths in the GN MNHR who were not lost to follow-up prior to delivery and delivered at or after 20 weeks (in keeping with the MNHR definition of stillbirth occurring at or after 20 weeks) between January 2017 and December 2020 , 30. We Our primary outcome was LBW, defined as birth weight < 2500 g by measured weight when available, or estimated weight. We selected LBW as a surrogate for preterm birth given the lack of reliable gestational age dating for the total birth population. We evaluated candidate predictors from the variables that are collected in the MNHR, focusing on characteristics that do not require the use of lab tests or ultrasound which may not be available in all resource-poor settings. We selected characteristics or complications that were present prior to the time of delivery since our focus was to build a predictive model that could direct care prior to delivery. We evaluated maternal characteristics of age , education , parity , height, maternal weight, socioeconomic status (SES) score   and prevWe completed exploratory data analysis of study outcomes, maternal characteristics, and pregnancy characteristics, looking for predictors that were highly correlated with each other, had no or little variation or were missing for many subjects. We generated descriptive statistics of frequencies for categorical variables and count, mean, and standard deviation for continuous variables.Scikit-learn in Python 3. We picked Belagavi as the reference because this site generally enrolled women earlier than the other sites, thus representing ‘best case scenario’ for having information available early in pregnancy. We picked parity of one as the reference since nulliparous women are at greater risk for poor outcomes and of the remaining parity groups, parity = 1 had the largest sample size.We prepared data for the models to exclude participants missing one or more of the predictors. The binary outcome for the predictive models was LBW. The variables described above were included as predictors. We prepared data and descriptive tables using SAS 9.4 and ran predictive models using We developed and tested five predictive models: decision tree and random forest (both tree-based models), logistic regression, K-nearest neighbors and support vector machines. The decision tree model, based on classification and regression trees (CART), splits the subjects into consecutive sub-groups based on the most important predictors at each node of the tree. The random forest model avoids overfitting that may happen with a decision tree model by using multiple trees. For both tree-based models, we used Gini impurity criterion to determine which predictors yielded the most information for each classification split. Thirdly, we employed a regularized logistic regression model, which used an L2 ridge regulation penalty to avoid overfitting. For our fourth model, we used K-nearest neighbors with weights set by distance. Finally, our fifth model type was support vector machine models where we ran linear, degree 2 polynomial and radial basis kernel functions. For all models, except the K-nearest neighbors, we improved the class imbalance in the study outcome by using balanced weights.To develop the models, we split the data into a training dataset (75% of available data) and test dataset (25% of available data). Hyperparameters were tuned using tenfold grid-search cross validation with scoring = ’roc_auc’. In addition to hyperparameter tuning, we varied the cut point for the probability used to classify an outcome as LBW for the logistic regression model from 0.1 to 0.9. We trained the models on the training data and validated the models on the test data. To validate the models, we generated predictive accuracy measures, including calculating area under the curve (AUC) and producing receiver operator characteristic (ROC) curves. We calculated precision , recall (sensitivity), and f1 scores using the classification_report method. To further evaluate model performance, we generated calibration curves. We implemented the permutation-based importance in Scikit-Learn as permutation_importance method. This method randomly shuffles each feature and computes the change in the model’s performance. The features which impact the performance the most are the most important ones. Additionally, we created partial dependency plots of the probability of LBW based on the predictors for the models that performed the best.Of the 179,953 women screened in the MNHR from January 2017 to December 2020, 145,206 (80.7%) women were eligible, consented, were not lost to follow-up prior to delivery, and delivered a singleton fresh stillbirth or livebirth with known LBW status and non-missing covariates , SES and clinical site (r = 0.54), parity and age (r = 0.5), and previous livebirth and age (r =—0.45). The distribution of the outcome and predictors among the training dataset  and test dataset  were similar (data not shown).The Pearson correlations for the variables included in the models were calculated (data not shown). Related variables include parity and previous livebirth  and approximately 20% (in each Asian site). We found that mothers of LBW infants were more likely to experience a complication of pregnancy, such as hypertensive disorder, severe antepartum hemorrhage, severe infection of pregnancy, or fresh stillbirth. We used five predictive modeling strategies to identify pregnancy characteristics that predict the outcome of LBW infants. Of the five models tested, the logistic regression model performed the best with an AUC of 0.72 and an accuracy of 61%. All of the top performing models identified clinical site, maternal weight, antenatal care, hypertensive disorders, and severe antepartum hemorrhage as key variables in predicting LBW.Our logistic regression model had reasonable performance to predict LBW using maternal and pregnancy characteristics prior to delivery. If we created a model that had predicted every outcome to be non-LBW, our accuracy rate would be 86%, given the 14% incidence of LBW in our sample; however, the recall of such a model would be 0. The recall, or sensitivity (proportion of true positives correctly identified), of our logistic regression model was 0.72. Since this model is intended to identify women with high-risk pregnancies for referral, a preferable model is one that errs on the side of over-identification  than under-identification . Our logistic regression model also had a precision, or positive predictive value (proportion of positives reported that are true positives) of 0.22. Lower performance for precision would increase the number of false positives, incorrectly identifying women as high-risk for LBW when they deliver a non-LBW infant. While over-predicting women who are at high-risk for delivering a LBW infant could put strain on an under-resourced health system, this is a reasonable allowance for a screening test to direct women to increased surveillance.A comparison of our results to those from prior studies illustrates the importance of studying critical variables in various populations/datasets and comparing across models. Our logistic regression model performed similarly to the predictive model for LBW that included different factors associated with LBW from a case–control study in North India with 500 neonates. That study identified inadequate maternal weight gain, inadequate maternal protein intake, prior preterm infant, prior LBW infant, anemia and passive smoking as factors significantly associated with LBW . Their pIn low-resource settings where prenatal ultrasound is infrequently available to evaluate fetal weight, identification of LBW in advance of delivery using predictive modeling could have a substantial impact on care. Our top performing models identified a consistent cluster of variables available prior to delivery as important predictors of delivering a LBW infant, including low maternal weight, hypertensive disorder and severe antepartum hemorrhage. Maternal weight, hypertensive disorder, and severe antepartum hemorrhage are detectable at a time when referral is still feasible, and thus could be feasibly incorporated into a clinical tool to predict LBW. In particular, maternal malnutrition is a major, potentially modifiable predictor of LBW identifiable early in pregnancy. Limited antenatal care was also identified as a risk factor, but this variable is confounded by the higher number of preterm infants that are LBW, since preterm delivery truncates the usual number of antenatal care visits. We suspect that improving data collection in these key domains could improve the reliability of the predictive model. For example, inclusion of additional clinical information such as specific maternal blood pressure, might improve the accuracy of the predictive tools and thereby enhance the clinical utility of the predictive models. Our predictive modeling study is the first step in the development of a clinical tool to support decisions regarding referral of pregnancies at high risk for LBW in low-resource settings. While our study did not identify novel predictors of LBW, a clinical decision support tool incorporating these results could enhance care by standardizing referral decisions related to the anticipated delivery of a LBW infant.Our study also identified clinical site as a consistent predictor of LBW across our top performing models. It is important to recognize that sites are not necessarily reflective of care across a country; future studies could consider analysis by geographic clusters with similar LBW rates as an alternative approach. Ultimately, the influence of site on prediction of LBW suggests that clinical tools to predict LBW should be developed within the site that they will be used. Our analysis provides a rubric for the development of similar tools in new sites, identifying an important set of predictors for collection that are not related to site, and indicating that a traditional logistic model is sufficient for analysis. Given the importance of site in the model, additional research could also focus on understanding how site differences are related to measurable characteristics, with replication of modeling with these new characteristics to improve predictive performance and reduce the importance of site identifiers in the model.Our study has several notable strengths. We used high quality and robust prospectively-collected research data from the NICHD GN MNHR. This unique, population-based dataset contains maternal characteristics, pregnancy characteristics and delivery outcomes collected for a large number of women in seven different LMICs in Latin America, Africa and Asia. Due to the paucity of detailed health records in LMICs, the MNHR is an exceptional resource by which to build a predictive model. We assessed and compared the performance of five different predictive models using independent training and test data. The side-by-side comparison showed that the logistic regression model performed similarly to the random forest and linear support vector machine models, which is encouraging since logistic regression models are widely used and less complex. However, our study also had some limitations. We were limited by the data collected in the MNHR to build the predictive models. We had missing data for socio-economic status and maternal height (primarily Kenya) in early 2017. Maternal weight and clinical site were both predictors of LBW but were related with lower average weights in the Asian sites compared to higher average weights in Guatemala and the African sites. While using BMI instead of weight might account for some of the difference in weight across sites, we chose to maintain maternal height and weight as separate variables in our modeling since the MNHR includes sites where stunting or underweight are serious issues. We did not have information from clinical records such as maternal blood pressure, fundal height, or other features that might have improved the precision of our model. Despite these limitations, we believe that the variables collected approximate the typical data that might be readily available in a low-resource area, where clinical variables might be difficult to obtain. We limited the analysis to five different model types; other models such as extreme gradient boosting may have performed better.We identified several predictive modeling strategies that risk-stratify women in LMICs based on their risk of delivering a LBW infant using clinical variables readily available prior to delivery in low-resource settings. Our creation of these predictive models is an important first step in the development of a clinical decision support tool to prompt early referral of women at high-risk of delivering a LBW infant in LMICs. Such a clinical tool could facilitate standard referral of these women before delivery, directing the limited resource of advanced neonatal care to infants at highest risk. Timely, advanced care for LBW infants could reduce mortality and serious morbidity of these infants.Additional file 1: Supplement Table 1. Maternal and pregnancy characteristics by LBW status for the analysis population.