Long-term Condition Management for Prisoners: exploring prevalence and compliance with QOF monitoring

Background: Long-term conditions (LTCs) are a signicant cause of morbidity and mortality and prisoner populations have a disproportionately high prevalence of risk factors for LTCs. The size and mean age of the prison population has increased rapidly in recent years. The UK Quality Outcomes Framework (QOF) is a national standardised framework embedded in community general practice with nancial remuneration linked to assessment and ongoing review of key clinical outcomes pertaining to LTCs. However, healthcare in prisons in England is not linked to nancial remuneration through the QOF framework and prison clinicians are not mandated to adhere to the framework. Methods: Secondary data analysis of data extracted from the prison primary care record pertaining to patient self-report of LTC, level of conrmation by supporting evidence and compliance with QOF monitoring frameworks. Results: 17% of the sample had at least one LTC, the most common condition being asthma, conrmed in 12% of the sample. Having an LTC was associated with female gender and increasing age. Prevalence rates for the other LTCs were hypertension 3%, epilepsy was 3%, coronary heart disease 2%, diabetes 2% and chronic obstructive pulmonary disease 1%. Just 34% of the eligible sample had had a QOF template completed. Higher rates of completion were associated with younger age and there were also statistically signicant inter-prison differences. Conclusion: There is a pressing need to embed standardised QOF monitoring systems within an integrated community/prison commissioning framework supported by connectivity between prison and community primary care records of not just the summary care record but also all activity related to QOF compliance. Amongst prisoner populations, there is high prevalence of risk factors for long-term conditions. This research quanties the burden of disease and highlights under-developed systems for monitoring. There is an opportunity to enhance connectivity between community and prison GP settings to facilitate seamless monitoring of QOF outcomes. research bias clinical practice with a prospective process. The following data were signicant


Background
Long-term conditions (LTCs) are those that are controlled, rather than cured through medication and/or other forms of therapy 1 . More than 15 million people in the UK suffer from a long-term condition 2 , with multi-morbidity also becoming increasingly problematic 1 .
The risk factors for LTCs disproportionately affect prisoner populations 3 . Currently in England and Wales, there are over 83,000 individuals imprisoned 4 . Compared to equivalent community populations, prisoners consult primary care doctors three times more frequently, other primary health care workers 80 times more frequently, and receive inpatient care at least 10 times more frequently 5 . They have higher mortality and morbidity rates from chronic disease 6 .
Internationally, ethical codes of practice highlight a "principle of equivalence" which states that prisoners have a right to an equivalent quality of healthcare as they would receive in the community. 7 However, there are signi cant threats to providing equivalent healthcare. For example, medical indemnity organisations acknowledge that imprisoned patients may be examined and treated in situations that are far from the norm for the rest of society. They provide examples of consultations taking place without access to GP/hospital records or being held in environments that could compromise safety for both patient and doctor. 8 This is particularly commonplace when patients enter remand prisons outside of normal working hours and are assessed in rst night prison reception centres. In such an environment, there are signi cant threats to effective medicines management. Prisoners commonly enter prison without their prescribed LTC medication and con rming medications with community services is not possible outside of normal working hours. Further, once medication is con rmed and prescribed, there are potential delays in dispensing and administering medications. With the exception of opiate substitution treatment, there are no national guidelines to inform best practice regarding when it is appropriate to offer substitute medications where medication cannot be con rmed on rst night assessment.
Regarding assessment for those entering prison, there is a process of healthcare screening through both " rst" and "second" tools that are integral to the patient record. The former addresses issues regarding acute health need (e.g. demographic details; assessment of withdrawal from substances; contact details for community pharmacists), whereas the second screen covers an assessment of the need for ongoing management of LTCs and current immunisations.
Once patients have undergone assessment and are established in prison, there is an opportunity to obtain con rmation of their self-reported LTC through liaising with their community GP or arranging the necessary clinical tests, thus permitting the health risks posed by LTCs to be more proactively managed. In community general practice in England, the key framework to achieve this objective is the Quality and Outcomes Framework (QOF). 10 Such a national standardised framework is now embedded in community general practice with nancial remuneration linked to assessment and ongoing review of key clinical outcomes pertaining to LTCs. However, healthcare in prisons in England is not linked to nancial remuneration through the QOF framework and prison clinicians are not mandated to adhere to the framework. Rather, the only mandated process is that of all prisoners entering prison undergo the screening process outlined above. Such a process places less of an emphasis upon clinical outcomes than that outlined in the QOF framework. Therefore, since compliance with QOF monitoring is voluntary in prisons, it is possible that an opportunity to improve clinical outcomes associated with LTCs is being missed. In response to such a gap in service provision, we explored the assessment and management of LTCs in four remand prisons. By extracting routinely collected clinical data, we explored the prevalence of LTCs, compliance with both rst and second assessment, and also QOF monitoring processes.

Methods
After acquiring the necessary ethical and governance approvals, data was extracted retrospectively from the clinical records of all new entrants to four remand (two male and two female) prisons between June 1st and June 30th, 2015. All relevant data recorded in the clinical record within 12months of arrival was extracted. Data extraction took place between June 2016 and June 2018. The rationale for retrospective data collection was that the research activity did not bias routine clinical practice which would have been a risk with a prospective process. The following data were extracted: demographics (including age, gender, ethnicity and sentence status); length of stay in prison (categorised as less than or greater than six months); prevalence of the following "tracer" physical health LTCs: diabetes, asthma, hypertension, coronary heart disease (CHD), chronic obstructive pulmonary disease (COPD), epilepsy; prevalence of co-morbid mental health (MH) conditions; proportion of QOF templates completed -and whether completion was full or partial; time to completion of QOF template; supporting evidence for the long-term conditions. The above LTCs were selected as tracer conditions because they are the physical health LTCs that commonly present in prison rst night receptions and because of their potential to cause signi cant morbidity and mortality. The LTCs were identi ed by the researchers through examining the patient's individual clinical record to retrieve self-reported information of the condition and whether it was con rmed by "supporting evidence." Supporting evidence was de ned as any one of evidence of prescribed medication(s) indicated for the condition, con rmation from patient's community GP of the LTC or biomedical/clinical test con rming prevalence of the condition. The following biomedical/clinical tests were regarded as supporting evidence for prevalence: Diabetes -de ned as an HbA1c of 48 mmol/mol (6.5%) or above Asthma -(FEV1/FVC) ratio of less than 70% but positive reversibility test as diagnosed on spirometry (i.e. an increase in FEV1 from baseline of > 12% in response to bronchodilators) Hypertension -blood pressure greater than 140/90 mmHg CHD -diagnosed by cardiologist from radiological ndings COPD -spirometry highlighting air ow obstruction de ned as FEV1 < 80% predicted and FEV1/FVC < 0.7 which does not show reversibility to bronchodilator therapy Epilepsy -diagnosed by a neurologist Following data extraction, analysis was undertaken to assess: Prevalence of associated co-morbid physical and MH conditions Proportion of prisoners still resident in the receiving study prison 6 months after entering and proportion with physical health or MH conditions Proportion with a physical health LTC that had the relevant QOF template completed either partially or in full (prisoners residing in the prison for less than one-month were excluded to acknowledge the signi cant throughput of prisoners on short sentences in remand prisons which acts as a barrier to effective monitoring of LTCs).

Time to completion of QOF template
Quali cation of the professional completing the QOF template Demographic associations with QOF completion Agreement between self-report of the LTC and con rmation with supporting evidence Descriptive statistics are presented as mean (standard deviation (SD) or n (%). Logistic regression analysis was undertaken of clinical data and the following statistical tests were undertaken: t-tests (continuous data), chi-square tests (categorical data) or Mann Whitney tests (ordinal data). Kappa was used to measure agreement between self-reported LTC and con rmation by supporting evidence. A P-value of < 0.05 was considered to indicate statistical signi cance. Data analysis was undertaken in IBM SPSS Statistics for Windows (version 24).

Results
In total, data was retrieved from the clinical records of 1,126 prisoners. Table 1 highlights the prisoner characteristics. 185 (17%) had at least one LTC. Regarding the association between total number of LTCs (i.e. diabetes, hypertension, asthma, CHD and COPD) and demographic characteristics, there was a signi cant difference by gender (P < 0.001) and age (P < 0.001), but not ethnicity (P = 0.153). Females were more likely to have an LTC (OR 2.12; 95% CI: 1.50, 3.00), and having an LTC was associated with older age (OR 1.05; 95% CI: 1.04, 1.07). Table 2 highlights the number of prisoners with each of the LTCs at each prison site as con rmed by supporting evidence (i.e. met the inclusion criteria for QOF monitoring). 221 LTCs were con rmed by supporting evidence, and the most common condition was asthma, which was con rmed in 12% (135) of the sample. Epilepsy is excluded from this table since the epilepsy indicator was a "register only" (i.e. no clinical assessment required) in the 2015-16 QOF framework.  Table 3 highlights QOF completion rates for each LTC and shows low levels of QOF completion as evidenced in Table 4. Just 34% (38/112) had a full QOF completed and 11 part-completed. There was signi cant variation for time to completion, with a range of 5-358 days. 35 of the QOF templates were completed by a nurse, with just one completed by a healthcare assistant (2 missing data). All of the 11 part-completed templates were undertaken by nursing professionals.  Table 5 highlights associations with QOF template completion and prisoner characteristics. By univariate analysis, younger age (P = 0.028), male gender (P < 0.001) and prison site (P < 0.001) were statistically signi cantly associated with QOF completion. There was no signi cant difference by ethnicity (P = 0.956) or sentence status (P = 0.470). A multivariate analysis in a logistic regression model highlighted that only younger age (P = 0.015) and prison site (P = 0.017) remained statistically signi cant. The level of agreement between self-reported LTC and con rmation by supporting evidence was good for all conditions (see Table 6). Our ndings demonstrate the signi cant challenges of managing LTCs in remand prisons, as highlighted by the fact that 75% of prisoners were no longer in the receiving study prison six months after entering prison. Of those still in prison, just 11% remained in the receiving study prison, with 14% transferred to another prison. The numbers were even higher for those with an LTC. Eighty-four percent of those with an LTC were no longer in the receiving study prison at six months. Regarding MH, 54% of those with a co-morbid MH condition were no longer in the receiving study prison at six months. Therefore, still being in the receiving study prison at six months was not associated with an increased likelihood of having either a comorbid MH or physical LTC.
Regarding prevalence, 17% of the sample had at least one LTC, the most common condition being asthma, con rmed in 12% of the sample. Having an LTC was associated with female gender and increasing age. The con rmed prevalence rates for the other LTCs were hypertension 3%, CHD 2%, diabetes 2% and COPD 1%. The prevalence of epilepsy was 3%, but there was considerable variability between prisons. The likely reason for such variability is coding practice for patients with either pseudo-seizures or alcohol withdrawal seizures.
Our ndings highlighted just 34% of the eligible sample had a QOF template completed. QOF completion rates varied between LTCs and was highest for asthma (40% completion rate), and lowest for diabetes (8% completion rate). Demographic variables were associated with QOF completion. Higher rates of completion were associated with younger age. There were also statistically signi cant inter-prison differences. This coupled with our nding of signi cant variation for time to completion triangulates with the ndings in our linked paper reporting qualitative ndings of differing clinical practice between prisons.

Strengths and Limitations
Our ndings make a signi cant contribution to the evidence base regarding prevalence of LTCs in prisons, which constitutes an under researched area. As far as we are aware, this research is the rst peer-reviewed study exploring existing processes regarding QOF monitoring in UK prisons. Whilst collated from four remand prisons, we are con dent that our ndings can be generalised across the remand prison estate, and also to training prisons, since all prisoners in such establishments have at some point been transferred from remand prisons.
Whilst extracting data from just four prisons could be perceived as a limitation, it remains the largest UK multi-centre research study to quantify LTC prevalence from clinical records and, the only UK study exploring prison based QOF monitoring processes.

Comparison with Existing Literature
Our prevalence statistics broadly concur with previous research conducted which highlights respiratory disease as the most prevalent LTC in UK prisons and prevalence statistics of lower than 5% for each of diabetes, CHD or hypertension. 11 Our research highlighted that the level of agreement between self-report in the rst night primary care consultation and subsequent supporting evidence was good for all the LTCs. This concurs with a UK data-linkage study in which self-report survey ndings regarding LTC prevalence were cross checked with prisoner patient records and highlighted a good level of agreement regarding the prevalence of physical LTCs. 3 The community prevalence rates for LTCs in the UK has been recorded as: asthma -8% (+3.6% compared to global prevalence); COPD -1.8% (-1.5%); CHD -11.2% (+2.2%); diabetes -7% (-1.8%), hypertension -24.2% (+9.2%); epilepsy -0.8% (-0.1%); opioid dependence -0.4% (equal to global prevalence); depression -3% (-0.9%); alcohol dependence -0.9% (-0.5%) and schizophrenia -0.95% (+0.67%). Within the UK, 1 in 4 people will experience a MH illness each year, the most common being General Anxiety Disorder (5.9%) and depression (3%). Differences can, therefore, be ascertained between the prison and community population.

Implications for Research and/or Practice
The key implication for future research is that for future prevalence studies of LTCs in prison settings, extraction and secondary data analysis of routinely collected clinical data will be as effective, but less costly than administering surveys to patients. We would recommend implementing research processes that ful l the necessary requirements pertaining to robust information governance to facilitate extraction and anonymisation of routinely collected clinical data.
Regarding implications for practice, since LTC prevalence is associated with increasing age, yet our ndings show an association between younger age and QOF completion, this presents a pressing training need to target QOF activity where the burden of disease is highest.

Conclusions
Such lack of engagement in QOF monitoring highlighted in our ndings was despite a national requirement of prison providers through the Health and Justice Indicators of Performance (HJIPs) to report on QOF data 11 . The relevant document states: "Through the use of SystmOne templates and standard reporting, providers are able to self-assess their LTC monitoring, and report this as part of their HJIP data submission; providing performance outcomes against the chronic disease register and achievement against nationally recognised quality outcomes framework. This reporting enables assurance that there is parity of treatment provision between residents of the secure estate and the wider community. Providers are able to access their QOF achievement outcomes via a report embedded in SystmOne." Therefore, a disconnect is apparent between national reporting requirements and clinical activity. This could be addressed, in part, through pending improvements in the electronic patient record linkage systems between community and prisons.
In our linked paper reporting qualitative ndings, such a prospect was universally welcomed by participants and, in addition to better meeting acute health need, it was felt that this development would support seamless monitoring of QOF activity between community and prison. Therefore, this presents an opportunity to introduce QOF monitoring systems, possibly supported by an integrated community/prison commissioning framework to enable future connectivity between prison and community records of not just the "summary care record" (a minimum dataset comprising: current medication; allergies and details of any previous bad reactions to medicines; name, address, date of birth and NHS number of the patient), 12 but also all activity related to QOF compliance. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests
The authors declare that they have no competing interests