4. Are children active and physically healthy?
This chapter reviews the available data on child physical health and well-being, and highlights the information required to develop better policies to promote children’s health. It considers key child health outcomes such as birth outcomes, physical development and self-reported health status. It examines how children’s activities and behaviours shape their physical health as well as the effects of children’s family situation and the community and built environment. It also considers the role of public policies in supporting child health. The chapter considers the cross-national data available on child physical heath and discusses the way forward, highlighting key data gaps and setting out priorities for data development.
The purpose of this chapter is to review the data on child physical health and highlight the information required to develop better policies to promote child health. The chapter assesses the available cross-national information to monitor children's health at different stages of childhood, and also makes use of national evidence to illustrate how some data gaps could be addressed. It draws on the scientific literature on child physical health and the relationship with later adult health highlighting the data gaps and priorities for data development. There are complex interactions between child physical health and other aspects of well-being, for example, mental health and material living conditions, which are discussed in Chapters 3 and 5, respectively.
Children’s physical health is a major component of well-being from early life through to adolescence. It is a key determinant of health status in later adult life (Palloni et al., 2009[1]; Conti and Heckman, 2013[2]; Currie, 2020[3]; Almond, Currie and Duque, 2018[4]; Mallo and Wolfe, 2020[5]). Having good physical health during childhood has been linked to higher levels of educational attainment and better employment and economic outcomes later in life; it also lays the foundation for greater psychological well-being and life satisfaction (Currie, 2005[6]; Currie, 2009[7]; Jackson, 2010[8]; Jackson, 2015[9]; Patton et al., 2016[10]; Poulton et al., 2002[11]). By contrast, adverse health events during childhood can have long-lasting effects on later adult health and other key outcomes, such as employment status and earnings (Currie, 2020[3]; Mallo and Wolfe, 2020[5]). Much evidence underlines that fact that social inequalities in health evident in middle-age actually first begin to emerge in childhood.
To promote child health, policy makers need a full understanding of the key determinants of child health and age specific vulnerabilities and risks. They need to understand the shortfall in family resources to invest in child health, and where policy interventions could be the most effective. The chapter provides the following key messages:
A large body of evidence underlines the linkages between physical health in childhood and later life outcomes. Illness and deprivation during childhood may have long-term consequences for health during adulthood, either directly through the illness itself or indirectly through socio-economic impacts (Mallo and Wolfe, 2020[5]). Policies aimed at improving children’s health have long-lasting benefits for both the individual and society because of increased human capital accumulation, better employment opportunities and health later in life (Currie, 2020[3]).
The “sensitive period model” suggests adverse experiences during sensitive periods of development (e.g., gestation, birth, childhood, and adolescence) lead to functional changes in organisms through biological programming. (Yang et al., 2017[12]). The importance of prenatal and perinatal conditions for later health is evidenced by many studies (Almond, Currie and Duque, 2018[4]), and there is large scientific evidence showing that the first 1 000 days of a child’s life are particularly important for their development and future health outcomes (Clark et al., 2020[13]).
Priority should be given to the development of age-appropriate data to capture the physical health status of children at different stages of childhood. Longitudinal data collection could track health outcomes at different life stages.
Good assessment of children’s and families access to resources protecting children from diseases and ensure they develop healthy is needed, as well as information on the main risks to child health at different ages. Protective and health-enhancing factors can be promoted from the early days of life to enhance child health resilience and foster child physical development. Access to high quality preventative and curative services are key resources to support children’s healthy development.
Important protective factors for child health include good neighbourhood environmental and housing quality, healthy nutrition and dietary intake, up-to-date vaccination, regular physical activity, respecting sleep patterns and nap time, etc. Conversely, high levels of air pollution, unsafe outdoor equipment, poor housing quality, poor dietary intakes, and lack of physical activity put child health at risk (and indirectly other well-being outcomes are also jeopardised). As children grow up, they are subject to additional health risks due to the development of risk-taking behaviours and exposure to toxic substance.
Key areas for improvement of data collection are also identified, including:
The need of data to better capture the significant social gradients affecting child health and to enable a better tracking of the formation of health inequalities from the early years, including in the first 1 000 days of life. Deprivation during childhood has important effects on health that can endure long into adulthood, even for adults who escape poverty and disadvantage (Poulton et al., 2002[11]). However, while there is good data on inequalities and social gradients in adolescent health and health behaviours, thanks mostly to the Health Behaviour in School-age Children (HBSC), there is a clear blind spot when it comes to younger children.
The lack of data on children who are exposed to high risk to physical health, such as child victims of maltreatment. Information on physical health of children in other vulnerable situation (e.g. children with disabilities, in out-of-home, homeless) is also sparse and beset by several measurement challenges.
The data needed to track children’s exposure to environmental risks such as unsafe air, contaminated water and food, including for infants and toddlers, who can be exposed to chemicals through contaminants in foods, toys and other products targeted at young children.
The need to improve data availability on maternal and child health care services coverage, as well as on the specific reasons for children not receiving service or treatment (e.g. lack of service or treatment availability or affordability). Systematic collection of data on children’s health checks at different stages of childhood is also needed to develop stronger preventative policies. Better information of countries spending on health services for children at different ages, including on preventative services, would also help countries assess where to prioritise public spending.
The need to better track the implementation and outcomes of recommendations on child health.
The lack of information on children’s knowledge on various health issues, including the main challenges for current and future health and well-being, what they can do to improve their physical health, and any support they can receive if needs be.
The need to develop data that allows to better examine how physical health affects other dimensions of children’s well-being, such as cognitive and social and emotional well-being.
The chapter starts with a discussion of the main aspects of children's physical health, organised to cover the different stages of childhood, i.e. birth (and the pre-natal period), early childhood, middle childhood and adolescence, respectively. The subsequent sections review the availability of data and indicators and discusses the key priorities that can guide the further development of indicators according to the trade-off that may exist between their relevance and the feasibility of collecting comparable data across countries. Finally, the chapter concludes by connecting the dots between future policy development and the need for an evidence-informed framework on children’s physical health.
Genetics are an important determinant of individual health. Lifestyle and environmental factors also have a significant role to play For example, protective health behaviours, such as regular physical activity, decrease susceptibility to chronic diseases and reduce the risk of obesity (Haskell, Blair and Hill, 2009[14]). Whereas air quality and pollution levels, dependant on where a person lives and works, can contribute to a number of adverse health outcomes, including respiratory diseases and cardiovascular conditions (Dominici et al., 2006[15]; OECD/European Union, 2020[16]). The availability, affordability and quality of health care services is also crucial to prevent or treat health problems.
Table 4.1 offers an overview of the central aspects of children’s physical health and well-being throughout childhood, considering four different stages: pregnancy and infancy, early childhood, middle childhood and late childhood (adolescence). Key health outcomes and their behavioural and environmental determinants are taken into account in accordance with the dimensions normally used to categorise the determinants of health (WHO, 2017[17]):
Panel A highlights child key physical health outcomes which includes direct measures of health status and physical development. This includes birth outcomes, such as low birth weight and preterm birth rates, physical development such as and anthropometric development (e.g. weight, height and head circumference) and body mass index (BMI). It also cover physical health status, considering outcomes such as the prevalence of certain diseases, injuries and self-reported health status.
Panel B focuses on children’s health-related behaviours, activities and processes. This includes nutrition and eating behaviours (e.g. breastfeeding, fruit and vegetable consumption, and sugar consumption). It also includes protective health behaviours such sleep patterns and levels of physical or sedentary activity. Also important here are risky health behaviours, for instance, substance use and unprotected and early sexual activity in older children. Accounting for different stages of childhood is important, as children’s nutritional needs evolve as well as them being able to exercise autonomy over what they do and what they eat as they older.
Panel C covers children’s settings and environments, broken down between the family and the home, and the community and built environment. Important family and home conditions include parental health and health behaviours, family financial resources and material conditions, and family violence and child maltreatment. Aspects of children’s physical and built environments include air and noise pollution, and considerations of neighbourhood crime and violence.
Lastly, Panel D covers public policies that can impact children’s physical health outcomes. Many different policies can play a role in shaping children’s health, through mechanisms that operate through various channels (Box 4.1). As a result, promoting child health involves considering policies that don’t specifically focus on health (such as cash or in-kind assistance, housing quality policies, environmental policies, or parenting education programs), in addition to policies focusing on access to health care or the direct provision of medical services (Currie and Reichman, 2015[18]).
Child health policy represents a patchwork of various efforts at national and regional levels. In some countries, the government has no affirmative obligation to promote child health and, more often than not, steps in only after a severe health risk has been identified. Responsibility is fragmented at national and regional levels, and among entities that control different aspects of children’s well-being, such as health care, education, social assistance and child protection. The result is a largely uncoordinated jumble of resources and services that can be extremely difficult to navigate. Children’s access to services can be dependent on where they reside.
Some health-related policies attempt to prevent the emergence of child health problems (e.g. by regular body and dental health checks), while others aim to treat them once they occur. A few policies such as maternal leave entitlements target women during or before pregnancy with the goal of improving the health of both mothers and new-borns. Some policies have a universal scope (e.g. health checks of new-borns), while others target low-income families children, as it is particularly the case when children grow up (OECD, 2009[19]). Health insurance systems also play a key role in raising health services affordability and accessibility by low income children. For instance, many OECD countries exempt children from co-payments (i.e. fixed charges) to guarantee access to health services (Paris, Devaux and Wei, 2010[20])
A broader range of policies influence child health by improving the quality of the settings and environments in which children live. These include housing and built environment policies, and environmental quality regulations and policies. These policies not only impact child health, but they also are crucial to enhancing child material and social well-being. Meeting the basic needs for food, shelter, safety, housing and economic security is fundamental to good health (Chapter 3). Children’s health and safety are also strongly influenced by children’s physical security at home and in the neighbourhood. Chronic and acute conditions such as obesity, asthma, lead poisoning, and injuries are associated with risk factors within a child’s built environment (APHA, 2010[21]). Other policies may also have an effect, such as those that allow parents to stay employed and thereby increase their income and escape poverty, but the link to child health is indirect and the evidence on the effects is not robust enough to be further explored.
Policies and programmes primarily operate to enhance family and community capacities to support the foundations of good child health (Figure 4.1). These foundations encompass the basic needs of all children, which are safe and secure environments, responsive caregiving, adequate and appropriate nutrition, and health-promoting behaviours. They, in turn, influence basic biological mechanisms that shape child development and health across the life span. There are two important contexts that play a moderating role in child health. The first context is the social, economic, and cultural determinants of health, which includes the direct and indirect effects of poverty, education, and discrimination. The second is the settings – or places – in which children and their families live, work, and develop.
Source: Mistry et al. (2012[22]), A New Framework for Childhood Health Promotion: The Role of Policies and Programs in Building Capacity and Foundations of Early Childhood Health, American Public Health Association, https://doi.org/10.2105/AJPH.2012.300687.
Birth outcomes and key pre-natal determinants
A child’s physical health in the first few months of life is critical for survival beyond infancy and to prevent developmental issues that can have long-reaching effects on many aspects of child to adult outcomes (see Box 4.2 as well as Conley and Bennett (2000[23]) and Almond, Currie and Duque (2018[4])). Infant mortality has traditionally been used to measure the outcomes of infants, but as infant mortality rates have dropped sharply across OECD countries over the past few decades, it has increased the need to complement it with many other indicators of infant/child health (OECD, 2019[24]). A greater focus is thus given to birth outcomes, such as birth weight and gestational age, and prenatal conditions that critically impair health and wellbeing.
The incidence of low birth weight is widely used to assess health status of children at birth, as it is associated with an increased risk of poor health. For example, children born with a low weight are at an elevated risk of experiencing developmental problems in the short- and long-term (Scharf and DeBoer, 2016[25]). In 2017, on average across OECD countries, 6.5% of babies were classified as having a low birth weight weighing less than 2 500 grams (OECD, 2019[26]).
Preterm births (i.e. births occurring before 37 completed weeks of gestation) are one of the leading causes of death in children below five years of age (Vogel et al., 2018[27]). In addition, preterm births often bring a series of health complications, as well as feeding difficulties, visual and hearing problems, and a higher risk of experiencing behavioural or learning difficulties relative to term-born babies (De Araújo et al., 2012[28]; Platt, 2014[29]; Mangin, Horwood and Woodward, 2017[30]; Moreira, Magalhães and Alves, 2014[31]; Johnson et al., 2015[32]; Cheong et al., 2017[33]). Pre-term births also typically result in low birth weight. As preterm babies require special care, incidence data at population level is valuable to highlight the needs for countries to develop appropriate neonatal services. Available data for European countries show a variance in the frequency of preterm births by a ratio of almost two: In 2015, about 6% of births in Denmark, Estonia, Finland, Iceland, Latvia, Lithuania, Norway and Sweden were preterm compared to more than 11% in Greece (Euro-Peristat Project, 2015[34]).
Children's health status at birth depends on many factors, including maternal physical health and both parents’ mental health during the pregnancy period (see Box 4.2). The risk of adverse birth outcomes also increases by maternal consumption of alcohol, tobacco or drug use during pregnancy. For example, prenatal nicotine exposure can impair respiratory functioning and increase metabolic and cardiovascular risk factors (Gibbs, Collaco and McGrath-Morrow, 2016[35]; Kelishadi et al., 2016[36]; Li et al., 2016[37]). Given these links, it is important to measure children’s exposure to unhealthy environments during gestation to guide any required policy action.
Prenatal care is care provided before the birth, primarily to prepare the expectant mother for delivery and to monitor and respond to warning signs for mother and child during pregnancy and childbirth. Higher prenatal care coverage in a country is linked with fewer low-weight births (OECD/WHO, 2018[38]). Prenatal maternal care consists of assessments and treatments, including estimates of the unborn child’s anthropometrics to provide indications of the child’s growth and development in the womb, which have been shown to predict later child physical and mental health conditions, for example overweight, asthma and hyperactivity. Using US data, Conti et al. (2018[39]) found that foetal anthropometrics in the third trimester of pregnancy predicted child growth (height and BMI) at six years of age. At the population and public policy level, such a finding reinforces the importance of the in-utero environment for child health and development.
A large spectrum of factors beyond genetics are found to critically influence birth outcomes, including:
Maternal physical health impacts birth outcomes through a variety of complex mechanisms, including placental functioning (Byrne and Phillips, 2000[40]; Skogen and Øverland, 2012[41]) and inflammation or stress (Calkins and Devaskar, 2011[42]; Skogen and Øverland, 2012[41]). Maternal pre-pregnancy underweight increases the risk of giving birth to a child who is small for gestational age size or of low birth weight; conversely, maternal overweight and obesity increases the risk of high birth weight, macrosomia and subsequent offspring overweight/obesity (Agius, Savona-Ventura and Vassallo, 2013[43]; Oken, 2009[44]; Yu et al., 2013[45]; De Jongh et al., 2014[46]).
Parental mental health. There is a positive relationship between positive affect experienced by an expectant mother and gestational length. (Pesonen et al., 2016[47]). Conversely, chronic stress and work stress exposure, as well as maternal mental depression symptoms are found to adversely impact gestational length and the birth weight of children (Lee et al., 2011[48]; Dunkel Schetter and Lobel, 2012[49]; Grote et al., 2010[50]; Chang et al., 2014[51]; Pesonen et al., 2016[47]). Though fathers are less often questioned about their psychological well-being during their partner's pregnancy, episodes of paternal depression can be associated with an increased risk of preterm birth (Liu et al., 2016[52]) and may influence the mothers state of well-being during pregnancy (Field et al., 2006[53])
Health-related behaviours. Physical activity of pregnant women, as opposed to a sedentary lifestyle, is associated with a lower incidence of preterm birth and low birthweight (Both et al., 2010[54]; Davenport et al., 2018[55]). Pregnant women engaging in physical activity during their free time are at reduced risk of preeclampsia, gestational diabetes mellitus and preterm birth (da Silva et al., 2017[56]). By contrast, poor nutrition (WHO, 2014[57]; Ramakrishnan, 2004[58]), as well as tobacco and alcohol use are associated with higher risk of pre-term and/or underweight births (Dubois and Girard, 2006[59]; Magnus et al., 2008[60]; Agrawal et al., 2010[61]; Knopik et al., 2016[62]; Pereira et al., 2017[63]; Goetzinger et al., 2012[64]; Panico, Tô and Thevenon, 2015[65]). Drug use is one factor posing the highest t risk to preterm birth. For instance, a 2011 analysis in the United States found that the risk of preterm birth among expectant mothers using cocaine stood at three and a half times more likely to deliver a preterm infant while using opioid increased the risk by almost three times (Ruane, 2018[66]).
Working conditions and family socio-economic status. Long working hours, shift work, lifting, standing and heavy physical workload tend to raise the risk of preterm delivery (Klebanoff, Shiono and Carey, 1990[67]; Mozurkewich et al., 2000[68]; Croteau, Marcoux and Brisson, 2007[69]; Bonzini et al., 2011[70]). Similarly, women with low levels of education and low family income have a higher likelihood to have a child with a low birth weight (Dubois and Girard, 2006[59]; Magnus et al., 2008[60]; Panico, Tô and Thevenon, 2015[65]).
Physical development
From the very first months of life, changes in children's height and weight (i.e. the anthropometric development) can be indicative of growth and developmental problems. Deviations from optimal growth are typically measured as stunting (low height relative to age), wasting (low weight relative to height) as well as under- (low weight relative to age) and overweight (high weight relative to height). Even though there are some recent critical discussions on the interpretation of these measures (see e.g. Scheffler et al. (2020[71])), they are particularly useful for assessing child health and nutritional status in early childhood in the absence of other measurable aspects, given the persistent effects of children’s height and weight of in the first years of life on later health outcomes, including adult obesity and cardio-vascular diseases (Victora et al., 2008[72]; Martin-Calvo, Moreno-Galarraga and Martinez-Gonzalez, 2016[73]; Liu et al., 2020[74]).
Motor development is important in the first few years of life because of its impacts on other development areas. Motor development can instigate a developmental cascade, including changes in perceptual, cognitive, language and social development (Adolph and Robinson, 2015[75]; Keenan, Evans and Crowley, 2016[76]; Leonard and Hill, 2014[77]; Gonzalez, Alvarez and Nelson, 2019[78]). However, developing an indicator assessing child motor development in the first few months of life is not appropriate because there is a large degree of heterogeneity in the age pattern of motor development without persistent consequences on child development (Adolph and Robinson, 2015[75]). Furthermore, measures of motor skills in the early years of life (i.e., before the age of two) have a limited power in predicting the development of children just a few years later (Santos et al., 2013[79]; Spittle et al., 2013[80]; Burakevych et al., 2017[81]). Thus, for this reason, it may be more beneficial to focus on children’s physical activity.
The nutritional status and physical development of children and adolescents lays the foundation for their later health outcomes. As such, deviations from recommended body and weight developments can have significant health consequences for children. Of importance is the weight-for-height Body-Mass-Index (BMI), which is used as a tool to classify individuals as over- or under-weight or obese. Obesity is persistent. More than half of children classified as obese will continue to be obese in adolescence; around three quarters of obese adolescents will stay obese later in life (Simmonds et al., 2016[82]). Thus, measuring obesity prevalence among children and adolescent is important given its serious immediate and later-in-life effects on health, such as hypertension and diabetes, and implications fora range of other organ systems and psycho-social outcomes (Kelly et al., 2015[83]; Güngör, 2015[84]).
Child health status
Injuries and illnesses contracted in childhood can have serious and long-lasting consequences for children's health and well-being. While the prevalence of infectious diseases, such as diphtheria, tetanus, pertussis, and measles, has become very low in countries where vaccination in the early years of life is widespread, the health burden of non-communicable diseases remains significant (Silverwood et al., 2019[85]). For example, the number of younger children suffering from respiratory diseases and allergies is increasing (Pearce et al., 2007[86]; Björkstén et al., 2008[87]). Table 4.2 ranks the health condition that are a cause of mortality and disability adjusted life years (DALYs). DALYs measure the overall burden of a disease by accounting for the years lost due to premature death or illness, capturing the long-term consequences of child health conditions.
Among young children, neonatal disorders, in particular preterm births or neonatal encephalopathy, are the most important factors leading either to death or impaired quality of life (de Vries and Jongmans, 2010[88]). Similarly, congenital birth defects, especially congenital anomalies of the heart, have high early-life mortality rates and implications for neurodevelopmental outcomes (Razzaghi, Oster and Reefhuis, 2015[89]). Lower respiratory tract infections, such as pneumonia or bronchitis, have significant impacts on later-life respiratory functioning (Grimwood and Chang, 2015[90]). Other important causes leading to death and DALYs for infants are sudden infant death syndrome or the presence of foreign objects in the body. Children between the ages of one and four years are also particularly susceptible to long-term impacts from dermatitis, asthma and diarrheal disease (Drucker et al., 2017[91]). In terms of mortality, road injuries, drowning and interpersonal violence are key cause of death. The preventable or avoidable nature of these event means that they deserve the require attention to reduce the numbers by even further (Sleet, 2018[92]). Overall, death rates and DALYs of child morbidities are particularly high for young children, especially those concentrated at birth or the first year of life.
In contrast to early childhood and infancy, the most common cause of mortality during middle childhood and adolescence differ from the risks leading to DALYs. Road injuries and interpersonal violence continue to be an important cause of mortality throughout middle-childhood and adolescence. Road injuries are a leading cause of death of adolescents worldwide, and a major cause of physical disability (Vos et al., 2016[93]). Other risks that become significant at this stage in childhood include cancer, especially leukaemia. Non-communicable diseases such as asthma has significant long-term and immediate quality of life consequences for children in middle childhood, increasing the susceptibility to chronic co-morbidities and reducing lung capacity (Fletcher, Green and Neidell, 2010[94]; Dharmage, Perret and Custovic, 2019[95]). Similarly, in adolescence pain disorders, such as migraine, and lower back pain, although are non-lethal, are important factors contributing to DALYs by reducing quality of life (Wöber-Bingöl, 2013[96]; Dunn, Hestbaek and Cassidy, 2013[97]). Mental health disorders, such as anxiety depression, become significant detriments to the quality of life from middle childhood on as well as substance use and incidences of self-harm. These topics are discussed in Chapter 5.
It important to also measure the prevalence of atopic conditions among child populations (although all types are not listed among the most significant child morbidities) Atopic conditions in children, such as asthma, eczema, hay fever and food allergies, have been on the rise over the last decades, potentially plateauing in developed countries more recently (Thomsen, 2015[99]; Moreno, 2016[100]). Due to similar underlying causes, many children suffering from one atopic disease are also likely to develop additional ones over the course of childhood, leading to increased disease burdens.
Tooth decay and cavities are among the most common chronic childhood conditions across OECD countries (OECD, 2009[101]; Griffin et al., 2016[102]). When left untreated, dental caries lead to severe toothache that may reduce school performance and general quality of life (Peres et al., 2019[103]). Despite the replacement of primary teeth with permanent dentition over the course of middle childhood, children with early caries are more likely to suffer from subsequent caries complications as well as insufficient physical development (Çolak et al., 2013[104]; Sheiham, 2006[105]). However, regular tooth-brushing, typically recommended at least twice a day, is an easy method of prevention (Kumar, Tadakamadla and Johnson, 2016[106]). While the evidence base for routine visits to the dentist office from early age is weak, many countries currently recommend early and frequent (about twice a year) dentist consultations for children (Bhaskar, McGraw and Divaris, 2014[107]; Sen et al., 2016[108]).
In early childhood, hearing and vision screenings are of critical importance to detect sensory impairments, such as refractive disorders as well as vision and hearing loss, early on. Compromised vision or hearing abilities can affect not only children’s quality of life, but can also literacy and language development (Lederberg, Schick and Spencer, 2013[109]), and neurological processing (Kral and O’Donoghue, 2010[110]), and lead to higher long-term economic costs (Wittenborn et al., 2013[111]). Early detection of sensory impairments through vision and hearing screenings can help children receive more timely treatment – leading to higher efficacy and better developmental outcomes in the long-term (Mathers, Keyes and Wright, 2010[112]; Evans, Morjaria and Powell, 2018[113]) – as well as earlier access to equipment and supports, such as skills training (e.g. sign language training).
In addition to objective estimates of child illnesses and physical health, it is also valuable to have information on the self-assessed health status of children. This may provide a more general picture of the physical health status of all children, not limited to clinically identified diseases or disorders. If the assessment uses age-appropriate methodologies, which account for children’s cognitive competencies and of their understanding of health and illness, these self-reports provide reliable summary information on the children’s perceived health status (Bevans and Forrest, 2010[114]; Greco, Lambert and Park, 2016[115]). Children as young as 5 years of age can provide details on aspect of their health-related well-being, and may know things that may not be visible to parents and health care professionals (Varni, Limbers and Burwinkle, 2007[116]).
Nutrition and eating habits
While infants, children and adolescents grow and their bodies develop, it is important that their dietary intake supplies the necessary micro- and macronutrients critical for physical development. The optimal nutritional supply changes with the age of the child, ranging from breastmilk for infants and young children to iron- and protein-rich food for adolescents. The importance of early-life nutrition is further underlined by evidence that dietary patterns in childhood are mirrored into adulthood (Due et al., 2011[117])
Breastmilk has a wide range of significant health benefits for children, including increased protection against infections, improved cognitive development and a lower risk of child mortality overall (Victora et al., 2016[118]; Sankar et al., 2015[119]). The share of children ever-breastfed vary substantially across the OECD. The WHO and UNICEF recommend that new-borns are exclusively breastfed within the first hour after birth and throughout the first six months of life, while receiving a mix of breastfeeding and complementary foods thereafter for the following 18 months (WHO, 2020[120]). After breast- and/or bottle-feeding, the contents of the regular childhood diet become critical. Undernutrition in children may hinder child development. It is important to focus on adequate nutrition and correction of nutritional deficiencies during the early years, as reversal may become very difficult beyond 2 years of age (Aboud and Yousafzai, 2015[121]).
Over the course of childhood and adolescence, some dietary requirements change as a result of maturation of the body. For example, the intake of amino acids and proteins becomes particularly important during adolescence to support growth and muscle development. Additionally, the required energy intake peaks over adolescence, and is higher for boys than for girls (Das et al., 2017[122]).
An ideal daily intake of calories varies depending on age, metabolism and levels of physical activity, among other things. Estimated needs for young children range from 1 000 to 2 000 calories per day, and the range for older children and adolescents varies substantially from 1 400 to 3 200 calories per day, with boys generally having higher calorie needs than girls (US Department of Health & Human Resources, 2015[123]). The general dietary recommendations made for children aged two years and older stress a diet that primarily relies on fruits and vegetables, whole grains, low-fat and non-fat dairy products, beans, fish, and lean meat (Association et al., 2006[124]; WHO, 2014[125]).
Consumption of food rich in micronutrients, especially fruits and vegetables, are a central part of healthy nutrition and may provide a wide range of protective health benefits (Wallace et al., 2019[126]). The WHO recommends "a minimum of 400g of fruit and vegetables per day (excluding potatoes and other starchy tubers)". The regular consumptions of fruits and vegetables, along with whole grains, is associated with better cognitive development over adolescence than diets containing high amounts of processed food and red meat (Nyaradi et al., 2014[127]). A higher consumption of fruit and vegetables in childhood may also reduce blood pressure and further protect from stroke and cancer later in life (Maynard et al., 2003[128]; Ness et al., 2005[129]; Moore et al., 2005[130]). Conversely, the regular consumption of carbonated drinks is strongly linked to weight gain and obesity among children and adolescents (Malik et al., 2013[131]; DeBoer, Scharf and Demmer, 2013[132]). Frequent consumption of sugary foods and drinks causes tooth erosion, which is especially critical once permanent dentation has been established (Salas et al., 2015[133]).
Growing evidence highlights the importance of regular breakfast consumption for children. Skipping breakfast in the morning has been linked to raised risk for overweight and cardiometabolic diseases (Smith et al., 2010[134]; Monzani et al., 2019[135]). While having a lower calorie intake over the whole day, children who skip breakfast show elevated appetite and a higher tendency to consume non-breakfast meals and snacks, leading to reduced intake of important nutrients and lower overall dietary quality (Kral et al., 2011[136]; Ramsay et al., 2018[137]). In addition, having breakfast is linked to improved school behaviour, with potential effects on academic performance (Adolphus, Lawton and Dye, 2013[138]).
Risky and protective behaviours
Early in life, timely vaccination is a critical and low-cost means to protect infants and children from a range of infectious diseases, such as diphtheria, tetanus, pertussis, and measles. Vaccinated children benefit directly through immunisation against several communicable and non-communicable diseases, often with complete or above 90% prevention rates. For communicable diseases, high vaccination rates provide further protection for the wider community addition to the individual protection (Andre et al., 2008[139]; Anderson et al., 2018[140]). A critical factor influencing vaccine effectiveness and the susceptibility to subsequent infection is the timeliness of vaccination (Curran et al., 2016[141]; Hughes et al., 2020[142]). The WHO recommends age of first dose as well as intervals further doses, while most initial vaccinations should be administered before age one (WHO, 2019[143]). Nevertheless, immunisation schedules may differ across countries. Any cross-country comparative data thus needs to account for actual immunization policies in the respective countries.
The behaviours and activities children engage in often have significant consequence for their development. Children become more autonomous as they grow older because they develop the capacity to make more of own choices and engage in a more varied range of behaviours. For example, children have more control over their diet and whether they engage in physical activity. Adolescence often represents an individual’s peak in risk-taking behaviour. Risk taking during the teenage years may be normative and functionally adaptive as the adolescent strives for independence from adults. These increases in risk-seeking can, in part, be attributed to an imbalance between the brain reward and cognitive control systems in the adolescent brain, as well as a lack of experience with new adult behaviours and activities (Romer, 2010[144]; Braams et al., 2015[145]; Shulman et al., 2016[146]). While substance abuse is a prominent risk behaviour that can have significant consequences on child physical health.
Physical activity
Physical activity broadly refers to any bodily movement produced by skeletal muscles that requires energy expenditure, including exercise, active games, and participation in sports programs, as well as active transportation, such as walking and cycling. For infants, the WHO recommends 30 minutes of physical activity per day, as well as 180 minutes for children ages two-four years (of which 60 minutes should be moderate to vigorous between age three and four) (WHO, 2020[147]). From middle childhood throughout adolescence, the WHO recommends also state 60 minutes of moderate to vigorous-intensity physical activity per day. Any additional activity will be provide additional health benefits at this stage. At the same time, activities that strengthen muscles and bones should be done at least three times per week.
The available evidence suggests that physical activity in early childhood has a positive effect on the development of motor skills (Zeng et al., 2017[148]), and on child health (Timmons et al., 2012[149]), spanning from cardio-vascular health (Proudfoot et al., 2019[150]) to overweight or obesity (Ulrich and Hauck, 2016[151]; Hills, Okely and Baur, 2010[152]; Nemet et al., 2005[153]). In addition, early physical activity appears to have ripple effects on children's cognitive development (Bidzan-Bluma and Lipowska, 2018[154]; Zeng et al., 2017[148]; Carson et al., 2016[155]). Physical activity levels in adolescence also track into adulthood, setting important foundations for later-in-life health (Due et al., 2011[117]). Finally, child physical activity is one aspect of infant and children’s life that parents and caregivers can influence without requiring expert knowledge of how children’s motor skills develop.
Sleep
Sleep is something that is critical for the well-being and development of children of all ages, though in different ways and in different amounts as children grow up. The WHO recommends that new-borns have 14-17 hours of sleep per day, including naps, reducing steadily to about 10-13 hours for three- to four-year-olds (WHO, 2019[156]). The recommendation for adolescents often sits between 8-10 hours per day (Hirshkowitz et al., 2015[157])[.
Sudden Infant Death Syndrome (SIDS) – the sudden unexplained death of seemingly healthy infants during sleep is risk that parents need to take measures against (Kinney and Thach, 2009[158]). Concerns around SIDS and associated risk factors have led to the introduction in many OECD countries of “safe sleep” guidelines and campaigns for infants, which typically include abstaining from bed-sharing and placing infants on their backs to sleep (CDC, 2018[159]; NHS, 2018[160]). These and similar initiatives have helped reduce the frequency of SIDS of the past few decades. In the United States, for example, the frequency of SIDS has fallen by almost 75% since the introduction of the American Academy of Paediatrics’ safe sleep recommendations in 1992 (CDC, 2021[161]).
Sleep patterns shift substantially across childhood, reflecting differences in developmental needs for sleep and biological changes in children’s bodies as they age. Especially among adolescents, short sleep durations are wide-spread, resulting for example from shifting biological clocks and early school starting times. These non-optimal sleep patterns have been linked to a wide range of physical and mental health outcomes ranging from childhood obesity, cardio-metabolic risk, poorer emotional regulation and worse overall well-being (Shochat, Cohen-Zion and Tzischinsky, 2014[162]; Chaput et al., 2016[163]; Hanlon, Dumin and Pannain, 2019[164]). Cognitive functioning is also impacted, leading to lower academic achievement, due to impaired attention, learning and memory (Curcio, Ferrara and De Gennaro, 2006[165]).
Sexual activity
Adolescence typically marks the beginnings of romantic relationships and becoming sexual active. Adolescents need to know about sexual health and safe sex practices to protect themselves from sexually transmitted diseases and unwanted pregnancies. In the United States in 2018, close to half of all sexually transmitted infections (STIs) occur among adolescents and young adults (Kreisel et al., 2021[166]). STIs, especially when untreated, can have profound effects on the health and well-being of individuals. For example, chlamydia can lead to pelvic inflammatory disease and infertility among women and gonorrhoea in men is associated with increased prostate cancer risk (Caini et al., 2014[167]; Price et al., 2016[168]; Mathur, Mullinax and Santelli, 2017[169]). Adolescents have different health-seeking behaviours than adults. They may worry about being judged and issues of confidentiality. Therefore, sexual health services and health promotion need to be adapted to young people’s needs to encourage uptake and protective health behaviours (Slater and Robinson, 2014[170]).
Children’s physical health and health behaviours depend on a range of factors relating to the local, family, and institutional context in which they live (WHO, 2018[171]). Environmental quality is one such dimension. Heavy exposure to air pollution and contaminants in food and water can have severe implications for children’s physical health and development. Indeed, slightly more than a quarter (28%) of deaths among children under five worldwide are estimated to be accountable to modifiable environmental factors (WHO, 2018[172]). Institutional and policy-related factors can also play an important role in shaping children’s physical health and development. Children’s safety, for example, can be promoted by measures to protect children from road injuries, to secure independent mobility, and to provide safe access to green and recreation spaces. The accessibility and quality of public health care systems, which is in part linked to the level of public health expenditure, also matters. Sufficient access to health care, as well as the quality of care provided, can affect the opportunities children and pregnant mothers’ have for receiving appropriate medical care when needed. The material living conditions of children and their families – as discussed in Chapter 3 – represents another important environmental dimension.
Physical environment
The quality of the immediate environment and exposure to various particles, bacteria, substances and contaminants can have important effects children’s physical health and well-being. For example, many children worldwide are affected by air pollution – especially in developing countries, but also in developed economies (WHO, 2019[173]). And while adults too are also impact by pollution and contaminants, children are often at greater risk, in part because they consume more pollutants and contaminants per-kilogram body-weight than adults (Landrigan et al., 2019[174]). Exposure early in life to even to low-doses of toxic chemicals, bacteria and pollutants can lead to illness, disability, and death in childhood, as well as complications later in life (Landrigan et al., 2018[175]; WHO, 2016[176]).
One example of a common and risky air pollutant is fine particulate matter (PM2.5). In high-income countries, more than a half of children below the age of 5 are subject to greater fine particulate matter pollution than is recommended by the WHO (WHO, 2018[177]), with substantial effects on health: the combined effects of ambient and residential particulate pollution leads to more than 1 200 premature deaths among children below the age of 15 in OECD countries each year (OECD, 2019[178]). Other air pollutants that can have adverse health effects in children include polycyclic aromatic hydrocarbons (PAHs), ozone, and nitrous oxide (NOx), among others (WHO, 2018[177]; Bushnik et al., 2020[179]; Lubczyńska et al., 2020[180]). Air pollution in general can have a variety of effects on children’s health, ranging from adverse birth outcomes, heart disease and neurodevelopmental difficulties, to respiratory conditions and childhood cancer (WHO, 2018[177]). Combating air pollution can also promote children’s outcomes in other areas, including learning outcomes, as the installation of air filters after industrial gas leaks has shown (Gilraine, 2020[181]).
Climate change is rapidly changing our realities, with severe consequences for child health outcomes. Extreme heat, air pollution, food insecurity and many other consequences of global warming are threatening the health and well-being of children, especially those from disadvantaged socio-economic backgrounds (Adrian et al., 2020[182]). Exposure to heat and extreme temperature can have harmful effects on birth outcomes, including low birth weight, stillbirth, or increased neo-natal stress. It also puts many children at risk of severe dehydration and heat related morbidity. Rising temperatures have also been linked to increases in mortality rates for children under age 5 and infants under age 1 (Basu and Ostro, 2008[183]).
Increases in air pollution are one aspect of climate change that can have a heavy impact on child health at all stages of their development. Pre-natal exposure to air pollution increases the frequency of adverse birth outcomes and neuro-developmental disorders. Additionally, changes in temperature and CO2 are altering the dynamics of pollen seasons and rendering children more prone to developing diseases such as asthma or hay fever. Most importantly, exposure to air pollution at different points in their lives negatively affects children’s long-term outcomes, exacerbating inter-generational inequalities. One study from Chile, for instance, found that an increase in foetal exposure to carbon monoxide is associated with lower fourth grade math tests scores (Bharadwaj et al., 2017[184]). Similarly, greater exposure to air pollution in the first year after birth has been found to negatively impact labour force participation and earnings at age 30 (Isen, Rossin-Slater and Walker, 2017[185]), while exposure during adolescence has been linked to the non-completion of secondary school and incarceration (Voorheis, 2017[186]). Children from low-income families are particularly vulnerable to air pollution as they are more likely to live in more polluted neighbourhoods, and to live in families that are less able to engage in avoidant behaviours or make investments to compensate for pollution.
Vector-borne diseases are also likely to spread as a result of global warming, deforestation, and ecological disruption. Rising temperatures are expanding suitable mosquito habitats, while human mobility facilitates the spread of illnesses to previously disease-free regions. Children living in overcrowded spaces with limited access to clean water and proper sanitation are particularly susceptible.
Natural disasters such as floods and extreme precipitation also carry health risks for children. Children are more likely than others to contract water-borne diseases due to their immature immune systems. In addition, infectious diseases and mental illness can be exacerbated by the destruction of homes and infrastructure. Exposure to contaminated drinking water and mould resulting from such phenomena are a further risk.
Prolonged drought resulting in reduced arable land and crop yields is likely to result in food insecurity and, potentially, malnutrition for children. Indeed, rising sea temperatures and coral bleaching triggered by global warming are radically decreasing per-capita capture-based fish consumption (Watts et al., 2018[187]). In addition, climate change may lead to smaller crop yields and degradation in the nutritional properties of plant foods, in turn potentially triggering price rises, with possible knock-on effects for child nutrient deficiencies and malnutrition, especially for disadvantaged children (Myers et al., 2017[188]; Taub, Miller and Allen, 2008[189]). Food insecurity already results in poorer families turning to more affordable foods, including fast- and processed foods and beverages, which increases risks of obesity, asthma, and chronic disability.
In addition to physical health, climate change may also affects children’s mental health in several ways. For instance, extreme weather events can lead to forced displacement, family loss, and/or changing social support systems. This may lead to children, especially those in poverty, developing diagnosable mental health issues such as post-traumatic stress disorder, depression or anxiety. In developed countries too, many children express anxiety and fear about the potential impact of climate change on their futures (Maibach and Feldman, 2010[190]).
Besides air pollution, constant high levels of ambient noise in the immediate environment, originating from, for example, road- and air traffic as well as neighbours and industrial activity, can impact on children’s health. Noise exposure can trigger stress responses in children and potentially impair cognitive skills, resulting in reduced memory, reading ability and test scores (Stansfeld and Clark, 2015[191]). Traffic-related noise has been shown to increase risks of sleep disturbances, attention disorders and high blood pressure in children (Liu et al., 2014[192]; Skrzypek et al., 2017[193]). However, further evidence is needed to fully appreciate the causal consequences of environmental noise pollution on children’s health. It may be that ambient noise exposure has a lower impact on child outcomes than personal noise exposure, such as from music and other portable devices, which appears to be associated with hearing loss (Swierniak et al., 2020[194]; Le Clercq et al., 2018[195]).
Noise and particulate matter pollution often originate from road traffic. Distance to high-volume traffic in itself has been linked to adverse health outcomes in children, including early cancer, especially leukaemia (Pearson, Wachtel and Ebi, 2000[196]; Houot et al., 2015[197]), atherogenesis (Armijos et al., 2015[198]) and respiratory disorders (Brown et al., 2012[199]; Skrzypek et al., 2013[200]).
Children are often also exposed to toxic chemicals and bacteria in food, water, and consumer products, some of which have been found to be harmful to child health and development, while others have never been tested for their toxicity to children (Landrigan and Goldman, 2011[201]). For example, contamination of drinking water with coliform bacteria can induce intestinal illnesses, such as diarrhoea or pneumonia, which can be especially dangerous for infants (Landrigan et al., 2019[174]; Mathew et al., 2019[202]). Even though death rates to chemical and bacterial contamination are relatively low in developed countries, exposure is still high and may potentially lead to adverse health outcomes (Landrigan et al., 2018[175]; Haug et al., 2018[203]).
Lead exposure is also a strong concern. A recent report by UNICEF and Pure Earth estimates that one third of the world’s children have been poisoned to some degree by lead, and that at least 900 000 premature deaths globally, or 1.6% of all deaths, are attributable to lead poisoning (Rees and Fuller, 2020[204]). Lead exposure is preventable, and there is no level of exposure to lead that is known to be without harmful effects (WHO, 2019[205]). Endocrine disrupting chemicals (EDCs) are also a significant source of concern for child health and development (WHO, 2012[206]). EDCs have been linked to a number of outcomes, including neuro-developmental conditions and learning disabilities, thyroid disorders, diabetes and obesity (Attina et al., 2016[207]; Trasande et al., 2015[208]),
Family and home environment
The family and home environment can play a key role in children’s child health behaviours and outcomes. First, poor housing quality can have important effects on children’s physical health, especially for very young children. Overcrowding, but also damp or difficulties in heating the home, are factors that contribute to the transmission of infectious diseases and the development of chronic lung diseases. Exposure to lead contained in building materials makes young children particularly vulnerable to lead poisoning (Santé Publique France, 2020[209]).
The role of parents is manifold. For instance, parents initiate healthy eating habits and have and important role to play in encouraging physical activity in infants. As children grow older, parents act as role models for healthy behaviours and can encourage and support their children to engage in healthy activities, such as practising sport and eating well. However, parental behaviour may not always be positive. Children can be exposed to neglect, maltreatment, and physical punishment, as well as to domestic violence, which can lead to severe injuries, death, or impair brain and nervous system development (Hillis et al., 2016[210]; WHO, 2019[211]).
The definition of child maltreatment is broad and includes neglect, physical and verbal abuse, sexual abuse and emotional abuse. Child maltreatment can lead to a number of health problems, such as non-organic failure to thrive (stunted growth associated with neglect and emotional abuse) (Nemeroff, 2016[212]; Jud, 2018[213]); non-accidental injuries (babies with fractured skulls, kids with broken bones caused by physical abuse) (Mulpuri, Slobogean and Tredwell, 2011[214]); and sexually transmitted diseases or pregnancies (caused by sexual abuse) (Bechtel, 2010[215]; Noll, Shenk and Putnam, 2009[216]). It can also lead to toxic stress, which can disrupt early brain development and impair the development of nervous and immune systems (Nemeroff, 2016[212]). As adults, maltreated children are at increased risk of behavioural, physical and mental health problems such as smoking, obesity, alcohol and drug misuse, depression, and perpetrating or being a victim of violence (Gilbert et al., 2009[217]; Brown, Fang and Florence, 2011[218]; Zielinski, 2009[219]; Thielen et al., 2016[220]; OECD, 2019[221]; WHO, 2014[222]). Child maltreatment often occurs “behind closed doors”, and is not always visible to the outside world. Nevertheless, good quality data on these exposures are highly valuable and should be centre stage in child health and well-being monitoring.
Passive tobacco smoke and nicotine exposure, especially early in life, can impair respiratory functioning as well as increase metabolic and cardiovascular risk factors (Gibbs, Collaco and McGrath-Morrow, 2016[35]; Kelishadi et al., 2016[36]; Li et al., 2016[37]). Household exposure to tobacco smoke in early childhood has also be linked to impaired executive functioning and increases in the likelihood of attention deficit and hyperactivity disorder (Pagani, 2014[223]). As a result, it is important to not only measure children and adolescent’s active use of tobacco, but also to monitor those exposed to second-hand tobacco smoke in the home.
Health care policies
Developing policies that can promote child health requires information on both the preventive and remedial aspects of health policy. It is also important to have information that covers the different stages of childhood: a preventive health policy will be more effective if potential health problems are identified at an early stage, but it requires medical vigilance to be maintained throughout childhood. The extent to which children are targeted by preventative measures can be assessed through different metrics, including whether the system is offering routine vaccination and regular health checks, and through the proportion of children regularly visiting doctors and dentists.
Measuring potentially avoidable hospitalisation is another way to assess the strength of preventive policies (DoPMC, 2020[224]; Procter et al., 2020[225]). Potentially avoidable hospitalisations include respiratory conditions (including asthma, pneumonia, bronchiolitis, etc.), gastroenteritis, skin infections, and vaccine-preventable illnesses. They also include unintentional injuries and, in some countries, hospitalisation due to assault or self-harm. Many childhood illnesses are preventable through more effective primary health care services or broader public health interventions that target health determinants. Poor housing conditions, inadequate or poor nutrition, failure to vaccinate, exposure to unsafe outdoor sport or leisure equipment, and exposure tobacco smoke are examples of drivers of potentially avoidable hospitalisations for children.
Estimates of unmet needs for medical and dental care provide an indication of the extent to which health care services are accessible and effective in treating medical needs. Some children have special health care needs which may not be addressed to the same extent as other, more general needs. Some groups of children may be particularly vulnerable (e.g. children of immigrants or refugees, children in foster care or in the juvenile justice system), and special policies may be needed to ensure that these children have access to health care services.
Data on public spending on children's health care services provide an indication of countries' efforts to meet children's health needs. As most health spending is directed toward older populations, in particular through end-of life services, the share of overall health care spending going to child health is low (OECD, 2016[226]). Data on early life public health care spending is sparse. To understand how expenditures relate to child well-being, one positive step would be to collect data on expenditure on health care for children in the early years, as well as expenditure on care services for pregnant mothers.
Overall health care expenditure can mask differences in the distribution of spending between services that prevent and treat health conditions. The United States, for example, has the highest level of health care spending in the OECD (in general and on children, see Thakrar et al. (2018[227])), yet also has poorer child health outcomes and higher child mortality rates than many other member countries. This is partially because the United States has relatively high pre-term birth and low birth weight rates, leading to subsequent health complications, Another factor is high health care prices, which drive up health expenditures (Thakrar et al., 2018[227]; Lorenzoni, Belloni and Sassi, 2014[228]). A substantial fraction of these expenditures can also be driven by low-value and unnecessary medical care that give no clear benefit to children’s well-being, such as cough medicines for young children or pap tests for pregnant women (Chua et al., 2020[229]). Therefore, it is important to remove spending on low-value services when creating indicators that measure health care spending and/or use for cross-national comparison. A potential list of such services can be found in Chua et al. (2020[229]).
There may be institutional or geographical barriers to health care services, both of which may have severe implications for children’s well-being and health. Institutional barriers can prevent children from having substantive health care insurance, in particular for those from lower socio-economic status households. Geographic barriers can limit the physical accessibility to health care and medical facilities. Issues with health care coverage often results in unmet health care needs and increased emergency department use among children (DeVoe, Tillotson and Wallace, 2009[230]; Gushue et al., 2019[231]). Child maltreatment is also less likely to be reported among uninsured children, because they have less contact with health care providers (Puls et al., 2020[232]). Children who do not live close to medical care facilities are also less likely to attend these services (Currie and Reagan, 2003[233]; Goodman et al., 2011[234]). The assessment of inequalities in children's access to health care then requires sound information on geographical disparities in the availability of health services for children, as well as on differences in service use by families’ socioeconomic status.
A wide range of information on children’s physical health outcomes exist at the national level. The underlying data usually originate from household surveys or, in rarer cases, administrative registers. However, if the aim is to compare the state of children’s physical health across countries, it is often hard to find data sources that are sufficiently similar in terms of concepts and methodology. For example, it is easier to develop indicators that measure infant mortality across countries, than it is to measure children subject to violent discipline or domestic sexual violence.
There are some comprehensive epidemiological data sources that allows for cross-national comparisons of children’s physical health. The Global Burden of Disease study (GBD) from the Institute for Health Metrics and Evaluation (IHME) offers morbidity and mortality estimates for all OECD countries. The GBD is built on a wide range of sources, including national household surveys, official statistics and academic publications. The modelled data include death rates and disability adjusted life years (DALYs) as well as prevalence and incidence rates for over 350 diseases and injuries as well as 80 risk outcomes. Importantly, the estimates are reported for very fine age groupings, which allows for measurement across different stages of childhood. A range WHO datasets also provide useful cross-national data. These include the WHO databases that track the Sustainable Development Goals (SDGs), as well as the NCD Risk Factor Collaboration (NCD-RisC), which models overweight and obesity prevalence across countries.
However, as also discussed in the appendix, global health metrics, especially the GBD data, are often not sufficiently transparent in their methods and risk oversimplifications of complex realities (Shiffman and Shawar, 2020[235]; Mahajan, 2019[236]). Occasionally, these data conflict with national statistical accounts, which raises various questions on the reliability and uncertainty involved in the creation of the estimates (Boerma, Victora and Abouzahr, 2018[237]; Rigby, Deshpande and Blair, 2019[238]; 2019[239]). Stronger confidence in the GBD data would require greater transparency obligations, which would enable users of the data to better understand the origins and the underlying levels of uncertainty. An alternative route would be stronger commitments from national statistical institutes to collect and public harmonised data and indicators, given internationally coordinated definitions and standard, which may reduce the reliance on global health metrics.
Many existing sources of data on children’s physical health lack information on inequalities. However, there a few cross-national surveys that allow for data disaggregation by various demographic and socio-economic factors. One example is the Health Behaviour in School-aged Children study (HBSC), which offers comprehensive cross- national data for adolescents aged 11-, 13- and 15 years in most OECD countries (except Australia, Canada, Chile, Colombia, Japan, Korea, Mexico, New Zealand and the United States). HBSC collects data on a variety areas relating to child health and health behaviours, ranging from dietary and eating behaviours to self-perceived health status, and can be disaggregated by various demographic and socio-economic markers. For European countries, the European Union Statistics on Income and Living Conditions (EU-SILC) can give insights on the living conditions and health behaviours of children, such as exposure to noise and consumptions of fruits and vegetables, as well as basic information on child health status, all of which can be disaggregated along various dimensions. Future rounds of the European Health Interview Survey may also contain questions on children’s physical health (Box 4.4). Unfortunately, beyond these sources, there are currently no other comprehensive cross-national dataset on children’s physical health that offer disaggregation by household socioeconomic characteristics.
The remainder of this section summarises the available information in each of the previously identified dimensions on children’s physical health, with a rough mapping presented in Table 4.3. More detailed information on each dimension and a more comprehensive mapping of the available data sources can be found in the Annex.
The European Health Interview Survey (EHIS) is a cross-national representative household survey that collects data on adult health status, health care use, and health determinants, as well as relevant socio-economic and demographic factors. The EHIS covers all EU member states plus Iceland and Norway, and runs every five years. The fieldwork for the most recent wave, Wave 3, took place in 2019.
As part of the latest wave, the EHIS ran a new test module on “Health of Children” in two EU member states (Bulgaria and Hungary). Two versions of the module were tested: one focusing on child disability (with questions that largely conform to the Washington Group/UNICEF Module on Child Functioning), and the other on child health behaviours and prevention. Both contained a set of basic questions on child health status. Responses to the test module were collected from parents or guardians.
Eurostat and the EU member states are currently in the process of deciding whether to include the “Health of Children” module (or part of it) in future waves of the EHIS.
Source: (Eurostat, 2021[240]); (UNICEF, 2016[241]); OECD Secretariat correspondence with Eurostat.
Data on physical health outcomes and pre-natal determinants
Comparable cross-national data on birth outcomes are widely available, with full OECD wide coverage and frequent data update cycles. For example, infant and under-five mortality rates are reported by OECD Health Statistics and by the UN Inter-agency Group for Child Mortality Estimation, respectively. The GBD reports estimates of the incidence of pre-term births across the OECD, while the WHO reports both observational and estimated data in the Global Preterm Birth Estimates. Information on low birthweight is available through the UNICEF-WHO Low Birthweight Estimates and through OECD Health Statistics. For some countries, information on birth outcomes is also available in administrative birth records (more details in the Annex).
The data availability for key pre-natal determinants is considerably sparser. While antenatal care coverage in middle and low income countries is monitored by the WHO as part of its tracking of SDG 3.7. (“By 2030, ensure universal access to sexual and reproductive health-care services […]”), no comparable information is available for most OECD members. Global data on maternal smoking during pregnancy can be found in Lange et al. (2018[242]), though, while valuable, this is only a one-off systematic review of estimates in the literature. It may be possible to source information on either antenatal care and/or maternal smoking from administrative datasets in some OECD countries. For example, the number of antenatal care visits for Danish mothers as well as information on whether mothers smoked during pregnancy is available in the Medical Birth Register. However, most countries do not have sufficient administrative data infrastructures to similarly measure birth outcomes.
Indicators of infant and early childhood anthropometric development are available as part of the monitoring of SDG 2.2. (“By 2030 end all forms of malnutrition […]”), which targets the prevalence of stunting, wasting and overweight among children under the age of 5. OECD-wide estimates might be sourced from the health-related SDGs estimates in the GBD, which cover stunting, wasting, and over-/underweight, in line with the WHO growth standards. An alternative source for data on infant and child height and weight status and/or development at different stages may be found in preventive health examinations records, though only a handful of countries collect this information.
Information on anthropometric status in middle-childhood and adolescence can be obtained from NCD-RisC, which reports estimated over-/underweight and obesity prevalence among children and adolescents aged 5-9 and 10-19 years old, including for all OECD countries, based on a variety of national population-based surveys. The prevalence rates are based on BMI levels along which over- and underweight is typically classified in the literature and the health care system. Alternatively, data from the HBSC survey also provides self-reported information on child height, weight and BMI. However, for a number of countries there are significant non-response rates, which may reduce reliability.
OECD-wide data on child morbidities are available through the GBD, which estimates prevalence and incidence rates for 350 diseases and injuries by detailed age groups, including children and adolescents (e.g. below 1, 1 to 4, 5 to 9, 10 to 14 and 15 to 19 years). The data is typically updated every two years and is well suited to continuous monitoring of child morbidities. Importantly, the GBD also covers estimates for oral health conditions, such as caries and periodontitis, as well as hearing and vision impairments, and can thus also be used to measure the oral and sensory health status of children and adolescents in OECD countries.
For adolescents, data on self-assessed health in available in the HBSC survey. Here, 11-, 13- and 15 year old children report how often they have experienced headaches, stomach aches and backaches over the previous six months, as well as their over-arching self-rated health status. The latter is answered on a simple “excellent, good, fair and poor”-scale, which may be subject to semantics bias, possibility reducing its reliability as a proper cross-country measure (Schnohr et al., 2016[243]). Similar questions are also asked to 15 year old children in the Programme for International Student Assessment (PISA), yet for many countries there are no responses in the most recent 2018 round. Unfortunately, none of these sources provide a self-assessed health status for children below the age of 11.
Data on child health behaviours and other health activities
Indicators that monitor the WHO recommendations on breast-feeding are readily available only for low- and middle-income countries, though this data gap can be overcome by synthesizing information from various national surveys in high-income countries (Victora et al., 2016[118]). However, most of these countries are not able to report information that is in line with WHO recommendations. As a result, it may be necessary to rely more general information (such as whether the child was ever breastfed; see, for example, OECD (2009[244])). A further complication is that many of these surveys are subject to significant non-response rates, a lack of recent information and long recall periods that may reduce accuracy. One alternative may to make use of administrative data on preventive health examinations, though the availability of such sources across OECD countries is rather sparse (more on this in the Annex).
In terms of comparable cross-national data on nutrition, either deficiency rates or the overall availability of nutrients, the sources are out-dated or lacking, especially for OECD countries. Both EU-SILC and HBSC provide alternative and more up-to-date information on general childhood nutrition. The latter collects information for adolescents on fruits and vegetable consumption, as well as the consumption of sweets, sugared drink, and breakfast on school days. Similar data on fruit and vegetable consumption, which is also available for younger children, can be found in EU-SILC. Here, the information covers children in households where at least one child aged 1-15 does not have either fresh fruits or vegetables at least once per day, as well as those that did not have one meal with meat, chicken or fish (or vegetarian equivalent). While both datasets do not provide direct information on micro- or macronutrient deficiency per se, they do allow for a basic assessment of nutritional deprivation along major food groups. Nevertheless, they are not well suited to assessing whether children reach the WHO recommendations of 400g of fruits and vegetables per day.
Global population-level information on physical activity are available for adolescents, either from pooled survey estimates in the scientific literature (e.g. in Guthold et al. (2020[245])) or through regular cross/national studies. For instance, the HBSC survey provides information on adolescents’ physical activity in the form of the share of reporting at least 60 minutes of moderate-to-vigorous activity per day, as well as the share of respondents that engaged 4 or more times in vigorous physical activity per week. The former is in line with the physical activity-related recommendation of the WHO, though does not identify how much time is spent on activity strengthening muscles and bones. Similarly, PISA data contains information on how often 15-year old children engaged in moderate or vigorous physical activity over the last 7 days outside of school. Unfortunately, the latest PISA round lacks information on this item for many countries and thus it may not be possible to use this source for further measurement, though future rounds will include some physical activity items for all countries.
Indicators on physical activity for younger children are not as readily available. Some countries, including the United Kingdom, collect information on physical activity for children aged 5 and over on a regular basis. Population-level indicators on physical activity for children under 5 years are often only available from child cohort surveys using parental reports, organised in-home observations or accelerometers (e.g. in Worobey (2014[246])). These are expensive methodologies that are unlikely to be extended beyond small sample or child cohort surveys, which involve nation-representative samples but are not conducted on a regular basis.
Even though the HBSC survey has information on self-reported sleep difficulties for adolescents, globally comparable data on children’s sleep patterns are rare. Nevertheless, some information is available in nationally representative surveys, such as the Longitudinal Study of Australian Children (LSAC). The data includes survey items on bed- and wake-time, though all participants have practically reached adulthood by now. A complication of similar surveys that collect data on bed- and wake-time is that these concepts do not necessarily indicate actual sleep-duration, especially among adolescents, who appear to frequently use smartphones and social media past bed-time and/or in the middle of the night.
Data on sexual activity is available in the HBSC survey for 15 year olds. Questions cover whether adolescents have engaged in sexual activity and whether or not they used a condom or contraceptive pill at last intercourse. In terms of preventive health behaviour, the HBSC survey also collects information on tooth brushing, in particular the share of adolescents that brush their teeth more than once per day, which makes it possible to measure attainment of WHO recommendations. Unfortunately, no globally comparable data source exists on tooth-brushing behaviour for younger children, though researchers have occasionally administered surveys on children in a wide range of countries (e.g. Llodra et al. (2014[247])).
Indicators on childhood vaccination are routinely reported as vaccination rates that reflect the share of children receiving a specific vaccination or a combination of those (e.g. combined DTP-vaccine against diphtheria, tetanus, and pertussis) at the recommended vaccination age. Information on global vaccination rates is available through the WHO/UNICEF estimates of national immunisation coverage. OECD Health Statistics also reports similar information for OECD countries. These indicators may be further extended to cover additional vaccination rates, such as for rubella-, rotavirus-, pneumococcus- and polio vaccines.
Data on environments and public policies
Information on risks related to particulate matter pollution are available from the GBD comparative risk assessment (CRA), which estimates exposure based on a combination of land use and satellite data, chemical transport models, and ground measurements of pollutants. While less important in the OECD context, data on risks relating to residential or household pollution are also estimated, using a wide range of surveys on the use of solid fuels for cooking. Both sources are reported as pollution-related deaths and DALYs, and can be broken down by detailed age groups. However, no details on specific exposure levels are available. Indicators on exposure to critical levels of different pollutants (above a critical threshold for a certain period) are available for EU countries through the European Environment Agency (EEA). Unfortunately, however, this data cannot be broken down by age, and only considers exposure in urban agglomerations. One route to building better indicators of children’s exposure to particulate matter pollution may be to use data collected as a by-product of pollution measurement by public and private initiatives operating air quality sensors across cities and along major roads, though this may be costly and complicated.
The GBD CRA also estimates death risks and DALYs relating to unsafe water sources and lead exposure. For food safety, the WHO reports annual indicators based on monitoring by the International Health Regulation (IHR) to detect and respond to foodborne disease and food contamination. While this data is not broken down by specific foods, such as early infancy dietary products, it nevertheless may give a good overview of national food safety levels.
Information on children’s exposure to noise and tobacco smoke can be obtained for EU countries from the EU-SILC survey, which collects the share of children under the age of 15 exposed to some or severe noise from neighbours or the street or tobacco smoke in the household. Data on exposure to heavy traffic are sometimes obtainable from road network and traffic volume data, which is often available to local authorities, typically based on road surveys and sensor data. Finland, for example, publishes real-time information on traffic volume for its entire road network on a fine-grained basis in the Digiroad and Digitraffic data.
Information on child maltreatment in the family and home environment is often collected through survey data or reports to child protection services (e.g. through primary care contacts or the school). For example, in order to track global attainment of SDG 16.2. (“End abuse, exploitation, trafficking and all forms of violence and torture against children”), the UN’s Global SDG Indicator Database reports the proportion of children aged 1-14 years who experienced physical punishment and/or psychological aggression by caregivers. The data is mainly obtained from Multiple Indicator Cluster Surveys (MICS) or other household surveys, though survey sources may severely under-report actual instances of child maltreatment and violence against children (MacMillan, Jamieson and Walsh, 2003[248]).
The availability of health policy is inconsistent and depends on the specific policy aspects and country. Relatively little data are routinely available to monitor the development of preventative health policies. Moreover, those data that are available, such as on antenatal health checks and vaccination rates, often covers only the early years of childhood. Data on health checks and visits to doctors or dentists by older children are not routinely reported on a large cross-national scale.
In Europe, the 2017 EU-SILC collected information on the proportion of children with unmet needs for medical and dental examination or treatment. About 1.3% of higher-income families with children, and 3% of income-poor families with children, reported unmet needs for medical examinations or treatment for at least one child in the household (Eurostat, 2019[249]). Similar question on unmet needs for child medical care will be included in EU-SILC 2021. A few countries, including Australia, Italy, New Zealand and the United Kingdom, produce statistics on potentially preventable hospitalizations of children based on administrative data from hospitals (Zucco et al., 2019[250]; Procter et al., 2020[225]; DoPMC, 2020[224]; Nuffield Trust, 2020[251]); the production of comparative data at the international level on this issue would require a greater harmonization of the situations covered by these statistics.
Data on health care spending for children are not widely available. In European countries, the HEDIC project (Health Expenditures by Diseases and Conditions) has demonstrated the general feasibility of collecting data on expenditure by age, but data collection is incorporated in routine collection of data on health expenditure in the European Statistical System (HEDIC, 2016[252]). The Global Health Expenditure Database (GHED) of the WHO for low and middle countries but with poor documentation about comparability. Cross-country information on health care utilization is typically only available for the general population, e.g. in the OECD Health Statistics. However, for a few countries it may be possible to use administrative health care records to assess the status if health care service utilization for children. The advantage of this method over self- (or parent-reported) household survey information is a usually stronger reliability of the data due to avoidance of self-report bias.
Data on coverage for child and maternal health care is often obtained from household surveys. Globally comparable data is, for example, compiled by the Countdown to 2030 initiative, though coverage extends only to low- and middle-income countries. Equivalent information for countries in the OECD may be obtained from national household surveys or approximated by the health care coverage for the general populations (as in OECD (2019[253])). In a few cases, data on the geographical accessibility of children’s health care services may be obtained from administrative data sources using information on children’s residential location and the location of health care service facilities. For the monitoring of the SDGs, a set of indicators on inequalities in maternal and child health coverage have been developed from Demographic and Health Surveys (DHS) carried out in low and middle income countries. Increasing relevance of to higher income countries remains a challenge (WHO/World Bank, 2017[254]).
Some cross-country measurement of health care quality is available from the OECD Health Care Quality and Outcomes (HCQO) programme. The most recent framework collects a total of 61 indicators on health care service quality. However, these indicators are typically age-standardised and not available for children below the age of 15. An alternative approach to measuring both healthcare access and the quality of the care provided may be the use of estimated summary indices, such as the GBD’s Healthcare Access and Quality (HAQ) Index. Using incidence and mortality rates, it approximates personal health care access and quality by estimating excess death rates which should not occur under effective health care systems. One drawback is that the estimates refer to health systems as a whole, without a particular focus on child health outcomes.
The discussion above has shown that the development of health surveys and comprehensive global disease estimation projects has greatly expanded the range of cross-national data on children’s health. These data cover physical health status, the prevalence of physical diseases and adolescent’s risky and protective behaviours fairly well. However, there is a lack of transparency in the data generating processes and underlying uncertainty behind the estimates. In addition, there are several limitations that prevent proper tracking of inequalities in children's health and of its determinants from pregnancy onward. These limitations range from a lack of information to account for the differences in physical health outcomes among children of different socio-economic backgrounds and (hidden) risk factors, to the degree of which health care and policy decrease children’s actual physical health risks through preventative measures.
Policies to promote children's physical health and well-being requires indicators that make it possible to properly identify the risks to children's health, including whether those risks are related to the environment in which children grow up or to individual circumstances. It also requires being able to identify health inequalities as soon as they emerge, even among the very young. However, the set of data presently available for child health in early and middle childhood is much more limited than for older children. For example, much of the currently available data, especially on children’s risky and protective behaviours, are only available for adolescent. Though risk-taking is generally higher among adolescents, information on risky and protective practices for younger children is valuable and should be a focus of future data collections. Although sometimes collected in national surveys, there is a lack of comprehensive cross-country data on children’s behaviours as well as on children’s views regarding health issues.
More data on the resources available to nurture child health are also needed. It would involve having data on maternal and children’s health care services coverage, and health checks for screening for preventive or curative services.
Improve the tracking of vulnerable children and child health inequalities
Inequalities in physical health develop as early as pregnancy and can have strong and long-lasting consequences on many aspects of adult outcomes, including education, employment, earnings, and the health of the next generation (Currie, 2017[255]; Spencer et al., 2019[256]). Therefore, it is important that indicators can be used to track health inequalities from pregnancy and the first years of life throughout childhood and adolescence. However, much of the data and indicators currently available on the health status and use of medical services provide information on the average situation – possibly for different age groups and genders – but very few provide information on disparities by income or other socio-demographic characteristics.
For policy-relevance, it would be useful if indicators on socio-economic health disparities capture different aspects of health status, health determinants and health care use at individual, family and neighbourhood levels. The prevalence of some infectious diseases, for example, is related to household living conditions, environmental health, hygiene and nutrition behaviours, and there can be a link here with the socio-economic status of the family (Spencer et al., 2019[256]). Similarly, there can be geographic disparities in health status and determinants, sometimes linked to the spatial and/or community-level concentration of disadvantage. The situation of some indigenous communities – such as in Canada, where access to clean drinking water, as well as cramped living conditions and inadequate nutrition, are ongoing issues for a number of First Nations communities (Geland and Harrison, 2013[257]; Government of Canada, 2021[258]) – provides one such example. Information on the social gradient of diseases prevalence would help to determine whether universal policies – such as vaccinations – are successful in reaching all children, and whether governments need to expand their efforts to better reach certain groups of children.
The GBD project is the most comprehensive data source in children’s physical health outcomes, and uses ex-post harmonisation of a wide range of national household surveys in order to track the incidence and prevalence of many diseases and risk factors. However, household surveys typically cover detailed information on each respondent’s income and living conditions. While likely requiring extensive synthetisation efforts, it may be nonetheless possible to enrich the estimates with further disaggregation along socio-demographic dimensions in the future, including by household type, migrant background and possibly indigenous identity.
Another important limitation of data on children’s physical health outcomes is that relatively little information is available on children with physical or intellectual disabilities and other vulnerabilities, such as those living in out-of-home care or who are homeless. These children often have additional health needs that, if unmet, can compromise different areas of development. For example, there is a lack of cross-national data on children with disabilities due to the fact that national surveys are not regularly conducted and/or are typically based on definitions of disabilities that vary across countries (OECD, 2020[259]; Hunt, 2019[260]). Even though international instruments like the UN Convention on the Rights of Persons with Disabilities propose definitions, the practical translation of this recommendation into disability surveys varies between countries. In particular, the inclusion of disabilities in social activities is subject to varying interpretations depending on the social norms that are diverse across countries. To this end, the Conference of European Statisticians has set up a Task Force mandated to review data gaps, sources, standards and definition and collection mechanisms used in UNECE countries on children with disabilities and to develop a set of recommendations for a harmonized improvement of the availability of data.
Expand the monitoring of child health across childhood, including the 1000 first days of life
The developmental period from conception to the end of the child’s second year has become known as the first 1 000 days and has helped frame the type of supports very young children and their families need in order to give children the best possible start to life. The special focus on the first 1 000 days comes from the growing body of scientific evidence showing the importance of the early life experiences for long-term healthy development and well-being. The First 1 000 Days of life are regarded as the period in people’s lives when public policy can have the most positive impact as brain plasticity as at its highest (Moore, 2018[261]; Riding et al., 2021[262]).
Birth outcomes have a significant impact on children’s healthy development in the early years of life and on later life outcomes. However, only a small list of prenatal conditions are currently measured and where indicators and data sources do exist, for example, such as those collected at post-birth hospital discharge or during preventive health examinations, they are typically not comprehensive and standardised enough for cross-country comparison. Measuring prenatal conditions and maternal behaviours during pregnancy is important as it can provide insight into whether information and preparation programmes during pregnancy are efficient in reaching expectant mothers to ensure that every baby starts life with the highest potential for healthy development.
It is crucially important that future data collections establish standardised frameworks for evaluating pregnancy and increase their focus on prenatal conditions, such as mother’s physical and mental health, physical activity and risky behaviour during pregnancy. The HEDIC project in Europe and the International Consortium for Health Outcomes Measurement (ICHOM), for example, propose a minimum standard set of internationally comparable measures to be collected during the pregnancy that incorporate some of these factors (Nijagal et al., 2018[263]; HEDIC, 2016[252]). A better mapping of prenatal risks for child health will be possible if enough countries adopted either of these proposed measurement agendas.
Having in place regular health screening programmes for children, especially during the first years after birth, is also crucial to reach children in vulnerable situations, increase the likelihood of early detection of developmental problems and diseases, and improve full completion of vaccine schedules. Depending on the country, medical check-ups in the first few years of life can be performed by regular home visits, or provided in other settings such as in day nurseries, childcare or healthcare centres (Riding et al., 2021[262]). However, not all children necessarily enjoy equal access to health check-ups. It is therefore important to have information on the existence and coverage of regular postnatal routine examinations/screening programmes, confirmation of whether these programmes provide general health, vision, hearing and dental care screening, and of procedures in place to ensure that the child receives the required follow-up.
A better assessment of child maltreatment
The capacity to monitor health risks is central to policies that aim to improve child health and reduce health inequalities across children. However, population-based estimates of child maltreatment, including exposure to intimate partner violence, child neglect and abuse, are often incomplete, in part due to the complexity of measurement (Annex 4.A). The development of indicators is key to giving visibility to cases of child maltreatment and to encourage countries to put in place policies to address it. But most data collection methods are very costly, often require lengthy in-person interviews that render a sufficiently large data collection complicated, and come with tricky ethical issues. However, a range of short form questionnaires on children’s experience with household physical, verbal and sexual abuse exists or are under development and validation, which could be embedded in cross-country surveys in the future (WHO, 2016[264]).
A better tracking of environmental risks
The thorough tracking of children’s exposure to environmental risks, such as unsafe air and contaminated water and food, is key to ensuring that children can grow up healthily (Currie, 2013[265]; Currie, 2017[255]; WHO, 2017[266]). To this end, it is desirable to have indicators to monitor the prevalence and concentration of environmental risks in some regions, including those resulting from climate change.
Currently, available data focuses on death rates relating to environmental pollution and contamination, but less frequently accounts for the global exposure of children to levels of risks that can jeopardise current and future health. A few features are essential for achieving good quality tracking. First, tracking must be done at the appropriate local level, which requires a high level of granularity in the data. Second, tracking must account for children’s exposure to environmental risks not only in the home, but also in school, during commutes, and partaking in other activities (see e.g. McConnell et al. (2010[267])).
In order to set and monitor policy priorities relating to environmental quality, it is importance to have detailed data that consider the full spectrum of children’s environments. Fortunately, recent advances have put individual-level measurement of environmental exposures on the scientific agenda. As a result, it might be possible to measure exposure levels more comprehensively and in more detail in future (Caplin et al., 2019[268]). Other major challenges for monitoring include collecting better data on infants’ and toddlers’ exposure to chemicals through contaminants in foods targeted at children (e.g. infant formula, baby food, cereals, etc.), as well in consumer products targeted for children (e.g. toys, child mattresses, etc.). A better understanding of the use patterns and behaviours of children and adolescents when using products such as paints, glues, hand sanitizers, and make up is also needed.
Develop data on child health check-ups and health care service policies
Detecting health problems as early as possible is essential for ensuring that children have access to appropriate health care and support. Medical check-ups during pregnancy and in the post-natal period are a key element of prevention systems and useful for directing families to appropriate care services. From a policy perspective, monitoring the number of children covered by medical check-ups (including especially vision and hearing screening) in the early years of life but also at later stages of childhood is critical for strengthening preventive health policies.
Access to relevant treatment and health care services by pregnant women and children is a key resource for improving children's health (Guio, Frazer and Marlier, 2021[269]). However, data on service availability, cost and child coverage are limited. These data are nevertheless important for the development of policies to promote access and to alleviate possible barriers to treatment and services. For infectious diseases, WHO and UNICEF have developed an indicator measuring the proportion of children receiving appropriate treatment, but this indicator is available for only a few low- and middle-income. This approach could be extended to other types of diseases and health issues by asking parents of children with a diagnosed health issue about their access to an adequate health service and the possible obstacles to service use.
Current data collections on health care spending are typically focussed on overall expenditure, mostly stretching across all age groups, although, in some cases, data are available on health care spending on children under the age 5. However, there are crucial differences between preventive and curative-rehabilitative measures when looking to link health expenditure to child health and well-being outcomes. A sufficient level of preventative health care is essential for keeping health risk under control and for providing services that detect emerging health issues early on. Preventative measure are also often particularly cost-effective, as they help detect and address health conditions that may become more severe and more costly later on (Merkur, Sassi and McDaid, 2013[270]).
Currently, OECD countries spend only about 3% of overall healthcare expenditure on preventive measures, such as immunisation, early detection and healthy condition monitoring, though there is a wide variation between countries. The level of preventative expenditure is also highly sensitive to the economic environment and has often been reduced as a response to recessions (Gmeinder, Morgan and Mueller, 2017[271]). In principle, it would be valuable to measure spending on, and children’s access to, specific preventive measures, but this is complicated by differences in preventive measure recommendations across countries (e.g. immunisation guidelines differ across OECD countries). In order to monitor whether children receive sufficient preventative measures, it is important to measure not just how countries spend on preventative health care, but also how this expenditure is distributed across children of different ages. Preventative measures for infants typically include immunisation programs and frequently re-occurring health examinations that aim to detect emerging health risks and conditions. In later childhood and adolescence, expenditure may be directed more towards education programs that inform children of the risk and benefits of different health behaviours, such as substance use, sex, sport and nutrition.
Collect information on children’s views and knowledge on health issues
Understanding what children know about health issues is important for assessing where, when and how to target health education. However, at present, there is no data on children’s knowledge of health issues, the supports they may receive, the risk they face now or in the future, and the positive or negative consequences that certain behaviours may have on their current and future health and well-being. Collecting this kind of data would be a first step towards better engaging children and young people on health issues, and could help ensure that guidance and information on health issues is presented in a "child-friendly" way. Consulting with children to understand how they best absorb information could also be valuable.
Collecting information on parents’ knowledge of children's health issues may also be important in view of helping them construct healthy environments, especially for parents with younger children and infants. Areas that could be covered include, among other domains, prenatal care, children’s developmental milestones, nutrition and eating practices, sleep patterns, and physical activity practices.
Track awareness and the implementation of child health-related recommendations
National and international recommendations on nutrition, physical activity and sleep set minimum standards that are intended to promote healthy child development and prevent health problems that may be linked to poor practices. However, to date, not all existing recommendations have corresponding data that can be used to monitor whether guidelines are being met. The importance of these gaps depends on the specific recommendation in question. For example, some datasets can already be used to give general information on a certain dimension. The EU SILC data on fruit and vegetable consumption, for instance, is not fully aligned with the WHO recommendations of consuming at least 400g (or alternatively five pieces) of fruits and vegetables per day, but nevertheless provides useful and relevant information on child nutrition. Regarding physical activity, some data exist for adolescents, but not for younger children. Finally, there are data on the existence of sleep disorders, but no data, regardless of age, on child sleep patterns indicating whether the recommendations are being met. The desirability of developing new data on child sleep that is consistent with existing child health recommendations is something that should be further explored.
To be effective, health recommendations must be known to those to whom they are directed at. Collecting data on children and parent’s awareness of recommendations may help highlight areas where better information and guidance on risky and protective health behaviours is needed.
Connecting the dots: physical health and other well-being outcomes
In order to provide children with a healthy start to life and foster healthy development throughout childhood and adolescence, it is crucial to know not just about children’s physical health outcomes, but also how their health interacts with other aspects of current and future well-being. The evidence reviewed in this and other chapters shows that good physical health depends on material living conditions (Chapter 3) and lays the foundation for children's learning (Chapter 6) and emotional and social development (Chapter 5). Measuring how differences in well-being outcomes are linked to differences in health status can help promote a more holistic understanding of the issues and aid the identification of areas where public action should focus its efforts. This requires data that allows for information on health status to be cross-tabulated with other aspects of children's development and well-being. This is not always be possible with existing sources, which often focus only on specific aspects of child well-being.
Developing a good mapping of children’s physical health situation and the challenges, risks and health-related resources available to children is key for fostering children’s abilities to reach their full potential. At the same time, there are still key gaps in child physical health data that need to be filled. Some of these gaps may be tackled by extending currently existing methodologies to measure additional aspects, while others may require the introduction of new surveys or additional items in existing questionnaires, and better linkages across different data sources.
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The goal of this annex is to provide information on whether child health indicators are already available and which sources could potentially be used to fill current data gaps.
Much of the existing information on children’s health is available in national demographic and household surveys. Non-response, recall- and sampling biases as well as small sample sizes can make it difficult to draw inferences to the population. It can also be difficult to synthesize information across countries due to differences in survey design. For some countries data gaps can in part be filled by administrative data sources, but these are rare and necessitate sufficient administrative information data infrastructures.
Promising alternatives are cross-country surveys and other global approaches to measure and estimate health outcomes, such as the Health Behaviour in School-aged Children study (HBSC) or the European Union Statistics on Income and Living Conditions (EU-SILC) (see Annex Box 4.A.1). Some useful items can also be obtained from OECD’s Programme for International Student Assessment (PISA), which collects school performance data for adolescents in the OECD, but also includes items on health and behaviour outside of the school context. To date, the health- and well-being items were administered on a voluntary basis to a subset of countries participating in the PISA. As a result, only nine countries have results relating to self-reported health status or body image. Forthcoming PISA rounds will include a new health and well-being module in the core questionnaire, which may become useful to measure health related outcomes among adolescents for a broader set of countries in the future.
It is also possible to make use of work done to synthesise information within and across countries to provide estimates of the prevalence and incidence of diseases and conditions, such as the Global Burden of Disease Study (GBD, see Annex Box 4.A.1). Other sources, such as WHO or UN datasets, track the Sustainable Development Goals (SDGs) or model overweight and obesity across countries, for example in the NCD Risk Factor Collaboration (NCD-RisC).
As with all data collection, there are limitations and caveats with cross country surveys and synthesised datasets. Surveys, such as the HBSC or EU-SILC do not cover all OECD countries and have differential rates of non-responses for some countries on specific items. Estimated data, such as the GBD are often subject to uncertainty and unclear reliability as well as occasional conflicts with other official observational data sources (Boerma, Victora and Abouzahr, 2018[237]; Rigby, Deshpande and Blair, 2019[238]; 2019[239]). A lack of clear information on the underlying methods that create these estimates raises questions on the transparency of the data and some of the estimates may risk oversimplifications of complex realities (Shiffman and Shawar, 2020[235]; Mahajan, 2019[236]). These issues may, in the future, require other coordinated efforts of national statistical institutes to replace the reliance on global health metrics or a more transparent data generating process of the IHME itself in order to fully understand the estimates and their resulting uncertainty. Despite the potential caveats, both cross-national surveys and estimated data are of great value in order to measure and compare child health and well-being across the OECD, especially where other data is not available. Careful use of these data can close a substantial amount of current data gaps with readily-available information at a low-cost.
Global Burden of Disease Study (GBD)
The Global Burden of Disease Study is an international observational epidemiological study of the Institute for Health Metrics and Evaluation (IHME) at the University of Washington. It estimates the burden of diseases, injuries, and risk factors across the world and provides data for all OECD countries available in the GBD results tool (IHME, 2018[98]). The wide range of estimates is available as cause-specific deaths and disability-adjusted life years across detailed age groups, including children and adolescents (e.g. below 1, 1 to 4, 5 to 9, 10 to 14 and 15 to 19 years), and many also include prevalence and incidence estimates. Estimates typically published every two years, with the latest update containing data for 2017. Due to its wide coverage and detailed age breakdown, the GBD data is particularly useful in order to measure cross-country differences in child health outcomes.
Health Behaviour in School-aged Children (HBSC)
The Health Behaviour in School-aged Children survey collects data on 11-, 13- and 15-year-old boys' and girls' health and well-being, social environments and health behaviours for most European countries and Canada (29 out of the 37 OECD member states). In collaboration with the WHO Regional Office for Europe, data is collected every 4 years with latest data available for 2018 (see results in WHO (2020[272])). The survey can illuminate cross-country differences over critical periods of adolescent development with increased personal authority Importantly and makes it is possible to break down information by gender and family affluence (highest/lowest quintile).
European Union Statistics on Income and Living Conditions (EU-SILC)
The European Union Statistics on Income and Living Conditions is a survey that collects cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions for most European Economic Area (EEA) countries, the United Kingdom and accession candidates. Data is collected by Eurostat, primarily on the household level and the resulting microdata can be used to build indicators on child health outcomes at different ages, disaggregated into socioeconomic groups. The most recent data is currently available for 2018.
As discussed, in the main chapter, there is generally a good availability of data on children’s physical health outcomes. The following section will detail the availability of indicators on these dimensions and account for potential caveats. An overview of the specific data sources can be found in Annex Table 4.A.1.
Birth outcomes and key prenatal determinants
Indicators on infant mortality are available from the OECD Health Statistics. Here infant mortality is based on either neonatal mortality, that is death under 28 days after birth, or infant mortality before age 1. For a few countries there, a slight differences in the recording that dampen the cross-country comparability to a small degree, but these differences are documented. For example, typically all live births are considered in the mortality statistics, but some countries register slightly different births conditional on specific characteristics. Poland, for example, registers only children born with at least 500 grams. Annex Figure 4.A.1 plots these data on infant mortality rates, which have become very low in most OECD countries. Like many of the following sources on children’s physical health outcomes, disaggregation for these data sources is not possible, which prevents the identification of intra-national health inequalities on these specific dimensions.
Alternative data on under age five mortality, but also neonatal and infant mortality, is available through the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which is based on comprehensive cross-country estimates that accounts for differences in data collections across countries as well as systematic measurement biases (UN IGME, 2019[273]).
Note: Deaths of children aged less than one year per 1 000 live births (no minimum threshold of gestation period or birthweight). For 1970, data for Mexico refer to 1971, for the Russian Federation to 1980, and for South Africa to 1974. For 1990, data for Korea refer to the mean of 1989 and 1991, and for Turkey to 1996. For 2018, data for Colombia and New Zealand refer to 2016, and for Costa Rica and the United States to 2017. The OECD-36 average excludes Turkey due to missing data.
Source: OECD Health Statistics, http://www.oecd.org/els/health-systems/health-data.htm, via the OECD Family Database, oe.cd/fdb.
Data on low birthweight are well suited for the evidence-informed framework, but would benefit from disaggregation to illuminate health inequalities at the beginning of life. Data on the number of low birthweight births is available from the OECD Health Statistics, as plotted in Annex Figure 4.A.2. The statistics are typically based on national health survey data. While cross-country comparison is possible, differences along demographic and socioeconomic lines are not visible. Alternative sources of data on low birthweight incidence can be found in the UNICEF-WHO Low birthweight estimates (WHO, 2019[274]). These estimates are in part based on administrative sources, such as vital statistic registers, as well as Multiple Indicator Cluster Surveys (MICS). Similar to the UN IGME mortality estimates, these estimates correct for underlying data insufficiencies.
Note: Data for China, Colombia and Costa Rica refer to 2012, for Germany to 2013, for the Russian Federation to 2015, and for Australia, Belgium, Chile, France, Japan, Netherlands and Sweden to 2016. Exact definitions of low birth weight and of live births may differ slightly across countries.
Source: OECD Health Statistics, http://www.oecd.org/els/health-systems/health-data.htm, via the OECD Family Database, oe.cd/fdb.
Cross-country comparable data on birth outcomes are typically widely available. For example, the Global Burden of Disease study (GBD, see Annex Box 4.A.1) reports estimates of the incidence of pre-term births across the OECD. On the other hand, the WHO reports both observational and estimated data in the Global Preterm Birth Estimates which are based on Chawanpaiboon et al. (2019[275]). However, latest data is only available for 2014 and it is not clear whether these estimations will see updates in the future.
In some countries, information on birth outcomes is also available in administrative datasets. For New Zealand birth outcomes, including gestation length (to identify preterm births) and birthweight, are collected in the National Maternity Collection (MAT) by the Ministry of Health (2011[276]). Similar data is also available in administrative register elsewhere, in particular the Nordics. For example, birth outcomes for all Danish children are available in the Medical Birth Register (MBR) (Bliddal et al., 2018[277]). However, the spread of these registers is, as of now, not yet very wide, but will likely an important part of future data collections.
Prenatal maternal care consists of assessments and treatments that differ along multiple dimensions, including variations in the time care starts, prescribed and actual care, the type and training of the provider, the location of care, and the availability of specialised services. Some forms of pre-natal intervention may also apply to fathers, including relationship advice, birth and parenting classes, and public health information. Due to this wide definition on antenatal care as well as differences in national policy and health care contexts, comparable data on actual visits received by pregnant women and visit content remains very limited, however.
WHO currently recommends a minimum of four antenatal visits, and antenatal care coverage is being monitored to ensure that access to prenatal care is integrated into national reproductive health-care services strategies and programmes by 2030 (Sustainable Development Goal 3.7). The indicator developed by the WHO measures the share of pregnant women who received the four recommended visits, or lack thereof, who had at least one visit during pregnancy. This information is available for many middle and low income countries but no comparable information is available for most OECD countries. The World Bank offers an indicator on whether pregnant women receive any prenatal care, but this is not available for most OECD countries.
This information is available from administrative datasets for some OECD countries. For New Zealand, the total number of antenatal publicly funded maternity and new-born services, which are available to all mothers, is collected in the National Maternity Collection (MAT). The number of antenatal care visits for Danish mothers is available in the Medical Birth Register (MBR), along with other information on the new-born’s anthropometrics and mothers risk behaviours during pregnancy, such as smoking. However, the use of administrative data to monitor the recommended antenatal visits often depends on the underlying funding structure of the health care system as these records are typically only available for publicly funded services. If a country has a substantial private health-care market, data solely from public services may not provide an accurate estimate. In New Zealand, even if a women chooses a private obstetrician (which makes up about 6% of all pregnancies), services are still publicly funded and thus covered in the MAT (Grigg and Tracy, 2013[278]).
Data on maternal prenatal smoking can be obtained from national sources, such as the Danish MBR or the Smoking Status at Time of Delivery (SATOD) data collection in the United Kingdom. Additionally, there have been academic reviews that collected national prevalence rates in scientific surveys in order to estimate the fraction of children born by mothers who smoked during pregnancy, such as Lange et al. (2018[242]).
Physical development
Indicators of infant and early childhood anthropometric development, such as stunting (low height relative to age), wasting (low weight relative to height) as well as under- (low weight relative to age) and overweight (high weight relative to height) can theoretically be constructed along different stages of the early life, but are most commonly reported as the prevalence among children below the age of 5. These indicators are manifested in the Sustainable Development Goals (SDGs) with targets for the prevalence of stunting, wasting and overweight (Sustainable Development Goal 2.2). Information on these indicators is available in the Joint Child Malnutrition Estimates that are compiled using a range of nationally representative household surveys (UNICEF/WHO/World Bank, 2020[279]). While most indicators sufficiently capture malnutrition, wasting prevalence can be volatile over a given year. As the survey data feeding into the Joint Child Malnutrition Estimates is usually collected at certain point in time and does not allow to collect wasting incidence, the indicator is not fully reliable (Chotard et al., 2010[280]). Related to other indicators on early child development, much of this information is also only available for low- and middle-income countries, without wide coverage of the OECD member states. Alternatively, comparable information might be sourced from the health-related SDGs indicators of the GBD that estimates worldwide attainment of the SDGs using over 90 000 different sources (Lozano et al., 2018[281]).
An alternative source for data on infant and child height and weight development may be found in preventive health examinations records. In Denmark for example, obligatory preventive health examinations provide information on height, weight and head circumference at different stages after birth. Information on height and weight in different ages of school children can be compiled in the Children’s Database (BDB) which is recommended for national monitoring of children’s health development by the Danish Health Data Authority (2018[282]) and can be linked to other background data for each child, allowing for a breakdown of the indicator by socioeconomic status. Comparable information is for example available in the Child Health Systems Programme Pre-School data for Scotland (ISD, 2019[283]) or in the B4 School Check data for New Zealand (Stats NZ, 2017[284]), though the latter is collected from a single examination before school start. Even though participation rates for preventive health examinations are routinely close to or above 90%, it cannot be ruled out that the group of non-participants, for which no administrative data anthropometric development exists, may be a selected subsample with a higher representation of children already at risk of being vulnerable (Michelsen et al., 2007[285]; ISD, 2010[286]; Stats NZ, 2017[284]).
Information on physical development in middle childhood and adolescence can be obtained from the NCD Risk Factor Collaboration (NCD-RisC) which reports estimated over-/underweight and obesity prevalence among children and adolescents aged 5-9 and 10-19 years old, broken down by gender. This data is collected through a variety of national population-based surveys covering data on height and weight as well as waist and hip circumference. Similar to the GBD data, this source provides information for countries worldwide, including OECD members. The current data is based on estimates from Abarca-Gómez et al. (2017[287]) for 2016. Alternatively, data from the Health Behaviour in School-aged Children (HBSC, see Annex Box 4.A.1) survey may be used. This data covers information on over-/underweight and the body image for children and adolescents aged 11, 13 and 15 in Canada and Europe. However, for a number of countries there is a significant fraction of non-response along most of these questions and data is not available for non-European OECD members, except Canada. Alternatively, the Programme for International Student Assessment (PISA) data contains information on the BMI of 15-year old adolescents, yet in the most recent 2018 round this information is available for less than 13 percent of students. This is because the relevant well-being questionnaire, which contained height and weight items, was not administered to adolescents in all countries.
Child health status
Using estimates of the GBD study (see Annex Box 4.A.1), the main chapter identifies the most important childhood morbidities, by ranking them according to death rates, which give a direct indication in terms of lives lost due to specific conditions, as well as disability-adjusted life years (DALYs), as a measure of healthy years of life lost due to illness, disability or early death. Both measures are important concepts to examine national indicators of child health. DALYs in particular include information on long-term implications of child morbidities – for example, while Asthma is itself is rarely a lethal condition, it significantly impairs the quality of life for many children,. Using the same data from the GBD study used to identify common child morbidities, it is then possible to build indicators for each OECD member across different age groups as the source contains not just death rates and DALYs, but also prevalence and incidence estimates by country, sex and age. Additionally, the GBD data also includes estimates for the incidence and prevalence of a number of oral diseases and sensory impairments across age groups in OECD countries, including caries and periodontitis, refractive disorders, and vision and hearing loss, which can be used to assess the oral and sensory health status of children.
Data on self-assessed health for adolescents in available in the HBSC survey. Here 11-, 13- and 15 year old children report how often they had experienced headaches, stomach aches and backaches over the last six months. The survey also includes a self-rated health status that may be used to compare countries in the EU and Canada. Similar questions are also asked to 15 year old children in the PISA questionnaire, yet for many countries there are no responses in the most recent 2018 round due to the reason already raised above.
As evident in the main chapter, data on nutrition, behaviours and processes can mainly be sourced from cross-country surveys, such as the HBSC and EU-SILC. Much of this does not cover younger children and countries other than in Europe and Canada, though it provides a good set of readily available indicators that can be disaggregated according to family affluence. A collection of the different potential data sources can be found in Annex Table 4.A.2.
Nutrition and eating habits
The WHO recommends that new-born children are exclusively breastfed within the first hour after birth and throughout the first 6 months of life, while receiving a mix of breastfeeding and complementary foods for the following 18 months. To measure the share of children being breast-fed, the WHO defined a set of indicators: early initiation of breastfeeding (within first hour of birth), exclusive breastfeeding under 6 months, continued breastfeeding at 1 year, and continued breastfeeding at 2 years. While some of these indicators are designed in a way to reduce recall bias, they may suffer from often misinterpreted exaggeration of the shares (Greiner, 2014[288]).
Most of the information regarding breast-feeding is only readily available for low- and middle-income countries, primarily collected through the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). In order to overcome this data gap, Victora et al. (2016[118]) additionally compile data from various national surveys in high-income countries. However, most of these countries are not able to report information that is in line with the WHO indicators and thus it might be necessary to use more general information (such as indicators on children ever breastfed), which are available in the OECD Family Database. Unfortunately, the data only covers breastfeeding rates in 2005 and as such appears outdated. The use of such data is complicated by the fact that these surveys are often subject to some degree of non-response bias, a lack of recent information and long recall periods that may diminish the accuracy of indicators. However, for some countries it may be possible to use information that is recorded in administrative dataset. In Scotland for example, information on the incidence of breastfeeding for new-born children is available in the Child Health Systems Programme Pre-School data which is collected with high coverage at hospital discharge and during preventive health examinations (Ajetunmobi et al., 2014[289]; IDS, 2019[290]). Similar data is available in the previously mentioned Danish Children’s, Database (BDB), which also collects administrative information on breastfeeding and exposure to smoking at home during preventive examinations.
The WHO operates a detailed set of biochemical indicators for assessing the prevalence of various vitamin and mineral deficiencies in the Micronutrients Database of the Vitamin and Mineral Nutrition Information System (VMNIS). When combined with health-, nutrition- and household surveys, this data for instance allows to use recorded haemoglobin concentrations to estimate the prevalence of anaemia caused by iron-deficiency in pre-school aged children (see e.g. Stevens et al. (2013[291])). Unfortunately, the data which is collected from a wide array of published research and reports is often not up to date, in particular for OECD countries. Children’s dietary intake of specific micro- and macronutrients may further be available from some national food surveys, such as in the United States (USDA and USHHS, 2018[292]).
The overall availability of micro- and macronutrients on country level can be obtained from the Global Nutrient Database by the Institute for Health Metrics and Evaluation (IHME) and the Food and Agriculture Organization of the United Nations (UN FAO), which compiles information from the scientific literature, estimates of food availability, sales data as well as nutrition- and household surveys in order to estimate global dietary risks (Schmidhuber et al., 2018[293]). Again, the latest available data is from 2013 and it is not certain whether there will be any subsequent updates to the database in the near future.
An alternative for more up-to-date information on general childhood nutrition may be found in either EU-SILC or HBSC data (see Annex Box 4.A.1). The latter survey provides respective information on children who consume neither fruits or vegetables, as well as data on sweet-, carbonated drink- and school-day breakfast consumption for children aged 11, 13 and 15 in European countries and Canada. Information on nutritional behaviour along these lines, especially fruit and vegetable as well as breakfast consumption, is valuable due to its well-documented relation to healthy child development even though it does not provide more detailed information on specific nutrients and deficiencies. The information on sweet and carbonate drink consumption can be used to measure the share of adolescents with high levels of risky dietary patterns. As mentioned above, the HBSC data can be disaggregated by the children’s gender and the household affluence. In contrast to many other data sources on child health, the latter can unveil important socioeconomic differences in healthy childhood nutrition, often found to be pronounced throughout childhood.
Note: Children are limited to those aged 1-15. “Fruits and vegetables” includes both fresh fruits and vegetables and frozen fruits and vegetables. Canned fruits and vegetables are not included.
Source: OECD Child Well-Being Data Portal based on EU-SILC 2014, https://www.oecd.org/els/family/child-well-being/data/.
Data on the consumption of fruit and vegetables, also available for younger children, can be found in the EU-SILC micro-data. Here, the information covers children aged 1 to 15 that live in households where at least one child aged 1-15 does not have either fresh fruits and vegetables at least once a day as well as those that did not have one meal with meat, chicken or fish (or vegetarian equivalent), see e.g. a combined indicator in Annex Figure 4.A.3 using EU-SILC data. While both datasets do not provide practical information on micro- or macronutrient deficiency per se, they allow to assess basic nutritional deprivation along major food groups which have shown to be important for child development.
Risky and protective behaviours
Indicators on childhood vaccination are routinely reported as vaccination rates that reflect the share of children receiving a specific vaccination or a combination of those (e.g. combined DTP-vaccine against diphtheria, tetanus, and pertussis) at the recommended vaccination age. Information on global vaccination rates is available through the WHO/UNICEF estimates of national immunization coverage. Using the same estimates, Annex Figure 4.A.4 reports vaccination rates for diphtheria, tetanus and pertussis (DTP), measles and hepatitis B at 1 year of age across OECD countries. The figure shows that vaccination rates are high, even though many still fall short of WHO recommended immunization levels (OECD, 2019[253]). These indicators may be further extended to cover additional vaccination rates that are available through the WHO/UNICEF estimates, such as for rubella-, rotavirus-, pneumococcus- and polio vaccines.
Note: 1. DPT data estimated. 2. Measles data estimated.
Source: OECD Health Statistics, http://www.oecd.org/els/health-systems/health-data.htm.
Cross-country information on physical activity for adolescents can be found in the scientific literature that pools survey estimates (e.g. in Guthold et al. (2020[245])) or through regular cross-national surveys that study adolescents behaviour in and outside of school. For instance, the HBSC survey provides information for physical activity of 11-, 13- and 15 year old boys and girls. The information includes data on the share of respondents reporting at least 60 minutes of moderate-to-vigorous activity per day as well as the share of respondents that engaged 4 or more times in vigorous physical activity per week. Similarly, Programme for International Student Assessment (PISA) data contains information on how often 15-year old children engaged in neither moderate nor vigorous physical activity over the last 7 days outside of school, which can be disaggregated by an index of socioeconomic status (see Annex Figure 4.A.5). Unfortunately, the latest PISA round lacks information on this item for many countries and thus it may not be possible to use this source for further measurement. However, items on physical activity will be included in the 2021 round and may thus become useful.
In contrast to adolescent’s information on physical activity, measures for younger children are not as readily available as international indicators. Nevertheless, some countries, such as the United Kingdom, run surveys that quantify physical activity for children aged 5 and over on a regular basis (Sport England, 2019[294]). However, population-level indicators on physical activity for children under 5 years of age seem to be unavailable at this point. The only information available comes from child cohort surveys using various of methods to collect data on children who are not old enough to answer a survey as well as to rate child activity: parental reports are used in some surveys, while others organise in-home observations or use accelerometers (Worobey, 2014[246]). These are expensive methodologies that are unlikely to be extended beyond small sample surveys or child cohort surveys which involve nation-representative samples but are not conducted on a regular basis.
Note: The PISA index of economic, social and cultural status (ESCS) is a composite measure used to estimate a student's socio-economic background. The index is derived from several variables related to students’ family background: parents’ education, parents’ occupations, a number of home possessions that can be taken as proxies for material wealth, and the number of books and other educational resources available in the home. The index itself is a composite score derived from these indicators via Principal Component Analysis (PCA). Here, however, students are divided into quartiles according to their position in the distribution of ESCS scores in their country or economy. B-S-J-G (China) refers to the four PISA-participating China provinces: Beijing, Shanghai, Jiangsu and Guangdong.
Source: OECD Child Well-Being Data Portal based on OECD PISA 2015, https://www.oecd.org/els/family/child-well-being/data/.
Even though the HBSC survey has information on self-reported sleep difficulties for adolescents aged either 11, 13 or 15, globally comparable data on children’s sleep patterns are rare. Nevertheless, some information is available in nationally representative surveys, such as the Longitudinal Study of Australian Children (LSAC). The data is based on two cohorts, children aged 0-1 years as well as those aged 4-5 in 2003 and the data is collected among primary carers and the children themselves (those aged 10 and older) every two years and includes survey items on bed- and wake-time (Evans-Whipp and Gasser, 2019[295]). Since most of the studies sample reached adulthood by now, it can unfortunately not be used anymore to track children’s sleep patterns in Australia. A further complication of similar surveys that collect data on bed- and wake-time is that these concepts do not necessarily indicate actual sleep-duration, especially among adolescents who appear to frequently use smartphones and social media past bed-time and/or in the middle of the night (Lemola et al., 2015[296]; Troxel, Hunter and Scharf, 2015[297]). The increasing availability of detailed smartphone usage data, such as touchscreen activity, may have future potential to measure sleep patterns among adolescents, even though current data collection requires particularly controlled settings (Rod et al., 2018[298]; Borger, Huber and Ghosh, 2019[299]).
Data on risky sexual activity is available in the HBSC survey on adolescents in Europe and Canada. The survey includes questions on sexual activity, though they are only available for 15 year olds, either as having had sexual intercourse or having used a condom or contraceptive pill at last intercourse (or used neither). As in other cases where HBSC data may be used, some countries may not have sufficient sample sizes to allow detailed breakdowns and countries outside the EU and Canada are not included.
Data on children’s environment and the policy context is considerably sparse, and as such, require future development. As evident in the main chapter, new measurement items in cross-country surveys or improved individual-level data collections could however improve the measure in the future. In terms of what is nevertheless available at the moments, Annex Table 4.A.3 present an overview of potential data sources.
Exposure to pollutions or contaminants
Information on exposure to particulate matter pollution is available in from the GBD comparative risk assessment (CRA) which estimates pollution-related risks by combining land use and satellite data with chemical transport models and ground measurements of pollutants. While less important in the OECD context, data on residential or household pollution is also readily available and estimated using a wide range of surveys on the usage of solid fuels for cooking (Stanaway et al., 2018[300]). Both data are reported as deaths and DALYs associated with particulate matter pollution and can be broken down into detailed age groups, yet no details on the specific exposure level are available.
Aggregate indicators relating to the share of population exposed to critical levels of different pollutants (above a critical threshold for a certain period) are for example available for European countries through the European Environment Agency (EEA). However, this data cannot be broken down by age and only considers urban agglomeration. It is thus not immediately suitable to measure the level of particulate matter pollution children are exposed to. An alternative avenue to build indicators on children exposed to particulate matter pollution may be data as a by-product of pollution measurement by public and private initiatives operating air quality sensors across cities and along major roads. Although common sources are mostly available for few urban spaces, some commercial providers have modelled detailed national pollution levels of, among others, particulate matter on postcode level (e.g. in the UK). The information is typically gathered from a chemical transport models employing range of sources, combining sensor-, weather-, traffic- and external air quality data. In order to estimate the share of children exposed to critical levels of pollution, the availability of geographical information, i.e. address data for each children or the number of children living in each postcode area, is necessary.
In terms of data sources, the GBD CRA also estimates death risks and DALYs related to unsafe water sources and lead exposure, broken down into detailed age groups. In terms of food safety, the WHO reports annual indicators based on International Health Regulation (IHR) monitoring to detect and respond to foodborne disease and food contamination. While this is not disaggregated into specific foods, such as early infancy dietary products, it nevertheless may give a good presentation of national food safety levels. Another approach is the barometer containing 30 safety indicators for the food chain in Belgium, as in Baert et al. (2011[301]), yet applying this methodology to other countries and deciding on the specific indicators might be hard.
Information on children exposure to noise can be obtained for EU countries from the EU-SILC survey (see Annex Box 4.A.1), which collects the share of children under the age of 15 exposed to some or severe noise from neighbours or the street. Both noise and particualte matter pollution are often originating in road traffic. Thus, it may be beneficial to build indicators on the share of children exposed to heavy traffic. This can for example be done using road network and traffic volume data which is often available at local authorities, typically based on road surveys and sensor data. Finland, for example, offers real-time information on traffic volume for its entire road network on a fine grained basis in the Digiroad and Digiotraffic data. This can, in theory, be combined with detailed address data for children.
Household environment
Data on passive exposure to tobacco smoke is for example available in the EU-SILC data (see Annex Box 4.A.1). The source measures daily exposure to tobacco smoke indoors by sex, age and educational attainment level in EU member states, the United Kingdom and accession candidates. However, individual exposure levels are only available for children 15 year old or older. Thus exposure for younger children would need to be identified from data on the number and age of younger children in each household.
Data collection on child maltreatment, is complicated and needs to be subject to strong ethical guidelines in order to minimize further risk to children. Information on child maltreatment in the family and home environment is often obtained from survey data or reports to child protection services (e.g. through primary care contacts or the school). For example, in order to track global attainment of SDG 16.2.1, the Global SDG Indicator Database of the United Nations reports the proportion of children aged 1-14 years who experienced physical punishment and/or psychological aggression by caregivers. The data is mainly obtained from Multiple Indicator Cluster Surveys (MICS) or other household surveys and defines physical punishment as actions intended to cause physical pain or discomfort without injuries as well as psychological aggression as shouting, yelling or screaming at a child.
Survey sources may severely under-report actual instances of child maltreatment and violence against children (MacMillan, Jamieson and Walsh, 2003[248]). With comparable problems, national child protection registers, such as those operated to administer and register cases of children referred to and assessed by social services, can be used. The use of administrative data reduces the need for children to disclose traumatic events of maltreatment and may thus severely lower the burden of recalling traumatic events (Hurren, Stewart and Dennison, 2017[302]). However, administrative data can always only cover the “tip of the iceberg” of children exposed to maltreatment In terms of measurement, Degli, Esposti et al. (2018[303]) build a rich data compilation (iCoverT) covering the incidence of reported child maltreatment over time with administrative datasets and registering cases referred to and assessed by social services for England and Wales. The referrals only cover a fraction of the actual number of child maltreatment incidences and while survey data also severely underreports these cases, there is no sufficient added value to be expected immediately even though these sources may reduce the burden on the children themselves.
Health care spending, quality and access
Data on health care spending, in particular among the population of children below the age of 5, is readily available in the Global Health Expenditure Database (GHED) of the WHO and for further age groups in the OECD Health Expenditure and financing database. This database collects accounts on health care expenditures using national reports as well as other sources, such as the OECD Health Expenditure and Financing Dataset. Cross-country information on health care utilization is typically only available for the general population, e.g. in the OECD Health Statistics. However, for some countries it may be possible to use administrative health care records to assess the status if health care service utilization for children. The advantage of this method over self- (or parent-reported) household survey information is a usually stronger reliability of the data due to avoidance of self-report bias (Reijneveld, 2000[304]; Dendukuri et al., 2005[305]). Administrative data on doctor consultations exists for example in the Danish National Health Service Register (SSR) which collects data on all services provided within the public health care and can be broken down by age (Sahl Andersen, de Fine Olivarius and Krasnik, 2011[306]).
Data on coverage for child and maternal health care is often obtained from household surveys and globally comparable data is, for example, compiled by the Countdown to 2030 initiative, though it includes only low- and middle-income countries. Equivalent information for countries in the OECD may be individually obtained from national household surveys or approximated by the health care coverage for the general populations (as in OECD (2019[253])). In some cases, data on geographical accessibility of children’s health-care services may be obtainable from administrative data sources in some countries. For many countries, administrative records contain the children’s precise (de-identified) address or an area/geographical unit, such as zip-/postal-/municipality- codes or census tracts, regularly collected from administrative registers in order to build census sampling frames or as a base for social statistics. Combining this information with central business registers (CBR), which contain the locations of businesses and public enterprises, it is then sometimes possible to build indicators on the access to medical care by determining approximate distances between the child’s home and specific medical facilities. However, while basic population data exists for many countries, including addresses, it is not always useable without problems and requires sufficient knowledge of recording processes.
Some cross-country measurement of health care quality is available in the OECD Health Care Quality and Outcomes (HCQO) programme. The most recent framework collects a total of 61 indicators on primary care, prescribing, acute care, mental health care, cancer care, patient safety, and patient experiences. The data are typically age-standardized and not available for children below the age of 15, but it may be possible to build some of these indicators for children alone. However, the HCQO programme might be too comprehensive to be used in an evidence-informed framework due to its large volume of data and indicators.
An alternative way to measure healthcare access as well as quality of the care provided may be the use of estimated summary indices, such as the Healthcare Access and Quality (HAQ) Index of the GBD study (Fullman et al., 2018[307]). This index approximates personal health-care access and quality by estimating excess death rates which should not occur under effective health care systems. In particular, using mortality, incidence and risk estimates from the GBD study, it uses risk-standardised death rates for most causes that are amendable to health-care as well as mortality-to-incidence ratios for cancers. As a result, these estimated indices make it possible to compare the effectiveness of national health care systems across countries. While the index is available for all OECD countries, it is estimated for overall systems, without particular focus on child health outcomes. Using similar methods and the open source GBD estimates it should nevertheless be possible to tailor similar indices for the quality of health care at young ages, potentially broken down into different childhood stages.