To what extent is stigma and discrimination putting the health outcomes of the homeless at risk

To what extent is stigma and discrimination putting the health outcomes of the homeless at risk

Homelessness can be defined as having no shelter or living in inadequate and temporary accommodation (Armstrong et al., 2021) It is a subject of concern as discrimination and stigma are common experiences for people who are homeless and seeking health care (Gilmer and Buccieri, 2020).

The purpose of this research is to determine the extent to which stigma and discrimination is putting the health outcomes of the homeless at risk. For a conclusion to be made on the research question, the following objectives will be explored. Firstly, what forms of stigmas and discrimination do the homeless face, and what are the consequences for individuals? Secondly, what factors may be contributing to the causes of stigma and discrimination, thereby affecting the health of homeless people? Finally, what contributions can health care professionals make to reduce health disparities for this underserved population? This paper will show that people who are homeless experience more stigma and discrimination when accessing healthcare, than that of the general population.

The research will discuss co-production and Kotters model of change. Kotters model of change was chosen for its clear guidance, and it will provide a structure for this paper. Discussion on co-production will comprise theoretical and practical components of the approach. Kotters model of change has a sequence of steps, providing guidance in the change process. It has been described as one that is straightforward and easier to understand compared with that of other models (Wentworth et al., 2018). (Lv and Zhang (2017) provide detail about the models process, which will briefly be discussed. Creating urgency and forming a guiding coalition forms the start of the process, where a need for the change is identified and communicated to highlight its importance. A team of people are brought together to establish a guiding coalition, who will lead the change. This is followed by a vision being formed, illustrating what the future will look like, which is shared to build interest and drive towards the common goal. Empowerment and short-term wins follow on from the vision. Empowerment creates autonomy and trust in people. The short-term wins build confidence and provide reassurance that there is steady progress. Kotters model has also been criticized by Pollack (2015), who suggests it comprises of too many stages, with not enough detail about how each should be achieved. Meanwhile, Krishnan and Metcalfe (2020) claim the model is excessively focused on changing systems, culture, and developing strategies, while neglecting the most important aspect of change, which is initially changing people's behaviours.

In comparison to Kotters change model, a systematic review by Taylor et al (2013) , PDSA was identified as a quality improvement tool that can help to determine whether an intervention for improvement will be successful. This tool has a flexible approach and has been promoted as being simple to use. However, the tool needs to be managed effectively and appropriately according to the nature and scale of the improvement. In a further study by Reed and Card (2016) the PDSA framework is much slower with regards to progression and requires strong team member support. It is not ideal for radical changes. (Reed and Card, 2016) also implies it cannot be used independently but should be supported by a separate change approach.

Co-production NHS England (2022) is an approach whereby organisations collaborate with people with lived experiences to improve service quality (NHS England,.2022). This approach would be advantageous in answering the researchers questions, although it was unable to be utilised for ethical reasons. As part of a future study investigating the impact of stigma and discrimination on health outcomes, co-production would be utilised, and patients could collaborate with health professionals to improve their health outcomes. Co-production has principles and values of which several will be discussed. Values comprising of accessibility and reciprocity. Accessibility allows involvement and access to all, with everyone having an equal opportunity to participate. Reciprocity is giving people something back for their contributions (SCIE, 2013). There are six principles, and these are assets, capacity, mutuality, networks, blurred roles and finally catalysts (SCIE, 2013). Mutuality is about people having shared and equal responsibilities and expectations (Minghella and Linsky, 2018). Assets are the people with lived experiences who can work collaboratively with professionals as equal partners (NHS England, 2022). It is critical to recognise the assets participants have, as their contribution is significant (NMC, 2022). Equality acknowledges that people have a variety of assets to contribute to the approach, such as skills, ability, and time (SCIE, 2013). (NICE., 2018) describes assets as experience and aspirations, community assets as networks and services. (Van Eijk and Steen,2016) claims that when people with lived experiences consider the research taking place to be worth their engagement and effort, this is defined as salience. The researcher sees that this should be considered when collaboration is needed in co-production, otherwise the outcome of the process may not be as successful. Furthermore, individuals who are homeless participating in co-production would be a great asset to this research, as they could provide a deeper understanding of the issues that they experience. For co-production to be successful it requires preparation, such as training about the principles and philosophy of the approach (SCIE2013) Some participants may not be familiar with co-production, what it entails and how to implement it (SCIE, 2013). Training and support will help to develop their understanding and what it involves (SCIE, 2013). Support is allowing people to express their views, contribute to conversations and have people to advocate for them as necessary (NICE., 2018) . A good working culture is also important, where people feel they are able to be open and honest. Furthermore, where there is value, respect and support everyone has capacity to take part (SCIE, 2013). However, there are barriers to co-production that should be considered. Practicalities including cost, time and other organisational pressures (Minghella and Linskey, 2018). Conflict can also happen when there are plans for change due to initial reluctance (Minghella and Linskey, 2018), the causes of which maybe a lack of understanding of the change and its implications (Wentworth et al., 2018). In a co-produced study, the researcher would use focus groups as the method of data collection. This qualitative research method, has become increasingly popular within healthcare research, being an ideal method for collecting in depth dimensions about a subject (Adler et al.,2019). However, (Kaehne et al (2018) suggest there is not enough quality evidence, to know what difference co-production has on service improvement. In addition, whether or not there is increased satisfaction from those who use the service.

The research methodology for this project was a questionnaire, providing qualitative data, which aimed to look at participants awareness, knowledge, perceptions and thoughts on how stigma and discrimination could be managed with the aim of eliminating them. The questions were created to assist the researcher in answering the research objectives and ultimately the research question. Data was collated to initiate critical discussion about the findings but was not necessarily designed to prove anything. Questionnaires have been successfully used in social care to look at reality and understand relationships between people (Queiros et al., 2017). They are an advantageous data collection tool, being able to reach many people in a short period of time (Rowley, 2014) Participants are able to respond at a time which suits them {Paraphoo ,2006). However, there are limitations with questionnaires. There is no guarantee participants will understand all the questions (Rowley, 2014).) In addition to this, response levels can be low, caused by time constraints and effort needed, which can be the case, when trying to obtain data from healthcare professionals (Paraphoo, 2006). The researcher considered what other data collection method could be considered for similar a study in the future.

Focus groups are a possible alternative to questionnaires. Queiros et al (2017) mentions these in literature, explaining they are good for investigating peoples behaviour, which essentially is the root cause of the issues discussed in this study. A further advantage of focus groups is that information is gained directly from groups, and any clarifications can be made (Queiros et al., 2017). The researcher suggests that, in a hypothetical situation, co-production using this method, could bring nurses and people experiencing homelessness together. This would allow for discussion and debate on issues homeless people experience in healthcare. (Halliday et al.,2021) supports the researcher by stating they can create broad discussions and the sharing of ideas between people. Despite the advantages, there are also disadvantages. The willingness of people to participate and small groups that may not represent the wider population (Queiros et al., 2017). Time is also required for preparation and the costs associated with organising an event, as well as bringing participants together, who may come from a wide geographical area (Halliday et al.,2021). Nonattendance can result from being unable to attend due to personal circumstances, limiting a wider range of participants and perspectives (Halliday et al.,2021).

The researcher recruited a combination of sixty nurses and healthcare associates from a local NHS trust to participate in this qualitative research. Convenience sampling was used which is a method used when participants are conveniently available (Paraphoo, 2006). As part of the governance process, approval for the research and data collection was given by the ward matron. The inclusion criteria were that participants worked within a healthcare setting. An email was circulated by the matron to members of her team, inviting them to complete the questionnaire. The email provided a brief outline of the research and its purpose. Participants were unable to be identified by their responses, thus maintaining anonymity and confidentiality. When obtaining data for this research was a challenge, as participants showed little interest in completing the questionnaire. As previously mentioned, Paraphoo (2006) says health professionals can be under time constraints which can have an impact on response rates.

In an attempt to optimise responses, the researcher issued paper copies of the questionnaire and spoke to team members intermittently inviting them to respond. This produced a small number of responses but was not as successful as one had hoped. Many health care settings have international nurses and other clinical professionals, as was the case for this study. Several team members explained that they could not answer, as they did not have knowledge about homelessness in England and some found the questions difficult to understand and provide responses. This gave some reasoning for low participation in the questionnaire.

The second data collection attempt also used convenience sampling. The researcher sent an email to many of her personal contacts, including friends, family, and co-workers inviting them to take part in the research. Potential participants were informed of the purpose of the questionnaire, as well as assurance that their responses would remain anonymous. Once consent was obtained to participate in the study, an email with a copy of the link to the questionnaire was given.

Based on the topics in the questionnaire, the results of this research have been divided into themes.

Awareness of stigma and discrimination

Assumptions about the main influences of stigma and discrimination

Barriers experienced by homeless people accessing healthcare

Contributions that healthcare professionals can make in reducing stigma and discrimination.

(Fig 1)

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Fig 1, illustrates how much awareness participants have about stigma and discrimination that is experienced by homeless people when accessing health care. It shows a greater number of participants are aware of the stigma and discrimination that homeless people experience in healthcare settings. The researcher identifies that although this does not link directly to the study objectives, the data provides some key information. Firstly, it demonstrates that there is stigma and discrimination against this marginalised population. This is shown by the high number of participants who are aware of it. Secondly, for that reason there is a need for action, to be able to improve health outcomes. (Fahoum and Al-Krenawi, 2021) describe stigma as a social attribute which affects the reputation of an individual or group in society, reducing them from being normal to being rejected and tainted. Similarly, Bresnahan and Zhuang (2016) define stigma as a concept in which people can be seen as outliers, unacceptable and not classed as human. Stigma and discrimination creates reluctance for homeless people to improve health behaviours or seek medical attention (Moore-Nadler et al 2019). Lived experiences of stigma are found in literature McCallum et al (2019) where people experiencing homelessness feel they are dehumanised, judged and disrespected. Behaviour that is discriminatory or labels a group or population in society, increases inequalities as homeless people become increasingly excluded (Mejia-Lancheros et al., 2021) These can be caused due to factors including socioeconomic status, income, physical and mental health (Mejia-Lancheros et al., 2021). . Overall stigma creates barriers in providing care for people who are homeless (Ramsay et al., 2019).

A systematic review by Thorburn and Lindley ( 2021) states that places of healthcare are unique places where discrimination happens. A prime example of discrimination and stigma towards homeless people is when they try to register with GP (General Practitioner) surgeries with an unstable housing status CITATION Kri20 l 2057 (Krishnan & Metcalfe, 2020). The Care Quality Commission (Care Quality Commission, 2022), clarifies that it is the responsibility of GP surgeries to register people who do not have a permanent address, are homeless and having no means of providing documentation (Care Quality Commission, 2022). Although having adequate housing or accommodation may appear to be something within a person's control Johnstone et al (2015) people can lose their homes due to unforeseen circumstances Moore-Nadler et al (2019), such as a history of being in prison or care, alcohol and drug issues as well as poverty and bereavement (Health matters: Rough sleeping, 2022). However, Johnstone et al (2015) claims that people who are homeless are seen as being responsible for their housing circumstances. Individuals experiencing homelessness become socially isolated and rejected by society, including by those who work in healthcare settings (Crisis | Together we will end homelessness, 2022. This affects health outcomes as trust in people, including professionals diminishes (Crisis | Together we will end homelessness, 2022). Loss of trust is also spoken about by a participant in a study by (Grech and Raeburn, 2021), who commented that trust has been lost because patients have been let down so much. (Dougall and Buck, 2021) asserts the importance of health professionals building trusting relationships with patients, as this can reduce patient stress, thus contributing to improving health. (ParadisGagn and PariseauLegault, 2020) elaborates on this, emphasising how in nursing practice, the ability to build trust depends on an understanding of the care receiver's life context (cultural, ethnic, or gender). (Ramsay et al., 2019) discusses how people experiencing homelessness face neglect, rejection and feeling unwelcome. She goes on further to say how this is viewed by individuals, as providers having no compassion for their circumstances and disregarding information they share about their health. This evidence by Ramsay et al (2019) relates to the researchers objectives, as it shows the consequences of stigma that is experienced. A valuable point in this evidence talks about areas homeless patients see as important in their care experiences. Factors such as inclusive and collaborative care, having access to health care when required that will meet their needs. Being involved in decisions regarding their health and finally being comfortable in declining advice if they so wish, without being penalised for it. The (NMC, 2022), states that nurses need to respect the level that patients want to be involved in decisions regarding their health and to respect the wishes of patients who choose to decline or refuse treatment.

The researcher considers some of this evidence to be trustworthy. The literature by (Thorburn and Lindley, 2021) as mentioned, was from a systematic review (Pollack and Berge, 2017). These reviews are high-quality evidence and essential for keeping practitioners up to date with ongoing development in research (Pollack and Berge, 2017. The Care Quality Commission (Care Quality Commission, 2022) is an organisation responsible for regulating health and social care services, to ensure that they provide high quality care. Furthermore, they aim to ensure services are safe, effective, and compassionate and strive for continuous improvement (Care Quality Commission, 2022).

left52895500(Fig 2)

The chart in (fig 2) illustrates how strongly participants believe that people who are homeless experience more health inequalities than the rest of the population. It clearly shows that the majority of thirty-four participants strongly agree and a further fifteen participants agree. This shows that there is a level of knowledge about health inequalities between those who do not work in health care. Health inequalities are the differences in health between individuals, groups and wider society (Dougall and Buck, 2021) They are disadvantages which are unwarranted and could be avoided (Dougall and Buck, 2021). Factors that cause inequalities are the social determinants of health, such as, income, housing and the conditions in which we are born (Dougall and Buck, 2021). The impact of health inequalities, will affect nursing and health care professions, as people who are homeless or at risk of homelessness, are more likely to visit emergency departments and have a higher number of hospital admissions than the general population (Health matters:Rough sleeping, 2022). However, it is in these clinical areas that this marginalised group experience attitudinal, practical, and administrative barriers (Jones et al., 2021). These are experienced widely within clinical settings, but even more so in primary care (Jones et al., 2021).

The sustainable development goals are in place to make changes towards a world which is safe, equitable and healthy. This literature by Rosa et al 2022) highlights that out comes are needed that will concentrate on improving health, equity, inclusion and respect. In order to do this, nurse leaders need to speak out and collaborate with stakeholders in a variety of fields and be involved in all policy decisions which are concerned with the determinants of health (Rosa et al., 2022).

(Fig 3)

-635-69532500This pie chart (Fig 3) illustrates what assumptions participants believe are some of the primary influences of stigma and discrimination. The NMC code NMC ( 2022) states that nurses must avoid making assumptions. According to the questionnaire results, it is clear the use of alcohol and drugs at thirty six percent does have an impact on peoples assumptions. However, according to (Crisis Together we will end homelessness, 2022), while drugs and alcohol can cause homelessness, this doesnt happen to everyone. (Crisis | Together we will end homelessness, 2022) also suggest that drug and alcohol usage is high amongst the homeless population. (Puddephat et al., 2021) supports this view and adds alcohol misuse is likely to be more prevalent amongst those who suffer from mental health issues, such as depressed mood, psychosis or anxiety.

There is an intricate relationship between homelessness and substance use (Doran et al., 2018). However, (Crisis | Together we will end homelessness, 2022) emphasises that this does not imply that all people have problems or addictions related to these. Amongst this population of people, the use of substances is considerably higher than those who are in stable housing (Ibabe et al.,2013). Similarly (Doran et al., 2018), agrees saying that substance use is above average in people experiencing homelessness. Along with substance use, the risk of issues with mental health, such as depression and anxiety are increased (Doran et al., 2018). This is also mentioned by (Ibabe, et al., 2013), who goes on to say that the level of substance use is likely to be related to the stressors that are experienced by those who are homeless, for example their social environment. Much evidence talks about the morbidity and mortality rates in homelessness (Moore-Nadler et al (2019) , a high percentage of this may be due to substance use as a leading cause of death due to overdoses (Doran et al., 2018). So how can health professionals contribute to improving these issues? According to Doran et al (2018) there is a need for increased understanding about the relationship between substance use and homelessness. This evidence relating to drug and alcohol use links to the researchers objectives as they are factors that contribute to the stigma and discrimination experienced by people who are homeless. In addition, they also have an impact on mental health.

The researcher identified this literature study by Grech and Raeburn (2021), which investigated the perceptions of registered nurses regarding patient discharge and discharge planning from hospital. The twelve nurses worked on wards including emergency department and wards for mental health and drug and alcohol related issues. The method for research was interviews lasting up to forty minutes and consisting of 14- 20 questions. Nurses shared potential reasons for stigma towards homelessness, one of which being because of a patients mental health rather than the fact that the patient was homeless. According to ParadisGagn and PariseauLegault (2020) mental health for those experiencing homelessness can result in being labelled as mentally ill. The researcher identifies this as another example of discrimination. Another nurse spoke about peoples appearance, the unkept of look which was found to make building relationships and rapport difficult. However, it is these negative experiences that cause difficulty for health care staff to build trust with those who are homeless (ParadisGagn and PariseauLegault, 2020). The perception of substance use created issues to engage with patients for treatment. (ParadisGagn and PariseauLegault, 2020) states that attitudes like these happen frequently and have the potential to cause exclusion from care as well as increasing social isolation.

In relation to the study by Grech and Raeburn (2021), the small number of nurses included came from wards that could potentially see more people experiencing homelessness than other wards, due to the nature of their specialities. Obviously, a sample size of twelve in comparison to a much larger sample is unlikely to be represent the nursing profession fully (Paraphoo, 2006) If this evidence was repeated, a larger number of nurses from a wider area of clinical settings and of different specialities, may provide further insight into the reasons that stigma happens. The valuable aspect of this evidence is the nurses shared reasons that initiate stigma for them, which could possibly help in reducing this issue. It could also be viewed as assumptions being made. Furthermore, Lee and Turney (2012), discuss how loss of social status leads to marginalisation, stereotyping and labelling, overall causing poor life outcomes. This is supported by (Arpey et al.,2012 ), who goes on to identify how some healthcare providers are not contributing to improve health outcomes, by perceiving people as unintelligent, irresponsible and unlikely to attend current and future appointments. Yet some health providers understand the importance of individualised care and about tailoring it to individuals, their circumstances and social status (Arpey et al.,2012 ). (Lee and Turney, 2012) further explain how people with lower social status struggle to deal with circumstances that cause them stress. They also lack the support to help them such as social, leisure and financial. This can result in being much more vulnerable to mental health issues.

The literature by Arpey et al (2017) relates to the researchers objectives about how healthcare providers can improve health outcomes, but also how they are in effect making them worse. The researcher acknowledges that the literature by Lee and Turney (2012) is ten years old though other literature identifies the same points. The researcher also acknowledges that the majority of the evidence for this subject area was not of the highest quality. Despite this, it has been consistent in the information that it has provided about mental health, alcohol and drug misuse, The participants responses have also reflected what the evidence has shown. The researcher did expect this outcome. What is interesting with the research results is that many of the participants do not work in healthcare settings, yet the responses are similar to the evidence in the literature. This section of the research relates to the authors objective on the factors that may be contributing to the causes of stigma and discrimination

The graph in fig four illustrates some of the key barriers that participants viewed as being experienced by people who are homeless

60385155707Fig 4

Fig 4

-95250-168910There is a wide spectrum of barriers to health care that homeless people face as the graph in fig 4 shows. Barriers cause further inequity for this marginalised population (Armstrong et al., 2021). Socioeconomic status is an example of an inequity, and this is associated with poor health outcomes (Gilmer and Buccieri, 2020). A combination of barriers and poor health results in higher morbidity and mortality (Moore-Nadler et al (2019). Stigma and discrimination are also a substantial barrier (Ramsay et al., 2019). The researchers questionnaire was completed by fifty participants and, out of these, thirty gave prejudice as a barrier and sixteen gave victimisation. It is these negative experiences that affect the patient and professional relationships and also have an impact on healthcare seeking behaviour amongst homeless individuals (Ramsay et al., 2019). (Wright, 2014) discusses other common barriers that participants also gave as responses such as issues with transport, finance, appointments and medication. (Understanding homeless health inequality in Birmingham (2021), supports this research in identifying common barriers when accessing health care. Similar to the questionnaire results, the evidence includes not having credit to make a phone call, access to internet, not readily available travel issues and lack of permanent address(Understanding homeless health inequality in Birmingham (2021), 2022).This report further explains that because of the barriers, health care needs are increased along with health inequalities and a higher number of visits to emergency departments (Understanding homeless health inequality in Birmingham (2021), 2022). According to (Moore-Nadler et al (2019), what is required is for those in positions of leadership with emotional intelligence and resilience to develop strategies for overcoming barriers.

The word cloud in (fig 5) shows words used by participants, about how health care professionals can contribute to reducing stigma and discrimination for people experiencing homelessness. The cloud shows some key values, many of which are within the NMC code of conduct.

(Fig 5)

02540As part of the questionnaire participants were asked what contributions if any, can be made by health professionals to reduce stigma and discrimination? The responses are illustrated in the word cloud. The data was illustrated in this format, due to a wide range of answers that were given. This would have been complicated to transform into a pie chart or bar graph.

The researcher views the participants responses as factors and values, which will contribute to reducing stigma and discrimination, thus improving health outcomes. Several of the words in the cloud are in the nurses code of conduct. The NMC code mentions respect in several areas, such as respecting human rights, treating people with respect and respecting peoples choices about how much people want to be involved in their care (NMC, 2022). In addition, dignity and encourage are within the code of conduct. Nurses must encourage patients to share decisions in relation to their care and treatment and maintain patient dignity at all times (NMC, 2022). Beneficence is one factor that does not appear as a response by participants. This is part of the health professionals role to act in the patients best interests (NMC, 2022). As well as this, non-maleficence is not causing harm to the patient or at least keeping it to a minimum. The researcher sees the impact of stigma and discrimination as causing patient harm because as Moore-Nadler et al (2019) suggests, homeless peoples health care needs are not being met). Furthermore, as previously mentioned, the way in which this population are treated causes behaviours, such as not engaging with clinical settings or seeking advice and treatment (Gilmer and Buccieri, 2020). (Walker et al (2022) says behaviours can be changed through giving support in making decisions and allowing choices, thus improving health outcomes.

As a start to recommendations for practice, the researcher suggests that clinicians are aware of their own unconscious and conscious biases, as these may be perceived as stigma or discrimination. The bias may be because of a patient's cultural or ethnic background, but also in relation to socioeconomic status. Arpey et al (2017) discusses educating clinicians about potential socioeconomic biases towards people who are homeless, and how these may be perceived. In addition, considering the impact this background has on their care. Furthermore (Arpey et al., 2017) says that regardless of a clinician's attitude toward a person's socioeconomic status, it is critical they consider the impact this has on their care (Arpey et al 2017.)

In addition to awareness of bias the researcher suggests an increase the number of community-based nurses or outreach teams, who work closely with homeless people. This would allow for more access, which is a concern raised in the findings and literature. According to ParadisGagn and PariseauLegault (2020) clinics and community-based nurses are effective in improving health, patient compliance and satisfaction. In addition to this the services also reduce barriers to healthcare. Similarly, services such as clinics and walk-in general practitioner appointments, were suggested by research participants. (Wright, 2014) discusses the effectiveness of using nurses in the community or primary care to support health for homeless people. The advantage that Wright (2014) points out is primary care may reduce stigma but cause more barriers due difficulties with access. Community nurses or outreach teams may not be cost effective due to the number of homeless living outside cities

Advocacy is defined by (ParadisGagn and PariseauLegault, 2020) as speaking up for patients and promoting awareness among teams and organisations. People who are homeless as a group require clinicians to advocate for them. This is something that the researcher suggest needs to be observed more in clinical settings. The nurse's responsibility includes advocating for patients, which is outlined in the NMC code (NMC, 2022). This states that nurses must advocate for persons who are vulnerable and oppose unsatisfactory and discriminatory practises (NMC, 2022).

Collaborative working is a common theme spoken about in research regarding improving health. Margaret Moore-Nadler discusses the need for collaborative working between systems including government healthcare settings and non-government organisations to improve person centred care. Similarly (Nursing care for people experiencing homelessness, 2022) states that there needs to be increased holistic care for homeless people. When healthcare professionals do not provide patient centred care, it puts health outcomes at risks and a lack of leadership, team work and professionalism (Moore-Nadler et al (2019).

Finally, evidence has suggested that people who are homeless struggle to navigate the health care system (Doran et al., 2018). The researcher identifies there is a gap in literature as to how this issue can be improved and recommends that the issue is explored in future research.

There were limitations to this study. The researcher was unable to find studies that have explored the similar objectives. This would have been interesting for a comparison. Quantitative data was also not available. A larger sample size and alternative method to convenience sampling may have given a wider range of perspectives. In addition, the sample could include a range of people who work closely with homeless people, not just those from clinical settings. This may give further information from those with lived experiences.

This study sought to explore how stigma and discrimination affects the health outcomes of those experiencing homelessness. The literature provided an insight into how homeless people are referred to within health care settings, how they are perceived by healthcare professionals and the implications that this has. The primary data collated showed that both healthcare professionals and members of the general public are aware of stigma and discrimination towards homeless people when they access healthcare. Literature discussed some of the consequences of stigma and discrimination. The inclination for homeless people to change health behaviours and to seek healthcare and engage with health providers is reduced. There is awareness of stigma and the evidence to prove that it happens, combined with the impact that it has. This suggests a need for action to be taken to reduce stigma and discrimination to a minimum or eliminate it, although this may not be possible. In relation to Kotters model of change, the researcher suggests the findings of this research and the literature have created urgency.

The primary data revealed that the majority of participants in the study, had the assumption that drug and alcohol use was the main influence for stigma and discrimination. The main points in literature were that drug and alcohol use is high amongst those with unstable housing compared to the general population. It also increases the risk of poor mental health and is possibly connected to the stressors that homeless people experience, as well as increased mortality and morbidity. The study focused on drug and alcohol use as a contributory cause of stigma and discrimination, as there was more literature to support this factor than any of the others identified in the primary data.

Co-production has been a theme in this research. Its positive aspects are the principles and values that it holds, that encourage people to collaborate to improve services and achieve a common goal. Co-production would have been beneficial to this research in providing qualitative data from people with lived experiences. Co-production can be applied to future research, which aims to improve the health and healthcare experiences for the homeless population and it has the potential to make difference in the fight against stigma, discrimination and health inequalities.

The researcher reflects on the progress of this project. The collation of data for this study was a challenge initially, although this was overcome successfully with an alternative sample. The final number of responses was an unexpected positive outcome, and the data provided a range of supporting information. The body of evidence was available to support this research, and it is likely to develop further in relation to inequalities that are experienced by people who are homeless. The initial objectives discussed in the introduction have contributed towards a conclusion for the research question. The research concludes that the extent to which stigma and discrimination puts the health outcomes of homeless people at risk is considerable .

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Addressing minority groups health inequalities when it comes to breast screening.

Student number:

NS3506 Practice Inquiry Project A 21/22

Abstract

Throughout health care there are inequalities to who receives care, this can be down to how deprived the area they live within, their ability to access care and services, previous experiences, and wider determinants of their health. When it comes to breast screening it is apparent that those who fall within a minority group are far less likely to attend surveillance screening. Thus, leading to a clinical audit to be carried out to identify strengths and weaknesses within the practice. Finally, leading to an improvement that is supported by national policies and guidelines to help reduce the inequalities and improve the minority groups that attend the screening to lead to better patient care.

Table of Contents

TOC o "1-3" h z u Introduction PAGEREF _Toc99393215 h 2Method- Data collection PAGEREF _Toc99393216 h 5Method- Results PAGEREF _Toc99393217 h 8Findings and Discussion PAGEREF _Toc99393218 h 9Strengths and limitations PAGEREF _Toc99393219 h 14Conclusions PAGEREF _Toc99393220 h 15References PAGEREF _Toc99393221 h 16Bibliography PAGEREF _Toc99393222 h 27Appendix 1 PAGEREF _Toc99393223 h 31Appendix 2 PAGEREF _Toc99393224 h 32Appendix 3 PAGEREF _Toc99393225 h 33Appendix 4 PAGEREF _Toc99393226 h 34Appendix 5 PAGEREF _Toc99393227 h 35Appendix 6 PAGEREF _Toc99393228 h 36Appendix 7 PAGEREF _Toc99393229 h 37Appendix 8 PAGEREF _Toc99393230 h 38Appendix 9 PAGEREF _Toc99393231 h 39Appendix 10 PAGEREF _Toc99393232 h 40

Introduction

The purpose of this research is to conduct a practice inquiry project looking into the health inequalities, that are faced by minority groups when attending breast screening. Breast screening is a surveillance programme that is part of the population screening timeline, (Public health England, 2021), run for women aged between fifty and seventy by the National Health Service (NHS) (2021), to help detect early stages of breast cancer before they are detectable through symptoms. Patients can attend a screening every three years; they have an option to attend this screening, but their General Practitioners (GP) normally advise it (Public Health England 2020). The NHS (2019) states that at least one in eight women will experience breast cancer within their lifetime, therefore making breast cancer the highest cancer detected in men and women (Cancer Research, 2020). It is suggested by Chorley et al (2017:69) that the prevalence of breast screening and early detection can be linked with a significantly better prognosis which is further supported by the National Institute of Health and Care Excellence (NICE, 2019).

NHS digital (2022) state that one point one nine million women are screened each year as part of their programme meaning that sixty-one-point six percent of these women have attended from their invitation. Rebolj et al (2019:17) have also explored that about one-third of women do attend their screening. This project will explore why the number of women attending screening is not higher and whether it is down to inequalities, inequalities are found within clinical care where those with differences or within a collected group of people are overlooked by the care system in unwarranted discrimination (Kings, 2021). Public health England (2021) highlights that those who experience inequalities are less likely to attend their breast screening as a result. Sin et al (2009:19) establish this inequality cannot just be overcome by treating everyone the same, there need to be reasonable adjustments for these patients. This is still evident today with the NHS England (2021) highlighting these inequalities and finding ways to overcome them. Coleman (2017:155) highlights the importance of early breast screening to reduce the severity of treatment and prolong patients life expectancy which can also be supported by Macmillan (2020). They identified the importance of following up appointments post-screening and discussed the inequalities of people attending screening, meaning that this audit will establish the rates of attendance and draw conclusions on the service previsions. Cancer Research (2021) highlights that due to the suspension of screening due to the Covid-19 pandemic several patients did not receive invitations for breast cancer screening meaning some patients were missing out on vital screening. Gathani, et al., (2021:712) understands the importance of screening and investigating the severity of the pandemic and how there may have been alternative ideas rather than suspending the screening programme, however, it does not establish the full impact of this pandemic on these patients who use the service, which is evidenced by Breast Cancer Now (2020).

This project will explore data from a primary care setting at a GP partnership within North Hampshire, access whether they are experiencing inequalities with their patients or if they have already overcome them. This project will start by exploring patients registered at the practice, with access to Egton Medical Information System (EMIS) (2022) the electronic clinical programme to access patient data. The practice currently has forty-two thousand and thirty-nine patients that are registered. Meaning that all the data collected from this practice will then be generated into an audit to help assess where the practice stands and ways of improvement. The data collected will explore all the eligible women within the age range of fifty to seventy and establish the rates of attendance.

It will also explore, the classification of the minority groups that are registered at the practice and which clinical coding they fall within. When looking at the minority groups of the practice to establish our target audience the population reporting is used to break them down into identifier groups which are Black, Asian and Minority Ethnic (BAME) population, registered blind, whose main language is not English, currently on the learning disability register, patients with a clinical safeguarding concern, severe mental illness (SMI) and registered under transgender patients.

This Project Inquiry and audit has three main aims that it wants to explore:

Identify screening uptake for women in minority groups

Explore the reasons for this uptake and determine whether it is above or below average

Identify factors leading to poor uptake, some of which was impacted by covid-19

Within this project it will be comparing the data found to the national findings on NHS digital (2021), to make the project more generalisable and to determine if there are any discrepancies within the projects findings. It will then compare the findings of previous years to establish correlations within the data. It will further be used to explore whether an audit has been used and determine if it can lead to better service improvement.

Methods

This chapter will highlight the ways in which the methodology data was collected in a quantitative way and how it applies to my research proposal. It will further discuss the aims, objectives of the project and the sort of approach that has been taken through the clinical audit process. In addition, it will compare the strengths and limitations that were found throughout this project.

Data collection

For this project a clinical audit is going to be used to explore the data collected, the NHS (2021) have demonstrated a clinical audit, as a way to establish standards that are already in place plus challenging the strengths and possible weaknesses of a service that thus, provides an environment for change through health improvement. As explained by Taylors, et al., (2016:99) who illustrates the importance of audits to provide adequate service improvements by making local performance changes. Hence this project will be using an audit cycle, to help manage and conduct decisions that best utilise the data collected leading to stronger practice engagement.

Brain, et al., (2011) highlight the importance of clinical audits by establishing quality improvements, by producing a framework tool to educate staff on how to carry out an audit which is used throughout this project. In relation to this, the data collected will be pasted back to the stakeholder, where necessary involvement is needed to create an improvement in patient care. Therefore, demonstrates that this project will be carried out via a clinical audit to establish the current practice in place and what quality improvements could be made to improve patient care.

This audit will be using data from within a cluster of three general practices that have aligned together to establish a partnership, it will be exploring the attendance rates of women registered at the practice that are eligible for screening. It will establish whether the attendance rate is above or below average, leading to the audit exploring those patients who are classified as being within a minority group. This audit will be collecting quantitative data that will be compared to national statics to compare the findings. Backhouse and Ogunlayi (2020) state audits are a fundamental part of bringing around quality improvement to allow best practice approaches to be applied for change.

The research found will then be analysed to highlight the areas of systematic improvement within the health and social care setting, in line with the National Health Service (NHS 2010) innovation and improvement strategies and long-term plan (2019). Though conducting this clinical audit, it needs to be established that an undergraduate study has limitations with the ways of collecting the data to the way it has been explored. It will use the clinical audit cycle to address issues that have arisen and how they have been overcome and compare data to NHS Digital (2021) which will make this research more of a quantitative result. To address the co-production of this project it will establish that the collaboration and feedback to staff members once this project has been completed. This will help establish the change improvement by providing the practice partnership with autonomy and empowerment for change, in turn positively impacting patient care. This is supported by Conguer et al (2021:59) who statues the importance of co-production in bring around change, by challenging changes within society and sway influence for change within the health and social care system.

When a clinical audit is being used there are several different cycles that you can use to establish this data but all of them are constructed from the seven-step method to ensure clinical governance is broken down into risk management, clinical audit, clinical effectiveness, complaint and complement, service user and staff experience and compliance and standards (Patient, 2019; Clinical Governance, 2021). This ensures that all the clinical data is being collected in an effective way, meaning that any of the different audit cycles used are fundamental aspects of change and improvement. Gillam and Siriwardena (2013:123) explore the PDSA model, which stands for plan, do, study, act. This has been applied to clinical governance within primary care, but it also allows for it to be more manageable. The PDSA model can be used in conjunction with clinical governance, it can also be used more regularly as it allows a shorter time scale to implement the change needed, which is ideal for this project (Limb et al, 2017:23). However, due to the simplistic style of this cycle and it is not having much structure within it if used it may lead to the audit losing its original aims. The healthcare quality improvement partnership (2021) will be used to break down the aspects of a clinical audit into four manageable areas which are, preparation and planning, measuring levels of performance, making improvements, and maintaining improvements. This style is like the PDSA but has more guidance in its structure, leading to smaller areas of errors within this clinical audit and is more fitting for this project.

If the audit cycle had not been used in this project, it could have failed to find data before it began. However, Boyle and Keep (2018) explore the effectiveness of clinical audits within emergency medicine and established that clinical audits are not always effective due to the fact they are not focusing on quality improvement. Nevertheless, due to this being about secondary care needs it needs to be looked at slightly differently but does support the way in which this project has not just done a clinical audit it has compared it to the NHS improvement (2018). Therefore, this audit focused on the information and data available at the time, which highlighted the lack of attendance within minority groups.

For this project the healthcare quality improvement partnership (2021) audit cycle will be used which is broken down into preparation and planning, measuring levels of performance, making improvements, and maintaining improvements. The first part of the cycle is the preparation and planning process which was paramount to determining the field for this project. Initially, the audit explored breast cancer screening uptake, comparing this to the likelihood of metastasis and the severity of cancer over a five-year period. However, whilst completing the audit cycle it became clear, these statics were not available within EMIS. It found that there was limited literature to support the exploration of this topic. This was also supported by the previous research into minority groups whilst completing the audit as it has become clear, these statics were not available within EMIS. Therefore, this audit focused on the information and data available at the time, which highlighted the lack of attendance within minority groups.

Following on with the audit cycle tool and using the measuring levels of performance, it was established the data software needed to be gathered through EMIS to have access to currently registered patients to collect the data sample. All data used through this project has been anonymised to protect patient safety. In line with the Data Protection Act by the Department of legislation (2018). The NMC Code of conduct (2018) highlights the importance of protecting the patients anonymity in all aspects of their care. The data collected for this project was from one general practice partnership, which covers three practices located within Hampshire. Where access to patient data was granted by the regional practice manager, by ethical practice and following the practices data protection policy. Understanding Patient data (2021) highlights the importance of patient data and the consequences of miss-use of the data affecting patient care. However, when accessing this at the practice difficulties were faced due to the project being an undergraduate study and having access to relevant information needed was at times not possible. This was due to data protection and restrictions from the administration team and having to rely on other sources to get the information required for the project.

Leading onto this the making improvements part of the audit cycle will be used. This is where the data collected will be comparing data found in the NHS digital (2021) and NHS innovation and improvement strategies (2010). This will be asking why these patients were not attending and exploring if it could be down to the EMIS coding (2022), meaning that their recalls are not happening and missing their appointments or somewhere in the administration team they are not giving the patients the correct minority grouping codings. Similarly, to maintain improvements, it could be upscaled and compared to other general practices in the area to see if they have similar data of their minority groups. It could be working on staff engagement as Wagner, et al., (2017) identify the importance of auditing through staff engagement to bring around positive patient change. Or possibly leading to a national campaign that might need to be run to encourage women to attend their breast screening supported by Public Health England (2016). Lastly leading onto maintaining improvements, this will be performed by making the positive change to encourage these women to attend their breast screening and completing a re-audit within 12 months to see If what was implemented has made positive improvements.

Results

When starting to carry out the searches on EMIS there needs to be access to population reporting for the practice sample for this project the parent population will be forty-two thousand and thirty-nine patients. This parent population is generated from the number of registered patients at the practice. The data collected was copied and pasted into labelled tables within a word document. When using the statistical package for the social sciences (SPSS), this statical analysis programme has been used to interpret findings within bar graphs. Excel has been used to provide formulas to visualise data and explore the correlation between values found through EMIS and provide accurate percentages (appendix 8-9).

When exploring the data collected, we first need to establish the amount of currently registered patients at the practice on the 7th of January 2022 (PRIMIS, 2021), when these searches were run there were forty-two thousand and thirty-nine patients, meaning that for most of the searches completed throughout this audit they will be referring to them as the patient population. Following this our target audience needed to be established, therefore searches needed to be run to determine the patients who are female and aged 50-70. From this parent population who were currently registered patients, we can establish the population count of our target audience (seen appendix 1). Where it states there are five thousand seven hundred and thirty-two patients older than or equal to 50 and younger than 71 years on the above search date.

Table 2 highlights the number of women who were unable to attend due to being declined from the screening service for health-related reasons, clinical coding or error/difference or the patients declining the service. One hundred and six patients who cannot attend due to health-related causes and thirty-four patients declined their screening. Excluding women who are attending screening due to health-related issues table, 3 highlighted two hundred and thirty female patients eligible for breast screening. Table 3 highlights the eligible breast screening patients five thousand and two patients. This is now the target population to establish the minority groups.

The number of patients, identified as being part of minority groups, is present in table 4. The total number of patients within minority groups is three hundred and nine patients registered to identify as BAME, fourteen who identify as blind, one hundred and seventy-eight whose main language is not English, twenty who are identified as having a learning disability, thirty patients were identified as having safeguarding concerns, sixty- nine coded with a server mental illness and zero patients identified as being transgender. Table 4 from the EMIS report gives the expression of less than one. Using the excel spreadsheet formation, we can establish how small these minority groups are seen in table 8. From the data in table 8, you can establish the total population of the minority groups; it identifies those as five thousand five hundred and two patients who were eligible for breast cancer screening.

Within table 9 it highlights the attendance record of persons within the minority groups for screening where you can establish the percentage of attendance. This change is to work out the percentage of the patients within each minority grouping (Appendix 6). This states that there are one hundred and seventy-three patients with the coding BAME meaning only fifty-six percent attended their screening, four patients with the clinical code of blind meaning only twenty-nine precent attended, eighty who are coded within main spoken languages that is not English meaning that only forty-five percent attended, eight who are clinically coded with a learning disability meaning that only forty precent attended, thirteen who are coded with safeguarding concerns meaning that forty-three percent attended, thirty with the coded with a learning disability meaning that forty-three percent attended and zero patients coded as transgender meaning zero percent.

From the data collected above, you can now establish the percentage as a whole in the minority groups (see appendix 10) which is only forty-nine percent of patients that are eligible to attend their screening, in comprising the whole screening population which is sixty percent. This means that there is an eleven percent difference within the two shown groupings which therefore highlights that minority groups are experiencing inequalities when it comes to breast screening.

Findings and Discussion This chapter will analysis and discuss the data collected from the audit as it will address concerns found within minority groups by exploring the data collected in several ways, to help draw conclusion to the findings. This will explore the salient of the aims and themes that have been found within the data and compare them to existing literature. Additionally, it will be referring to 1-10 appendices which has the data collected.

Public Health England (2021) highlight that woman within an ethnic minority group are less likely to attend breast screening (see appendix 10). Within these minority groups, we can explore the BAME patients, where Bamidele et al (2017:217) identified that ethnic minorities may have a lack of education on breast screening. This could result in religious beliefs, anxiety and stigma associated with breast cancer screening. When applying this to the data collected (see appendix 6) it can establish that only fifty-six percent of the eligible patients are attending meaning that a vast minority of these patients could go on to develop a form of breast cancer that goes undetected until it shows symptoms when it is more aggressive. This is supported by Jack, et, al, (2014) who found similar data within their study, which explores the uptake of women for breast screening within the boroughs of London. Who identified that even with a greater ethnic minority background it established British white women are more likely to attend. This is supported by Whelhan, et al (2016) in a qualitative study that established uptake is lower due to the fear of having a mammogram within the BAME group due to again lack of knowledge and information that is not always accessible and the fear of having a mammogram, which could be improved by better communication. However, Gathani et al, (2014:226) concluded that women from BAME backgrounds have a lower breast cancer rate than white women, however, it is imperative that they attended screening (University of Oxford, 2014).

When further exploring the minority groups and investigating the patients who are registered at the practice with the clinical coding when English is not their main language. It is evident within table 6 where the attendance is only forty-five percent meaning that fifty-five percent are not attending. Woof et al (2019:137) state that patients from a Pakistani and Bangladeshi background where their first language isnt English will have a lower attendance rate for breast screening, which could be down to barriers in communicating and understanding the invitation letters and challenges with attending the appointment with a family member. This could affect the sample size of this clinical audit due to the fact from 2020-2022 there has been a worldwide pandemic. The covid pandemic is a respiratory based virus that spread throughout the world leading to the social distances in place to limit infections (Public health agency, 2022). This meant that patients were not able to bring relatives to their appointments who may have acted as a translator. National Institute for Health Research (2020) has established the fallings within breast screening for British -Pakistani women when it comes to communication and identifying barriers to improve uptake. Yong and Germain (2022) highlights the importance to improve the language barrier to increase awareness and attendance. They also explore the low attendance rate to the impact of the coronavirus crisis and those women may already be discriminated against when communicating and assessing information. However, fails to explore the impact of barriers affecting other groups when attending breast screening.

Those patients who are registered with a server mental illness (SMI) are less likely to attend the screening (see appendix 8). Woodhead et al, (2016) established from the research carried out within London, that women are twenty-two to twenty-eight times more likely to not attend their screening. Moreover, he has also established that for these patients to have the clinical coding of the SMI they need to have a final diagnosis and it is coded on their health records. For example, mental health first aid England (202) highlight that it can take up to six years to receive a correct Bipolar diagnosis, meaning that these patients might have to wait a while for the correct clinical code, therefore that they might not be getting the support that they need. This can be compared to the data collected within this audit (See appendix 9), which established that only forty-six percent of SMI patients attend their screening meaning that fifty-four percent did not attend. Werneke et al (2006) can recognise that there are inequalities among patients with SMI and the impact this then has on their life expectancy, however once again he was exploring the boroughs of London. Public health England (2018) needs to be explored to see if these findings are transferable to the wider population, which established similar findings but does not outline ways to change the systems or improve communication from the screening services. Further supported by the NHS five-year view on mental health (2016) which highlight the importance of screening, but no preventative measures are in place. It is bringing round the question of the patients who might fall within more than one category meaning that they might be at risk of experiencing higher inequalities.

To make this audit more reliable it can be compared to NHS digital (2020) which is the national database with clinical data that is accessible to the public to make improvements to care. The breast screening statics are being accessed from 2011 to 2021 to explore the statical colourations of data. When applying the data collected through this audit (see appendix 5&6) you can establish that sixty percent of the practice eligible patients attended their breast screening.

Though the NHS digital data is evidence that for breast screening eligibility, the national average for 2021 was sixty-four point two precent meaning that the data from this practice is slightly under average. Nevertheless, this could have altering data due to when searches were run with the demographic for this audit running within 2019-2022. Also, these findings may differ since the practice only sends the invitations for the upcoming screening in January, so when these searches were run some of the patients might not have been within the system. When exploring the national guidance of where they want the level of patients screened to be (NHS digital, 2021) stating that their acceptable level is 70 precent or greater and their achievable level is 80 precent or greater. It can be established from the data collected (see appendix 10) that the minority groups are within only 50 precent of patients attending, therefore they are 20 precent under the acceptable level.

You can also compare the yearly findings on NHS digital (2021) to establish whether the number of patients has changed at all over previous years, when looking at the report from 2019-to 2021 the number of eligible women, seen in 2020 had 6.16 million and in 2021 having 6.25 million is increasing but the coverage rate of attendance is declining. This can be seen in the number of women that attended screening in the last three years, 2020 being 4.57 million and 2021 is 4.02 million. When comparing the eligible women to the women that were screened you can see that within 2020 there were 1.59 million who didnt attend and for 2020 there were 2.23 million patients not having screening done. Nevertheless, this data could have been affected as throughout this time there has been the coronavirus pandemic, which halted the screening service for several months. Macmillan (2020) and the breast screening programme (2022) highlights the backlog of patients and the impact this is having on the patients accessing services. Maroni, et al., (2021:740) identify the womens age regarding when they should be invited for their first mammogram and the impact of some patients not getting a screening until the age of 53. This plus the backlog of patients that need to attend screening means that there is now more pressure on staff members and services, as talked about by Dique (2021) when disusing the impact covid has had on the screening service. To ensure they are overcoming the inequalities women face plus ensuring their screening is performed within a timely manner. Duff et al, (2022) has highlighted the subsequent impact on this through patients outstanding screening and thus affecting the additional deaths. On the contrary, Duff et al (2022) doesnt discuss the impact on minority groups consequently supporting the argument that minority groups are still experiencing inequalities.

The NHS (2019) have produced a long-term plan outlined in their framework for the NHS to reach by 2023/24 by aiming to improve the uptake around breast screening and reduce the health inequalities within healthcare. This can be met within the practice if it was possible for them to bring their average of patients attending breast screening to about fifty-two percent and especially working on their patients in the remit of minority groups who make up eleven percent of patients eligible is only forty-nine percent (this is seen within appendix 10). However, when considering the effects of covid on the NHS long term plan Thorlby et al (2021:26) has explored the impact of covid on where the NHS wanted to be in 2023/24 which found it may take longer to reach the end goal. Thorlby et al (2021) and NHS (2020) understand the vital changes that need to be made to encourage women to attend their screening with clear knowledge of the impact this will subsequently be having on minority groups. Despite this publication not having a direct discussion, anything around minority groups and attending their breast screening. It was the whole eligible population which is supporting the case that their minority groupings are overseen when it comes to breast screening.

For this change improvement to be facilitated it is vital that there is support from the stakeholders within the practice area. Brain, et al., (2011) highlights the importance of staff engagement and clinical audit collected which in turn is denoted back to the stakeholders. Establishing change by using the clinical audit cycle helps to make the improvements that may be needed, within the general practice. To help bring about this change in improvements with a service there needs to be supported within the team to enable this change to happen. Therefore, the GP should start to encourage all women to attend their breast screening. Thus, linking to the audit cycle, leading to maintaining improvement and leading to better patient services. For this audit there was a direct relay of the findings back to the stakeholders, to allow this change and encouragement of patients within minority groups to attend the screening. Kelly, et, al, (2015) states that change needs to be in line with cultural and societal, meaning the need to be further supported, not just within the GP but within the wider media to increase breast screening rates NHS digital (2018) and the successful attendance from social media campaigns. This will future be in line with the NHS (2019) long-term plan to digitally reform the way patients access their health services in line with the modern way.

If this project was going to be re-audit to establish whether change improvement has developed from this audit. The audit would have a greater sample size of other GP practices within the geographical area to establish if there are repeatability low attendance rates of those patients within the clinical coding of minority groups. This would institute a stronger standing for change within the wider community to overcome barriers that are faced by patients who are already at a disadvantage due to being within a minority grouping.

Strengths and limitationsThis project has been carried out by an undergraduate while completing placement hours in general practice, meaning that there may be concerns about the reliability of the data collected. Jones, et al, (2008:89) Illustrates the issues faced with students completing clinical projects within the general practice but identifies the overall benefit their practice can bring within the practice area. However, there were challenges faced with little literature found to support the challenges faced by undergraduate nursing students completing a research project and can be seen within the development of being added to the aspect of the nursing qualification.

As this is a small-scale project that was completed by an undergraduate meaning that the limitations are faced was accessing resources. This was a party down to administration difficulties with accessing the data required and due to the administration team not understanding that student nurses now complete these sorts of projects within their placement. Moreover, the sample size of the data collected was from three GPs within North Hampshire that only make up a small percentage of the representation population within the local community. This means that if there was more data available from other practices within the area, there would be a stronger mean of the percentage of minority groupings and the levels of inequalities that are being faced within that area.

A strength of this research is understanding that there are errors when it comes to completing audits due to the numerical data being used and discussing the ways in which this has been overcome. It has further investigated how all eligible women for breast screening might have a lower attendance to help establish how this might further impact patients within minority groups. Whelehan, et, al, (2016) identifies that attendance may be low due to the perception of mammograms, peoples first attendance and lack of information and discomfort felt while the mammogram is being carried out and patients not being eligible for screening for other health reasons (healthtalk, 2016).

This project has also determined that there is literature lacking research into minority groups as a whole and are only explored as individual groups. This means their need to be changed within the way research is conducted and explored, therefore those who fall within greater inequality grouping are at a greater risk of not attending their breast screening. This project has compared NHS digital to make the project more reliable to try and overcome the limitations of its small sample size. In the process of making this project stronger, it highlighted that the national statics of breast screening is lacking a breakdown of minority groupings and their breast screening attendance, meaning that the data cannot be compared and contrasted to the data within this audit.

ConclusionsTo conclude this small-scale research project has explored the way in which a clinical audit can be used to establish findings within a minority of patients. This audit has explored the ways in which patients within minority groupings are experiencing inequalities due to their lower attendance for breast screening. It has used the audit cycle throughout to allow this project to overcome barriers that it has faced due to it being an undergraduate piece of work. However, it has explored considerably literature supporting these inequalities in minority groups as individuals, but there has been little research supporting the minority groups seen though out this project when using the EMIS database.

This project has explored the fact that inequalities are present within todays society, it has explored breast screening and the inequalities that are faced by different groupings while exploring the reasoning behind it, and finally highlighting the impact covid-19 had on services. This has helped to contribute to real questions asked about what more needs to be done to support these patients. This can be done through several campaigns and programmes and by raising awareness and communication between the general practice and the screening service.

It has compared its findings to a national database (NHS Digital 2019) to understand

that the number of women that are attending, to establish whether the findings collected through this audit are generalisable. It was recognised that the overall attendance has decreased in the previous years with the eligible patients increasing, this has been reconsidered throughout and explored to determine if it is down to coronavirus and inequalities within the screening programme. It has understood the limitations of this project and ways in which they could be overcome in the future, by carrying out a re-audit to see if these inequalities are still in place or completing future audits within the local area to establish whether these finds are comparable.

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Appendices

-199390535305Table 1

Female age 50-70

Parent population Currently registered regular patients

Population count Parent % Excluded

5,732 42,039 13% 36,307

Rule 1

Included patients with patient where: the age is older than or equal to 50 and younger than 71 years on the search date and the Gender is female.

Age/sex (brands 10 yearly brands)

Age Female

50-59 3164

60-69 2419

70-79 214

00Table 1

Female age 50-70

Parent population Currently registered regular patients

Population count Parent % Excluded

5,732 42,039 13% 36,307

Rule 1

Included patients with patient where: the age is older than or equal to 50 and younger than 71 years on the search date and the Gender is female.

Age/sex (brands 10 yearly brands)

Age Female

50-59 3164

60-69 2419

70-79 214

Appendix 1-304800403225Table 2

Breast cancer screening exclusion+ declines breast screening

Parent population -Currently registered patient

Population count Parent % Excluded

Screening Exclusion 196 42,039 <1% 41,843

Declined breast screening 34 42,039 <1% 42,005

All exclusions 230 42,039 <1% 41,809

Rule 1

Includepatents withclinical code where: theclinical code is on gold standards palliative care framework, palliative care, palliative care service, palliative care mot indicatedEITHER ordering bydate select the latest and check that: the clinical code is not palliative are not indicatedORincludepatients with clinical codewhere: theclinical code is malignant tumour of breast, and thedate is after or on 2 years before the search date.

Rule 2

IncludePatients with theclinical code where: theclinical code is mastectomy of left breast (excluding excision of part of the left breast)AND includepatients withclinical codes where: theclinical code is mastectomy of right breast (excluding excision of part of the right breast).

Rule 3

Includepatients withclinical code where: theclinical code is bilateral mastectomy.

Rule 4

Includedpatients withpatient details where: theage is older than or equal to 50 years and younger than and the clinical coding is that the patients has returned form deferring breast screening and 71 years on the search date and thegender is female.

00Table 2

Breast cancer screening exclusion+ declines breast screening

Parent population -Currently registered patient

Population count Parent % Excluded

Screening Exclusion 196 42,039 <1% 41,843

Declined breast screening 34 42,039 <1% 42,005

All exclusions 230 42,039 <1% 41,809

Rule 1

Includepatents withclinical code where: theclinical code is on gold standards palliative care framework, palliative care, palliative care service, palliative care mot indicatedEITHER ordering bydate select the latest and check that: the clinical code is not palliative are not indicatedORincludepatients with clinical codewhere: theclinical code is malignant tumour of breast, and thedate is after or on 2 years before the search date.

Rule 2

IncludePatients with theclinical code where: theclinical code is mastectomy of left breast (excluding excision of part of the left breast)AND includepatients withclinical codes where: theclinical code is mastectomy of right breast (excluding excision of part of the right breast).

Rule 3

Includepatients withclinical code where: theclinical code is bilateral mastectomy.

Rule 4

Includedpatients withpatient details where: theage is older than or equal to 50 years and younger than and the clinical coding is that the patients has returned form deferring breast screening and 71 years on the search date and thegender is female.

Appendix 2

Appendix 3-162560340360Table 3

Eligible breast screening women aged 50-70

Parent population -Currently registered patient

Population count Parent % Excluded

5,502 42,039 13% 36,537

Rule 1

Includepatients withpatient details where: thegender is female, and theage is older than or equal to 50 years and younger than to equal to 70 yearson the search date.

00Table 3

Eligible breast screening women aged 50-70

Parent population -Currently registered patient

Population count Parent % Excluded

5,502 42,039 13% 36,537

Rule 1

Includepatients withpatient details where: thegender is female, and theage is older than or equal to 50 years and younger than to equal to 70 yearson the search date.

Appendix 4-160020262255Table 4

Cancer: breast screening population - Groupings

Parent population: currently registered patients

Grouping Population count Parent % Excluded

All (above) 5502 42,039 13% 36,537

1. BAME 309 42,039 <1% 31,730

2. Blind 14 42,039 <1% 42,025

3. Language 178 42,039 <1% 41,861

4. Learning disability (LD) 20 42,039 <1% 42,019

5. Safeguarding 30 42,039 <1% 42,009

6. Server Mental Illness (SMI) 69 42,039 <1% 41,970

7. Transgender 0 42,039 0% 42,039

Rule 1 (same rule for all)

Includepatients withpatient details where: theage is older than or equal to 50 years and younger than 71 years on the search date and thegender is female.

Rule 2

Include patients from search:BAME population

Included patients with clinical code where:the clinical code is registered blind

Include patients from search:Main spoken language not English

SNOMED CT Currently on learning disability register

IncludePatients withclinical code where: theclinical code is safeguarding issues, adult safeguarding concerns, referral to safeguarding adult team, safeguarding adults protection plan agreed, etc/. ordering bydate select the latest and check that: theclinical code is not adult no longer safeguarding concern

Include patients from search:SMI register (excluding in remission)

Include patients with clinical codes where; clinical code is gender dysphoria, gender reassignment patients, gender reassignment surgery

Rule 3 (same rule for all)

Include patients from search:breast cancer screening exclusion

00Table 4

Cancer: breast screening population - Groupings

Parent population: currently registered patients

Grouping Population count Parent % Excluded

All (above) 5502 42,039 13% 36,537

1. BAME 309 42,039 <1% 31,730

2. Blind 14 42,039 <1% 42,025

3. Language 178 42,039 <1% 41,861

4. Learning disability (LD) 20 42,039 <1% 42,019

5. Safeguarding 30 42,039 <1% 42,009

6. Server Mental Illness (SMI) 69 42,039 <1% 41,970

7. Transgender 0 42,039 0% 42,039

Rule 1 (same rule for all)

Includepatients withpatient details where: theage is older than or equal to 50 years and younger than 71 years on the search date and thegender is female.

Rule 2

Include patients from search:BAME population

Included patients with clinical code where:the clinical code is registered blind

Include patients from search:Main spoken language not English

SNOMED CT Currently on learning disability register

IncludePatients withclinical code where: theclinical code is safeguarding issues, adult safeguarding concerns, referral to safeguarding adult team, safeguarding adults protection plan agreed, etc/. ordering bydate select the latest and check that: theclinical code is not adult no longer safeguarding concern

Include patients from search:SMI register (excluding in remission)

Include patients with clinical codes where; clinical code is gender dysphoria, gender reassignment patients, gender reassignment surgery

Rule 3 (same rule for all)

Include patients from search:breast cancer screening exclusion

Appendix 5-220213179705Table 5

Breast cancer screening uptake, including non-screening codes in Last 3 years

Parent population Eligible breast cancer screening women aged 50-70

Population count Parent % Excluded

3,308 5,502 60% 2,194

Rule 1

Includedpatients withclinical codes where: theclinical code is breast neoplasm screening, breast neoplasm screening normal, breast neoplasm screening abnormal, breast neoplasm screen NOS, mammography- screening, [V] screening for malignant neoplasm of beast, etc. and thedate is after or on 3 years before the search date.

0Table 5

Breast cancer screening uptake, including non-screening codes in Last 3 years

Parent population Eligible breast cancer screening women aged 50-70

Population count Parent % Excluded

3,308 5,502 60% 2,194

Rule 1

Includedpatients withclinical codes where: theclinical code is breast neoplasm screening, breast neoplasm screening normal, breast neoplasm screening abnormal, breast neoplasm screen NOS, mammography- screening, [V] screening for malignant neoplasm of beast, etc. and thedate is after or on 3 years before the search date.

-160020407670Table 6

Cancer: Screening groups + breast uptake

Parent population: Cancer: Breast screening population- (Screening grouping)

Parent population: Cancer: Breast screening population - BAME

Parent population: Cancer: Breast screening population -Blind

Parent population: Cancer: Breast screening population - Language

Parent population: Cancer: Breast screening population - LD

Parent population: Cancer: Breast screening population - Safeguarding

Parent population: Cancer: Breast screening population -SMI

Parent population: Cancer: Breast screening population - Transgender

Grouping Population count Parent % Excluded

1. BAME 173 309 56% 136

2. Blind 4 14 29% 10

3. Language 80 178 45% 98

4. (LD) Learning disability 8 20 40% 12

5. Safeguarding 13 30 43% 17

6. (SMI) Server Mental Illness 30 69 43% 39

7. Transgender 0 00 0% 0

All minority groups 308 620 49% 312

Rule 1

Include patients from search: Breast cancer screening done in last 3y 6m

00Table 6

Cancer: Screening groups + breast uptake

Parent population: Cancer: Breast screening population- (Screening grouping)

Parent population: Cancer: Breast screening population - BAME

Parent population: Cancer: Breast screening population -Blind

Parent population: Cancer: Breast screening population - Language

Parent population: Cancer: Breast screening population - LD

Parent population: Cancer: Breast screening population - Safeguarding

Parent population: Cancer: Breast screening population -SMI

Parent population: Cancer: Breast screening population - Transgender

Grouping Population count Parent % Excluded

1. BAME 173 309 56% 136

2. Blind 4 14 29% 10

3. Language 80 178 45% 98

4. (LD) Learning disability 8 20 40% 12

5. Safeguarding 13 30 43% 17

6. (SMI) Server Mental Illness 30 69 43% 39

7. Transgender 0 00 0% 0

All minority groups 308 620 49% 312

Rule 1

Include patients from search: Breast cancer screening done in last 3y 6m

Appendix 62522491427896Grouping Population count Parent % Excluded

All 5502 42039 13.1% 36537

1. BAME 309 42039 0.74% 41730

2. Blind 14 42039 0.03% 42025

3. Language 178 42039 0.42% 41861

4. Learning Disability 20 42039 0.05% 42019

5.Safeguarding 30 42039 0.07% 42009

6.Server Mental illness (SMI) 69 42039 0.16% 41970

7. Transgender 0 42039 0.00% 42039

Total number of Grouping patients from currently RG patients 1.5% 00Grouping Population count Parent % Excluded

All 5502 42039 13.1% 36537

1. BAME 309 42039 0.74% 41730

2. Blind 14 42039 0.03% 42025

3. Language 178 42039 0.42% 41861

4. Learning Disability 20 42039 0.05% 42019

5.Safeguarding 30 42039 0.07% 42009

6.Server Mental illness (SMI) 69 42039 0.16% 41970

7. Transgender 0 42039 0.00% 42039

Total number of Grouping patients from currently RG patients 1.5% -173421372789Table 7

Cancer: Breast screening population - Groupings Parent population- Currently Registered regular patients 0Table 7

Cancer: Breast screening population - Groupings Parent population- Currently Registered regular patients Appendix 7Appendix 8-301625183515Table 8

Table explore the patients within minority groups from target audience.

Parent population Eligible breast screening -women aged 50-70

These data table are compiled within SPSS, from the data collected above from the population count. For this table the All population hasnt been used for clarity within the table.

00Table 8

Table explore the patients within minority groups from target audience.

Parent population Eligible breast screening -women aged 50-70

These data table are compiled within SPSS, from the data collected above from the population count. For this table the All population hasnt been used for clarity within the table.

2044701209741Grouping Population count Parent % Excluded

All 5502 5502 100% 0

1. BAME 309 5502 5.6% 5193

2. Blind 14 5502 0.3% 5488

3. Language 178 5502 3.2% 5324

4. Learning Disability 20 5502 0.4% 5482

5.Safeguarding 30 5502 0.5% 5472

6.Server Mental illness (SMI) 69 5502 1.3% 5433

7. Transgender 0 5502 0.0% 5502

Total number of Grouping patients from breast screening population

11.3% 00Grouping Population count Parent % Excluded

All 5502 5502 100% 0

1. BAME 309 5502 5.6% 5193

2. Blind 14 5502 0.3% 5488

3. Language 178 5502 3.2% 5324

4. Learning Disability 20 5502 0.4% 5482

5.Safeguarding 30 5502 0.5% 5472

6.Server Mental illness (SMI) 69 5502 1.3% 5433

7. Transgender 0 5502 0.0% 5502

Total number of Grouping patients from breast screening population

11.3% 724821512217200

1164634594169500738987319845200-17961007199Grouping Population count Parent % Excluded

1. BAME 173 309 56% 136

2. Blind 4 14 29% 10

3. Language 80 178 45% 98

4. Learning Disability 8 20 40% 12

5.Safeguarding 13 30 43% 17

6.Server Mental illness (SMI) 30 69 43% 39

7. Transgender 0 0 0% 0

00Grouping Population count Parent % Excluded

1. BAME 173 309 56% 136

2. Blind 4 14 29% 10

3. Language 80 178 45% 98

4. Learning Disability 8 20 40% 12

5.Safeguarding 13 30 43% 17

6.Server Mental illness (SMI) 30 69 43% 39

7. Transgender 0 0 0% 0

-301625328361Table 9

Cancer: screening groups + Breast screening uptake Parent Population- Cancer: breast screening Population

These data table are compiled within SPSS, from the data collected above from the population count.

These data table are compiled within SPSS, from the data collected above from the population count.

00Table 9

Cancer: screening groups + Breast screening uptake Parent Population- Cancer: breast screening Population

These data table are compiled within SPSS, from the data collected above from the population count.

These data table are compiled within SPSS, from the data collected above from the population count.

Appendix 91257301117600- Population Count Parent % Excluded

All Patients 3308 5502 60% 2194

Minority Groups 308 620 50% 312

00- Population Count Parent % Excluded

All Patients 3308 5502 60% 2194

Minority Groups 308 620 50% 312

Appendix 10-144145117212Table 10

Breast screening uptake, comparison of all eligible patients and minority groups Parent Population - Eligible Breast screening - women aged 50-70 0Table 10

Breast screening uptake, comparison of all eligible patients and minority groups Parent Population - Eligible Breast screening - women aged 50-70