Data

We used a range of sources of administrative and spatial data. The core dataset that contains primary care workforce information is provided by NHS Digital, and we obtained data as of 30 September 2016, published on 29 March 2017 [12]. At the practice level, information is available on geography (Clinical Commissioning Group (CCG) and NHS region), patient list size by age groups and also numbers and full-time equivalent (FTE) for GPs, by country and region of qualification. At the individual level, the same geographical information is available, as well as staff type (e.g. GP, nurse, administrator), role (e.g. GP partner, junior doctor), country and region of qualification for GPs only, age, sex and FTE. Individual records are not linked to practices to protect anonymity [13].

Additional information at the practice level included the average deprivation of patients, the overall morbidity as measured by the QOF and the average payment made to primary care per patient. NHS payments to general practice for the financial year 2015/2016 and for the whole of England [14] were used to calculate average pay per patient minus prescribing and dispensing fee payments.

Deprivation was quantified with the 2015 release of the Index of Multiple Deprivation (IMD), a complete aggregate measure which is widely used to quantify deprivation and affluence [15]. The measure quantifies relative deprivation across the following seven domains: income, employment, education and skills, health and disability, crime, barriers to housing and services, and living environment. Deprivation scores are calculated and assigned to very low UK geographical units (Lower Super Output Areas), and the overall IMD is calculated as a weighted mean across the seven domains, with income and employment deprivation given the largest weight (22.5% each), followed by health and education deprivation (13.5% each), and with the other three domains given equal weights (9.3%). To calculate the average deprivation levels of the practice population, rather than the practice location, we made use of a dataset linking practice populations to low geographies [16], allowing us to calculate a weighted average of deprivation for each practice.

To quantify overall morbidity at the practice level, we used 2015/2016 data from a national primary care pay-for-performance programme, the QOF [17]. The programme has underpinned high quality of recording in primary care [18], and under its umbrella, recording, management and treatment of a large number of clinical domains was financially and reputationally incentivised. In 2015/2016, there were 21 incentivised domains: Atrial Fibrillation, Asthma, Cancer, Cardiovascular Disease Primary Prevention, Coronary Heart Disease, Chronic Kidney Disease (for those aged 18 or older), Chronic Obstructive Pulmonary Disease, Dementia, Depression (18 or older), Diabetes both types (17 or older), Epilepsy (18 or older), Heart Failure, Hypertension, Learning Disability, Severe Mental Illness, Obesity (18 or older), Osteoporosis (50 or older), Peripheral Artery Disease, Palliative Care, Rheumatoid Arthritis (16 or older), Stroke. For each of the practices participating in the QOF, covering more than 99% of all registered patients [19], we calculated the total sum of all condition registers, a cumulative QOF register.

Finally, 2016 spatial coordinates for NHS organisational units were obtained from the Office for National Statistics (ONS) Open Geography portal [20]. We focused on two organisational levels, the lower CCGs with 209 units, and the higher NHS regions with 14 units.

Analyses

For all aspects of data manipulation and analysis we used Stata v14.1. Whenever medians are reported, we also report the 25th and 75th centiles. Spatial maps were plotted using the spmap command [21]. An alpha level of 5% was used throughout.

We quantified the characteristics of GPs for the whole of England and for each of the 14 NHS regions in 2016. For each region and overall, we estimate and report the following individual aggregates: number, percentage of males, median age and median FTE. All individual-level aggregates are reported overall by GP country of qualification. We also report practice-level aggregates on median number and FTE (overall only) per 10,000 patients, per 1000 patients aged 75 or older and per 10,000 counts on the cumulative QOF register. Finally, and also at the practice level but overall and by GP country of qualification, we present the median residence location deprivation of the average practice patient and median of the average pay per patient (minus prescription and dispensing costs). For deprivation, we first calculated the weighted deprivation mean within each practice, and next we estimated its weighted median (weighted by the practice list size for overall estimates or by the product of the list size and the percentage of GPs qualified from each region for qualification regions). The process was similar for pay, the only difference being the first step where we used the average pay per patient in the practice. This weighting approach allowed us to estimate patient deprivation and pay medians by GP country of qualification (on numbers, which were available as country of qualification aggregates at the practice level, when FTE was not).

In a second approach, we quantified the characteristics of practices (median FTE per 10,000 patients, 1000 patients aged 75 or older and 10,000 counts on the cumulative QOF register; also average pay and patient residence location deprivation) at different levels of presence of overseas qualified GPs: 0%; above 0% and up to 20%; above 20% and up to 40%; and above 40%. To evaluate if pay differed for various strata of EEA and elsewhere qualification more robustly, we performed multiple linear regressions at the practice level, associating average pay to percentage of EEA, percentage of elsewhere and percentage of EEA or elsewhere qualification, adjusted for the percentage of patients aged 75 or older and the cumulative QOF register (i.e. adjusting for proxies of health need).

Spatial graphs at the CCG level with additional information on NHS regions were plotted for various variables of interest for GPs, overall and by region of qualification: number and percentage aged 55 or older (FTE weighted), cumulative FTE, mean age, FTE per 10,000 patients and FTE per 10,000 counts on the cumulative QOF register. The primary aim of these graphs was to identify areas more dependent on overseas qualified GPs.

Strengths and limitations of the study

The main strength of this study is the use of numerous national administrative datasets of high data quality that allows us to obtain a complete picture for the whole of England. Nevertheless, some limitations exist.

First, data were not available for all GPs, the headcount of which was 41,865 on 30 September 2016 [12]. Information was reported for 37,792 GPs or 90.3% of all those practicing in England, for 1202 (3.2%) of which the country of qualification was unknown. However, we think missing data are unlikely to introduce bias to our findings; for example, it seems unlikely that most of the GPs for which we could not obtain data were UK qualified and working in deprived areas.

Second, as with all analyses of observational data, we report associations and not causal paths. However, the directionality can be reasonably assumed in most cases (e.g. non-UK qualified GPs do not lead to an increase in deprivation).

Third, aggregating the observed practice-level association to the CCG level to make them more relevant to the current organisational structure of the NHS is challenging. This is due to the fact that CCGs are higher level units which usually serve highly heterogeneous areas in terms of deprivation and populations. Therefore, associations between country of qualification and patients’ health needs or deprivation can be obscured at the CCG level.

Findings

The over-dependence on overseas qualified GPs has the potential to have an impact on and exacerbate the current recruitment and retention crises in general practice, especially in the short term (the next 10 years). Unfortunately, it is likely to affect areas of the country which have the greatest health needs, thus adding another dimension to Julian Tudor Hart’s inverse care law [22, 23].

General practice remains critical to the long-term sustainability of the NHS [24]. Despite longstanding initiatives to increase GP training numbers to 3250 per annum, GP recruitment has remained well below this target at around 2700 per annum [25]. The crisis in GP recruitment prompted the Secretary of State for Health to promise to increase the number of GPs by 5000 by 2020, in the run-up to the 2015 elections [26]. However, current evidence suggests that the GP workforce did not increase between September 2015 and September 2016, but even fell by 0.3% (FTE) [27]. The question is, how will the 2020 target be met, especially in the context of hardening public attitudes to immigration? Even though the Secretary of State has promised 1500 new medical graduates per year from 2018 [28], it will take at least 10 years for this cohort to be trained in general practice, assuming that we can persuade more than the current 30% of UK graduates to choose general practice as their first career choice [29–31].

So, at least in the short term, the demand for EEA and non-EEA international graduates is likely to increase substantially. Furthermore, the adverse political climate around immigration and Brexit is already resulting in a large number of EU citizens leaving the NHS — recent figures suggest that there has been an 83% increase in doctors from the EU leaving NHS England [27]. This will have an additional impact on retention of the medical workforce, potentially exacerbating problems in shortage areas, especially considering that 36% of GPs aged 50–54 and 80% aged 55 or older intend to retire from general practice in the next five years [10]. As demonstrated by our findings, a disproportionate number of these older GPs are EEA and elsewhere qualified. In a specialty that has always relied on medical migration to cover the shortfall in the GP medical workforce, immigration policy changes that we have described earlier, together with our findings, have the potential to exacerbate the recruitment and retention problems in general practice. Worryingly, as far as we can tell, these important issues have not registered in the calculations of the Department of Health or with HEE.

Our data also suggest a continuing hypocrisy in the way that non-UK qualified doctors are treated. We are dependent on overseas qualified doctors because policy makers are unwilling to take measures to promote self-sufficiency in the medical workforce. The reasons are very likely financial, with one estimate suggesting that non-UK qualified doctors may have contributed £15 billion in saved medical school fees [32]. Yet despite the positive financial contribution they make, arguably even higher than the positive contribution of the average EEA migrant [33], and the fact that they work in deprived areas with patients who have greater needs (and are probably paid less for doing this), they remain a marginalised and stigmatised group of doctors [34]. Despite the recruitment crises in general practice, they have a higher failure rate in the postgraduate exams for general practice, even when adjusted for academic ability. They are more likely to be disciplined by their employers and be brought before the GMC [35]. The reality is that overseas qualified doctors are part of the solution to the recruitment crises facing general practice, and this needs to be acknowledged by policy makers and our politicians.