Effects of interpregnancy interval on pregnancy complications: protocol for systematic review and meta-analysis

Introduction: Interpregnancy interval (IPI) is the length of time between a birth and conception of the next pregnancy. Evidence suggests that both short and long IPIs are at increased risk of adverse pregnancy and perinatal outcomes. Relatively less attention has been directed towards investigating the effect of IPI on pregnancy complications, and the studies that have been conducted have shown mixed results. This systematic review will aim to provide an update to the most recent available evidence on the effect of IPI on pregnancy complications. Method and Analysis: We will search electronic databases such as Ovid/MEDLINE, EMBASE, CINAHL, Scopus, Web of Science and PubMed to identify peer-reviewed articles on the effects of IPI on pregnancy complications. We will include articles published from start of indexing until 12 February 2018 without any restriction to geographic setting. We will limit the search to literature published in English language and human subjects. Two independent reviewers will screen titles and abstracts and select full-text articles that meet the eligibility criteria. The Newcastle-Ottawa tool will be used to assess quality of observational studies. Where data permit, meta-analyses will be performed for individual pregnancy complications. A subgroup analyses by country categories (high-income vs low and middle-income countries) based on World Bank income group will be performed. Where meta-analysis is not possible, we will provide a description of data without further attempt to quantitatively pool results. Ethics and Dissemination: Formal ethical approval is not required as primary data will not be collected. The results will be published in peer-reviewed journals and presented at national and international conferences. Prospero Registration Number: CRD42018088578

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

The association between child maltreatment and health risk behaviours and conditions throughout life in the Australian Child Maltreatment Study

Objective To estimate associations between all five types of child maltreatment (emotional abuse, neglect, physical abuse, sexual abuse, and exposure to domestic violence) and health risk behaviours and conditions. Design, setting, participants Nationally representative survey of Australian residents aged 16 years and older conducted by computer-assisted telephone interviewing. Main outcome measures Associations between child maltreatment and the following health risk behaviours and conditions: current smoker, binge drinking (at least weekly in past 12 months), cannabis dependence (according to the Cannabis Severity of Dependence Scale), obesity (based on body mass index), self-harm in past 12 months, and suicide attempt in past 12 months. Results A total of 8503 participants completed the survey. All five types of child maltreatment were associated with increased rates of all of the health risk behaviours and conditions that we considered. The strongest associations were in the youngest age group (16–24-year-olds). Sexual abuse and emotional abuse were associated with the highest odds of health risk behaviours and conditions. Cannabis dependence, self-harm and suicide attempts were most strongly associated with child maltreatment. Experiencing more than one type of child maltreatment was associated with higher rates of health risk behaviours and conditions than experiencing one type of child maltreatment. Conclusions Child maltreatment is associated with substantially increased rates of health risk behaviours and conditions. Prevention and intervention efforts should be informed by trauma histories, and holistic psychosocial care should be incorporated into programs focusing on behaviour change

Child sexual abuse by different classes and types of perpetrator: prevalence and trends from an Australian national survey

Background Little evidence exists about the prevalence of child sexual abuse (CSA) inflicted by different relational classes of perpetrators (e.g., parents; institutional adults; adolescents), and by individual types of perpetrators (e.g., fathers and male relatives; male teachers and male clergy; known and unknown adolescents). Objective To generate evidence of the prevalence of CSA by different perpetrators, and trends by victim gender and age group. Participants and setting The Australian Child Maltreatment Study collected information about CSA victimisation from a nationally-representative sample of 8503 individuals aged 16 and over. Methods We analysed data about 42 perpetrator types, collapsed into eight classes. We generated national prevalence estimates of CSA inflicted by each perpetrator class and individual perpetrator type, and compared results by victim gender and age group. Results Australian CSA prevalence was 28.5%, with the following prevalence by perpetrator classes: other known adolescents (non-romantic): 10.0%; parents/caregivers in the home: 7.8%; other known adults: 7.5%; unknown adults: 4.9%; adolescents (current/former romantic partners): 2.5%; institutional caregivers: 2.0%; siblings: 1.6%; unknown adolescents: 1.4%. Women experienced more CSA by all perpetrator classes except institutional caregivers. Age group comparison showed significant declines in CSA by parents/caregivers, and other known adults; and increases in CSA by adolescents (current/former romantic partners). Individual perpetrator type comparison showed declines in CSA by fathers, male relatives living in the home, non-resident male relatives, and other known male adults; and increases in CSA by known male adolescents, current boyfriends, and former boyfriends. Conclusions CSA by adults has declined, indicating positive impacts of prevention efforts. However, CSA by adolescents has increased. Further declines in CSA by adults are required and possible. Targeted prevention of CSA by adolescents must be prioritised

The prevalence of child maltreatment in Australia: findings from a national survey

Objectives To estimate the prevalence in Australia of each type of child maltreatment; to identify gender- and age group-related differences in prevalence. Design, setting Cross-sectional national survey; mobile telephone interviews using random digit dialling (computer-generated), Australia, 9 April – 11 October 2021. Retrospective self-report data using validated questionnaire (Juvenile Victimisation Questionnaire-R2 Adapted Version (Australian Child Maltreatment Study). Participants People aged 16 years or more. The target sample size was 8500 respondents: 3500 people aged 16–24 years and 1000 respondents each from five further age groups (25–34, 35–44, 45–54, 55–64, 65 years or more). Main outcome measures Proportions of respondents reporting physical abuse, sexual abuse, emotional abuse, neglect, and exposure to domestic violence to age 18 years, assessed with the Juvenile Victimization Questionnaire-R2 Adapted Version (Australian Child Maltreatment Study), overall and by gender and age group, and weighted to reflect characteristics of the Australian population aged 16 years or more in 2016. Results Complete survey data were available for 8503 eligible participants (14% response rate). Physical abuse was reported by 32.0% of respondents (95% confidence interval [CI], 30.7–33.3%), sexual abuse by 28.5% (95% CI, 27.3–29.8%), emotional abuse by 30.9% (95% CI, 29.7–32.2%), neglect by 8.9% (95% CI, 8.1–9.7%), and exposure to domestic violence by 39.6% (95% CI, 38.3–40.9%). The proportions of respondents who reported sexual abuse, emotional abuse, or neglect were each statistically significantly larger for women than men. The reported prevalence of physical abuse by respondents aged 16–24 years was lower than for those aged 25–34 years, and that of sexual abuse was lower than for those aged 35–44 years, suggesting recent declines in the prevalence of these maltreatment types. Conclusions Child maltreatment is common in Australia, and larger proportions of women than men report having experienced sexual abuse, emotional abuse, and neglect during childhood. As physical and sexual abuse may have declined recently, public health policy and practice may have positive effects, justifying continued monitoring and prevention activities

The prevalence and nature of multi‐type child maltreatment in Australia

Objectives To determine the prevalence in Australia of multi-type child maltreatment, defined as two or more maltreatment types (physical abuse, sexual abuse, emotional abuse, neglect, or exposure to domestic violence) and to examine its nature, family risk factors, and gender and age cohort differences. Design Retrospective cross-sectional survey using a validated questionnaire. Setting and participants Mobile phone random digit-dial sample of the Australian population aged 16 years and older. Main outcome measures National estimates of multi-type child maltreatment up to age 18 years using the Juvenile Victimisation Questionnaire-R2: Adapted Version (Australian Child Maltreatment Study). Results Of 8503 participants, 62.2% (95% CI, 60.9–63.6%) experienced one or more types of child maltreatment. Prevalence of single-type maltreatment was 22.8% (95% CI, 21.7–24.0%), whereas 39.4% (95% CI, 38.1–40.7%) of participants reported multi-type maltreatment and 3.5% (95% CI, 3.0–4.0%) reported all five types. Multi-type maltreatment was more common for gender diverse participants (66.1% [95% CI, 53.7–78.7%]) and women (43.2% [95% CI, 41.3–45.1%]) than for men (34.9% [95% CI, 33.0–36.7%]). Multi-type maltreatment prevalence was highest for those aged 25–44 years. Family-related adverse childhood experiences — especially mental illness and alcohol or substance misuse — increased risk. Exposure to domestic violence was the maltreatment type most often present in multi-type maltreatment patterns. Conclusions Multi-type child maltreatment is prevalent in Australia and more common in women and gender diverse individuals. Child protection services, health practitioners, and prevention and intervention services must assess and manage multi-type maltreatment in children and address its health consequences across the lifespan. Public health policy should consider prevention services or strategies that target multi-type child maltreatment

Child maltreatment and health service use: findings of the Australian Child Maltreatment Study

Objectives To examine associations between child maltreatment and health service use, both overall, by type and by the number of types of maltreatment reported. Design, setting Cross-sectional, retrospective survey using the Juvenile Victimization Questionnaire-R2: Adapted Version (Australian Child Maltreatment Study); computer-assisted mobile telephone interviews using random digit dialling, Australia, 9 April – 11 October 2021. Participants Australians aged 16 years or more. The target sample size was 8500 respondents: 3500 people aged 16–24 years and 1000 respondents each from the five age groups (25–34, 35–44, 45–54, 55–64, 65 years or more). Main outcome measures Self-reported health service use during the past twelve months: hospital admissions, length of stay, and reasons for admission; and numbers of consultations with health care professionals, overall and by type. Associations between maltreatment and health service use are reported as odds ratios adjusted for age group, gender, socio-economic status, financial hardship (childhood and current), and geographic remoteness. Results A total of 8503 participants completed the survey. Respondents who had experienced child maltreatment were significantly more likely than those who had not to report a hospital admission during the preceding twelve months (adjusted odds ratio [aOR], 1.39; 95% confidence interval [CI], 1.16–1.66), particularly admission with a mental disorder (aOR, 2.4; 95% CI, 1.03–5.6). The likelihood of six or more visits to general practitioners (aOR, 2.37; 95% CI, 1.87–3.02) or of a consultation with a mental health nurse (aOR, 2.67; 95% CI, 1.75–4.06), psychologist (aOR, 2.40; 95% CI, 2.00–2.88), or psychiatrist (aOR, 3.02; 95% CI, 2.25–4.04) were each higher for people who reported maltreatment during childhood. People who reported three or more maltreatment types were generally most likely to report greater health service use. Conclusions Child maltreatment has a major impact on health service use. Early, targeted interventions are vital, not only for supporting children directly, but also for their longer term wellbeing and reducing their health system use throughout life

Autism and Intellectual Disability Are Differentially Related to Sociodemographic Background at Birth

Background: Research findings investigating the sociodemographics of autism spectrum disorder (ASD) have been inconsistent and rarely considered the presence of intellectual disability (ID). Methods: We used population data on Western Australian singletons born from 1984 to 1999 (n = 398,353) to examine the sociodemographic characteristics of children diagnosed with ASD with or without ID, or ID without ASD compared with non-affected children. Results: The profiles for the four categories examined, mild-moderate ID, severe ID, ASD without ID and ASD with ID varied considerably and we often identified a gradient effect where the risk factors for mild-moderate ID and ASD without ID were at opposite extremes while those for ASD with ID were intermediary. This was demonstrated clearly with increased odds of ASD without ID amongst older mothers aged 35 years and over (odds ratio (OR) = 1.69 [CI: 1.18, 2.43]), first born infants (OR = 2.78; [CI: 1.67, 4.54]), male infants (OR = 6.57 [CI: 4.87, 8.87]) and increasing socioeconomic advantage. In contrast, mild-moderate ID was associated with younger mothers aged less than 20 years (OR = 1.88 [CI: 1.57, 2.25]), paternal age greater than 40 years (OR = 1.59 [CI: 1.36, 1.86]), Australian-born and Aboriginal mothers (OR = 1.60 [CI: 1.41, 1.82]), increasing birth order and increasing social disadvantage (OR = 2.56 [CI: 2.27, 2.97]). Mothers of infants residing in regional or remote areas had consistently lower risk of ASD or ID and may be linked to reduced access to services or underascertainment rather than a protective effect of location. Conclusions: The different risk profiles observed between groups may be related to aetiological differences or ascertainment factors or both. Untangling these pathways is challenging but an urgent public health priority in view of the supposed autism epidemic

Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments

Although inequalities in health and socioeconomic status have an important influence on childhood educational performance, the interactions between these multiple factors relating to variation in educational outcomes at micro-level is unknown, and how to evaluate the many possible interactions of these factors is not well established. This paper aims to examine multi-dimensional deprivation factors and their impact on childhood educational outcomes at micro-level, focusing on geographic areas having widely different disparity patterns, in which each area is characterised by six deprivation domains (Income, Health, Geographical Access to Services, Housing, Physical Environment, and Community Safety). Traditional health statistical studies tend to use one global model to describe the whole population for macro-analysis. In this paper, we combine linked educational and deprivation data across small areas (median population of 1500), then use a local modelling technique, the Takagi-Sugeno fuzzy system, to predict area educational outcomes at ages 7 and 11. We define two new metrics, “Micro-impact of Domain” and “Contribution of Domain”, to quantify the variations of local impacts of multidimensional factors on educational outcomes across small areas. The two metrics highlight differing priorities. Our study reveals complex multi-way interactions between the deprivation domains, which could not be provided by traditional health statistical methods based on single global model. We demonstrate that although Income has an expected central role, all domains contribute, and in some areas Health, Environment, Access to Services, Housing and Community Safety each could be the dominant factor. Thus the relative importance of health and socioeconomic factors varies considerably for different areas, depending on the levels of each of the other factors, and therefore each component of deprivation must be considered as part of a wider system. Childhood educational achievement could benefit from policies and intervention strategies that are tailored to the local geographic areas' profiles