A picture of health: determining the core population served by an urban NHS hospital trust and understanding the key health needs

Background NHS hospitals do not have clearly defined geographic populations to whom they provide care, with patients able to attend any hospital. Identifying a core population for a hospital trust, particularly those in urban areas where there are multiple providers and high population churn, is essential to understanding local key health needs especially given the move to integrated care systems. This can enable effective planning and delivery of preventive interventions and community engagement, rather than simply treating those presenting to services. In this article we describe a practical method for identifying a hospital’s catchment population based on where potential patients are most likely to reside, and describe that population’s size, demographic and social profile, and the key health needs. Methods A 30% proportional flow method was used to identify a catchment population using an acute hospital trust in West London as an example. Records of all hospital attendances between 1st April 2017 and 31st March 2018 were analysed using Hospital Episode Statistics. Any Lower Layer Super Output Areas where 30% or more of residents who attended any hospital for care did so at the example trust were assigned to the catchment area. Publicly available local and national datasets were then applied to identify and describe the population’s key health needs. Results A catchment comprising 617,709 people, of an equal gender-split (50.4% male) and predominantly working age (15 to 64 years) population was identified. Thirty nine point six percent of residents identified as being from Black and Minority Ethnic (BAME) groups, a similar proportion that reported being born abroad, with over 85 languages spoken. Health indicators were estimated, including: a healthy life expectancy difference of over twenty years; bowel cancer screening coverage of 48.8%; chlamydia diagnosis rates of 2,136 per 100,000; prevalence of visible dental decay among five-year-olds of 27.9%. Conclusions We define a blueprint by which a catchment can be defined for a hospital trust and demonstrate the value a hospital-view of the local population could provide in understanding local health needs and enabling population-level health improvement interventions. While an individual approach allows tailoring to local context and need, there could be an efficiency saving were such public health information made routinely and regularly available for every NHS hospital.

Using Catchment Population to Estimate Sporadic Creutzfeldt–Jakob Disease Incidence

ABSTRACT Introduction Catchment populations have several uses. A method using catchment population to estimate the incidence of sporadic Creutzfeldt–Jakob disease (sCJD) is described. Materials and Methods A cohort of nine consecutive patients diagnosed with sCJD, symptom onset spanning 26 months, were observed at a rural tertiary university medical center that has approximately 40,000 hospital discharges annually. An effective catchment population was determined using surrounding county utilization frequency that captured all nine sCJD patients and accounted for over 87% of discharges. Results The effective sCJD hospital catchment population was 1.266 million, implying an annual sCJD incidence rate of 3.39 per million (95% CIs, 1.55-6.43), assuming a Poisson distribution for sCJD occurrence. Conclusions This annual incidence rate suggests that many sCJD patients are unrecognized and unreported. An advantage of this catchment population method is independence from death certificate accuracy, important in rare diseases that are both rapidly and invariably fatal. The relative absence of significant healthcare systems competition in this rural population enhances the reliability of this finding. The most likely explanation for the high sCJD incidence rate suggested by this study is enhanced clinical suspicion and improved diagnostic accuracy.

Efficiency in Utilization of the Resources Allocated to Lower Level Health Facilities in Uganda: a Case Study of Health Centre IVs

Background: Everyone has a right to quality life with good health of the household and, thus, health sector financing should be a top priority because when the population is healthy, it is very productive and wealthy. In Uganda, Health Centre IVs (HCIVs) created under Uganda National Minimum Health Care Package provide curative, prevention and promotion services. The efficiency of these HCIVs is as critical as people’s health and this paper measures efficiency in utilization of resources allocated to them.Methods: The study used Hospital and HCIV Census data for 2014 and health sector data for FY2015/16 reported by MOH in the Annual Health Sector Performance Report. STATA software was used to perform Data Envelopment Analysis for a preferred model was out-put oriented that optimizes variable returns to scale. In this way, efficiency scores for every HCIV were calculated. Also, a Tobit regression model was run to estimate the factors contributing to the adjusted inefficiency scores for HCIVs.Results: Overall, 7 HCIVs (23.3%) were operating under constant returns to scale, implying that they were efficient (both pure technical and scale efficiency) while the 19 (63.3%) were operating under increasing returns to scale, implying that their health service outputs would increase by a greater proportion compared to any proportionate increase in health services if more inputs were added in the facility. Four HCIVs (13.3%) were operating at decreasing returns to scale meaning an additional input to the HCIVs would produce a less proportional change of outputs. The study identified catchment population, average length of stay, bed occupancy rate, and outpatient department visits as a proportion of inpatient days as the main factors of efficiency among HCIVs.Conclusions: This study has shown how Data Envelope Analysis methods can be applied at the HCIV level of the health system to gain an insight into variation in efficiency across health centers using routinely available data. And, with the majority of HCIVs operating at increasing returns to scale, it showed that there is a need to increase inputs like staff, medicines and beds to achieve the desired optimal scale in case of constant returns to scale.

Attention-deficit hyperactivity disorder in people with intellectual disability: statistical approach to developing a bespoke screening tool

Background Attention-deficit hyperactivity disorder (ADHD) is common among people with intellectual disability. Diagnosing ADHD in this clinically and cognitively complex and diverse group is difficult, given the overlapping psychiatric and behavioural presentations. Underdiagnoses and misdiagnoses leading to irrational polypharmacy and worse health and social outcomes are common. Diagnostic interviews exist, but are cumbersome and not in regular clinical use. Aims We aimed to develop a screening tool to help identify people with intellectual disability and ADHD. Method A prospective cross-sectional study, using STROBE guidance, invited all carers of people with intellectual disability aged 18–50 years open to the review of the psychiatric team in a single UK intellectual disability service (catchment population: 150 000). A ten-item questionnaire based on the DSM-V ADHD criteria was circulated. All respondents’ baseline clinical characteristics were recorded, and the DIVA-5-ID was administered blinded to the individual questionnaire result. Fisher exact and multiple logistic regressions were conducted to identify relevant questionnaire items and the combinations that afforded best sensitivity and specificity for predicting ADHD. Results Of 78 people invited, 39 responded (26 men, 13 women), of whom 30 had moderate-to-profound intellectual disability and 38 had associated comorbidities and on were medication, including 22 on psychotropics. Thirty-six screened positive for ADHD, and 24 were diagnosed (16 men, eight women). Analysis showed two positive responses on three specific questions to have 88% sensitivity and 87% specificity, and be the best predictor of ADHD. Conclusions The three-question screening is an important development for identifying ADHD in people with intellectual disability. It needs larger-scale replication to generate generalisable results.

Spotlight influenza: Extending influenza surveillance to detect non-influenza respiratory viruses of public health relevance: analysis of surveillance data, Belgium, 2015 to 2019

Background Seasonal influenza-like illness (ILI) affects millions of people yearly. Severe acute respiratory infections (SARI), mainly influenza, are a leading cause of hospitalisation and mortality. Increasing evidence indicates that non-influenza respiratory viruses (NIRV) also contribute to the burden of SARI. In Belgium, SARI surveillance by a network of sentinel hospitals has been ongoing since 2011. Aim We report the results of using in-house multiplex qPCR for the detection of a flexible panel of viruses in respiratory ILI and SARI samples and the estimated incidence rates of SARI associated with each virus. Methods We defined ILI as an illness with onset of fever and cough or dyspnoea. SARI was defined as an illness requiring hospitalisation with onset of fever and cough or dyspnoea within the previous 10 days. Samples were collected in four winter seasons and tested by multiplex qPCR for influenza virus and NIRV. Using catchment population estimates, we calculated incidence rates of SARI associated with each virus. Results One third of the SARI cases were positive for NIRV, reaching 49.4% among children younger than 15 years. In children younger than 5 years, incidence rates of NIRV-associated SARI were twice that of influenza (103.5 vs 57.6/100,000 person-months); co-infections with several NIRV, respiratory syncytial viruses, human metapneumoviruses and picornaviruses contributed most (33.1, 13.6, 15.8 and 18.2/100,000 person-months, respectively). Conclusion Early testing for NIRV could be beneficial to clinical management of SARI patients, especially in children younger than 5 years, for whom the burden of NIRV-associated disease exceeds that of influenza.

Geo-Analysis: The Distribution of Community Health Workers in Relation to the HIV Prevalence in Kwazulu-Natal Province, South Africa

Background: The Ward Based Primary Health Care Outreach Team (WBPHCOT) policy framework states that community health workers (CHW)s should be distributed mainly according to poverty lines for each catchment population. We aimed to describe the spatial distribution of CHWs in relation to the HIV prevalence which has been associated with poverty in previous studies.Methods: We utilized geographic information system (GIS) method to visualize the distribution of CHWs in relation to HIV prevalence in KwaZulu-Natal (KZN) districts. Dot density mapping, was used to visualize the random distribution of CHWs in relation to HIV prevalence in the districts. The districts’ HIV prevalence, number of PLWH, CHW: PLWH ratio and poverty scores were mapped using choropleth mapping. MapInfo 17, a GIS software was used to map geospatial presentation of the data.Results: Ten out of 11 KZN districts had more than a required CHW:PLWH ratio of 1:160-267, which indicates a significant shortage of CHWs regardless of the HIV prevalence or poverty scores. Furthermore, our findings showed extensive geospatial heterogeneity with no clear pattern in the distribution CHWs in relation to the HIV prevalence and poverty scores in the districts Conclusion: This finding represents inequalities in the provision of HIV services by the CHWs. It is critical to strengthen response to the HIV epidemic through the well informed and justified distribution of CHWs especially to the poor districts.

Prevalence and Factors Associated with Active Trachoma among Children 1-9 years of Age in the Catchment Population of Tora Primary Hospital, Silte zone, Southern Ethiopia, 2020

Background: Trachoma is the foremost cause of wide-reaching, preventable blindness. According to the World Health Organization report, nearly 1.3 million human beings are sightless due to trachoma, whereas about eighty-four million are hurt from active trachoma. A survey revealed that the countrywide prevalence of active trachoma among children aged 1–9 years in Ethiopia was 40.1%. Limited data are present regarding the study area; therefore, the aim of this study was to determine the magnitude and factors associated with active trachoma among 1-9 years of children in the catchment population of Tora Primary Hospital, South Ethiopia. Methods: A community-based cross-sectional study was performed on 589 children in a study place from February 15 to March 13, 2020. We used Epi data program version 3.1 and SPSS version 20 for data entry and analysis, respectively. Results: The overall occurrence of active trachoma in the catchment was 29.4% [CI=25.7, 33.12]. Of these cases, the trachomatous follicle (TF) 90.9%, TI (4.8%), and combination of TF/TI (4.2%) were found. Households’ educational status, frequency of face washing, knowledge about trachoma, source of water for washing purposes, and garbage disposal system were the independently associated factors of active trachoma. Conclusion: In this study area, the occurrence of active trachoma was high. Hence, it needs instant attention, such as constructing a responsiveness application in the community, inspiring children and parents to try out face washing, improving knowledge about trachoma and appropriate excreta disposal.

Evaluating the Implementation of a National COVID-19 Hospital Guideline in Wales

BackgroundThe COVID-19 pandemic created a unique situation where a national clinical guideline would address uncertainty, and provide a trusted source for up-to-date information and advice. We developed a dynamic online infrastructure together with a dedicated implementation team to deliver this at scale and pace. The guideline was implemented through a digital implementation framework (SIMPSI framework) deploying facilitators to maximise guideline adoption, particularly targeting senior clinical decision makers (consultants) involved with the care of COVID-infected patients across six Health Boards (HB) in Wales. Methods We evaluated guideline implementation using the Taxonomy of Implementation Outcomes Model. The primary outcome was consultant engagement, with a target of 193 registrations. We assessed wider impact through analysis of guideline platform activity and a user survey, with additional sensitivity analysis to derive penetration ratios, catchment population, clinical staff, acute beds, and COVID-19 admissions. ResultsThe guideline platform had 4521 total registrants, with over 170,000 page views during the first wave. We exceeded the target nearly six-fold (1159 consultant registrants). This represented 45% of all medical consultants in Wales, and made up the highest proportion of guideline registrants of all professional groups (23%, 1159/4521). We observed significant variation in guideline penetration across the six HBs, ranging from 31% to 74% of consultants registered. The HB with highest penetration had the most active guideline facilitator. The HB with the lowest penetration was the region first impacted and most affected by COVID-19 at the time of guideline publication (37% inpatients of peak, versus 10% or less for the other HBs). ConclusionWe utilised a digital implementation framework to construct a system that could be rapidly applied throughout all hospitals in Wales. Whilst we exceeded the intended target demonstrating full implementation, we identified two key factors to account for differences in the penetration rates across the different HBs. First, an experienced and active facilitator with the capacity to undertake the role was associated with significantly better penetration. Second, timeliness of implementation was crucial as evidenced by lower penetrance is the one HB that was impacted earliest by COVID-19 at the time of guideline dissemination. Nevertheless, the rapid implementation of the guideline has coincided with Wales demonstrating more favourable intensive care survival rates and maintaining one of the lowest mortality rates when compared to the UK as a whole for the first wave of the COVID-19 pandemic.

Incidence of NUT carcinoma in Western Australia from 1989 to 2014: a review of pediatric and adolescent cases from Perth Children’s Hospital

Background NUT carcinoma (NC), previously known as NUT midline carcinoma, is a rare and very aggressive cancer that occurs in both children and adults. NC is largely chemoresistant, with an overall survival of less than 7 months. Because the carcinoma is not restricted to a particular organ, diagnosis is often a challenge. In the absence of a clearly determined incidence for NC, we sought to study the diagnosis of patients in a well-defined population. Methods We systematically reviewed records of all patients that presented to the Oncology Department of the Princess Margaret Hospital for Children from 1989 to 2014. This institution in the geographically isolated state of Western Australia has a catchment population of around 2 million. We then identified all high grade undifferentiated sarcomas or carcinomas in the 0–16 year age group. Results Over 26 years, we found 14 patients of 16 years or younger with undifferentiated malignant tumors. Of these, five tumors were positive by immunohistochemistry for the NUT/NUTM1 (Nuclear Protein in Testis) protein and/or the translocation t(15;19). Three patients presented with thoracic tumors, one with a para-spinal tumor, and one had an upper airway nasopharyngeal carcinoma. In all five cases, there was an initial response to therapy and then progression. This 26-year survey was conducted in a geographically isolated state with a well-defined population, and we determined an estimated incidence of NC of around 0.41 per million child years (0–16 yrs. of age) at risk. From three patients it was feasible to derive cell lines for further genetic analyses and drug screening. Conclusions For the first time, the incidence of NC could be determined in a well-defined geographic area. The calculated rate of NC incidence is consistent with a history of under-recognition for this malignancy. These findings indicate that improved diagnostic detection of NC would enable better management and counselling of patients. Our findings emphasize the heterogeneity of NC, and they highlight the need to develop personalised therapy options, and to consider a diagnosis of NC in undifferentiated malignant tumors.

OP0281 EXCESS GIANT CELL ARTERITIS CASES ARE ASSOCIATED WITH PEAKS IN COVID-19 PREVALENCE

Background:Immediately following the first wave of the COVID-19 pandemic, the number of giant cell arteritis (GCA) diagnoses noticeably increased at the Royal National Hospital for Rheumatic Diseases in Bath, UK. Furthermore, there was an increase in the proportion of patients with visual complications [1]. The finding supports the viral hypothesis of GCA aetiopathogenesis as previously described [2]. This not only has ramifications for understanding the underlying disease mechanisms in GCA but also has implications for the provision of local GCA services which may have already be affected by the pandemic.Objectives:The objective of the study was to estimate the incidence of giant cell arteritis during the COVID-19 pandemic years of 2020 – 2021 and compare it to 2019 data. Given that there have now been two distinct peaks of COVID-19 as reflected by hospital admissions of COVID-19-positive patients this has allowed us to investigate if there is a temporal relationship between the prevalence of COVID-19 and the incidence of GCA.Methods:The incidence of GCA was calculated by assessing emailed referrals to the GCA service and the hospital electronic medical records to identity positive cases from 2019 to the current date. Local COVID-19 prevalence was estimated by measuring the number of hospital beds taken up by COVID-19 positive patients, available publicly in a UK Government COVID-19 dataset [3].Results:There were 61 (95% Poisson distribution confidence interval [CI] 47 - 78) probable or definite GCA diagnoses made in 2020 compared to 28 (CI 19 – 40) in 2019 (Figure 1). This is an excess of 33 cases in 2020, or an increase in 118%. Given that 41% of the hospital’s catchment population is over 50, this equates to an annual incidence rate of 13.7 per 100,000 in 2019 and 29.8 per 100,000 in 2020. This compares to a previously estimated regional incidence rate of 21.6 per 100,000 for the South West of the UK [4].Figure 1. Prevalence of hospital COVID-19 and incidence of GCA (2019 – 2021). Graph showing the number of hospital beds occupied by COVID-19-positive patients in 2020 – 2021 (blue circles), number of daily GCA diagnoses in 2020 – 2021 (red circles), and previous GCA diagnoses in 2019 (green circles). The broken lines represent moving averages with a period of 7 days for COVID-19 cases and 28 days for GCA diagnoses.A peak in COVID-19-positive inpatients was seen on 10th April 2020 with a corresponding peak of GCA diagnoses on 29th May 2020, giving a lag period of approximately 6 weeks between these peaks (Figure 1).Conclusion:The incidence of GCA in Bath was significantly increased in 2020 compared to 2019. This may be the result of the widespread infection of the local population with the COVID-19 virus as a precipitating factor. Possible mechanisms include, but are not limited to, endothelial disruption by the virus, immune system priming towards T helper cell type 1 (Th1) cellular immunity and/or activation of the monocyte-macrophage system. More work is currently underway to assess the causal relationship between the two diseases.There was a lag period of 6 weeks between the peak during the first wave of the pandemic and the rise in GCA cases. We shall be closely monitoring the number of referrals that follow the current wave of the pandemic.References:[1]Luther R, Skeoch S, Pauling JD, et al. Increased number of cases of giant cell arteritis and higher rates of ophthalmic involvement during the era of COVID-19. Rheumatol Adv Pract 2020;4:1–4. doi:10.1093/rap/rkaa067[2]Russo MG, Waxman J, Abdoh AA, et al. Correlation between infection and the onset of the giant cell (temporal) arteritis syndrome. Arthritis Rheum 1995;38:374–80. doi:10.1002/art.1780380312[3]England PH. GOV.UK Coronavirus (COVID-19) in the UK. 2021.https://coronavirus.data.gov.uk/details/download (accessed 25 Jan 2021).[4]Smeeth L, Cook C, Hall AJ. Incidence of diagnosed polymyalgia rheumatica and temporal arteritis in the United Kingdom, 1990-2001. Ann Rheum Dis 2006;65:1093–8. doi:10.1136/ard.2005.046912Disclosure of Interests:Ben Mulhearn Speakers bureau: Novartis UK, 2019, Grant/research support from: Chugai, 2019, Jessica Ellis: None declared, Sarah Skeoch: None declared, John Pauling: None declared, Sarah Tansley: None declared