Names-based classification of accident and emergency department users

The burden of acute eye conditions on different healthcare providers: a retrospective population-based study

BACKGROUND

The demand for acute eyecare exponentially outstrips capacity. The public lacks awareness of community eyecare services.

AIM

To quantify the burden of acute eyecare on different healthcare service providers in a national population through prescribing and medicines provision by GPs, optometrists, and pharmacists, and provision of care by accident and emergency (A&E) services. A secondary aim was to characterise some of the drivers of this burden.

DESIGN AND SETTING

A retrospective data-linkage study set in Wales, UK.

METHOD

Analysis of datasets was undertaken from the Secure Anonymised Information Linkage Databank (GP and A&E), the Eye Health Examination Wales service (optometry), and the Common Ailments Scheme (pharmacy) during 2017-2018.

RESULTS

A total of 173 999 acute eyecare episodes delivered by GPs (168 877 episodes) and A&E services (5122) were identified during the study. This resulted in 65.4 episodes of care per 1000 people per year. GPs prescribed a total of 87 973 653 prescriptions within the general population. Of these, 820 693 were related to acute eyecare, resulting in a prescribing rate of 0.9%. A total of 5122 eye-related and 905 224 general A&E attendances were identified, respectively, resulting in an A&E attendance rate of 0.6%. Optometrists and pharmacists managed 51.8% (116 868) and 0.6% (2635) of all episodes, respectively. Older females and infants of both sexes were more likely to use GP prescribing services, while adolescent and middle-aged males were more likely to visit A&E. GP prescribing burden was driven partially by economic deprivation, access to services, and health score. Season, day of the week, and time of day were predictors of burden in GP and A&E.

CONCLUSION

Acute eyecare continues to place considerable burden on GP and A&E services in Wales, particularly in urban areas with greater economic deprivation and lower overall health. This is likely to increase with a rapidly ageing population. With ongoing pathway development to better utilise optometry and pharmacy, and improved public awareness, there may be scope to change this trajectory.

Quality of ethnicity data within Scottish health records and implications of misclassification for ethnic inequalities in severe COVID-19: a national linked data study

BACKGROUND

We compared the quality of ethnicity coding within the Public Health Scotland Ethnicity Look-up (PHS-EL) dataset, and other National Health Service datasets, with the 2011 Scottish Census.

METHODS

Measures of quality included the level of missingness and misclassification. We examined the impact of misclassification using Cox proportional hazards to compare the risk of severe coronavirus disease (COVID-19) (hospitalization & death) by ethnic group.

RESULTS

Misclassification within PHS-EL was higher for all minority ethnic groups [12.5 to 69.1%] compared with the White Scottish majority [5.1%] and highest in the White Gypsy/Traveller group [69.1%]. Missingness in PHS-EL was highest among the White Other British group [39%] and lowest among the Pakistani group [17%]. PHS-EL data often underestimated severe COVID-19 risk compared with Census data. e.g. in the White Gypsy/Traveller group the Hazard Ratio (HR) was 1.68 [95% Confidence Intervals (CI): 1.03, 2.74] compared with the White Scottish majority using Census ethnicity data and 0.73 [95% CI: 0.10, 5.15] using PHS-EL data; and HR was 2.03 [95% CI: 1.20, 3.44] in the Census for the Bangladeshi group versus 1.45 [95% CI: 0.75, 2.78] in PHS-EL.

CONCLUSIONS

Poor quality ethnicity coding in health records can bias estimates, thereby threatening monitoring and understanding ethnic inequalities in health.

Raising ethnicity recording in NHS Lothian from 3% to 90% in 3 years: processes and analysis of data from Accidents and Emergencies

BACKGROUND

The disproportionate burden of COVID-19 on ethnic minority populations has recently highlighted the necessity of maintaining accessible, routinely collected, ethnicity data within healthcare services. Despite 25 years of supportive legislation and policy in the UK, ethnicity data recording remains inconsistent, which has hindered needs assessment, evaluation and decision-making. We describe efforts to improve the completeness, quality and usage of ethnicity data within our regional health board, NHS Lothian.

METHODS

The Ethnicity Coding Task Force was established with the aim of increasing ethnicity recording within NHS Lothian secondary care services from 3 to 90% over 3 years. We subsequently analysed these data specifically focusing on Accident and Emergency (A&E) use by ethnic group.

RESULTS

We achieved 91%, 85% and 93% completeness of recording across inpatients, outpatients and A&E, respectively. Analysis of A&E data found a mixed pattern of attendance amongst ethnic minority populations and did not support the commonly perceived relationship between lower GP registration and higher A&E use within this population.

CONCLUSIONS

We identified a successful approach to increase ethnicity recording within a regional health board, which could potentially be useful in other settings, and demonstrated the utility of these data in informing assessment of healthcare delivery and future planning.

Ethnic inequalities in hospital admissions in England: an observational study

BACKGROUND

Ethnic inequalities in health are well-known and partly explained by social determinants such as poorer living and working conditions, health behaviours, discrimination, social exclusion, and healthcare accessibility factors. Inequalities are known both for self-reported health and for diseases such as diabetes, cardiovascular diseases, respiratory diseases, and non-specific chest pains. Most studies however concern individual diseases or self-reported health and do not provide an overview that can detect gaps in existing knowledge. The aim of this study is thus to identify ethnic inequalities in inpatient hospital admission for all major disease categories in England.

METHODS

Observational study of the inpatient hospital admission database in England enhanced with ethnicity coding of participants' surnames. The primary diagnosis was coded to Level 1 of the Global Burden of Disease groups. For each year, only the first admission for each condition for each participant was included. If a participant was readmitted within two days only the first admission was counted. Admission risk for all major disease groups for each ethnic group relative to the White British group were calculated using logistic regression adjusting for age and area deprivation.

RESULTS

40,928,105 admissions were identified between April 2009 and March 2014. Ethnic inequalities were found in cardiovascular diseases, respiratory diseases, chest pain, and diabetes in line with previous studies. Additional inequalities were found in nutritional deficiencies, endocrine disorders, and sense organ diseases.

CONCLUSIONS

The results of this study were consistent with known inequalities, but also found previously unreported disparities in nutritional deficiencies, endocrine disorders, and sense organ diseases. Further studies would be required to map out the relevant care pathways for ethnic minorities and establish whether preventive measures can be strengthened.

Names-based ethnicity enhancement of hospital admissions in England, 1999-2013

BACKGROUND

Accurate recording of ethnicity in electronic healthcare records is important for the monitoring of health inequalities. Yet until the late 1990s, ethnicity information was absent from more than half of records of patients who received inpatient care in England. In this study, we report on the usefulness of a names-based ethnicity classification, Ethnicity Estimator (EE), for addressing this gap in the hospital records.

MATERIALS AND METHODS

Data on inpatient hospital admissions were obtained from Hospital Episode Statistics (HES) between April 1999 and March 2014. The data were enhanced with ethnicity coding of participants' surnames using the EE software. Only data on the first episode for each patient each year were included.

RESULTS

A total of 111,231,653 patient-years were recorded between April 1999 and March 2014. The completeness of ethnicity records improved from 59.5 % in 1999 to 90.5 % in 2013 (financial year). Biggest improvement was seen in the White British group, which increased from 55.4 % in 1999 to 73.9 % in 2013. The correct prediction of NHS-reported ethnicity varied by ethnic group (2013 figures): White British (89.8 %), Pakistani (81.7 %), Indian (74.6 %), Chinese (72.9 %), Bangladeshi (63.4 %), Black African (57.3 %), White Other (50.5 %), White Irish (45.0 %). For other ethnic groups the prediction success was low to none. Prediction success was above 70 % in most areas outside London but fell below 40 % in parts of London.

CONCLUSION

Studies of ethnic inequalities in hospital inpatient care in England are limited by incomplete data on patient ethnicity collected in the 1990s and 2000s. The prediction success of a names-based ethnicity classification tool has been quantified in HES for the first time and the results can be used to inform decisions around the optimal analysis of ethnic groups using this data source.

Sense-making strategies and help-seeking behaviours associated with urgent care services: a mixed-methods study

Background

Policy has been focused on reducing unnecessary emergency department attendances by providing more responsive urgent care services and guiding patients to ‘the right place’. The variety of services has created a complex urgent care landscape for people to access and navigate.

Objectives

To describe how the public, providers and policy-makers define and make sense of urgent care; to explain how sense-making influences patients’ strategies and choices; to analyse patient ‘work’ in understanding, navigating and choosing urgent care; to explain urgent care utilisation; and to identify potentially modifiable factors in decision-making.

Design

Mixed-methods sequential design.

Setting

Four counties in southern England coterminous with a NHS 111 provider area.

Methods

A literature review of policy and research combined with citizens’ panels and serial qualitative interviews. Four citizens’ panels were conducted with the public, health-care professionals, commissioners and managers (n = 41). Three populations were sampled for interview: people aged ≥ 75 years, people aged 18–26 years and East European people. In total, 134 interviews were conducted. Analyses were integrated to develop a conceptual model of urgent care help-seeking.

Findings

The literature review identified some consensus between policy and provider perspectives regarding the physiological factors that feature in conceptualisations of urgent care. However, the terms ‘urgent’ and ‘emergency’ lack specificity or consistency in meaning. Boundaries between urgent and emergency care are ill-defined. We constructed a typology that distinguishes three types of work that take place at both the individual and social network levels in relation to urgent care sense-making and help-seeking.Illness workinvolves interpretation and decision-making about the meaning, severity and management of physical symptoms and psychological states, and the assessment and management of possible risks. Help-seeking was guided bymoral work: the legitimation and sanctioning done by service users.Navigation workconcerned choosing and accessing services and relied on prior knowledge of what was available, accessible and acceptable. From these empirical data, we developed a model of urgent care sense-making and help-seeking behaviour that emphasises that work informs the interaction between what we think and feel about illness and the need to seek care (sense-making) and action – the decisions we take and how we use urgent care (help-seeking).

Limitations

The sample population of our three groups may not have adequately reflected a diverse range of views and experiences. The study enabled us to capture people’s views and self-reported service use rather than their actual behaviour.

Conclusions

Much of the policy surrounding urgent and emergency care is predicated on the notion that ‘urgent’ sits neatly between emergency and routine; however, service users in particular struggle to distinguish urgent from emergency or routine care. Rather than focusing on individual sense-making, future work should attend to social and temporal contexts that have an impact on help-seeking (e.g. why people find it more difficult to manage pain at night), and how different social networks shape service use.

Future work

A whole-systems approach considering integration across a wider network of partners is key to understanding the complex relationships between demand for and access to urgent care.

Study registration

This study is registered as UKCRN 32207.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

Ethnicity estimation using family naming practices

This paper examines the association between given and family names and self-ascribed ethnicity as classified by the 2011 Census of Population for England and Wales. Using Census data in an innovative way under the new Office for National Statistics (ONS) Secure Research Service (SRS; previously the ONS Virtual Microdata Laboratory, VML), we investigate how bearers of a full range of given and family names assigned themselves to 2011 Census categories, using a names classification tool previously described in this journal. Based on these results, we develop a follow-up ethnicity estimation tool and describe how the tool may be used to observe changing relations between naming practices and ethnic identities as a facet of social integration and cosmopolitanism in an increasingly diverse society.

Surnames and ancestry in Brazil

This paper presents a method for classifying the ancestry of Brazilian surnames based on historical sources. The information obtained forms the basis for applying fuzzy matching and machine learning classification algorithms to more than 46 million workers in 5 categories: Iberian, Italian, Japanese, German and East European. The vast majority (96.7%) of the single surnames were identified using a fuzzy matching and the rest using a method proposed by Cavnar and Trenkle (1994). A comparison of the results of the procedures with data on foreigners in the 1920 Census and with the geographic distribution of non-Iberian surnames underscores the accuracy of the procedure. The study shows that surname ancestry is associated with significant differences in wages and schooling.

Ethnicity and population structure in personal naming networks

Personal naming practices exist in all human groups and are far from random. Rather, they continue to reflect social norms and ethno-cultural customs that have developed over generations. As a consequence, contemporary name frequency distributions retain distinct geographic, social and ethno-cultural patterning that can be exploited to understand population structure in human biology, public health and social science. Previous attempts to detect and delineate such structure in large populations have entailed extensive empirical analysis of naming conventions in different parts of the world without seeking any general or automated methods of population classification by ethno-cultural origin. Here we show how ‘naming networks’, constructed from forename-surname pairs of a large sample of the contemporary human population in 17 countries, provide a valuable representation of cultural, ethnic and linguistic population structure around the world. This innovative approach enriches and adds value to automated population classification through conventional national data sources such as telephone directories and electoral registers. The method identifies clear social and ethno-cultural clusters in such naming networks that extend far beyond the geographic areas in which particular names originated, and that are preserved even after international migration. Moreover, one of the most striking findings of this approach is that these clusters simply ‘emerge’ from the aggregation of millions of individual decisions on parental naming practices for their children, without any prior knowledge introduced by the researcher. Our probabilistic approach to community assignment, both at city level as well as at a global scale, helps to reveal the degree of isolation, integration or overlap between human populations in our rapidly globalising world. As such, this work has important implications for research in population genetics, public health, and social science adding new understandings of migration, identity, integration and social interaction across the world.