Epidemiology and world-wide impact of visual impairment in children


Introduction

This chapter first presents a number of key considerations and issues relevant to understanding and applying evidence from epidemiological studies of childhood visual impairment (VI), severe visual impairment (SVI), or blindness ( Box 1.1 and Box 1.2 ) – collectively “VI” for brevity. Thereafter, the impact of childhood VI is considered from different perspectives. This is followed by consideration of childhood VI in the broader context of child health and childhood visual disability. A synthesis of current epidemiological data, grouped by the GBD super regions, is presented as the key data required for planning clinical services, and for policies. Finally, an overview is presented of primary, secondary and tertiary prevention strategies with consideration of the role of ophthalmic professionals. Areas where critical evidence is lacking are highlighted throughout and potential future directions for research are discussed. Readers are referred to the online extensive supplementary material for a full bibliography.

Box 1.1
What is ophthalmic epidemiology?

Ophthalmic epidemiology (literally “studies upon people”) has both its origins and its applications in clinical and public health ophthalmology.

The aim of primary or secondary (e.g. systematic literature review, meta-analysis and modeling) research is to:

  • provide quantitative information for planning services

  • shed light on the causes and natural history of ophthalmic disorders

  • enhance the accuracy and efficiency of diagnosis

  • improve the effectiveness of treatment and preventive strategies.

Box 1.2
Epidemiological reasoning

This is based on the following principles:

  • the occurrence of disease is not random, rather a balance between causal and protective factors

  • that disease causation, modification, and prevention are studied by systematic investigation of populations to gain a more complete view than can be achieved by studying individuals

  • that any inference that an association between a risk factor and a disease is causal can only be made after two specific steps in reasoning: (1) the exclusion of chance, bias, or confounding as alternative explanations for the observed association, and (2) evidence of a consistent and strong statistical association, which is biologically plausible, in the correct temporal sequence, and preferably exhibits a dose–response relationship.

Specific Issues in the Epidemiological Study of Visual Impairment in Childhood

  • Case definition: A standard definition applicable to all children remains challenging, see below “Who is a visually impaired child?” .

  • Rarity: As childhood VI is uncommon, large-scale studies are required to achieve representative samples of affected children for precise and unbiased analysis.

  • Complex, multidisciplinary management: For a complete picture, information must be sought from all professionals involved in the care of VI or blind children, as many, in some settings the majority, have additional significant non-ophthalmic impairments or chronic disorders.

  • Life course approach: Within child health, life course approaches are now common, to understand the complex interplay between biological, environmental, and lifestyle/social influences at all life stages (preconceptional, prenatal, perinatal, and childhood), and how they combine to set and change health trajectories into adult life. Life course epidemiological and epigenetics approaches are increasingly applied to the study of VI and eye disease affecting children or originating in childhood.

  • Developmental perspectives: In all research on children, developmental issues (as distinct from age-related issues per se ) must be taken into account in assessing outcomes and their relationship with risk factors.

  • Long-term outcomes: Assessment of meaningful outcomes, such as final visual function or educational placement, requires long-term follow-up, into adult life for some outcomes. This is challenging and is increasingly addressed through data science and health informatics approaches. These use routinely collected data, often as electronic or “e” records, in health care (e.g. personal e health records, EHR/EM) and other health or education or welfare administrative systems with record (data) linkage using established methods to minimize errors to create complete datasets for analysis.

  • Ethics: Issues of proxy consent (by parents) and children's autonomy increasingly impact participation in ophthalmic epidemiological research.

Framing the Question

It is useful to think about the evidence needed to make clinical, service provision or policy decisions using a “four-part question” based on the PICO mnemonic P opulation I ntervention/Indicator C omparator and O utcomes). A good question incorporates the reference population (e.g. children under 2 years with infantile esotropia), the risk factor or the intervention and, where appropriate, a comparator (e.g. preterm versus term birth, or strabismus surgery versus no surgery), and the outcomes (e.g. parent-reported improvement in cosmesis and objective improvement in alignment and stereopsis).

The importance of framing the question well lies in the fact that the question will determine the type of study (study design) that can answer it, e.g. a descriptive, cross-sectional prevalence survey, or an analytical study of which of the two broad types are “observational,” e.g. case–control or cohort studies, or “interventional,” e.g. randomized controlled trials.

Who is a visually impaired child?

The affected child, their parents, teacher, social worker, rehabilitation specialist, pediatrician or ophthalmologist will often offer different but equally valid answers to this question. The issue is not which is “correct” but which definition to choose for epidemiological research, for assessing/planning services and in clinical practice.

A standardized definition is necessary in order to compare reliably the frequency, causes, treatment, or prevention of VI within and between countries and over time. The World Health Organization (WHO) classification of visual impairment ( Table 1.1 ) is now based on the “presenting” acuity in the better-seeing eye (i.e. at the level of the person not the eye) measured with optical correction, if usually worn, rather than the best corrected acuity used previously. Thus, uncorrected refractive error, i.e., the individual does not already have optical correction, now falls within the definition. This recognizes the fact that access to optical correction is very limited in some populations, making uncorrected refractive error a significant cause of functional impairment. Since refractive error is common, this important change in classification must be taken into account when comparing both the overall prevalence reported and relative importance of different causes of VI in studies conducted using the prior classification. As such, the data presented in this chapter do not include children with visual impairment due to undiagnosed or uncorrected refractive error alone . This group possibly comprises over 12 million children, most living in Southeast Asia with uncorrected myopia ( Box 1.3 ).

Table 1.1
World Health Organization classification of visual impairment3
Category Presenting distance visual acuity in better eye
Worse than: Equal to or better than:
0 No vision impairment 6/12 (LogMAR 0.3)
5/10 (0.5)
20/40
1 Mild vision impairment 6/12 (LogMAR 0.3) 6/18 (LogMAR 0.48)
5/10 (0.5) 3/10 (0.3)
20/40 20/70
2 Moderate vision impairment 6/18 (LogMAR 0.48) 6/60 (LogMAR 1.0)
3/10 (0.3) 1/10 (0.1)
20/70 20/200
3 Severe vision impairment 6/60 (LogMAR 1.0) 3/60 (LogMAR 1.3)
1/10 (0.1) 1/20 (0.05)
20/200 20/400
4 Blindness 3/60 (LogMAR 1.3) 1/60 (LogMAR 1.7)
1/20 (0.05) 1/50 (0.02)
20/400 No light perception 5/300 (20/1200) or CF at 1 meter
5 Blindness 1/60 (LogMAR 1.7) Light perception
1/50 (0.02)
5/300 (20/1200)
6 Blindness No light perception
9 Undetermined or unspecified
Category Presenting near visual acuity
Near vision impairment Worse than N6 or M0.8 with existing correction tested with both eyes open
CF, counting fingers; MAR, minimum angle of resolution.
If the extent of the visual field is taken into account, patients with a visual field of the better eye no greater than 10° in radius around central fixation should be placed under “binocular” blindness. For monocular blindness, this degree of field loss would apply to the affected eye.

Box 1.3
Key gaps in current knowledge about epidemiology and the impact of visual impairment on children

For many regions of the world, there is currently very limited contemporary population-based information about frequency, burden, and etiology.

There is limited understanding of the following:

  • long-term ophthalmic disease, general and mental health, educational, occupational, and social outcomes for affected children and the adults they become

  • social, economic, and personal impact on the families of affected children

  • economic consequences – including financial and other costs associated with medical treatment, rehabilitation, social support and care, as well as loss of productivity.

The opportunities and infrastructure for research to address these questions are better in industrialized countries, so there is currently a differential information gap.

The WHO classification is used in epidemiological research, despite the difficulties of measuring visual acuity in very young children and those unable to cooperate with formal testing. Investigators map behavioral responses/qualitative methods (e.g. using the central, following and maintaining (CSM) fixation notation) to broad categories of vision. Technological innovations for testing vision in young children are likely to emerge over time, such as eye tracking software. Nevertheless, there is a need in epidemiological research and also arguably in clinical practice for a better classification system applicable to children of different ages. This should consider other aspects of vision, such as binocularity and contrast sensitivity, as well as normal visual development. However, developing suitable methods for use in non-clinical research settings will be challenging.

Adoption of the WHO International Classification of Functioning, Disability and Health (ICF) reflects the new framework for understanding disability and the relationship between health conditions, personal, and societal factors. Also, the importance of measuring patient-reported outcomes (PROs/PROMs) using self-rated/self-completed questionnaires, is now accepted within many healthcare systems, to help improve the quality of care. Two types of PROMs are particularly relevant to pediatric ophthalmology. Firstly, functional vision measures of child’s/young person's own rating of their ability (difficulty or ease) for tasks of daily living dependent on vision, such as navigating independently. Secondly vision-related quality of life that elicits the child's or young person’s view of the gap between his/her expectations and his/her actual experiences with respect to the physical, emotional/psychological, cognitive, and social impacts of the visual disorder and its therapy.

Measures of frequency and burden of childhood visual impairment

The analogy of a barrel of white and red grapes with a hole in the bottom can be used to illustrate measures of frequency and the burden of disease. In this analogy, white grapes represent those without the condition of interest (“healthy”) and red grapes represent those who have the condition of interest (“diseased”). The total number of grapes (white plus red) in the barrel represents the population of interest (e.g. all children aged 0–15 years). The proportion of all the grapes in the barrel that are red at any given time denotes prevalence . The total number of red grapes in the barrel at any given time reflects the magnitude or burden of the disease in the population. The speed at which red grapes enter the barrel equates with incidence , i.e. the rate of new occurrence of disease in a given population over a specified time. For example, in the UK the annual incidence of congenital cataract was estimated to be 2.5 per 10,000 children aged ≤1 year in 1995.

However, the proportion in a population that is diseased (i.e. the prevalence) is dynamic, as some grapes (both red and white) leave the barrel through the hole in the bottom. The “diseased” (i.e. red grapes) can leave through mortality, which may be higher amongst those diseased, or by out-migration, or as a result of treatment that means they are no longer classified as diseased. At the same time, more red and white grapes are being added to the barrel. The prevalence (proportion of grapes that are red) at any given time is, therefore, a balance between how fast red (and white) grapes are added and how quickly they leave the barrel. For example, the current UK prevalence in children of amblyopia with an acuity of worse than logMAR 0.3 (6/12, 20/40, 0.5) is about 1%.

Prevalence and incidence data provide complementary information. Incidence identifies and monitors trends over time that reflect changing exposure to risk factors, or the emergence of new exposures or the introduction of effective public health measures for control (such as rubella immunization and childhood cataract). Incidence data are useful for planning the provision of services and research, e.g. estimating likely recruitment time in clinical trials. Prevalence indicates the proportion of the population with the condition at a given time . It helps allocate resources and can be used to evaluate services, if changes in prevalence can be attributed solely to changes in outcome or duration of disease as a result of treatment rather than changes in underlying incidence.

Measures of disease frequency do not, however, give any indication of the health economic impact (or “burden”) nor the consequences of the disease. These aspects are very important in order to determine priorities and allocate resources, providing a metric at population level that complements PROs assessed at an individual level. Measures of “utility” such as disability-adjusted life years (DALYs) or quality-adjusted life years (QALYs) are often used for this purpose in research with adults, as they incorporate morbidity or mortality into a single measure that can be used to compare different states of health within and between countries. Disability weights, which range from 0 (perfect health) to 1.0 (death), are used to calculate DALYs. These were revised in 2015; the weight for moderate vision impairment is unchanged at 0.034, but the weight for blindness was reduced from 0.6 to 0.17. There are concerns that the lower weight and hence lower DALYs for blindness, including among children, may have negative consequences for advocacy, benchmarking, and resource allocation. The direct applicability of these measures to children and young people is not fully established. As such, and also because large-scale population-based epidemiological studies of childhood visual impairment remain scarce, health economics research in the area of childhood VI remains limited.

Potential Sources of Information About Visual Impairment

There are a number of sources of epidemiological information about childhood VI or blindness but, in reality, only a few are available in most countries. This explains the currently incomplete picture (see Box 1.3 ).

  • Population-based prevalence studies: These represent a source of precise, representative estimates of burden (frequency) and causes. However, the few studies of whole populations of children with VI, such as prior and upcoming national birth cohort studies in the UK, need to be very large (a study of 100,000 children is required in an industrialized country to identify 100–200 children with VI or blindness): these are costly and difficult to do.

  • Population-based incidence studies : Studies of all-cause incident (newly occurring) VI are even more difficult, explaining their rarity.

  • Special needs/disability registers and surveillance: Specific studies and/or surveillance systems or registers of childhood disability can provide information about VI, but it is important to recognize the potential for bias, as certain visually impaired children may be over-represented in these sources, e.g. those with multiple impairment.

  • Studies of intervention/service-based populations: In developing countries, studies of children in special education provide information on causes, but these may be biased because many affected children (particularly those with additional non-ophthalmic impairments) do not have equal access to special education. With other service-based studies, e.g. from clinic attendees, the interpretation of findings and their extrapolation to other populations needs to take these biases into account, as children with treatable conditions are likely to be over-represented.

  • Visual impairment registers: These exist in many industrialized countries but, if registration is voluntary and not essential for accessing special educational or social services, registers may be incomplete as well as biased, as they will reflect differences in parental preferences and professionals' practices regarding registration of eligible children.

  • Visual impairment teams: Increasingly, children in industrialized countries are evaluated by multidisciplinary teams. They can provide useful information if these teams serve geographically defined populations.

  • Disorder-specific ophthalmic surveillance schemes: Uncommon eye conditions in children can be studied using population-based surveillance schemes, and have enabled studies, for example, of congenital eye anomalies, congenital glaucoma and adverse drug reactions. Under-ascertainment can occur. In the United Kingdom, the national active surveillance scheme includes all senior ophthalmologists (the British Ophthalmological Surveillance Unit). This unit facilitates studies of uncommon disorders, including the first population-based incidence study of SVI and blindness in childhood and subsequently the first study of VI, SVI, and blindness. This is an important resource for pediatric ophthalmic epidemiological research in the UK and a potential model for other settings.

  • Community-based rehabilitation programs: In many developing countries, rehabilitation of blind and VI children occurs within the community. If the size of the catchment population is known, it is possible to estimate prevalence and obtain population-based data on causes.

  • Case ascertainment using key informants: In many developing countries, it may be possible to identify key community and religious leaders, healthcare workers, and others who know their communities well and thus can identify children believed to have VI or ocular disorders. This can be combined with the size of the population at risk, to estimate prevalence and provide population-based data on the causes.

  • Household surveys are commonly used in many low-income countries to collect data on a range of health indices. This approach can also be used to identify children who are blind.

  • Data modeling can also be used for specific conditions, as has been used to estimate the global incidence of blindness and visual impairment from retinopathy of prematurity.

Regardless of the sources, ascertainment is often incomplete and/or biased. For example, in industrialized countries, families from socially disadvantaged groups or ethnic minorities are less likely to participate in research on health services for visually impaired children. Participation/selection bias affects our ability to generalize findings, especially in research on rare disorders. Also, in some communities in low-income countries, having a disabled child is a source of stigma, which can lead to under-ascertainment in community-based key informant or household surveys. Using multiple sources generally provides a more complete and reliable picture of causes and frequency of childhood VI.

Impact of Visual Impairment

Visual impairment in childhood impacts on all aspects of the child's development and shapes the adult he/she becomes, influencing health, well-being and education, particularly in low-income countries, and employment, social prospects, and lifelong opportunities. Visual impairment also impacts the families of affected children, both in terms of their own general health, mental health and well-being, and on their resources. Although the prevalence and incidence of VI are lower in children than in adults, the years of life lived with VI (“person-years of visual impairment”) are considerable. Personal and social costs are important, but difficult to measure (see Box 1.3 ). The economic costs of childhood VI in terms of loss of economic productivity are significant, a quarter of the costs of adult blindness in some countries. For example, in India an estimated annual cumulative loss of gross national product attributable to childhood VI was US$22 billion.

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