In evaluating oral health, interferences in physical, psychological and social functioning are important, as the traditional epidemiologic clinical indicators do not provide an insight into individual’s abilities in performing their roles and activities. Most of the currently available OHRQoL instruments have succeeded in measuring the impact of oral health on physical, functional, social and emotional wellbeing of an individual. Children like adults are also prone for various oral disorders, all of which can likely compromise functioning, well-being and QoL. But the concept of OHRQoL in children has increased dramatically only in recent years. A systematic literature review reported that the number of articles published on child OHRQoL between 2000 and 2006 was three times higher than between 1995 and 1999.
The Wilson and Cleary model of health-related QoL demonstrates that individual perceptions of QoL are influenced by several individual, environmental characteristics and also non-medical factors. However, evaluating the determinants of OHRQoL in children seems to be a new concept as there were no studies older than 2005 in spite of certain OHRQoL instruments being introduced between 2002 and 2005. Admittedly, the OHRQoL instruments for preschool children, such as ECOHIS and SOHO-5, were developed in 2007 and 2012 respectively. The latter instrument is a self-reported OHRQoL measure for 5-year-old children. While the ECOHIS questionnaire was widely used in preschool children, there were no studies that have used SOHO-5, which might be due to its recent development. The CPQ11-14 was the most widely used self-reported OHRQoL instrument in studies that were conducted on children and adolescents, and it is found to be valid as well as reliable in many cultural settings. Although the literature on the determinants of children’s OHRQoL is abundant, it is unequally represented, with more than half of the studies conducted in Brazil.
This review indicates that the findings on the correlates of OHRQoL from studies are varied and non-uniform, with different measures being considered by different authors. Moreover, not all studies included in the review aimed to test the association between the parental attributes and children’s OHRQoL. Findings from both the adjusted and unadjusted analysis were tabulated separately for each study. In a few studies, the significant effect of exposure on the outcome that was observed in unadjusted analysis was not observed in multivariate analyses after adjusting for the effect of confounders. The importance of statistical adjustment becomes more pronounced in cross-sectional studies, and especially in those studies that aim to ascertain the influence of many interrelated exposures on an outcome.
Most of the studies were of moderate quality and only three were strong. This is because of the quality assessment criteria used, which rates only those whose study designs are experimental or longitudinal in nature as good. However, experimental or longitudinal study designs are rarely used in OHRQoL studies of our interest. Furthermore, a few studies that were of moderate quality were rated as weak in ‘selection bias’ component of EPHPP as they did not report response rates in the articles. There were four prospective studies[23, 27, 28, 45] one of which was conducted with an objective to evaluate the effect of orthodontic treatment on OHRQoL. Due to the static nature of the exposure data (i.e., socio-economic and home environment characteristics), most of the studies were of cross-sectional design. However, it would be interesting to observe the dynamic effect of these exposure characteristics along the life course on children’s OHRQoL, which was done in one of the studies that assessed the influence of early life social conditions on children’s OHRQoL.
The composite measure of SES or area-based deprivation failed to show its effect on children’s OHRQoL in most of the studies. However, family income or family economy indicators and parental education levels were found to be significant predictors of children’s OHRQoL. Nevertheless, their effect was not observed after adjusted analysis in a few of the studies. Further, the influence of family economy or parental education was associated with only few dimensions of children’s OHRQoL. This discrepancy in results between the studies is due to the statistical methods adopted, i.e., a few studies analysed the effect of family income or parental education on overall OHRQoL score, while the others analysed the effect of these socio-economic variables on overall OHRQoL, as well as its dimensions. In addition, some studies performed statistical adjustment for the effect of confounders when analysing the influence of parental characteristics on children’s OHRQoL and few have not made any attempt to do so. As anticipated, family economy and parental education were directly proportional to children’s OHRQoL in all the studies that have found significant associations. Children of parents with high educational level and family income were more likely to have better OHRQoL. Low educational level may lead to reduced income and lower income is related to material deprivation. Children from poor families have limited access to health care and preventive interventions which might lead to a poor quality of life. None of the studies observed parents’ occupation to be significantly associated with children’s OHRQoL. Based on the findings from a few studies, it can be conceptually summarised that a mother’s work activity is a significant predictor during the early childhood while father’s occupation is significant during late childhood.
Mothers’ or caregivers’ age significantly predicted better OHRQoL in children, which might be due to younger mothers feeling less secure in caring for their child. Moreover, children of parents who are not native to the study location were found to be more prone to poor OHRQoL than those children whose parents are native to the area. This might be due to the indirect influence of SES, as migrants tend to have a lower SES than others. The marital status of the parents failed to influence children’s OHRQoL. Mother or other family members being the caregiver of the family did not influence children’s OHRQoL, except in one study on children with AIDS. It might be because of the additional care needed by these children than others as they are more prone to poor oral health. It is evident from the studies reviewed that children living with biological parents and those with nuclear families have better OHRQoL. More than half the studies that evaluated the relationship of crowding found it to be significantly associated with children’s OHRQoL, but only in unadjusted analysis. Household crowding is a proxy indicator of SES, and thus its association with children’s OHRQoL might have been masked by SES in adjusted analysis. Single children reported lesser impact of oral health on quality of life than those who have siblings, while the effect of the number of siblings a child has on their OHRQoL is inconclusive from the results of the reviewed studies. Other factors that significantly influence children’s OHRQoL comprise familial use of deleterious substances, maternal dental anxiety and dental services usage.
This is first study that has systematically reviewed the literature on the effect of parental socio-economic and home environment characteristics on children’s OHRQoL. A systematic review has been published recently that evaluated the effect of socio-economic characteristics on OHRQoL, which also included studies on children. In order to avoid exclusion of potential articles that had keywords other than those we have used, a broader term “Oral Health Related Quality of Life” was used to search “all fields”. We have not included other studies with the predictors “ethnicity”, “urbanisation”, “school type”, “dental fear” and “dental visits” as these are not directly related to either socio-economic or home environment characteristics. One of the limitations of the present review is the lack of quantitative data presentation by meta-analysis. Meta-analysis was not possible due to extremely heterogeneous data from the studies included, with categorisation of both the outcomes and exploratory variables differing between the studies.