Ethnic inequalities in oral health within the United Kingdom: a scoping review.

BackgroundEthnic inequalities in oral health among British adults remain largely unexplored. This study explored the role of socioeconomic position (SEP) in explaining ethnic inequalities in oral health; and the consistency of socioeconomic inequalities in oral health across ethnic groups.MethodsData from 45,599 adults, aged 16 years and over, who participated in the Health Survey for England were pooled across 5 years. The seven ethnic groups included were White British, Irish, Black Caribbean, Indian, Pakistani, Bangladeshi and Chinese. Edentulousness and toothache were the outcome measures. A composite measure of SEP was developed based on education, social class, income and economic activity using confirmatory factor analysis. Ethnic inequalities in oral health were assessed in logistic regression adjusting for sex, age, survey year and SEP.ResultsIndian (OR: 0.55, 95%CI: 0.40–0.76), Pakistani (0.56, 0.38–0.83), Bangladeshi (0.35, 0.23–0.52) and Chinese (0.41, 0.25–0.66) were less likely to be edentulous than White British after controlling for SEP. Irish (1.22, 1.06–1.39) and Caribbean (1.37, 1.19–1.58) were more likely and Bangladeshi (0.83, 0.69–0.99) were less likely to have toothache than White British after controlling for SEP. Socioeconomic inequalities in edentulousness were consistently found across almost all ethnic groups while socioeconomic inequalities in toothache were found among White British and Irish only.ConclusionThis study shows that the role of SEP in explaining ethnic inequalities in oral health depended on the outcome being investigated. Socioeconomic inequalities in oral health among minority ethnic groups did not consistently reflect the patterns found in White British.

Ethnicity matters in medicine and public health. Health professionals, both in public health and medicine, should be aware of the influence of ethnicity on health (care) and target health (care) services accordingly. In his paper, Bhopal discusses some of the issues that are relevant to health professionals who want to get familiar with this issue. These include the classification of ethnic groups, the use of ethnicity versus race as a basis for classification of groups and the use of absolute versus relative risks to describe inequalities in health. Bhopal also discusses some of the factors that produce ethnic inequalities in health. If health (care) policy is to respond effectively to these inequalities, we need to have a clear understanding of the factors that account for these inequalities, e.g. the higher burden of diabetes mellitus in immigrant populations with a South Asian background can only effectively be prevented if we have a detailed insight into the factors that are responsible for the increased risks of these groups. Currently, there is a paucity of evidence on these factors and mechanisms, and further research into these issues is warranted. An explanation that gets very little attention in Bhopal’s paper is that from socio-economic factors. Ethnic minority groups, in general, do have a lower socio-economic status than the ‘majority’ population in the host country. Given the well-known association between socio-economic status and health, it is not surprising that ethnic inequalities in health are, to at least some extent, socio-economic in nature. Many empirical studies support this hypothesis. As a general rule, explanation of ethnic inequalities in health should recognize that these inequalities are rooted in socio-economic factors. This is not to say, however, that ethnic inequalities in health can simply be understood by generalizing insights in socio-economic inequalities in health in the ‘majority’ population towards immigrant populations. Instead, we should aim to understand the complex way in which ethnic inequalities are linked up with socio-economic inequalities. The first point to realize is that socio-economic position is a multidimensional concept. It includes key components such as educational level and occupational class, but also employment status, income level and other indicators for material welfare. Different types of socio-economic determinants may be relevant to ethnic minority groups as compared with the majority population. For example, first-generation migrants may be disproportionally affected by lack of formal education. The lack of formal education, together with migrants’ problems of acculturation and integration, may particularly affect their later socio-economic career, including occupational positions, wealth accumulation and residential career. Thus, a ‘false start’ early in the socioeconomic career may affect migrant groups in particular. This implies that, if ethnic inequalities are to be addressed by policies on socio-economic determinants of health, particular emphasis may need to be placed on the root socioeconomic factors shaped in the early life of migrants. Second, the pervading relationship between socioeconomic factors and health (care) may take different forms in different ethnic groups. Recent studies showed that socioeconomic inequalities in health within ethnic minority groups often were smaller (or sometimes larger) than in the total national population. Illustrations for this were provided for example in recent Dutch studies on mortality by cause of death, metabolic syndrome prevalence and hospitalization rates. Such an effect modification has been found to be

The circumstances of the area where people live may affect their health and ethnic minority groups are often overrepresented in deprived areas. This study explored ethnic inequalities in adult oral health and the contribution of area deprivation to explain such inequalities. Data from 15667 adults across 8 ethnicities (White British, Irish, Black Caribbean, Black African, Indian, Pakistani, Bangladeshi, Chinese) in the Health Survey for England 2010/2011 were analysed. Oral health was indicated by having a non-functional dentition, poor self-rated oral health and oral impacts on daily activities. Survey logistic regression and the Blinder-Oaxaca decomposition method were used. There were ethnic inequalities in the non-functional dentition, but not in self-rated oral health or oral impacts. Compared to White British adults (19.7%, 95% CI: 18.9, 20.6), a non-functional dentition was more common in Irish (33.1%, 95% CI: 25.9, 41.2) and less common in Black Caribbean (14.9%, 95% CI: 9.9, 21.7), Black African (6.9%, 95% CI: 3.9, 11.9), Indian (10.5%, 95% CI: 6.3, 17.2), Pakistani (7.2%, 95% CI: 4.5, 11.5), Bangladeshi (12.7%, 95% CI: 4.3, 32.3) and Chinese (2.2%, 95% CI: 0.6, 7.9) adults. In decomposition analysis, observed population characteristics explained over half of the ethnic inequalities in the non-functional dentition. Age, area deprivation and SEP were the main contributors, although results varied by ethnicity. Ethnic inequalities in adult oral health varied according to oral health measure and ethnicity. Area deprivation and SEP contributed to, but did not fully, explain such inequalities.

Reducing inequalities in health has become one of the main health policy issues in the late 1990s. The Labour Government set up an independent inquiry into inequalities in health under Sir Donald Acheson to make recommendations on approaches to reducing health inequalities. This paper reviews the evidence on inequalities in oral health in Britain. Dramatic improvements in dental health in children and young adults have taken place in the past 30 years. The levels of caries in permanent teeth of children is low. Widening inequalities in oral health however exist between social classes, regions of England, and among certain minority ethnic groups in pre-school children. The main social class and minority ethnic differences in dental caries is in pre-school children. Wide district and regional differences also exist in prevalence of caries in young children. The area differences relate very strongly to deprivation. In adults the differences in decay experience is less unequal than in children but there are marked social class inequalities in edentulousness. Dental caries decreased in all social classes in the United Kingdom. The main causes of the inequalities are differences in patterns of consumption of non milk extrinsic sugars and fluoridated toothpaste. Improvements in oral health that have occurred over the last 30 years have been largely a result of fluoride toothpaste and social, economic and environmental factors. Oral health inequalities will only be reduced through the implementation of effective and appropriate oral health promotion policy. Treatment services will never successfully tackle the underlying cause of oral diseases.

Ethnic-minority health is a public health priority in Europe. This study compares strategies for tackling ethnic inequalities in health from two countries, Scotland and Belgium. We compared the countries using the Whitehead framework. Official policy documents were retrieved and reviewed and two databases related to immigrant health policies were also used. Ethnic inequalities in health were compared using the UK and Belgian Censuses of 2001. We analysed the recognition of the problem, the policies and the services and described ethnic health inequalities. Scotland has recognized the problem of ethnic inequalities in health, thanks to better data and the Scottish Government has come up with a bold strategy. Belgium is a later starter, unable to properly monitor ethnic inequalities. In addition, there is no clear government commitment to tackling either health inequalities or ethnic inequalities in health. Both countries provide health-care services to ethnic minority groups through the mainstream services, although ethnic minority groups have more choice in Belgium than in Scotland. Overall, ethnic heath inequalities are lower in Scotland than in Belgium. Scotland has provided a more advanced and comprehensive response to tackling ethnic inequalities in health than Belgium. It has acknowledged that discrimination exists and that ethnic minority groups may have different needs. Belgium still assumes non-discrimination in health care and effectively denies the need for policy to tailor services to meet these needs. In Scotland, public organizations have been made accountable for promoting equality in health. This is an important contribution to European health policy.

Social inequalities in health and oral health continue to present a major challenge to public health. Progress towards the development of interventions to reduce health inequalities is currently being hampered by an incomplete understanding of the causes of inequalities in health. This paper aims to provide oral health researchers with an overview of four current explanations for inequalities in oral health and to suggest further areas of research needed to advance our understanding of the causes of social inequalities in oral health.

BackgroundAlthough ethnic health inequalities remain a worldwide problem, underlying factors remain contested. Theories include genetic differences, culturally-patterned behavioural disparities, disadvantageous environmental exposures, and discrimination – as a psychosocial stressor and...

Socio-economically disadvantaged individuals and societies suffer a disproportionately high burden of oral diseases than their advantaged counterparts almost universally. Studies that examine socio-economic inequalities in oral health have applied many individual- and household-level measures of social position: income, education, occupation and social class. Social inequalities in oral health are also evidenced between populations using measures of area-level disadvantage, including country-level income, gross domestic product, gross national income, deprivation, social development and income inequality. Despite capturing some form of social disadvantage, each measure uniquely represents an underlying social and economic process that is shaped politically and historically; ignoring which can be misleading in understanding the extent of oral health inequalities, and most importantly, its solutions. Apart from the choice of measure, the scale on which socio-economic inequality in oral health is measured is of key importance. Oral health inequalities can be presented in absolute and relative scales. Strategies to reduce inequalities on one scale may insufficiently address inequalities on the other scale. Finally, discussion on epidemiological tools and theoretical explanations that enhance the current understanding of socio-economic inequalities in oral health is vital. This chapter discusses oral epidemiological research on socio-economic inequalities in oral health with an emphasis on the measurement of socio-economic inequalities in oral health, theoretical explanations and epidemiological methods that can assist in improving current knowledge on social inequalities in oral health.

Social inequalities in adult oral health in 40 low- and middle-income countries

Dental caries remains one of the most prevalent but preventable diseases among children worldwide and especially affects children with a lower socioeconomic status or ethnic minority background. It is important that all groups of children are reached by preventive interventions to reduce oral health inequalities. So far, it is unknown whether children from different social and ethnic groups benefit equally from potentially effective oral health interventions. This scoping review aimed to identify European public health interventions that report their effect on dental caries across different social groups. Four databases were searched for studies evaluating the effect of oral health interventions on dental caries among children from 0 to 12 y, and studies were included when results were presented by children of different social groups separately. A total of 14 studies were included, representing 4 different countries: 3 randomized and 11 nonrandomized studies. Most studies were performed at schools. Six studies showed results indicative of a reduction in oral health inequalities, 4 studies showed results that potentially widen oral health inequalities, and 5 studies showed results that were indicative of no impact on oral health inequalities. Interventions that contain early approaches, with a high frequency, approaching multiple levels of influence, and including at least the broader organizational or public policy level, may have the potential to reduce oral health inequalities among children from birth to young adolescence. We recommend researchers to perform high-quality intervention studies and to evaluate the effectiveness of oral health intervention always in different socioeconomic or ethnic groups separately, to better understand their contribution toward oral health (in)equalities. This review offers insight in the differential effects that oral health interventions might have across different social groups. Its results can be used to develop interventions that might reduce oral health inequalities among children. Also, we recommend future researchers to always evaluate the effects of any preventive oral health measure in different social groups separately.

The COVID-19 pandemic and the resulting modifications to health services have exacerbated the global number of negative maternal and perinatal outcomes. Using data from electronic health records, a study by Piekos and colleagues showed that mild or moderate SARS-CoV-2 infection early on in pregnancy resulted in increased risk of preterm delivery and stillbirth, emphasising the importance of prioritising pregnant people for COVID-19 vaccination. However, the pandemic has also elucidated social and racial disparities in these outcomes; a rapid report by MBRRACE-UK found that six of the ten women reviewed who died during or after pregnancy from COVID-19 or its complications were from Black or minority ethnic groups. Such disparities have been previously recognised to play a role in adverse pregnancy outcomes. Using data from 2017 to 2019, results of a maternal deaths and morbidity report by MBRRACE-UK showed persistent inequalities in maternal mortality rates; compared with White women, those who are Asian, of mixed ethnic background, or Black have a higher risk of dying in pregnancy. Likewise in the USA, data from 2014 to 2017 showed that Black, American Indian, and Alaska Native women were more likely to die during or within 1 year after pregnancy than women of other racial and ethnic groups. Studies have investigated underlying systemic causes, such as socioeconomic status. For example, analyses of data from England from 2015 to 2017 found that 24% of stillbirths, 19% of preterm births, and 31% of births with fetal growth restriction could be attributed to socioeconomic inequality, and adjusting for factors including ethnic group substantially reduced these percentages. Outdated government policies can also have long-standing impacts. In the USA, redlining is a historical discriminatory practice of delineating areas and basing the safety of investments on the racial composition of these areas, negatively impacting people of colour. This practice has been linked to poor health outcomes; within redlined areas, the rate of preterm births is higher in neighbourhoods graded as hazardous compared with better graded neighbourhoods, emphasising the enduring consequences of structural racism. Digital technologies have been recognised as a potential tool to address racial and ethnic health inequalities. A partnership between the NHS AI Lab and the Health Foundation is funding four artificial intelligence-based projects aiming to tackle this issue, such as developing standards to improve the representativeness of health datasets and using machine learning to investigate factors contributing to harmful outcomes experienced by mothers of different ethnic groups. Given that implicit biases within health-care systems can affect the care and treatment received, the Irth app was developed to enable expectant people of colour in the USA to search and write reviews of doctors or hospitals to empower others like them to make more informed choices about where and from whom to receive care. The use of digital tools can also speed up the implementation of system-wide changes. An example of this is the removal of race and ethnicity variables from a calculator used to estimate the likelihood of a successful vaginal birth after caesarean (VBAC), which systematically predicts lower chances of success for people of colour; the updated tool was shown to be accurate, and as it is available online, its dissemination and use could potentially occur more rapidly and provide equitable options for safe delivery. It is clear that COVID-19 has only served to worsen maternal health inequalities, and addressing them requires a multifaceted approach. It is imperative to ensure equitable access to health care and treatments, including COVID-19 vaccines, to prevent adverse outcomes, and eliminate biases in patient–provider interactions. Acknowledging and addressing the systemic causes of inequalities outside of the health system (such as providing greater education and employment opportunities) would also go some way towards improving health outcomes. Digital health services like telemedicine have proved pivotal for managing health conditions during the pandemic, but more investment is needed to increase the availability of these services to underserved and rural communities. It is also vital to collect globally representative data on maternal and perinatal outcomes to push for accountability at the government level, and drive policy changes to ensure a safe, healthy, and equitable maternal experience for all. Adverse pregnancy outcomes attributable to socioeconomic and ethnic inequalities in England: a national cohort studyOur results indicate that socioeconomic and ethnic inequalities were responsible for a substantial proportion of stillbirths, preterm births, and births with FGR in England. The largest inequalities were seen in Black and South Asian women in the most socioeconomically deprived quintile. Prevention should target the entire population as well as specific minority ethnic groups at high risk of adverse pregnancy outcomes, to address risk factors and wider determinants of health. Full-Text PDF

This study aimed to estimate social and racial inequalities in self-rated oral health in adults from the Brazilian birth cohort study. This study belongs to 1982 Pelotas (Brazil) birth cohort study. Data from this study was collected for oral health conditions 31 years old (Oral Health Study). The outcome was self-rated oral health, dichotomized into positive (good/very good) and negative (regular/bad/very bad). Analyses were stratified by gender, racial/skin color groups, schooling level and income. For statistical analysis, the slope index of inequality (SII) and the concentration index (CIX) were used. The prevalence of negative self-rated oral health was 36.1%. Social inequalities were observed in self-rated oral health in both absolute and relative terms. A SII of -30.0 (95%CI: -43.6; -16.4) was observed for income, and -27.7 (95%CI: -41.9; -13.4) for schooling level. Both the individuals' income and the schooling level had negative CIX (CIXincome -14.6 [95%CI: -21.2; -8.0] and CIXschooling level -14.1 [95%CI: -20.7; -7.5]). Furthermore, the prevalence of negative self-rated oral health in black/brown/indigenous individuals from the highest income/schooling level was comparable to prevalence of the outcome in the white individuals belonging to the lowest income/schooling levels. This study results demonstrate racial disparities in oral health regardless of income and schooling levels. Furthermore, a higher concentration of negative self-rated oral health was identified among the most socioeconomically vulnerable individuals. Our findings reinforce the presence of racial and socioeconomic inequalities in oral health.

To determine whether social support explains ethnic inequalities in oral health among English individuals. Data from 42704 individuals across seven ethnic groups in the Health Survey for England (1999-2002 and 2005) were analysed. Oral health was indicated by self-reports of edentulousness and toothache. Social support was indicated by marital status and a 7-item scale on perceived social support. Confounder-adjusted regression models were fitted to evaluate ethnic inequalities in measures of social support and oral health (before and after adjustment for social support). Overall, 10.4% of individuals were edentulous and 21.7% of dentate individuals had toothache in the past 6 months. Indian (Odd Ratio: 0.50, 95% Confidence Interval: 0.32-0.78), Pakistani (0.50, 95%CI: 0.30-0.84), Bangladeshi (0.29, 95%CI: 0.17-0.47) and Chinese (0.42, 95%CI: 0.25-0.71) individuals were less likely to be edentulous than white British individuals. Among dentate participants, Irish (1.21, 95%CI: 1.06-1.38) and black Caribbean individuals (1.37, 95%CI: 1.18-1.58) were more likely whereas Chinese individuals (0.78, 95%CI: 0.63-0.97) were less likely to experience toothache than white British individuals. These inequalities were marginally attenuated after adjustment for marital status and perceived social support. Lack of social support was associated with being edentulousness and having toothache whereas marital status was associated with edentulousness only. The findings did not support the mediating role of social support in the association between ethnicity and oral health. However, perceived lack of social support was inversely associated with worse oral health independent of participants' sociodemographic factors.

Most work on ethnic inequalities in health in the UK has focused on genetic and cultural difference, ignoring issues relating to class disadvantage. However, more recent work, and that conducted in the US, suggests that material disadvantage might be crucial. Nevertheless, the wider sociological literature illustrates that ethnicity and ‘race’ cannot simply be reduced to class.This paper uses data from the Fourth National Survey of Ethnic Minorities to examine three alternative approaches to ethnic inequalities in health. Epidemiological approaches are driven by empirical findings and make little explicit acknowledgement of theoretical understandings of ethnicity, but they carry the assumption that ethnicity provides a natural and fixed division between population groups. Consequently, explanations for differences tend to be reduced to ahistoric and de‐contextualised genetic and cultural factors. Structural approaches generally focus on material explanations for inequalities, but there are important methodological difficulties in assessing these. We also need to consider other elements of the structural disadvantage faced by ethnic minority groups, such as their experiences of racism or concentration in particular geographical locations. Approaches that focus on ethnic identity emphasise the importance of group affiliation and culture, while acknowledging the contingent and contextual nature of ethnicity. However, despite the promise carried by identity based approaches, there has been little empirical work undertaken.These varying approaches illustrate how important ethnic inequalities in health might be to a wider understanding of mechanisms producing inequalities in health. However, a concern with mechanisms in health inequalities research can lead to a focus on technical interventions along causal pathways, with the roots of health inequalities, wider social inequalities, being ignored.

The sudden and rapid advancement of the novel Coronavirus (COVID-19) pandemic has led to an unanticipated and unprecedented global crisis Since its emergence in the United States, there is increasing discussion surrounding the impact of the virus among vulnerable populations Older adults, young children, and persons with chronic medical or mental health conditions, persons with disabilities, pregnant women, immunocompromised persons and those who are institutionalized or homeless are considered most vulnerable to death and lost quality of life (World Health Organization, 2020)