discrimination in care homes case study

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Patient-Reported Experiences of Discrimination in the US Health Care System

1 Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor

Minakshi Raj

2 Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign

Melissa Creary

Sharon l. r. kardia.

3 Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor

Jodyn E. Platt

4 Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor

Accepted for Publication: October 23, 2020.

Published: December 15, 2020. doi:10.1001/jamanetworkopen.2020.29650

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2020 Nong P et al. JAMA Network Open .

Author Contributions: Ms Nong and Dr Raj had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Nong, Creary, Platt.

Acquisition, analysis, or interpretation of data: Nong, Raj, Kardia, Platt.

Drafting of the manuscript: Nong, Raj, Creary, Platt.

Critical revision of the manuscript for important intellectual content: Raj, Creary, Kardia, Platt.

Statistical analysis: Nong, Raj, Kardia, Platt.

Obtained funding: Kardia, Platt.

Administrative, technical, or material support: Creary, Kardia.

Supervision: Platt.

Conflict of Interest Disclosures: Dr Kardia reported receiving grants from the National Institutes of Health during the conduct of the study. No other disclosures were reported.

Funding/Support: This work was funded by grant 5R01 CA214829-03 from the National Cancer Institute.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Associated Data

eFigure 1. Five Most Common Reasons for Experiencing Discrimination, by Gender

eFigure 2. Five Most Common Reasons for Experiencing Discrimination, by Race

eAppendix. Sampling and Recruitment

What are the national prevalence, frequency, and main types of discrimination that adult patients report experiencing in the US health care system?

In this nationally representative cross-sectional survey study, 21% of 2137 US adult survey respondents indicated that they had experienced discrimination in the health care system, and 72% of those who had experienced discrimination reported experiencing it more than once. Racial/ethnic discrimination was the most frequently reported type of discrimination respondents experienced.

Experiences of discrimination in the health care system appear to be more common than previously recognized and deserve considerable attention.

Although considerable evidence exists on the association between negative health outcomes and daily experiences of discrimination, less is known about such experiences in the health care system at the national level. It is critically necessary to measure and address discrimination in the health care system to mitigate harm to patients and as part of the larger ongoing project of responding to health inequities.

To (1) identify the national prevalence of patient-reported experiences of discrimination in the health care system, the frequency with which they occur, and the main types of discrimination experienced and (2) examine differences in the prevalence of discrimination across demographic groups.

Design, Setting, and Participants

This cross-sectional national survey fielded online in May 2019 used a general population sample from the National Opinion Research Center’s AmeriSpeak Panel. Surveys were sent to 3253 US adults aged 21 years or older, including oversamples of African American respondents, Hispanic respondents, and respondents with annual household incomes below 200% of the federal poverty level.

Main Outcomes and Measures

Analyses drew on 3 survey items measuring patient-reported experiences of discrimination, the primary types of discrimination experienced, the frequency with which they occurred, and the demographic and health-related characteristics of the respondents. Weighted bivariable and multivariable logistic regressions were conducted to assess associations between experiences of discrimination and several demographic and health-related characteristics.

Of 2137 US adult respondents who completed the survey (66.3% response rate; unweighted 51.0% female; mean [SD] age, 49.6 [16.3] years), 458 (21.4%) reported that they had experienced discrimination in the health care system. After applying weights to generate population-level estimates, most of those who had experienced discrimination (330 [72.0%]) reported experiencing it more than once. Of 458 reporting experiences of discrimination, racial/ethnic discrimination was the most common type (79 [17.3%]), followed by discrimination based on educational or income level (59 [12.9%]), weight (53 [11.6%]), sex (52 [11.4%]), and age (44 [9.6%]). In multivariable analysis, the odds of experiencing discrimination were higher for respondents who identified as female (odds ratio [OR], 1.88; 95% CI, 1.50-2.36) and lower for older respondents (OR, 0.98; 95% CI, 0.98-0.99), respondents earning at least $50 000 in annual household income (OR, 0.76; 95% CI, 0.60-0.95), and those reporting good (OR, 0.59; 95% CI, 0.46-0.75) or excellent (OR, 0.41; 95% CI, 0.31-0.56) health compared with poor or fair health.

Conclusions and Relevance

The results of this study suggest that experiences of discrimination in the health care system appear more common than previously recognized and deserve considerable attention. These findings contribute to understanding of the scale at which interpersonal discrimination occurs in the US health care system and provide crucial evidence for next steps in assessing the risks and consequences of such discrimination. The findings also point to a need for further analysis of how interpersonal discrimination interacts with structural inequities and social determinants of health to build effective responses.

This cross-sectional study examines the responses to a recent National Opinion Research Center survey to assess the prevalence, frequency, and main types of discrimination experienced by adult patients in the US health care system.

Introduction

Health systems in the US are increasingly expressing concern about understanding and responding to social determinants of health (ie, the social and environmental conditions that may influence individual health and the differences in health and health outcomes between groups). 1 , 2 , 3 Considerable analytical work has identified a range of factors associated with inequities in treatments, outcomes, and mortality across race, sex, socioeconomic status, and various other social identities. 1 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 Some of these include patient-clinician discordance, physician bias, and daily experiences of discrimination. 1 , 3 , 12 , 13 Daily experiences of discrimination in other contexts (eg, while shopping, in employment, or in housing) have been studied extensively in association with downstream health outcomes, including but not limited to hypertension, cardiovascular disease, poor sleep, mental health symptoms, lower trust in the health care system, delayed or avoided care, and underuse of mental health services. 14 , 15 , 16 , 17 , 18 , 19 , 20 Despite considerable knowledge about the association between discrimination and health care utilization rates and health outcomes and the relevance of discrimination to health inequity, to our knowledge, experiences of discrimination in the health care system itself are understudied.

More specifically, previous work has provided important insights regarding the association between discrimination and health but has not identified patient-reported lifetime experiences of discrimination in the health care system at a national level in a way that captures the frequency and that allows for a full self-selection of the types of discrimination experienced. For example, some studies have drawn from narrow regional samples or limited respondent reports to the previous 12 months, 19 , 21 , 22 , 23 whereas other studies have asked participants to report discrimination associated with a single aspect of their identity, such as race or sex, as preselected by the research team. 24 , 25 In addition, there is limited information on the frequency of different types of discriminatory treatment, which may be a significant risk factor for chronic disease given the association between discrimination and health over the life course. 26 , 27

To better understand and respond to interpersonal discrimination in the health care system, as well as the potential downstream effects of discrimination in the context of structural inequity, it is necessary to identify these experiences and the frequency with which they occur at the national level. The objective of our study was to characterize patient-reported experiences of discrimination in a nationally representative sample of the US population in terms of (1) prevalence, (2) primary types of discrimination, and (3) frequency. To our knowledge, this is the first study to examine the prevalence, frequency, and types of discrimination in the health care system using a nationally representative sample that does not limit the respondents’ reporting time frame to 1 year or less.

We used the National Opinion Research Center (NORC) AmeriSpeak Panel probability-based, nationally representative sample of English-speaking US adults to conduct an online survey in May 2019. Prior to data collection, the survey instrument was pretested (n = 320). The research team conducted 17 cognitive interviews to assess comprehension and to improve the clarity of the survey questions, and NORC conducted a pilot survey with 115 respondents. Of 3253 surveys sent, 2157 individuals responded and completed the final survey (for a response rate of 66.3%) after being recruited via the NORC panel. We oversampled African American respondents, Hispanic respondents, and respondents with annual household incomes below 200% of the federal poverty level. Poststratification survey weights were calculated by NORC based on age, sex, educational level, race/ethnicity, housing tenure, telephone status, and Census division from the Current Population Survey. They also included weights for survey nonresponse (eAppendix in the Supplement ). This study followed the American Association for Public Opinion Research ( AAPOR ) reporting guideline for survey studies. The institutional review board of the University of Michigan reviewed and approved this project and waived the requirement to obtain informed consent because the research involved no more than minimal risk to participants, who had already provided informed consent to NORC.

To assess experiences of discrimination, we adapted the Major Experiences of Discrimination measures and the Experiences of Discrimination measures from the Coronary Artery Risk Development in Young Adults study. 28 , 29 We asked respondents (1) whether they had ever been discriminated against, hassled, or made to feel inferior while getting medical care and, if so, (2) what they believed was the main reason for that experience, and (3) how frequently they experienced this discrimination. A response of “yes” to the first question was defined as an experience of discrimination. Respondents chose from a list of 13 potential reasons for the discrimination, adapted from the Major Experiences of Discrimination measures, including an open-ended response for other reasons not listed. Two research team members (P.N. and M.C.) separately coded the free-text responses under “other” and reconciled any differences with a third team member (M.R.). Those responses were classified under extant categories or under additional categories that emerged through thematic analysis. Remaining free-text responses retained the “other” designation if they remained miscellaneous. After coding, there were 18 total types of discrimination for analysis.

The survey instrument defined the health care system as “the healthcare professionals and institutions that you personally interact with when getting health care.” Respondents self-reported their sex and racial or ethnic identity and reported their current insurance status as a binary measure of whether they currently had health insurance. They also indicated when they last received medical care and reported their health status on a 5-point scale ranging from poor to excellent health. We excluded 20 observations that had missing data for any of the measures included in the analysis.

Statistical Analysis

We analyzed survey responses from 2137 participants with complete data. We first compared respondents who had experienced discrimination in the health care system with those who had not, using various demographic measures, including sex, age, race/ethnicity, educational level, income, health insurance status, rural or urban residency, having a regular source of medical care, having received care in the last 12 months, and self-reported health status. We conducted weighted bivariable and multivariable logistic regressions to examine associations between these variables and reported experiences of discrimination. We defined statistical significance as P  < .05 in 2-tailed tests. Next, we enumerated the reported types of discrimination, identified the most commonly reported types of discrimination, and then assessed the frequency of experiencing the 5 most commonly given reasons for discrimination. All reported percentages are weighted to provide population estimates. All analyses were conducted with Stata, version 14 (StataCorp).

Table 1 summarizes the demographic characteristics and general health status of all 2137 survey respondents and of the 458 respondents who reported experiences of discrimination, with unweighted frequencies and weighted percentages. Based on weighted percentages, approximately half of all respondents (1047 [52.3%]) were male (unweighted, 51.0% female). The mean (SD) age of respondents was 49.6 (16.3) years (range, 21.0-91.0 years), and the sample reflected the racial/ethnic composition of the US. 30 The majority of respondents had at least some college education (1675 [77.9%]), and approximately half (1022 [50.2%]) earned at least $50 000 in annual household income. Most respondents had health insurance (1890 [89.4%]) and lived in metropolitan areas (1899 [89.3%]). A large majority of respondents reported receiving care in the last 12 months (1809 [85.3%]) and having a regular source of care (1708 [81.0%]). Overall, 916 respondents (43.3%) reported being in good health. Just over one-fifth of respondents (458 [21.4%]; SE, 0.009) reported that they had experienced discrimination while getting medical care. The majority of respondents reporting discrimination were female (289 [63.1%]) and reported less than $50 000 in annual household income (279 [60.9%]). Compared with non-Hispanic White respondents (252 [20.3%]), higher proportions of Hispanic respondents (96 [22.9%]), non-Hispanic Black respondents (77 [22.8%]), and non-Hispanic respondents with other racial/ethnic identities (33 [23.4%]) reported experiences of discrimination (eTable in the Supplement ).

We observed statistically significant differences in reported experiences of discrimination across demographic groups and health-related characteristics ( Table 2 ). In bivariable analysis, those more likely to experience discrimination were female (odds ratio [OR], 1.87; 95% CI, 1.52-2.32), had poor or fair self-reported health status (OR, 1.71; 95% CI, 1.34-2.17 compared with good health), or lacked health insurance (OR, 1.50; 95% CI, 1.11-2.02). Those less likely to experience discrimination had an annual household income of at least $50 000 (OR, 0.64; 95% CI, 0.52-0.79), were older (OR, 0.98; 95% CI, 0.98-0.99), or had a regular source of medical care (OR, 0.74; 95% CI, 0.58-0.95). In multivariable analysis, these associations remained statistically significant with the exception of having a regular source of care (OR, 0.91; 95% CI, 0.68-1.14) and insurance coverage (OR, 1.21; 95% CI, 0.87-1.68). In multivariable analysis, the odds of experiencing discrimination were higher for respondents who identified as female (OR, 1.88; 95% CI, 1.50-2.36) and lower for older respondents (OR, 0.98; 95% CI, 0.98-0.99), respondents earning at least $50 000 in annual household income (OR, 0.76; 95% CI, 0.60-0.95), and those reporting good (OR, 0.59; 95% CI, 0.46-0.75) or excellent (OR, 0.41; 95% CI, 0.31-0.56) health compared with poor or fair health.

Abbreviation: OR, odds ratio.

The 5 most commonly reported types of discrimination among 458 respondents were based on race/ethnicity (79 [17.2%]), educational or income level (59 [12.9%]), weight (53 [11.6%]), sex (52 [11.4%]), and age (44 [9.6%]). Just over one-quarter of respondents reporting discrimination selected “other reasons” for discrimination (121 [26.4%]). After coding free-text responses, some of which overlapped with extant categories, we identified 6 additional types of discrimination. The most common of these included insurance and health finances or ability to pay for care (21 [4.6%]). One respondent described this type of discrimination by writing “I felt that with Medicaid [you] get pushed aside but when I had Blue Cross Blue Shield I [was seen] immediately.” Drug use and medication use were also sources of discrimination for some respondents (18 [3.9%]). This category referred to stigma and discrimination based on the medications that respondents were taking, prior substance use, or assumed drug-seeking behavior. For example, 1 respondent reported that “I was honest about having a drug addiction. They treated me like I was not important at all and insinuated that I was just trying to get pills.”

Discrimination based on mental health status was also reported by 9 respondents (2.0%) in free-text responses, and lifestyle (eg, having tattoos) was reported by 5 respondents (1.1%). Forty-two respondents (9.2%) who reported discrimination felt hassled or discriminated against because of their clinician’s attitude or behavior. This included feeling dismissed or disrespected by clinicians in a way that was not captured by the multiple-choice responses. Responses coded as “provider attitude” reflected comments that described experiences of being treated poorly, disbelieved, or brushed off while seeking care. Reasons that remained miscellaneous retained the “other” label (18 [3.9%]). Table 3 gives the frequencies for the primary types of discrimination that respondents reported and includes all reported reasons for discrimination as selected by respondents for the entire sample and by race. Although racial/ethnic discrimination was the most commonly reported type of discrimination, race/ethnicity was not significant in the bivariable or multivariable analysis. This is a statistical power issue because non-Hispanic White respondents, predominant in the sample, reported far less racial discrimination (10 [4.0%]) than non-Hispanic Black (42 [54.6%]), Hispanic (21 [21.9%]), and other racial and ethnic minority (6 [18.2%]) respondents. Table 3 also gives the differences in proportions of respondents reporting discrimination by race.

Among 458 respondents who reported discrimination in the health care system, 330 (72.1%) said that they had experienced it more than once. We report the frequency of these experiences in Table 4 . The majority of respondents who experienced discrimination across all 5 of the most commonly reported types of discrimination reported experiencing it 2 or 3 times. In fact, 16 respondents (20.3%) who experienced racial discrimination and 13 respondents (22.0%) who experienced discrimination based on their educational or income level experienced it 4 or more times. Sex (5 respondents [9.6%]) and age discrimination (3 respondents [6.8%]) were less frequently reported as occurring 4 or more times.

Our study estimates that, overall, more than 1 in 5 adults in the US have experienced discrimination at least once while receiving health care. Racial discrimination was the most commonly reported type of discrimination, followed by discrimination based on educational or income level, weight, sex, and age. After conducting multivariable logistic regressions, we found that respondents who were younger, identified as female, had lower annual household income, and reported poor or fair health were statistically significantly more likely to report experiences of discrimination.

Our results are consistent with previous studies examining experiences of discrimination in health care as well as in other settings. For example, prior work has found between 25.2% and 43.5% of survey respondents reporting ever experiencing discrimination in any setting. 31 Estimates of discrimination in the health care system have varied based on the use of different reporting time frames and sampling approaches. For example, one national study of discrimination found that 7.3% of respondents had experienced discrimination in the health care system only in the previous 12 months, whereas a community survey found approximately 14% of respondents reported ever experiencing discrimination in the health care system. This proportion was higher for Black respondents and Latino respondents, which was also true in our sample. 24 , 28 Although there may be a lower prevalence of discrimination in the health care system compared with some other settings, such as housing or policing, discrimination is still a frequent experience among patients, and health care is not immune to larger national trends. 28 , 29 Health care settings are also distinct; for instance, patients may be more forgiving or may not recall discriminatory incidents after a visit if they were very concerned about a serious illness. Patient experiences of discrimination may actually be higher and require further study through mixed-methods approaches.

Experiences of discrimination in the health care system harm patients by negatively impacting trust, communication, and health-seeking behaviors. 13 , 16 , 32 , 33 Our findings underscore the importance of understanding aspects of patient identity, especially with regard to race/ethnicity, not as risk factors for discrimination or the downstream effects of those experiences; rather, exposure to discrimination and racism are the risks. 34 , 35 The prevalence of discrimination identified in this study points to a need to examine discrimination in the health care system as a risk factor for other negative effects. Future work on interpersonal discrimination in the health care system should examine the types of discrimination we have identified herein, with the understanding that they are harms imposed on patients rather than caused by or reflective of patient demographic characteristics. 36 This future work should also explore the ways that discrimination is manifested and where in the health care system it is occurring most often.

Our study analyzes a wide variety of types of discrimination, including several highlighted directly by respondents. Each of these types of discrimination requires focused analysis and particular policy responses. For example, in supplementary analyses (eFigure 2 in the Supplement ), most of the respondents experiencing racial discrimination were Black persons. To effectively respond to the harms of racial discrimination in the health care system, anti-Black racism needs to be specifically analyzed and addressed. Furthermore, recognizing that discrimination is not discrete or necessarily additive, future work should also inform policy responses by building on existing literature to investigate the effects of layered or interacting types of interpersonal discrimination. For example, the vast majority of respondents reporting weight-based discrimination in the present study were women (eFigure 1 in the Supplement ). The intersection between sex- and weight-based discrimination represents only 1 example of how policy will need to respond to intersections of identity and discrimination. Intersectional policy and practice guidance will need to be built on and responsive to these multiple dimensions of discrimination to effectively respond to them. 37 Such work should complement and inform efforts to address systemic inequities.

Patient self-reports of discrimination are challenging to measure because the specific types of discrimination occurring may be unclear. This survey was able to capture only a single type of discrimination, which may mean that the reports underestimated patient experiences of discrimination. Some of these discriminatory experiences may also be internalized and denied, which suggests that reports may further underestimate the prevalence of discrimination. 38 Nevertheless, patient perspectives are critical in analyses and in policy designs that aim to address discrimination and health inequities. The prevalence of discrimination in the health care system that we identified builds on existing evidence that it is a problem requiring large-scale policy responses. As health care institutions throughout the country reckon with how systems interact to produce inequity, our study provides national estimates of the prevalence, frequency, and types of discrimination useful for policy, serving as 1 step in the process of building evidence-based responses.

In addition to a broad reckoning with discrimination, there are also localized approaches that may be appropriate to reduce harm in the shorter term. These include seeking information about experiences of discrimination and using that information to alter system-level policies to address inequality. 39 Health care systems can include measures of experiences of discrimination in their patient surveys to identify the occurrence of discrimination in their organizations and its effects on their patient populations to respond appropriately. Our study may provide guidance on the types of questions to include because the survey items used here build on previously validated measures. Furthermore, our analysis of “other” types of discrimination may suggest additional categories for inclusion in patient surveys. For example, the frequency with which medication or drug use and insurance-based discrimination were reported in this study indicates that further analysis of these health-specific types of discrimination may be warranted. This expanded data collection will enable health care systems to identify the particular types of discrimination occurring in their organizations and, most importantly, address them systematically.

Limitations

There are a few limitations to this study that should be considered in interpreting the results. The survey questions allowed participants to report only 1 primary type of discrimination, which limits our understanding of the multidimensionality of discrimination and the nature of encounters during which experiences of discrimination occurred. 40 , 41 Furthermore, self-reports of discrimination are challenging to measure because some of these experiences may be internalized and denied by individuals, making our estimate of approximately 20% of people who have ever experienced discrimination while receiving health care potentially underestimated. 38 Also contributing to a potential underestimation are the limits of race/ethnicity response categories that did not specifically capture American Indian, Alaska Native, or Middle Eastern identities. The survey was conducted only in English, which may have excluded some potential respondents. Finally, although we include some supplementary analyses that began to analyze across multiple demographic categories and types of discrimination, future analysis will need to address these numerous dimensions of identity and discrimination in more detail.

Conclusions

This is the first study, to our knowledge, that has examined the prevalence, frequency, and types of discrimination in the health care system using a nationally representative sample without limiting respondents’ reporting time frame. We found that experiences of discrimination in the health care system (21.4%) were more common than previously known and that these experiences typically occurred more than once. The 5 most commonly reported primary types of discrimination that we identified were based on race/ethnicity, educational or income level, weight, sex, and age. Addressing the immediate harms of these types of discrimination in the health care system should be an immediate policy and health care system priority.

Supplement.

eTable. Weighted Row Percentages of Descriptive Statistics (n = 2,137)

• Research article
• Open access
• Published: 09 January 2020

Discrimination in healthcare as a barrier to care: experiences of socially disadvantaged populations in France from a nationally representative survey

• Joshua G. Rivenbark   ORCID: orcid.org/0000-0002-7120-6677 1 , 2 &
• Mathieu Ichou 3  

BMC Public Health volume  20 , Article number:  31 ( 2020 ) Cite this article

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People in socially disadvantaged groups face a myriad of challenges to their health. Discrimination, based on group status such as gender, immigration generation, race/ethnicity, or religion, are a well-documented health challenge. However, less is known about experiences of discrimination specifically within healthcare settings, and how it may act as a barrier to healthcare.

Using data from a nationally representative survey of France ( N  = 21,761) with an oversample of immigrants, we examine rates of reported discrimination in healthcare settings, rates of foregoing healthcare, and whether discrimination could explain disparities in foregoing care across social groups.

Rates of both reporting discrimination within healthcare and reporting foregone care in the past 12 months were generally highest among women, immigrants from Africa or Overseas France, and Muslims. For all of these groups, experiences of discrimination potentially explained significant proportions of their disparity in foregone care (Percent disparity in foregone care explained for: women = 17%, second-generation immigrants = 8%, Overseas France = 13%, North Africa = 22%, Sub-Saharan Africa = 32%, Muslims = 26%). Rates of foregone care were also higher for those of mixed origin and people who reported “Other Religion”, but foregone healthcare was not associated with discrimination for those groups.

Conclusions

Experiences of discrimination within the healthcare setting may present a barrier to healthcare for people that are socially disadvantaged due to gender, immigration, race/ethnicity, or religion. Researchers and policymakers should consider barriers to healthcare that lie within the healthcare experience itself as potential intervention targets.

Peer Review reports

People within minority or otherwise socially disadvantaged groups are confronted with a multilevel web of challenges that negatively impact their health and wellbeing [ 1 , 2 , 3 ]. Among these numerous factors, research has increasingly focused on experiences of discrimination and how they may relate to individuals’ health [ 4 , 5 ]. In addition to a direct influence on health via physiologic stress pathways, experiences of discrimination are also thought to influence health indirectly via behavioral responses [ 6 , 7 ]. Indeed, a meta-analysis reported a significant association between perceptions of discrimination and health-related behaviors such as diet, exercise, sleep, or substance use [ 8 ]. However, one health-related behavior that has received comparatively less attention in its association with discrimination is the utilization of healthcare.

Individuals who have experienced discrimination in the past may be more reluctant to seek health care, as they may perceive it as a setting of increased risk for discrimination (i.e., refusal of service or lower quality of care). This may be especially true for those who have experienced discrimination within the health care setting itself. Prior work has hypothesized that experiences of discrimination within the healthcare setting may have a negative effect on individuals’ trust in and satisfaction with the healthcare system, increasing the likelihood of delaying or foregoing seeking care [ 9 , 10 , 11 , 12 ]. Further, individuals who interact with the healthcare system most often may, simply by greater exposure to the setting, be more likely to experience discrimination in healthcare, and consequently delay or forego future care [ 13 ].

Research from the United States (USA) has documented disparities in rates of discrimination in healthcare settings across race/ethnicity, immigrant status, language proficiency, and insurance status [ 14 , 15 ]. Further research has investigated possible links between discrimination within healthcare and utilization, with mixed findings [ 9 , 10 , 11 , 12 , 13 , 16 ]. A large-scale survey conducted in New Zealand documented an association between experiences of racial discrimination within healthcare and lower rates of preventive care use [ 12 ], whereas a separate large-scale survey in the USA found that nearly all significant unadjusted associations between discrimination and preventive services were no longer significant once sociodemographic characteristics were controlled for [ 16 ]. A number of studies have documented an association between experiences of discrimination within the healthcare setting and delayed or foregone care, both in the USA [ 9 , 10 , 17 ] and in Europe [ 18 ]. However, a nationally representative sample of USA women found that discrimination was linked with more frequent healthcare visits, though the authors note that this may not relate to foregone or delayed care [ 13 ]. Parallel evidence comes from research among people living with human immunodeficiency virus (HIV), which has consistently shown that higher perceptions of HIV-related discrimination and stigma within care settings is associated with lower retention in care [ 19 , 20 ].

In addition to the mixed findings above, the existing literature is limited by studies often focusing on a single dimension of social stratification (e.g., disparities in discrimination by race or gender). Research with large-scale nationallyrepresentative samples remains relatively rare [ 10 , 12 ], making the generalizability of findings to a population level more difficult. Further, the USA remains the site of most existing research on discrimination within healthcare and healthcare utilization, with a small number of studies outside the USA [ 12 , 18 ]. Finally, although some prior research has tackled the issue of statistical association between discrimination in healthcare settings and healthcare utilization, we know of only one study [ 16 ] (and none outside of the USA) that investigates the extent to which discrimination in healthcare can account for gaps in foregone care between groups.

France has a number of distinguishing characteristics that make it an important place for the study of discrimination in healthcare settings and its consequences. France has long been a country of immigration, as significant immigration flows began well before the Second World War [ 21 ], and the immigrant population in contemporary France is both numerous and diverse. Among all European countries, France has the second largest population of immigrants born outside the European Union (EU) after Germany, reaching 6 million in 2017 (approximately 9% of the total population) [ 22 ]. The largest immigrant groups come from North Africa (Algeria, Morocco, and Tunisia), Southern Europe (especially Portugal), Sub-Saharan Africa, Turkey, Southeast Asia (Vietnam, Cambodia, and Laos) and, more recently, China [ 23 ].

France also has a distinct political model of immigrant assimilation and ethnic diversity management, known as the French republican model [ 24 ]. Ethnic and racial distinctions are not recognized by the state; as a result ethnic statistics are not collected for official purposes, and ethnic minorities are not considered as targets of social policies [ 25 ]. Data and knowledge of discrimination on the basis of ethnicity or migration status are thus extremely scarce, despite the potential insight they could provide on the lived experience of minority groups in France.

Finally, the French healthcare system provides high levels of quality and access to care [ 26 ]. It is largely funded by public spending; more than three quarters of total health expenditures are publicly financed. Health insurance has a compulsory and universal coverage [ 27 ], and it includes state-funded health services for undocumented immigrants residing in France. This national context, in which the entire population should have access to healthcare, offers a valuable setting for analyzing foregone care and its potential explanatory factors.

In this study, we use data from a nationally representative study in France – with an oversampling of immigrant households – to examine social disparities in discrimination within healthcare, foregone healthcare, and how they are related. These data are of particular interest both for their large-scale, representative nature, and for the demographic diversity of the sample. We leverage these sample strengths and build on prior research by documenting population disparities, both in terms of discrimination and foregone care, across numerous demographic characteristics, including gender, immigrant status, country of origin, and religion. We also explicitly examine the extent to which discrimination in healthcare settings could explain any disparities in foregone healthcare between groups.

Data come from the Trajectories and Origins (TeO) study [ 23 ], a large-scale, nationally representative cross-sectional survey of France. The survey was conducted from 2008 to 2009 with in-person home interviews across France. The sample consisted of 21,761 individuals aged 18 to 59, with oversamples of immigrants and individuals born to at least one immigrant (> 8000 of each group).

Theoretical framework

Models were conceptualized in line with the adapted Behavioral Model for Vulnerable Populations described by Gelberg and colleagues [ 28 ], in which the use of healthcare services represents a health behavior that is influenced by upstream population characteristics. The main population characteristics of interest in this study include demographic characteristics (“predisposing” factors) of gender, ethnicity, immigrant generation, and religion. Other factors that we attempt to account for given the available data include the “predisposing” factors of age, marital status, education, and employment; the “enabling” factor of family income; and the “need” factor of perceived and evaluated health status.

Healthcare experiences

Discrimination in healthcare was measured with a single yes/no question: “Has a doctor or other medical care worker ever treated you less well or received you less well than other patients?” Likewise, foregone healthcare was also assessed with a yes/no question: “During the past 12 months, have you foregone health care for yourself?”. Each measure was coded dichotomously.

Demographic characteristics

As this study was explicitly interested in group disparities in healthcare experiences, we conducted analyses across a series of demographic measures, all of which were self-reported in the survey. Characteristics of interest include gender, immigrant generation (“French-born”, which refers to French-born individuals to French-born parents; first generation immigrant; or second generation immigrant), country of origin (for either the individual or parent, depending on the relevant immigrant generation, grouped into geographic categories), and religion.

Additional survey items were included as control variables in this study, including age (weighted M  = 39.1, SD  = 12.4), marital status (married = 46.7%, weighted) socioeconomic status, and health status. Socioeconomic status was measured with three variables for self-reported monthly income (weighted M  = 1681€, SD  = 954€), educational attainment (weighted: less than middle school equivalent = 11.3%, middle school equivalent = 13.3%, vocational training = 26.9%, high school equivalent or higher = 48.6%), and employment status (weighted: employed = 73.1%, unemployed = 8.8%, student = 5.4%, inactive = 12.7%). Health status was also measured with three variables, consisting of self-rated health (weighted M  = 1.83, SD  = .79), history of chronic illnesses (yes = 27.1%, weighted), and number of healthcare visits in the last year (weighted: none = 8.2%, once = 24.4%, several = 67.5%).

Analyses proceeded in three main steps. First, we described rates of discrimination in healthcare settings experienced by various groups as the predicted probabilities of experiencing discrimination based on demographic characteristics. We calculated these predicted probabilities from logistic regression models of healthcare discrimination, and we contrasted coefficient estimates against a reference group for statistical comparison. For each demographic factor of interest (gender, migrant generation, origin, and religion), we constructed three nested models. The first model included the demographic predictor, with age and gender (if gender was not the factor investigated) as covariates; the second model added covariates for socioeconomic status; the third model added covariates for health status.

Second, we reported the predicted probabilities of foregoing healthcare across the demographic groups of interest, and then calculated the average marginal effects (AMEs) of the demographic characteristics of interest on those predicted probabilities. We did this by modeling reports of foregone healthcare across three nested logistic regression models: the first included only the demographic factor of interest; the second added discrimination; and the third added all other demographic characteristics, socioeconomic status, and health status. We present our findings as AMEs for two main reasons. First, AMEs are less affected by bias arising from unobserved heterogeneity across nested logistic models than odds ratios or raw logistic regression coefficients [ 29 , 30 , 31 ]. Second, we believe that AMEs provide a more intuitive description of effect size than odds ratios or logistic regression coefficients, as AMEs can be read as percentage-point increases in predicted probability.

Finally, we determined how much of the disparities in foregoing healthcare across various groups is potentially explained by experiences of discrimination in healthcare. We did this by calculating the percentage of the Model 1 AME (that is, the AME of a group demographic characteristic) explained by the addition of discrimination as a covariate in Model 2, so that: % explained  = 1 – ( AME Model 2 / AME Model 1 ). Statistical significance of the “percent explained” was tested by contrasting a demographic characteristic’s AME in Model 2 against the same AME in Model 1. Put another way, we tested the null hypothesis that the addition of discrimination in the model resulted in no change in the estimated AME for a demographic characteristic.

Descriptive statistics of the sample are shown in Table  1 . Overall, the survey-weighted prevalence of reporting discrimination in healthcare settings was 3.9%, with a range of 2.6 to 9.3% across the various demographic groups examined. In bivariate comparisons, significantly higher rates of discrimination were observed for: women compared to men; 1st generation immigrants compared to French-born; those with origins in Overseas France, Africa, and Turkey compared to those from Mainland France; and Muslims and those with no religion compared to Christians.

Also seen in Table 1 , the survey-weighted rate of foregone healthcare was 10.9% overall, ranging from 6.2 to 22.0% across demographic groups. Bivariate comparison tests are displayed in the table, and represented graphically in Fig.  1 , as predicted probabilities of foregoing healthcare across demographic groups. Blue bars correspond to the reference groups, black bars indicate significant difference from reference group levels, and grey bars indicate no significant difference. The probability of foregoing care was higher for: women compared to men; second-generation immigrants compared to French-born; people with origins in Overseas France, North Africa, or mixed origin (partially from France) compared to those from Mainland France; and Muslims and those who reported “Other Religion” compared to Christians. In contrast, the probability of foregoing care was lower for people of Southeast Asian origin.

Predicted probabilities of foregoing healthcare. Predicted probabilities were derived from logistic regression of foregoing healthcare on demographic characteristics, with no covariates ( N =  21,729). Bar colors represent statistical significance in logistic regression of foregoing healthcare on demographic characteristics: blue = reference group; black = ( p  < .05); grey = ( p  > .05)

Predicted probabilities of foregoing healthcare were then calculated across a series of nested models; the results are displayed in Table  2 and illustrate three main findings. First, discrimination in healthcare settings was strongly associated with having foregone healthcare across all models in which it was included (Models 2 and 3). In the fully adjusted Model 3, the AME of discrimination was 0.14 – the largest effect size of all covariates, corresponding to a 14-percentage point increase in the predicted probability of foregoing care. Second, the AMEs associated with women, Muslim, Buddhist, or other religion, as well as origin in North Africa or Southeast Asia, which were statistically significant in Models 1 and 2, were no longer significant with the addition of other sociodemographic factors as covariates in Model 3. Third, the AME of certain demographic characteristics was not fully explained by any of the added covariates (i.e., it remained statistically significant even in the most strictly controlled model). Namely, in Model 3 there were significant AMEs of foregoing healthcare for second-generation immigrants, those with an origin in Overseas France, or those with mixed origin (regardless of whether or not it was partially from France).

Finally, we examined the proportion of the disparities in foregone healthcare potentially explained by reporting discrimination in healthcare settings; the results are shown in Table  3 . Discrimination explained a statistically significant proportion of the disparity for women relative to men (17%), second-generation immigrants relative to French-born individuals (8%), people with origins in Overseas France (13%), North Africa (22%), and Sub-Saharan Africa (32%) relative to those with origins in Mainland France, and Muslims (26%) relative to Christians.

This study used data from a national population-representative survey to look at the experiences of people who are socially disadvantaged due to gender, immigration, race/ethnicity, and religion, within the healthcare setting in France. We examined rates of reported discrimination and how they may explain disparities in rates of foregoing healthcare among those groups. Overall, our findings suggest that discrimination in healthcare is associated with foregoing medical care, and that this is especially important for women and people in minority racial or religious groups.

More specifically, our results suggest three main points. First, we showed that disadvantaged social groups – particularly women, immigrants, those of African origin, and Muslim religion – are more likely to have experienced discrimination in healthcare settings. The population prevalence of discrimination of 3.9%, which was in line with prior research across more than 30 European countries documenting national rates of discrimination in primary care between 1.4 and 12.8% [ 32 ], obscures the heterogeneity across groups, with rates nearly doubling for disadvantaged groups. For many of these groups, this finding is consistent with a broad base of existing literature, as they have been shown to face higher risks of discrimination in French society. Immigrants and their children from Sub-Saharan Africa, North Africa, and the French overseas territories report higher rates of perceived discrimination, measured through both general and setting-specific discrimination questions (at school, on the labor or housing markets, etc.) [ 33 ]. These minority groups also face racism more frequently [ 34 ]. Among religious groups, our observation of a high rate of discrimination against Muslims in the healthcare system echoes previous findings of discrimination in other settings [ 33 ], especially the labor market [ 35 ], and high levels of anti-Muslim prejudice in French society overall [ 36 ]. In contrast, there seems to be a specificity of the healthcare setting for women. Our findings are consistent with qualitative evidence showing that women tend to report discrimination in healthcare settings more often than men [ 37 ], but differ from findings in other settings (school, the labor and housing markets) where women are less likely to perceive discrimination [ 33 ]. One possible factor contributing toward this setting-specificity could be the higher rate of healthcare utilization by women, which would in turn increase their exposure to the possibility of experiencing discrimination within that setting.

Second, our analysis documented disparities in the rates of foregoing medical care across populations of social disadvantage due to gender, immigration, race/ethnicity, and religion. Many of the groups with higher rates of foregoing healthcare were the same as those who reported higher rates of discrimination in healthcare – women, immigrants (though second-generation, rather than first), people with origins in Africa or Overseas France, and Muslims. Other groups with comparatively high rates of foregoing healthcare were those with mixed origins, and those who reported as “Other Religion”. For some groups, these findings are in line previous research on foregoing care: for example, there is evidence of higher rates of foregoing healthcare among adult women in Sweden and adolescent girls in the USA [ 18 , 38 ]. Similarly, prior research has consistently documented higher rates of foregoing care among disadvantaged racial and ethnic minority groups in the US [ 39 , 40 ]. However, there is less existing research on migrant generation and foregoing care, and our finding of higher rates of foregoing care among second-generation immigrants in France differs from a study of immigrant children in the USA, which documented higher rates of foregone care for first-generation immigrants, but not second-generation [ 41 ]. We are not aware of other reports of foregone healthcare by religion.

Finally, we examined the potential explanatory role of experiences of discrimination in the healthcare setting on foregoing healthcare. We found reports of discrimination to be robustly linked with foregoing care: in our fully adjusted model of foregoing care, discrimination in the healthcare setting was associated with an average 14 percentage-point increase in the predicted probability of foregoing care. Of note, this contrasts with a prior study that found the link between discrimination and decreased healthcare utilization to be explained by socioeconomic status [ 16 ]. These findings can also be considered alongside a USA-based study that found discrimination to be associated with more frequent healthcare visits [ 13 ] together, these studies are consistent with the model described in this paper, in which healthcare need (observed as frequency of visits) is an enabling factor for discrimination in healthcare, which results in a higher likelihood of foregoing future care [ 28 ]. Overall, findings in this study are consistent with existing research on discrimination as a barrier to healthcare: in addition to the previously mentioned Swedish study linking discrimination with foregone healthcare, qualitative research from Spain has described experiences of discrimination as a factor limiting access to healthcare [ 42 ], and experiences of discrimination have been linked to avoiding dental care in Australia [ 43 ].

We also contextualized this relationship by determining the potential proportion of disparities in foregoing care that could be explained by experiences of discrimination in healthcare. Groups for whom discrimination explained an especially large proportion of disparities in foregone care were people with origins in Sub-Saharan Africa (32%) and Muslims (26%). Also of note were women (17%); although the proportion explained was lower for women than for some other groups, the fact that they constitute half of the population points toward a large potential effect of discrimination when considered at the level of French society. Interestingly, the proportion of the disparity in foregoing care for second-generation immigrants explained by discrimination was small (8%). Taken together with the findings by region of origin, this suggests that discrimination may be of particular importance for healthcare utilization among immigrants who are more readily racialized based on their appearance and face higher levels of racism already.

This study has a number of limitations that should be noted. First, this was cross-sectional and thus no causal inference regarding discrimination and foregoing healthcare can be made – it is for this reason that results are framed in terms of the potential explanatory nature of discrimination. Future studies should consider possible natural experiments or other quasi-experimental designs in order to more rigorously test any causal relation between discrimination and foregoing healthcare. Second, we used a single-item measure of discrimination in healthcare settings, framed as being treated poorly compared to other patients. It is possible that a different assessment of discrimination, such as an adapted version of the Everyday Discrimination Scale [ 44 ], would reveal a different pattern of rates of discrimination. Third, we did not examine the specific type of healthcare that individuals reported having foregone, and thus do not know to what extent the foregone care was necessary. Finally, although this study was nationally representative of France, findings may be dependent on the societal dynamics and healthcare setting specific to France at that time (2008–2009), and consequently not generalizable to other settings. However, the rates of both discrimination in healthcare settings and of foregoing care are generally similar to those described in Sweden [ 18 ] – which has a different healthcare system and a more homogenous population – suggesting that similar trends may exist at least in other parts of Europe. Further, given the contemporary increase in far-right voting and associated anti-immigration politics in France, we would hypothesize that our estimates here represent lower bounds for experiences of discrimination in the present.

With these potential limitations in mind, the implications of this study can be discussed. We observe disparities between social groups in terms of discrimination in healthcare settings – a negative phenomenon itself – as well rates of foregone healthcare, an important hurdle in the functioning of any health system [ 45 ]. The affected groups represent large sections of French society (e.g., women, major immigrant groups, etc.), suggesting a substantial burden when considered at the national level. These disparities stand in opposition to the global goals of health equity [ 46 , 47 , 48 ], and should be considered in the discussion and design of interventions and health policies. Suggested interventions to reduce discrimination in healthcare settings include provider-level interventions, grounded in psychology research, that aim to improve provider understanding of bias and increase perspective-taking and empathetic behaviors [ 49 ], such as an intervention involving feedback on biased behaviors and interactions with a virtual patient that may reduce racial bias in pain medicine prescribing [ 50 ]. More systemic actions include policies that increase organizational accountability for discrimination, or social marketing campaigns that aim to shift population norms with anti-discrimination messaging [ 51 ]. The robust linkage between experiences of discrimination and foregoing healthcare observed in this study, especially among women, immigrants of African origin, and Muslims, adds additional context to the web of barriers that people in socially disadvantaged groups face and points to potential high-priority groups around which interventions may be structured.

The health status of disadvantaged and minority populations is a topic of increasing policy and scientific relevance for many countries around the world [ 52 , 53 , 54 ]. This study provides evidence that discrimination within healthcare settings may partially explain disparities in rates of foregone healthcare, contributing to the health inequalities observed across various disadvantaged groups. Researchers and policymakers who aim to improve the health of disadvantaged groups should be mindful that some barriers to healthcare for disadvantaged populations may lie in the experiences of healthcare itself, and those experiences are a potential place of action from which future policy and research can proceed.

Availability of data and materials

The data that support the findings of this study are available from the French Institut national d’études démographiques (INED), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of INED.

Abbreviations

Average marginal effects

European Union

Human immunodeficiency virus

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This work was supported by funding from the INED International Relations and Partnerships Department. The funding body played no role in the design of the study, the collection, analysis, and interpretation of data, or in writing the manuscript.

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Rivenbark, J.G., Ichou, M. Discrimination in healthcare as a barrier to care: experiences of socially disadvantaged populations in France from a nationally representative survey. BMC Public Health 20 , 31 (2020). https://doi.org/10.1186/s12889-019-8124-z

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How unconscious bias can discriminate against patients and affect their care

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This article has a correction. Please see:

• How unconscious bias can discriminate against patients and affect their care - November 13, 2020
• Kathy Oxtoby , journalist
• kathyoxtoby1{at}gmail.com

Our assumptions, which we may not even be aware of, can lead to erroneous clinical decisions. More research, awareness, training, and diverse teams are needed to tackle this, Kathy Oxtoby finds

Older patients are typically sedentary, you assume—without realising it. A patient you relate to well receives that bit more time and attention, although you’re not aware of giving preferential treatment. Or maybe you speak to some patients in a certain way because of your beliefs about their social class. If not these exactly, you’re likely to have made assumptions like them, and all are examples of unconscious bias.

Sarah Mumford, programmes director at IVE, a company that delivers workshops on unconscious bias, describes this type of bias as “what happens when our brains make snap judgments about people, places, and things based upon past experiences.”

Like the wider population, healthcare professionals exhibit unconscious bias, a 2017 systematic review found. 1 Scarlett A McNally, vascular surgeon and a council member of the Royal College of Surgeons of England, says that doctors are “only human” and are therefore not exempt from making assumptions about someone “because of their age, race, sexual orientation, or religion.”

This unconscious bias, also known as implicit bias, is important in a medical setting “because it may affect decision making about how care proceeds,” she says.

Discrimination against patients

Many types of implicit bias discriminate against patients ( box 1 ). Pete Jones, a chartered psychologist at Shire Professional Chartered Psychologists, which provides unconscious bias training, says that it “leads us to value some groups more than others, based on such factors as ethnicity, gender, and disability, which all play a critical role in patient care.”

Patients’ implicit biases

It’s not only healthcare professionals who will have unconscious bias—their patients will too. “Unconscious bias can cut both ways in the patient-doctor relationship,” says Scarlett A McNally, vascular surgeon and a council member of the Royal College of Surgeons of England. “Patients may assume that the person looking after them is not senior if they are not the stereotypical image of a senior consultant, and this could lead to a breakdown in communication.”

Ganesh Subramanian, consultant in stroke medicine at Nottingham University Hospital NHS Trust, who has given lectures on unconscious bias, says, “Some patients may dislike being treated by females because they can’t accept them in positions of authority. I have heard this from a number of my female colleagues when they have dealt with some patients. I have also seen some men refuse to let male doctors examine their wives.”

And some patients may exhibit unconscious racial bias. “I had one of my junior doctors, who was a black doctor, told by a white patient that he wanted to see a ‘real’ doctor. I can only assume it was a racial bias,” says Subramanian.

A patient’s social background can lead clinicians to jump to the wrong conclusions, for example. Sakkarai Ambalavanan, a consultant physician in respiratory medicine at Glan Clwyd Hospital in Rhyl, north Wales, recalls a patient who came from an area with a high incidence of alcoholism. He says, “As they were vomiting blood I assumed they were an alcoholic. ‘No, I don’t drink—haven’t you read my notes?’ the patient said. Bias can direct you in the wrong direction for diagnosis and treatment.”

Doctors “don’t consciously set out to be biased against a patient or patient group,” says Camilla Kingdon, vice president for education and professional development at the Royal College of Paediatrics and Child Health and a consultant neonatologist at the Evelina London Children’s Hospital.

But it can be difficult not to make assumptions about patients when you’re working in a busy healthcare environment. Kingdon says that clinicians work in “incredibly pressurised and often somewhat resource limited settings, so are often forced into making rapid decisions.” She says that the sequence of decision making that doctors are trained to carry out—diagnosis, action plan, further investigation, or treatment—“is ripe for unconscious bias.”

She explains, “To be efficient and timely with each step you have to make some assumptions. Nine times out of 10 you’ll be absolutely correct, but sometimes assumptions are simply wrong,” as clinicians have either ignored some possibilities or overemphasised others.

Even if doctors have longstanding relationships with their patients—as is typically the case with GPs—this doesn’t prevent them from making assumptions about diagnoses and treatments. Richard Vautrey, chair of the BMA’s General Practitioners Committee, says that, although continuity of care enables GPs to get to know individual patients better than a simple interaction, “this can blind you to something obvious.”

“Sometimes it’s helpful for other clinicians, such as a locum or colleague in the practice, to see your patient,” he says, “as they can spot something you haven’t because you’ve been focusing on a specific aspect of their care.”

Culturally sensitive services

Narinder Kapur, visiting professor of neuropsychology at University College London and honorary consultant neuropsychologist at Imperial College Healthcare NHS Trust, agrees that unconscious bias can result in misdiagnosis. He explains that a black male patient who behaves aggressively and has been drinking heavily could be assumed to be doing so “because they are black and are just like that, when in fact they may have frontal lobe dementia or a frontal lobe tumour.”

Ananta Dave is a medical director and consultant psychiatrist at Lincolnshire Partnership NHS Foundation Trust and chair of the Royal College of Psychiatrists’ Task and Finish group on covid-19 risks in minority ethnic staff. She believes that quality of care is affected if patients don’t have access to “culturally sensitive services” and that discrimination is likely from staff, towards patients and other staff, “if they are not appropriately trained and supported in equality and diversity.”

One systematic review in 2012 showed that black or African American patients were 22% less likely than white Americans to be given analgesia for pain. More recently, in August this year, a US study reported that black newborn babies who were cared for by black doctors were more likely to survive than those cared for by white doctors. Researchers analysed 1.8 million hospital births in Florida from 1992 to 2015 and found that deaths were lower by 257 in 100 000 among black newborns who were under the care of black doctors, when compared with care by white doctors. 2

A 2015 systematic review showed that low to moderate levels of implicit racial or ethnic bias were found among healthcare professionals in all but one of 15 studies. 3 The results also showed that implicit bias was “significantly related to patient-provider interactions, treatment decisions, treatment adherence, and patient health outcomes.”

Bias of experience

Other types of unconscious bias may stem from the doctor’s experience of certain conditions, particularly if it is recent. With the pandemic currently at the forefront of everyone’s mind, for example, Kapur says that doctors may assume that a patient who presents with a high temperature and breathlessness has covid-19, ignoring other possible diagnoses. This is an example of availability bias ( box 2 ).

Types of unconscious bias in clinical medicine 4 5 6

Anchoring bias.

A failure to adjust initial prognoses on the basis of later information, or a failure to modify initial diagnoses because of new events.

Example : This may occur in anaesthesia during difficult airway management when repeated instrumentation efforts cause the effectiveness of mask ventilation to deteriorate. Anchoring bias on the initial “easy mask” conditions may lead to a delay in recognising that the patient’s clinical status is changing.

Availability bias

Where diagnosis and treatment are guided by recent information you have had access to or recent events you have been exposed to.

Example : After recently seeing several patients with appendicitis, you assume that a patient presenting with abdominal pain probably has appendicitis.

Confirmation bias

The tendency to look for confirming evidence to support a diagnosis rather than look for disconfirming evidence to refute it—despite the latter often being more persuasive and definitive.

Example : Suspecting that the patient has an infection and then using raised white cells to prove this, rather than questioning why the white cells are raised and asking what other findings there are.

Ganesh Subramanian, a consultant in stroke medicine at Nottingham University Hospital NHS Trust, says that such availability bias can lead to misdiagnosis of conditions such as pulmonary embolism (PE) and subarachnoid haemorrhage (SAH). “These are common conditions which are both underdiagnosed and overdiagnosed at the same time,” he explains. “If you have seen a PE in a patient presenting with some atypical features, you associate those atypical features with a PE in the future.”

Conversely, he says, “If someone presents with atypical features and you have done the test in the past and it was negative, then the next time you will not entertain the possibility of PE, even after you have ruled out other conditions. This is because you are influenced by your previous experience—which is implicit bias.”

The consequences of this bias can be fatal. “I know of a case where a patient had minor orthopaedic surgery and developed pain in their leg,” says Subramanian. “They sought advice from one of the doctors who said it was unlikely to be DVT [deep vein thrombosis] because the leg was not red or hot. Unfortunately, two days later the patient died of PE.”

More research needed

Despite the serious consequences implicit bias can have, little is known about it in everyday clinical practice. The chartered psychologist Jones says that knowledge is lacking about this bias in medicine, and he hasn’t seen any tenders for unconscious bias training in the NHS. He says that the reason for this lack of awareness could be that it’s not yet seen as a patient care issue.

As a neonatologist, Kingdon believes that this lack of understanding about implicit bias and what it means for clinical practice arises because “doctors are trained that clinical decision making should be precise and rigorous, and they could find it almost insulting to think the decisions they make are affected by ‘softer,’ often unspoken and subconscious influences.”

Research into unconscious bias in the healthcare profession has included a white paper by the Institute for Healthcare Improvement. The paper offers guidance on how healthcare organisations can reduce health disparities related to characteristics such as race, religion, and gender. 7 This includes making health equity a strategic priority, developing structures and processes to support health equity work, and decreasing institutional racism within organisations.

The authors of the 2017 systematic review highlighted that “more research in actual care settings and a greater homogeneity in methods employed to test implicit biases in healthcare is needed.” 1 And the researchers in last August’s US study on newborns’ and doctors’ race concluded that their “results underscore the need for research into . . . why black physicians systemically outperform their colleagues when caring for black newborns.” 3

The neuropsychologist Kapur says, “I’d like to see more funding for research into reasoning flaws such as unconscious bias, which would then drive the knowledge base.”

Mitigation of bias

While clinicians “can’t eradicate unconscious bias—it’s ingrained in all of us—we can mitigate it,” says Subramanian. Doctors can undergo training to improve their understanding of unconscious bias and ways to deal with it. However, this training has been criticised by the media as being ineffective.

“Sadly, the headline is always ‘unconscious bias training doesn’t work,’ which is wildly misleading,” says Jones. He says that studies highlighted in the media have criticised some unconscious bias training for failing to change behaviours, when it “only set out to raise awareness. And the two are not the same.”

Unconscious bias training is unlikely to change behaviours if it’s delivered only as a tick box exercise. Rather than “three injections of unconscious bias training, then ‘goodbye,’” Mumford says that IVE’s workshops advocate “a full developmental process over three to six months,” where organisations are encouraged to embed diversity and inclusion in their policies and practice.

Kapur explains, “An important way for clinicians to really become aware of unconscious bias issues is through regular training at undergraduate and postgraduate level.” There are examples, 8 but this training does not seem to be widespread, he says—adding that it should be part of the standard curriculum and that, when working in clinical practice, doctors should have regular refresher courses on unconscious bias.

Unconscious bias awareness, while necessary, is “not sufficient” to mitigate such bias. Ben Fuchs, senior consultant in leadership and organisational development at the King’s Fund, says that it’s helpful to have “more than one set of eyes” looking at an issue. “Teams are stronger than individuals, particularly when there’s diversity in a team to give a broader perspective,” he says. “A diverse team of clinicians can help mitigate the risks of unconscious bias in treating a diverse patient population.”

Kapur believes that awareness of decision making processes such as unconscious bias, as they affect both patient care and staff wellbeing, needs to be at “every level, from the health secretary to porters. And people at the top need to realise that psychological biases are out there and are affecting healthcare.”

Even if people are aware of biases, they need to feel confident that they can speak up, says Ananta Dave. She explains, “When people experience bias—unconscious or otherwise—directed against them, they need to be able to speak up about it. We need to create safe spaces for that to happen, and this has to come from the trust board.”

So: more research, training, and action from healthcare leaders is needed to establish the nature and extent of unconscious bias in the medical profession—as well as among the patients and clients they care for—and how to tackle it. But in the meantime, practitioners should themselves be mindful of the issue. As Fuchs says, “You have to develop the habit of questioning your own judgments and conclusions and challenging yourself and each other about your assumptions.”

Competing interests: I have read and understood BMJ policy on declaration of interests and have no relevant interests to declare.

Provenance and peer review: Commissioned; not externally peer reviewed.

• FitzGerald C ,
• ↵ Greenwood BN, Hardemanb RR, Huangc L, Sojourner A. Physician-patient racial concordance and disparities in birthing mortality for newborns. Proc Natl Acad Sci USA 2020 (published online 17 Aug). https://www.pnas.org/content/early/2020/08/12/1913405117 .
• Chapman MV ,
• ↵ Croskerry P. 50 cognitive and affective biases in medicine. 2013. http://sjrhem.ca/wp-content/uploads/2015/11/CriticaThinking-Listof50-biases.pdf .
• O’Sullivan ED ,
• Schofield SJ
• ↵ Stiegler MP, Tung A. Cognitive processes in anesthesiology decision making. Anesthesiol 2014;120;204-17. https://pubs.asahq.org/anesthesiology/article/120/1/204/11717/Cognitive-Processes-in-Anesthesiology-Decision .
• ↵ Wyatt R, Laderman M, Botwinick L, Mate K, Whittington J. Achieving health equity: a guide for health care organizations. Institute for Healthcare Improvement. 2016. http://www.ihi.org/resources/Pages/IHIWhitePapers/Achieving-Health-Equity.aspx .
• ↵ Khatri U, Zeidan A, LaRiviere M. An evaluation of implicit bias training in graduate medical education. MedEdPublish 2019. doi: 10.15694/mep.2019.000109.1 . https://www.mededpublish.org/manuscripts/2399 .

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Black Americans are underrepresented in residential care communities, AP/CNHI News analysis finds

Eberline Nugent, left, Johnny Griffin, Jay Cossey and Carrie Dickson play bingo during activity time at The Retreat at Kenwood assisted living facility in Texarkana, Texas on Friday, May 17, 2024. Cossey recalls, “My brother came and said he wanted to take me home. ... I told him I am home. I’m home because I feel good here.” (AP Photo/Mallory Wyatt)

Jay Cossey plays bingo with other residents during activity time at The Retreat at Kenwood assisted living facility in Texarkana, Texas on Friday, May 17, 2024. The former lawyer moved here after multiple strokes more than seven years ago that caused him to lose most of his short-term memory. He’s one of a handful of Black residents at a facility that is blocks away from his old apartment. (AP Photo/Mallory Wyatt)

Eberline Nugent, left, Johnny Griffin, Jay Cossey, Carrie Dickson and Joyce Schiessl play bingo during activity time at The Retreat at Kenwood assisted living facility in Texarkana, Texas on Friday, May 17, 2024. Cossey’s church community urged the 70-year-old to move in, though his family in Alabama has pushed for him to come live with them. (AP Photo/Mallory Wyatt)

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Norma Upshaw, 82, was living alone south of Nashville, when her doctor said she needed to start in-home dialysis.

Her closest family lived 40 miles away, and they’d already scrambled once when the independent senior living facility she had called home — a community of largely Black residents — had closed with 30 days’ notice. Here they were searching, yet again, for an assisted living facility or maybe an affordable apartment that was closer.

They couldn’t find either, so Upshaw’s daughter built a small apartment onto her home.

“Most of her doctors, her church, everything was within Nashville,” said Danielle Cotton, Upshaw’s granddaughter, “... this was the best option for us.”

Nearly half of Americans over 65 will pay for some version of long-term health care , the landscape of which is quickly transitioning away from nursing homes and toward community living situations.

Black Americans are less likely to use residential care communities, such as assisted-living facilities, and more likely to live in nursing homes, CNHI News and The Associated Press found as part of an examination into America’s long-term care options. The opposite is true for white Americans.

The disparity is well-known to those who work in and research assisted-living settings, and experts say the reasons why are complicated. Where to place a parent or loved one is driven in part by personal and cultural preferences, but also insurance coverage and physical location of residential care communities. All of these factors vary state by state, family by family.

The result is older Black Americans may be left out of living situations that can create community, prevent isolation and provide help with daily tasks while allowing for a level of personal independence.

“The bottom line is white, richer people have a solution now — which is these incredible assisted-living communities — and minorities and low-income people don’t,” said Jonathan Gruber, an economist at Massachusetts Institute of Technology. “That is the fundamental challenge facing our country as our demographics are shifting.”

Complex causes

The AP and CNHI News analyzed data from the most recent National Post-acute and Long-term Care Study, published in 2020, and found Black people are underrepresented in residential care communities nationally by nearly 50%.

Black Americans account for about 9% of people over 65 in the U.S. But they are underrepresented in residential care communities at 4.9% of the population, and overrepresented in nursing homes — about 16% of residents.

The situation is flipped for white Americans, who make up 75% of Americans over 65 but are 88% of the people in residential care communities. The AP-CNHI News analysis also found that other ethnic and racial groups are underrepresented in assisted living facilities, but only Black Americans were also overrepresented in nursing homes.

Lacking a universal definition for assisted living, the federal study created the “residential community care” category to represent settings that serve people who cannot live independently, but also do not require the more comprehensive care provided in nursing homes.

In short, they’re places where people can live and receive help with activities of daily living like bathing, dressing and managing medications, but do not provide round-the-clock nursing care.

Financial barriers affect low-income people of all races, experts said, but they’re heightened for older Black Americans. Black workers make $878 weekly compared to $1,085 earned by white workers, according to data from the U.S. Bureau of Labor Statistics , which shows this national gap has existed for decades.

That affects both the potential to spend on long-term care — and, earlier in life, homeownership rates. Many residents sell their homes to fund senior care, and more than 7 in 10 homeowners in the U.S. are white, according to 2020 U.S. Census Bureau data.

One month in an assisted living facility runs $4,500 a month or $54,000 a year , according to a national median cost from the National Center for Assisted Living, which represents assisted living providers.

Most people pay privately, often through personal funds or long-term care insurance; nursing homes can be covered by Medicaid. That puts assisted living out of reach for many Black Americans, explained Cotton, who also founded and runs a Nashville nonprofit that helps financially strapped seniors find housing.

She said many can barely pay for government-subsidized housing, let alone expensive living communities: “It leaves them in a gap. Those are the seniors that are really not even considered or thought about.”

In Palo Alto, California, the nonprofit Lytton Gardens uses funding from the U.S. Department of Housing and Urban Development to subsidize housing costs for low-income assisted living residents. But the cost of care — scheduled meals, help with bathing and taking medications — is still on the individual.

Staff have tried to reach Black and Hispanic seniors through social workers, libraries and senior centers. But the residents are still mostly white and Asian.

“Most of the time, I’m begging somebody to move in,” said Donna Quick, housing administrator for Lytton Gardens. “But it’s just a matter of finding somebody who has the funds for this assisted living program.”

The process of paying for long-term care is “as opaque as it can be,” said Linda Couch, senior vice president of policy and advocacy at LeadingAge, which represents nonprofit long-term care providers and researches long-term care . “Because we don’t have a comprehensive and cohesive long-term care financing system in this country, we are left with this patchwork,” Couch said.

Researchers’ major question as more assisted facilities open up across the U.S. — are they located near Black communities? — is hard to answer, too.

“The federal government doesn’t even have a list of assisted living (facilities),” said Lindsey Smith, health systems management and policy researcher at the Oregon Health and Science University-Portland State University School of Public Health. “There is not, like, a registration. When COVID hit, they did not have a list.”

Desire to stay home

LaShuan Bethea, executive director of the National Center for Assisted Living, said more research is needed to fully know whether fewer Black people accessing assisted living means they are missing out on needed care, or if they are finding that support in other ways.

“It’s really important to do the work ... trying to understand: What does this mean when Black and brown people can’t access assisted living, knowing what it brings in terms of quality and outcomes?” Bethea said.

While affordability is one determining factor, researchers say it doesn’t completely explain why more Black people are not moving into assisted living.

“I think the other piece is that expectation that we want to keep people home as long as possible,” said Candace Kemp of Georgia State University’s Gerontology Institute. “And within families of color, African American communities in particular, there’s this desire to take care of family members.”

Steven Nash’s father could afford the most expensive assisted living facilities, but the former judge wanted to stay home. So while Nash ran one of the nation’s last remaining Black-owned nursing homes in the Washington, D.C., area, he also helped care for his father until he died at the age of 87.

“Even though it was very difficult for the family, we still kept that promise,” he said. “We try as hard as we can to honor the wishes of our elders.”

As smaller nursing homes and facilities that once catered to Black residents closed, there’s a cultural competency gap, Nash said. He pointed to the kitchen, where beloved cultural food options are removed in favor of generic menu mainstays.

“People want to live out their life the way they’ve lived,” he said.

That’s why the 95-year-old mother of Indiana state Sen. Gregory Porter still lives where she has for six decades, cared for by Porter, other family members and in-home health professionals. Porter’s daughter has promised to care for him similarly as he ages, a commitment that gave him “a level of comfort.”

“It means a lot,” Porter said. “It gives you the will to live.”

But for others, assisted living is an option for independence even as their daily needs grow.

Older Black Americans are twice as likely to have Alzheimer’s or other dementias compared to older white people, according to the Alzheimer’s Association. Nash said he’s seen more Black Americans interested in assisted living for dementia care; he’s even planning to open a targeted facility in the coming years.

In Texarkana, Texas, former lawyer Jay Cossey moved into an assisted living facility after multiple strokes more than seven years ago that caused him to lose most of his short-term memory. He’s one of a handful of Black residents at a facility that is blocks away from his old apartment.

His church community urged the 70-year-old to move in, though his family in Alabama has pushed for him to come live with them.

“My brother came and said he wanted to take me home,” Cossey recalled. “I told him I am home. I’m home because I feel good here.”

Gerber reported from Kokomo, Indiana; Shastri reported from Milwaukee; and Forster reported from New York.

___ The share of the U.S. population older than 65 keeps rising — and will for decades to come. Since nearly half of Americans over 65 will pay for some version of long-term health care, CNHI News and The Associated Press examined the state of long-term care in the series the High Cost of Long-Term Care , looking at adult day cares to high-end assisted living facilities, to understand the challenges in affordability, staffing and equity that exist today and lie ahead.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group and the Robert Wood Johnson Foundation.

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How to Promote Equality & Diversity in Care Homes

Estimated Reading Time: 7 minutes

Customer Care Expert

Last Updated: 21/04/2024

All people have the right to access health and social care that treats them with dignity and respect . This means it is essential for all providers to consider how to promote equality and diversity in a care home.

Whether it be race, religion or beliefs, or sexual orientation, each person deserves to have their identity and individual needs recognised. Without this, quality healthcare cannot be provided.

In this article, Lottie delves into equality and diversity within care services, examining what must be done to meet legislation and how homes can go beyond this to help their residents thrive.

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What Does Equality & Diversity Mean?

Although they are often grouped as a single concept, equality and diversity are distinct terms and it is important to consider them separately.

What is Equality?

Equality means ensuring that the opportunities and rights afforded to individuals are the same, regardless of their background and identity. It also means that everyone is treated equally. As a care provider, you must pursue equality in terms of both staff and residents in the home.

Viewing each person as deserving of the same chances is vital for giving quality care to those you are responsible for. Without this, you may negatively impact their happiness and risk failing to meet the standards set out by law.

What is Diversity?

Diversity in health and social care refers to the need to recognise the unique experiences of individuals, showing appreciation for each person’s culture, lifestyle and values. You must display respect for these differences in the care that you offer.

Diversity and equality work hand-in-hand to guarantee that a service user’s identity is seen, without it causing them to feel alienated.

Why It’s Important in a Care Home

Promoting equality and diversity must be central to the management of any social care company. This is necessary to comply with the requirements set out for you by law, but also to meet the needs of those who’s care has been entrusted to you.

Laws & Regulations

To ensure fair treatment and equal opportunities for people in care, successive governments have created legislation protecting their rights.

The Equality Act 2010

The Equality Act was signed into law in 2010, with the aim of unifying anti-discrimination legislation under one banner. It outlaws unfair treatment for any of the following reasons:

• Religion or belief
• Sexual orientation
• Marriage or civil partnership
• Pregnancy or maternity
• Gender assignment

Under the Equality Act 2010 , these are all considered protected characteristics. Should someone feel they have not been treated fairly because they belong to one of these groups, they can take action through civil courts.

As a care provider, you are legally obliged to protect residents from this unfair treatment. Issues you must be particularly aware of include:

• Denying an applicant a space in a home due to one of these characteristics
• Neglect or abuse of a resident due to one of these characteristics

Freedom from abuse and neglect are the minimum a person in your care should expect. You should be taking further action to promote equality and aid each to live happily.

The Human Rights Act 1998

The Human Rights Act 1998 (HRA) outlines the basic rights and protections each person in the UK is entitled to. Although the legislation was not originally applied in care homes, it’s scope was extended to include them in 2008 .

The act covers a wide range of possible injustices, with the rights it covers best summarised as the following:

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The Care Act 2014

The Care Act 2014 was created to specifically protect the rights of those in need of support, so they can live a free and fulfilling life.

The act aims to personalise services and guarantee that support is individualised, to meet each person’s needs. Additionally, it enshrines prevention at the heart of care, emphasising the need to lead healthy lifestyles and to protect older people from neglect or abuse.

Centralising assessment and care regulation under a single legal framework, the act makes care easier to understand. All care providers should familiarise themselves with the protections and processes set out by this law.

Additionally, care homes must consider how prevention can be incorporated throughout their operations. Vigilance to avoid infringements of people’s rights and dignity in care is therefore paramount.

The Mental Capacity Act 2014

The Mental Capacity Act (MCA) was passed in 2014, to protect those with impaired mental capacity. It aims to empower these people, by setting out a framework for assessing their ability to make decisions and advising on how best to assist them.

Elderly people are more likely to be covered by the act, due to their increased risk of experiencing health difficulties. Medical conditions, such as dementia or strokes, can impair cognitive function and mean help is required to make decisions.

Guidance on which service users may be in need of support through the MCA, as well as the process for evaluating their requirements, can be found through the NHS .

Promoting Equality & Diversity

To meet legal requirements, as well as their social responsibility, all care home providers must promote diversity and equality. Proactivity is key here, with action to protect and uplift the most vulnerable considered in every decision.

Code of Conduct

The first step any care home wanting to improve diversity and inclusion should take is to implement a code of conduct. This will set out minimum expectations for all staff, providing a structure to deliver equal opportunities and care for all.

As a basis, the Skills for Care ‘ Common Core Strategic Equality and Diversity Principles ’ can be used.

Commitment to Equality, Diversity and Human Rights Values

Definitively committing to these principles sets the tone and direction for the service that your care home offers. A clear declaration of this must therefore be made within your policy.

Promoting Equality, Diversity and Human Rights in Decision-Making

Diversity and inclusion should be considered at all levels of your business. Representation at the most senior levels of your company is vital to ensuring this in high level decision-making, whilst training for more junior employees will encourage it in everyday operations.

Advancement of Equality, Diversity and Human Rights

Your code must incorporate the need for work to improve equality and diversity. Any new business developments would thus be planned with these ideas considered.

Monitoring Equality, Diversity and Human Rights

Monitoring the progress of diversity and equality is important so that your business can learn what’s effective and implement changes. Your code should therefore set out a system for measurement and review of all efforts.

Commitment to Equal Access and Open Standards

Finally, you must ensure that your service is open to all, regardless of background or identity. To do so, you must identify barriers that individuals may face and provide assistance to help overcome them.

Diversity in the Workplace

You cannot promote equality and diversity without listening to the voices of people who are different from you. This means it’s vital to hire staff from varied backgrounds, so that different perspectives can be heard in the management of the facility.

Initiatives to guarantee equal access to employment at all levels of your business will help to diversify both the decision-makers and employees responsible for daily caregiving. This will increase awareness of challenges people may experience and encourage vigilance against them.

Further Reading & Resources

For care providers wanting to learn more about equality and diversity, the below resources can provide further information.

Common Core Strategic Equality and Diversity Principles

Care Act 2014

Equality Act 2010

Human Rights Act 1998

Mental Capacity Act 2014

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Case study: Pennine Care NHS Foundation Trust

Introduction.

Pennine Care NHS Foundation Trust (Pennine Care) has sought to address its disciplinary gap by increasing the capacity of the senior HR team to enable greater scrutiny and oversight, coupled with anti-racism training and education. Pennine Care provides mental health, learning disability, and autism services to a population of 1.3 million people in parts of Greater Manchester and Derbyshire. The trust has a workforce of approximately 4,000 staff, of which 17.9% are from an ethnic minority background. Data collected by the trust as part of the annual NHS Workforce Race Equality Standard (WRES) submission for 2022/23 showed a decrease against metric three (with ethnic minority staff 1.1 times more likely, where 1 is equally as likely, to be taken through the formal disciplinary process). This was an improvement on the previous year, where ethnic minority staff were three times more likely to enter a formal disciplinary process than their white colleagues. The NHS Providers Race Equality programme and Hempsons team spoke to Nicky Littler, director of workforce, and Shawnna Gleeson, deputy director of workforce, at Pennine Care to hear more about the organisation’s work on this topic. This case study shares details of the interventions they have implemented, their impact, the challenges faced, and advice they would give to other board members.

• Increased capacity and support in the senior HR team, by appointing a deputy director of workforce who was tasked with strengthening the oversight of cases. They review the employee relations caseload monthly in partnership with the employee relations team and head of workforce. Where disproportionality of ethnic minority staff is identified immediate reviews of these cases are undertaken.  
• A review of how employee relations data is monitored and report ed by protected characteristic and pay band. This has been in response to data that shows lower banded Agenda for Change staff are more likely to enter a disciplinary process and that there is a higher representation of ethnic minority staff in these bands.  
• Appl ying a person-centred and inclusive lens to the disciplinary and grievance triage processes, examining the cause behind the action/behaviour demonstrated , where previously the focus had been procedure focused.    
• Delivering ongoing training to enable HR colleagues to have better conversations about race. This includes programmes ranging from bullying and harassment, neurodiversity awareness, cultural awareness as well as refreshers on supporting fair employment processes.  
• Commissioning specialist 'lessons learnt' training from employment tribunals for the HR team delivered by Hempsons . This can involve highlighting procedural failings or a revised approach for a better outcome.   
• Increasing support from senior HR leadership to managers and members of the HR team to help navigate power imbalances that occur when a management decision conflicts with HR guidance and advice, for example a management decis ion to proceed to a disciplinary which a more junior HR advisor or partner may not feel able to sufficiently challenge.  
• Mandating trust - wide anti - racism training. This is in addition to mandated equality, diversity, and inclusion (EDI) training. The training is due to be rolled out across the trust and provide s clarity on what is unacceptable behaviour and the trust's zero tolerance approach to any form of discrimination .  
• Reporting bi-annually on casework at all levels , including at the people and workforce committee and board by protected characteristic, pay band and role type.  

Increased governance and scrutiny, as well as staff engagement have been key enablers to reducing inequality of experience for ethnic minority staff within the disciplinary process. This includes sharing reports amongst both senior, departmental and team managers. In addition, regular staff engagement, including monthly focus sessions, is undertaken to discuss any thematic issues highlighted by the data.

The trust's Race Equality Network (REN) has been engaged and involved in the development of the EDI action plan. More specifically, the REN lead collaborates with other staff network leads, partnership (union) leads, members of the senior leadership and operational leadership teams in the trust's EDI steering group to coproduce and deliver the EDI action plan.

Increased scrutiny of workforce data and employee relations caseload (by protected characteristic) from the people and workforce committee and board, have ensured that focus on race equality and the WRES is maintained year round, where any disproportionality is identified and escalated early, and actions are taken to understand and address the cause. Any subsequent learnings are shared across the workforce team and reported into both the trust management board and operational management meetings.

Prior to this work on improving the disciplinary process, the majority of disciplinary cases received an outcome of, 'no case to answer'. The introduction of case triage and applying a person-centred approach at the start of the process has resulted in fewer cases being taken forward unnecessarily/where there is 'no case to answer.'  

Being able to evidence improvement in WRES metric three has helped the trust maintain the engagement and support of ethnic minority staff, and led to more open discussion on the metrics where more time and renewed focus is needed to deliver impact.   

As a result of the increased scrutiny there have been some unintended benefits. These include earlier and more appropriate decision making and improvements to wider employee relations processes resulting in fairer outcomes and better experiences for staff.   

The increased reporting and governance scrutiny has highlighted inconsistencies in both the application of trust policies and processes and behaviours of some operational teams.  

Discussion at a local leadership level has identified that this is primarily a result of people feeling a real discomfort when talking about race. In response, over the past year, the trust has supported senior managers and board members on how to have the 'uncomfortable' conversations about race via facilitated workshops. Work to roll out training and support to the operational managers is ongoing, with the importance of this being championed by the chief executive and other board members through conversations at senior management meetings and regular trust-wide communication.    

The trust acknowledges that whilst good progress has been made, this has not always been sustained or prioritised. Work to implement a more restorative just and learning culture was paused during the pandemic and has been recently revived by the director of nursing.   

The board recognises that the issue of inequality within the disciplinary process could not be addressed in isolation. It needed to be part of wider work to develop an anti - racist and restorative just and learning culture. They acknowledge previous challenges in being able to maintain progress and recognise the need for ongoing scrutiny by the board. The additional governance and resource for key teams have enabled more measurable progress and are important in sustaining focus and momentum .    

Pennine Care NHS Foundation Trust's four top tips for other board members

• Be brave and face the issue. Create safe spaces to have the conversations, challenge each other in an appropriate way and do not let fear of saying the wrong thing prevent you from talking about the issues and taking action.
• Have a strong evidence base - use the data.
• Take a person-centred approach and embed this insight across all your work.
• Be ready to discuss challenging issues and get to the core of what the concern is.

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Case studies

Many organisations have been innovative to improve care for people who use their services, including these 9 examples.

The examples are not individually endorsed by the co-signatories to this publication and the descriptions of the providers, businesses and services are not judgements based on formal CQC inspections. Innovation is not a single methodology and different models may be needed in different contexts.

These examples are based on contributions from the services where we have seen innovation and adoption. They are intended bring to life the six principles in this publication to illustrate how they can be implemented in practice and stimulate discussion between providers about how to innovate well.

• Newcastle-upon-Tyne Hospitals NHS Foundation Trust
• St Mary’s Mount Care Home
• The Manor Surgery/AccuRx
• Durham County Council and County Durham and Darlington NHS Foundation Trust
• Support and permission to innovate at Royal Cornwall Hospitals NHS Trust
• Lewisham and Greenwich NHS Trust
• The Good Care Group
• Leeds Teaching Hospitals NHS Trust

These case studies were collected before the coronavirus pandemic. There are also some resources available which highlight examples of innovation in response to coronavirus, including:

• Innovation and inspiration : examples from CQC of how providers are responding to coronavirus
• Coronavirus adult social care provision : information and examples from Think Local Act Personal of emerging practice during the pandemic
• Digital innovation in adult social care : how communities have been supported during the pandemic

This publication was made possible by the Regulators’ Pioneer Fund from The Department for Business, Energy and Industrial Strategy (BEIS) and administered by Innovate UK .

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Building on the six principles

Innovation and why it is important

Developing a shared view of innovation

The six principles

Common misconceptions

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Disability Discrimination Case Study – The Equality Act 2010

BY ADISH FARKHAD, EMPLOYER LAW  

Taken from: NRAS magazine, Autumn 2012  

The following is a real case which  Adish  dealt with…  

Joe suffers from left hip early osteoarthritis with femoroacetabular impingement. He believes that this condition amounts to a disability within the meaning of the Equality Act 2010.    Joe is currently employed as a Personal Trainer at “All About Health” gymnasium (his “Employer”) and has worked with them for the past 10 years. Joe was diagnosed with hip early osteoarthritis with femoroacetabular impingement 3 years ago. He feels that he has been treated less favourably by his Employer because of his disability, contrary to the Equality Act 2010.    Joe has, on several occasions, made his Employer aware that he is suffering from a disability for which he requires reasonable adjustments to his working practices. Joe has requested the following adjustments:    1.    regular breaks from his shifts so that he can rest to ease the pain in his hip;  2.    a reduction in his hours but not so much of a reduction that would prevent him from earning a living. He wants to work 27 hours per week;  3.    an adjustment to the shift pattern for Personal Trainers to allow him to work Mondays and Tuesday which are his busiest days (so that he can continue to look after his key clients); and  4.    that his Employer waives its unreasonable request that Joe works every weekend (the quietest times) as part of his working hours as Joe wants to be treated in the same way as his non-disabled colleagues who only have to work one weekend per month.    Employee Booklets

Whilst Joe’s Employer has been on notice of his disability for over 3 years; it has persistently failed to make any adjustments to accommodate his disability. Joe’s manager regularly picks on him for demonstrating his hip pain in the way that he sometimes walks around the gym. His manager’s view is that Joe’s physical impairment does not create a positive image for the gymnasium and its Personal Trainers.    The subjecting of Joe to disability discrimination has meant that Joe has been prevented from working the reduced hours he requested and this has had a detrimental effect on his current health which has exacerbated the effects of his disability. Two months ago, Joe raised a formal grievance as he felt that he had no alternative but to do so in circumstances where all of his previous concerns raised verbally had been ignored. Joe’s Employer did not uphold his grievance and denied all liability for discrimination. Joe’s Employer did, however, agree to reduce his hours to 20 hours per week (with no flexibility or adjustment to enable him to work in excess of that should the need arise), requesting that he works at the quietest times every weekend and preventing him from working at the busiest times on Mondays and Tuesdays. He has also been allowed to take a 10-minute break when he feels in pain on the condition that he authorises the break with his manager so that his manager is aware of his whereabouts.    Joe’s Employer wishes to vary Joe’s terms and conditions of employment to reflect his new working hours (20 hours per week) and days of work to include working every weekend. Joe was told that he would face “proceedings” if he does not accept the proposed varied terms.    Joe considers that his Employer has failed to give any good reason for not agreeing to make the adjustments he requested and that the proposed adjustments that it is willing to make are unreasonable in the circumstances. Joe is aware that new staff are being recruited or being asked to cover Mondays and Tuesdays (his Employer has the maximum number of Personal Trainers already because it is allowing employees without a disability to work on a Monday and Tuesday instead of him).    Joe went to see a solicitor for legal advice to see if he had any potential employment claims against his Employer. He was advised that the Equality Act 2010 requires employers to make reasonable adjustments for employees who have a disability. Also, that employees with a disability should not be treated less favourably because of a disability. In Joe’s case, his employer did not provide any business reasons as to why it could not allow Joe to work 27 hours per week and on a Monday and/or Tuesday. Joe’s Employer had not sought a medical opinion from an Occupational Health Therapist about his disability and what recommended adjustments could be made. In all the circumstances, therefore, Joe’s Employer had failed to make reasonable adjustments. In addition to this, Joe’s Employer subjected him to less favourable treatment by insisting he works at the quietest times every weekend (when his colleagues who did not suffer from a disability did not have to work every weekend) and by insisting that he seeks his manager’s approval before taking breaks, in circumstances when it knew Joe had been bullied by him and that it would not always be possible to obtain such authority.    In addition to a claim for disability discrimination, Joe could also claim victimisation under the Equality Act 2010 because he was subjected to further less favourable treatment because he made a complaint (by raising his grievance) about disability discrimination, as his Employer threatened that he would face ‘proceedings’ if he does not accept the proposed variation to his terms and conditions of employment.    Joe was advised that if he were to pursue a claim in the Employment Tribunal for disability discrimination, he would be entitled to compensation for his injury to feelings, his future loss of income (if he were to resign and leave the gymnasium) and possibly the personal injury he had suffered due to his condition becoming worse as a result of his Employer’s failure to accommodate his disability. It was also explained to Joe that the Employment Tribunal would make a recommendation about reasonable adjustments for his continued employment (if he did not leave).    At the interview with his solicitor, Joe was concerned about the costs involved in pursuing an Employment Tribunal claim. However, when his solicitor discussed the matter with him, it became clear that he had Legal Expenses Insurance which would fund legal assistance. Joe was very surprised he had not realised he had such cover in his Home and Contents Policy. Joe’s solicitor assisted him to apply to his insurers for funding and then issued an Employment Tribunal claim on his behalf.    Employer Law     The Equality Act 2010 is the law which bans unfair treatment and helps achieve equal opportunities in the workplace and in wider society.  For further information and to download publications visit:  www.homeoffice.gov.uk/equalities/equality-act  

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• About Adverse Childhood Experiences
• Risk and Protective Factors
• Program: Essentials for Childhood: Preventing Adverse Childhood Experiences through Data to Action
• Adverse childhood experiences can have long-term impacts on health, opportunity and well-being.
• Adverse childhood experiences are common and some groups experience them more than others.

What are adverse childhood experiences?

Adverse childhood experiences, or ACEs, are potentially traumatic events that occur in childhood (0-17 years). Examples include: 1

• Experiencing violence, abuse, or neglect.
• Witnessing violence in the home or community.
• Having a family member attempt or die by suicide.

Also included are aspects of the child’s environment that can undermine their sense of safety, stability, and bonding. Examples can include growing up in a household with: 1

• Substance use problems.
• Mental health problems.
• Instability due to parental separation.
• Instability due to household members being in jail or prison.

The examples above are not a complete list of adverse experiences. Many other traumatic experiences could impact health and well-being. This can include not having enough food to eat, experiencing homelessness or unstable housing, or experiencing discrimination. 2 3 4 5 6

Quick facts and stats

ACEs are common. About 64% of adults in the United States reported they had experienced at least one type of ACE before age 18. Nearly one in six (17.3%) adults reported they had experienced four or more types of ACEs. 7

Preventing ACEs could potentially reduce many health conditions. Estimates show up to 1.9 million heart disease cases and 21 million depression cases potentially could have been avoided by preventing ACEs. 1

Some people are at greater risk of experiencing one or more ACEs than others. While all children are at risk of ACEs, numerous studies show inequities in such experiences. These inequalities are linked to the historical, social, and economic environments in which some families live. 5 6 ACEs were highest among females, non-Hispanic American Indian or Alaska Native adults, and adults who are unemployed or unable to work. 7

ACEs are costly. ACEs-related health consequences cost an estimated economic burden of $748 billion annually in Bermuda, Canada, and the United States. 8

ACEs can have lasting effects on health and well-being in childhood and life opportunities well into adulthood. 9 Life opportunities include things like education and job potential. These experiences can increase the risks of injury, sexually transmitted infections, and involvement in sex trafficking. They can also increase risks for maternal and child health problems including teen pregnancy, pregnancy complications, and fetal death. Also included are a range of chronic diseases and leading causes of death, such as cancer, diabetes, heart disease, and suicide. 1 10 11 12 13 14 15 16 17

ACEs and associated social determinants of health, such as living in under-resourced or racially segregated neighborhoods, can cause toxic stress. Toxic stress, or extended or prolonged stress, from ACEs can negatively affect children’s brain development, immune systems, and stress-response systems. These changes can affect children’s attention, decision-making, and learning. 18

Children growing up with toxic stress may have difficulty forming healthy and stable relationships. They may also have unstable work histories as adults and struggle with finances, jobs, and depression throughout life. 18 These effects can also be passed on to their own children. 19 20 21 Some children may face further exposure to toxic stress from historical and ongoing traumas. These historical and ongoing traumas refer to experiences of racial discrimination or the impacts of poverty resulting from limited educational and economic opportunities. 1 6

Adverse childhood experiences can be prevented. Certain factors may increase or decrease the risk of experiencing adverse childhood experiences.

Preventing adverse childhood experiences requires understanding and addressing the factors that put people at risk for or protect them from violence.

Creating safe, stable, nurturing relationships and environments for all children can prevent ACEs and help all children reach their full potential. We all have a role to play.

• Merrick MT, Ford DC, Ports KA, et al. Vital Signs: Estimated Proportion of Adult Health Problems Attributable to Adverse Childhood Experiences and Implications for Prevention — 25 States, 2015–2017. MMWR Morb Mortal Wkly Rep 2019;68:999-1005. DOI: http://dx.doi.org/10.15585/mmwr.mm6844e1 .
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Adverse Childhood Experiences (ACEs)

ACEs can have a tremendous impact on lifelong health and opportunity. CDC works to understand ACEs and prevent them.

• Open access
• Published: 27 May 2024

Patients’ satisfaction with cancer pain treatment at adult oncologic centers in Northern Ethiopia; a multi-center cross-sectional study

• Molla Amsalu 1 ,
• Henos Enyew Ashagrie 2 ,
• Amare Belete Getahun 2 &
• Yophtahe Woldegerima Berhe   ORCID: orcid.org/0000-0002-0988-7723 2  

BMC Cancer volume  24 , Article number:  647 ( 2024 ) Cite this article

Metrics details

Patient satisfaction is an important indicator of the quality of healthcare. Pain is one of the most common symptoms among cancer patients that needs optimal treatment; rather, it compromises the quality of life of patients.

To assess the levels and associated factors of satisfaction with cancer pain treatment among adult patients at cancer centers found in Northern Ethiopia in 2023.

After obtaining ethical approval, a multi-center cross-sectional study was conducted at four cancer care centers in northern Ethiopia. The data were collected using an interviewer-administered structured questionnaire that included the Lubeck Medication Satisfaction Questionnaire (LMSQ). The severity of pain was assessed by a numerical rating scale from 0 to 10 with a pain score of 0 = no pain, 1–3 = mild pain, 4–6 = moderate pain, and 7–10 = severe pain Binary logistic regression analysis was employed, and the strength of association was described in an adjusted odds ratio with a 95% confidence interval.

A total of 397 cancer patients participated in this study, with a response rate of 98.3%. We found that 70.3% of patients were satisfied with their cancer pain treatment. Being married (AOR = 5.6, CI = 2.6–12, P  < 0.001) and being single (never married) (AOR = 3.5, CI = 1.3–9.7, P  = 0.017) as compared to divorced, receiving adequate pain management (AOR = 2.4, CI = 1.1–5.3, P  = 0.03) as compared to those who didn’t receive it, and having lower pain severity (AOR = 2.6, CI = 1.5–4.8, P  < 0.001) as compared to those who had higher level of pain severity were found to be associated with satisfaction with cancer pain treatment.

The majority of cancer patients were satisfied with cancer pain treatment. Being married, being single (never married), lower pain severity, and receiving adequate pain management were found to be associated with satisfaction with cancer pain treatment. It would be better to enhance the use of multimodal analgesia in combination with strong opioids to ensure adequate pain management and lower pain severity scores.

Peer Review reports

Introduction

Pain is defined as an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage [ 1 ]. The prevalence of pain in cancer patients is 44.5-66%. with the prevalence of moderate to severe pain ranging from 30 to 38%, and it can persist in 5-10% of cancer survivors [ 2 ]. Using the World Health Organization’s (WHO) cancer pain management guidelines can effectively reduce cancer-related pain in 70-90% of patients [ 3 , 4 ]. Compared to traditional pain states, the mechanism of cancer-related pain is less understood; however, cancer-specific mechanisms, inflammatory, and neuropathic processes have been identified [ 5 ]. Uncontrolled pain can negatively affect patients’ daily lives, emotional health, social relationships, and adherence to cancer treatment [ 6 ]. Patients with moderate to severe pain have a higher fatigue score, a loss of appetite, and financial difficulties [ 7 ]. Patients fear the pain caused by cancer more than dying from the disease since pain affects their physical and mental aspects of life [ 8 ]. A meta-analysis of 30 studies stated that pain was found to be a significant prognostic factor for short-term survival in cancer patients [ 9 ]. Many cancer patients have a very poor prognosis. However, adequate pain treatment prevents suffering and improves their quality of life. Although the WHO suggested non-opioids for mild pain, weak opioids for moderate pain, and strong opioids for severe pain, pain treatment is not yet adequate in one-third of cancer patients [ 10 ].

Patient satisfaction with pain management is a valuable measure of treatment effectiveness and outcome. It could be used to evaluate the quality of care [ 11 , 12 , 13 ]. Patient satisfaction affects treatment compliance and adherence [ 12 ]. Studies have reported that 60-76% of patients were satisfied with pain treatment, and a variety of factors were found associated with levels of satisfaction [ 3 , 14 , 15 , 16 ]. Studies conducted in Ethiopia reported the prevalence of pain ranging from 59.9 to 93.4% [ 17 , 18 ]. These studies indicate that cancer pain is inadequately treated. Assessment of pain treatment satisfaction can help identify appropriate treatment modalities and further its effectiveness. We conducted this study since there was limited research-based evidence on cancer pain management in low-income countries like Ethiopia. Our research questions were: how satisfied are adult cancer patients with pain treatment, and what are the factors associated with the satisfaction of adult cancer patients with pain treatment?

Methodology

Study design, area, period, and population.

A multi-center cross-sectional study was conducted at four cancer care centers in Amhara National Regional State, Northern Ethiopia from March to May 2023. Those cancer care centers were found in the University of Gondar Comprehensive Specialized Hospital (UoGCSH), Felege-Hiwot Comprehensive Specialized Hospital (FHCSH), Tibebe-Ghion Comprehensive Specialised Hospital (TGCSH) and Dessie Comprehensive Specialized Hospital (DCSH). We selected these centers as they were the only institutions providing oncologic care in the region during the study period.

The UoGCSH had 28 beds in its adult oncology ward and serves 450 cancer patients every month. Three specialist oncologists and 12 nurses provide services in the ward. The FHCSH had 22 beds and provides services for 325 cancer patients every month. Two specialist oncologists, two oncologic nurses, and 7 comprehensive nurses provide services. The TGCSH had eight beds and serves 300 cancer patients every month. There were three specialist oncologists and four oncologic nurses at the care center. The cancer care center at DCSH had 10 beds. It serves 350 cancer patients every month. There was one specialist oncologist, three oncologic nurses, and three comprehensive nurses.

All cancer patients who attended those cancer care centers were the source population, and adult (18+) cancer patients who were prescribed pain treatment for a minimum of one month were the study population. Unconscious patients, patients with psychiatric problems, patients with advanced cancer who were unable to cooperate, and patients with oncologic emergencies were excluded from this study.

Variables and operational definitions

The outcome variable was patient satisfaction with cancer pain treatment, which was measured by the Lubeck Medication Satisfaction Questionnaire. The independent variables were sociodemographic (age, sex, marital status, monthly income, and level of education), clinical (site of tumor, stage of cancer, metastasis), cancer treatment (surgery, chemotherapy, radiotherapy), level of pain, and analgesia (type of analgesia, severity of pain, adequacy of pain treatment, adjuvant analgesic).

• Patient satisfaction

perceptions of the patients regarding the outcome of pain management and the extent to which it meets their needs and expectations. It was measured by a 4-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = agree, 4 = strongly agree) using the LMSQ which has 18 items within 6 subscales that have 3 items in each (effectivity, practicality, side-effects, daily life, healthcare providers, and overall satisfaction) [ 19 ]. Final categorization was done by dichotomizing into satisfied and dissatisfied by using the demarcation threshold formula.

\((\frac{\text{T}\text{o}\text{t}\text{a}\text{l}\,\,\text{h}\text{i}\text{g}\text{h}\text{e}\text{s}\text{t}\,\,\text{s}\text{c}\text{o}\text{r}\text{e} - \text{T}\text{o}\text{t}\text{a}\text{l}\,\, \text{l}\text{o}\text{w}\text{e}\text{s}\text{t}\,\, \text{s}\text{c}\text{o}\text{r}\text{e} }{2}\) ) + Total lowest score [ 20 ]. The highest patient satisfaction score was 70 and the lowest satisfaction score was 26. A score < 48 was classified as dissatisfied, and a score ≥ 48 was classified as satisfied.

The Numeric rating scale (NRS) is a validated pain intensity assessment tool that helps to give patients a subjective feeling of pain with a numerical value between 0 and 10, in which 0 = no pain, 1–3 = mild pain, 4–6 = moderate pain, 7–10 = severe pain [ 21 ].

The Adequacy of cancer pain treatment was measured by calculating the Pain Management Index (PMI) according to the recommendations of the WHO pain management guideline [ 22 ]. The PMI was calculated by considering the prescribed most potent analgesic agent and the worst pain reported in the last 24 h [ 23 ]. The prescribed analgesics were scored as follows: 0 = no analgesia, 1 = non-opioid analgesia, 2 = weak opioids, and 3 = strong opioids. The PMI was calculated by subtracting the reported NRS value from the type of most potent analgesics administered. The calculated values of PMI ranged from − 3 (no analgesia therapy for a patient with severe pain) to + 3 (strong opioid for a patient with no pain). Patients with a positive PMI value were considered to be receiving adequate analgesia, whereas those with a negative PMI value were considered to be receiving inadequate analgesia.

Sample size determination and sampling technique

A single population proportion formula was used to determine the sample size by considering 50% satisfaction with cancer pain treatment and a 5% margin of error at a 95% confidence interval (CI). A non-probability (consecutive) sampling technique was employed to attain a sample size within two months of data collection period. After adjusting the proportional allocation for each center and adding 5% none response, a total of 404 study participants were included in the study: 128 from the University of Gondar Comprehensive Specialized Hospital, 99 from Dessie Comprehensive Specialized Hospital, 92 from Felege Hiwot Comprehensive Specialized Hospital, and 85 from Tibebe Ghion Comprehensive Specialized Hospital.

Data collection, processing, and analysis

Ethical approval.

was obtained from the Ethical Review Committee of the School of Medicine at the University of Gondar ( Reference number: CMHS/SM/06/01/4097/2015) . Data were collected using an interviewer-administered structured questionnaire and chart review during outpatient and inpatient hospital visits by four trained data collectors (one for every center). Written informed consent was obtained from each participant after detailed explanations about the study. Informed consent with a fingerprint signature was obtained from patients who could not read or write after detailed explanations by the data collectors as approved by the Ethical Review Committee of the School of Medicine, at the University of Gondar.

Questions to assess the severity of pain and pain relief were taken from the American Pain Society patient outcome questionnaire [ 24 ]. Patients were asked to report the worst and least pain in the past 24 h and the current pain by using a numeric rating scale from 0 to 10, with a pain score of 0 = no pain, 1–3 = mild pain, 4–6 = moderate pain, 7–10 = severe pain.

The Pain Management Index (PMI) based on WHO guidelines, was used to quantify pain management by measuring the adequacy of cancer pain treatment [ 25 ]. The following scores were given (0 = no analgesia, 1 = non-opioid analgesia, 2 = weak opioid 3 = strong opioid). Pain Management Index was calculated by subtracting self-reported pain level from the type of analgesia administered and ranges from − 3 (no analgesic therapy for a patient with severe pain) to + 3 (strong opioid for a patient with no pain). The level of pain was defined as 0 with no pain, 1 for mild pain, 2 for moderate pain, and 3 for severe pain. Patients with negative PMI scores received inadequate analgesia.

The pain treatment satisfaction was measured by the Lübeck Medication Satisfaction Questionnaire (LMSQ) consisting of 18 items [ 19 ]. Lübeck Medication Satisfaction Questionnaire (LMSQ) has six subclasses each consisting of equally waited and similar context of three items. The subclass includes satisfaction with the effectiveness of pain medication, satisfaction with the practicality or form of pain medication, satisfaction with the side effect profile of pain medication, satisfaction with daily life after receiving pain treatment, satisfaction with healthcare providers, and overall satisfaction. Satisfaction was expressed by a four-point Likert scale (4 = Strongly Agree, 3 = Agree, 2 = Disagree, 1 = Strongly Disagree). The side effect subclass was phrased negatively, marked with Asterix, and reverse-scored in STATA before data analysis.

Data were collected with an interviewer-administered questionnaire. The reliability of the questionnaire was assessed by using 40 pretested participants and the reliability coefficient (Cronbach’s alpha value) of the questionnaire was 91.2%. The collected data was checked for completeness, accuracy, and clarity by the investigators. The cleaned and coded data were entered in Epi-data software version 4.6 and exported to STATA version 17. The Shapiro-Wilk test, variance inflation factor, and Hosmer-Lemeshow test were used to assess distribution, multicollinearity, and model fitness, respectively. Descriptive, Chi-square and binary logistic regression analyses were performed to investigate the associations between the independent and dependent variables. The independent variables with a p-value < 0.2 in the bivariable binary logistic regression were fitted to the final multivariable binary logistic regression analysis. Variables with p-value < 0.05 in the final analysis were considered to have a statistically significant association. The strength of associations was described in adjusted odds ratio (AOR) at a 95% confidence interval.

Sociodemographic and clinical characteristics

A total of 397 patients were involved in this study (response rate of 98.3%). Of the participants, 224 (56.4%) were female, and over half were from rural areas ( n  = 210, 52.9%). The median (IQR) age was 48 (38–59) years [Table  1 ]. The most common type of cancer was gastrointestinal cancer 114 (28.7%). Most of the study participants, 213 (63.7%), were diagnosed with stage II to III cancer. The majority of the participants were taking chemotherapy alone (292 (73.6%)) [Table  2 ]. Over 90% of patients reported pain; 42.3% reported mild pain, 39.8% reported moderate pain, and 10.1% reported severe pain. Pain treatment adequacy was assessed by self-reports from study participants following pain management guidelines, and 17.1% of patients responded to having inadequate pain treatment. The majority of patients, 132 (33.3%), were prescribed combinations of non-opioid and weak opioid analgesics for cancer pain treatment. Only 34 (8.6%) cancer patients used either strong opioids alone or in combination with non-opioid analgesics.

Patients’ satisfaction with cancer pain treatment and correlation among the subscales

Most participants strongly agree (243, (61.2%)) with item LMSQ18 in the “overall satisfaction” subscale and strongly disagree (206, (51.9%)) for item LMSQ2 in the “side-effect” subscale respectively [Table  3 ]. The highest satisfaction score was observed in the side-effect subscale, with a median (IQR) of 10 (9–11) [Table  4 ].

Two hundred and seventy-nine (70.3%) cancer patients were found to be satisfied with cancer pain treatment (CI = 65.6−74.6%). The highest satisfaction rate was observed in the “side-effects” subscale, to which 343 (86.4%) responded satisfied [Fig.  1 ]. A Spearman’s correlation test revealed that there were correlations among the subscales of LMSQ; and the strongest positive correlation was observed between effectivity and healthcare workers subscale (r s = 0.7, p  < 0.0001). The correlation among the subscales is illustrated in a heatmap [Fig.  2 ].

Patient satisfaction with cancer pain treatment with each LMSQ subclass, n  = 397

A heatmap showing the Spearman correlation of each subclass of pain treatment satisfaction, n  = 397

Factors associated with patient satisfaction with cancer pain treatment

In the bivariable binary logistic regression analysis, marital status, stage of cancer, types of cancer treatment, severity of pain in the last 24 h, current pain severity, types of analgesics, and pain management index met the threshold of P-value < 0.2 to be included into the final multivariable binary logistic regression analysis. In the final analysis, marital status, current pain severity, and pain management index were significantly associated with patient satisfaction (P-value < 0.05). Married and single cancer patients had higher odds of being satisfied with cancer pain treatment compared to divorced patients (AOR = 5.6, CI, 2.6–12.0, P  < 0.001), (AOR = 3.5, CI = 1.3–9.7, P  = 0.017), respectively. The odds of being satisfied with cancer pain treatment among patients who received adequate pain management were more than two times greater than those who received inadequate pain management (AOR = 2.4, CI = 1.1–5.3, P  = 0.03). Patients who reported a lesser severity of current pain were nearly three times more likely to be satisfied with cancer pain treatment (AOR = 2.6, CI = 1.5–4.8, P  < 0.001) [Table  5 ].

The objective of the present study was to assess patients’ satisfaction with cancer pain treatment at adult oncologic centers. Our study revealed that most cancer patients (70.3%) have been satisfied with cancer pain treatment. This is consistent with studies done by Kaggwa et al. and Mazzotta et al. [ 16 , 26 ]. Whereas, it is a higher rate of satisfaction compared to other studies that reported 33.0% [ 27 ] and 47.7% [ 28 ] of satisfaction. The differences might be possibly explained by the use of different pain and satisfaction assessment tools, the greater inclusion (about 70%) of patients with advanced stages of cancer, the duration of cancer pain treatment, and the adequacy of pain management. In the current study, only 19.6% of patients have been diagnosed with stage IV cancer: patients should take treatment at least for a month, and over 80% of patients have received adequate pain management according to PMI. However, some studies have reported higher rates of satisfaction with cancer pain treatment [ 15 , 29 ]. The possible reason for the discrepancy might be the greater (over 40%) use of strong opioid analgesics in the previous studies. Strong opioids were prescribed only for 8.6% of patients in our study. Due to the complex pathophysiology, cancer pain involves multiple pain pathways. Hence, multimodal analgesia in combination with strong opioids is vital in cancer pain management [ 30 ]. Furthermore, the use of epidural analgesia could be another reason for higher rates of satisfaction [ 29 ].

Regarding satisfaction with subscales of LMSQ, about 80% of patients were satisfied with the information provided by the healthcare providers [ 27 ]. In our study; 67.8% of patients were satisfied with the education provided by healthcare providers about their disease and treatment. In contrast, a higher proportion of participants were satisfied with information provision in a study conducted by Kharel et al. [ 29 ]. Furthermore, we observed the lowest satisfaction rate in the daily life subscale. About 48% of cancer patients were not satisfied with their daily lives after receiving analgesic treatment for cancer pain.

Married and single (never married) cancer patients were found to have higher odds of being satisfied with cancer pain treatment as compared to divorced cancer patients. These findings could be explained by the presence of better social support from family or loved ones. Better social support can enhance positive coping mechanisms, increase a sense of well-being, and decrease anxiety and depression. It also improves a sense of societal vitality and results in higher patient’ satisfaction [ 31 , 32 ].

Patients who had a lower pain score were satisfied compared to those who reported a higher pain score, and this is supported by multiple previous studies [ 16 , 26 , 27 , 29 , 33 , 34 ]. This could be explained by the negative impacts of pain on physical function, sleep, mood, and wellbeing [ 35 ]. Moreover, higher pain severity scores could increase financial expenses because of unnecessary or avoidable emergency department visits; and has a consequence of dissatisfaction [ 23 ]. On the contrary, there are studies that state pain severity does not affect patients’ satisfaction [ 36 , 37 ].

Positive PMI scores were significantly associated with cancer pain treatment satisfaction. Patients who received adequate pain management were highly likely to be satisfied with cancer pain treatment. This finding is similar to that of a study done in Taiwan [ 38 ]. However, a study conducted by Kaggwa et al. has denied any association between PMI scores and cancer pain satisfaction [ 16 ].

Satisfaction with healthcare workers and effectivity of analgesics

This study showed that there was a moderately positive correlation between satisfaction with healthcare workers and satisfaction with patients’ perceived effectiveness of analgesics. This might be explained by a positive relationship between healthcare professionals and patients receiving cancer pain treatment. Healthcare providers who provide health education regarding the effectiveness of analgesics may improve patients’ adherence to the prescribed analgesic agent and improve patients’ perceived satisfaction with the effectiveness of analgesics. A systematic review showed that the hope and positivity of healthcare professionals were important for patients to cope with cancer and increase satisfaction with care [ 39 ]. Increased patient satisfaction with care provided by healthcare workers may change attitude of patients who accepted cancer pain as God’s wisdom or punishment and create a positive attitude toward the effectiveness of analgesics [ 40 ]. Another study supported this finding and stated that healthcare providers who deliver health education regarding the prevention of drug addiction, side effects of analgesics, timing, and dosage of analgesics improve patient attitude and cancer pain treatment [ 41 ].

Correlation of each subclass of cancer pain treatment satisfaction

A Spearman correlation was run to assess the correlation of each subclass of LMSQ using the total sample. There was strong positive correlation (r s = 0.5–0.64) between most of LMSQ subclass at p  < 0.01.

A cross-sectional study stated that the effectiveness of analgesic, efficacy of medication and patient healthcare provider communication were associated with patient satisfaction [ 42 ]. In this study, 58.2% of patients were satisfied with the practicability of analgesic medications. Comparable to this study, a cross-sectional study stated that patients who were prescribed convenient, fast-acting medications were more satisfied [ 43 ]. Another study stated that 100% of patients who received sufficient information on analgesic treatment and 97.9% of patients who received sufficient information about the side effects of analgesic treatment were satisfied with cancer pain management [ 44 ]. Patients who were satisfied with their pain levels reported statistically lower mean pain scores (2.26 ± 1.70) compared to those not satisfied (4.68 ± 2.07) or not sure (4.21 ± 2.21) [ 27 ]. This may be explained by the impact of pain on daily activity. Patients who report a lower average pain score may have a lower impact of pain on physical activity compared to those who report a higher mean pain score. Another study also supports this evidence and states that patients who reported a severe pain score and lower quality of life had lower satisfaction with the treatment received [ 45 ].

As a secondary outcome, only 16% of patients were diagnosed to have stage I cancer. This finding could indirectly indicate that there were delays in cancer diagnosis at earlier stage. Further studies may be required to underpin this finding.

In this study, baseline pain before analgesic treatment was not assessed and documented. As a cross-sectional study, we could not draw a cause-and-effect conclusion. Since questions that were used to measure oncologic pain treatment satisfaction were self-reported, answers to each question might not be trustful. The expectation and opinion of the interviewer also might affect the result of the study. These could be potential limitations of the study.

Conclusions

Despite the fact that most cancer patients reported moderate to severe pain, there was a high rate of satisfaction with cancer pain treatment. It would be better if hospitals, healthcare professionals, and administrators took measures to enhance the use of multimodal analgesia in combination with strong opioids to ensure adequate pain management, lower pain severity scores, and better daily life. We also urge the arrangement of better social support mechanisms for cancer patients, the improvement of information provision, and the deployment of professionals who have trained in pain management discipline at cancer care centres.

Data availability

Data and materials used in this study are available and can be presented by the corresponding author upon reasonable request.

Abbreviations

Adjusted Odds Ratio

Crude Odds Ratio

Confidence Interval

Dessie Compressive and Specialized Hospital

Felege-Hiwot Compressive and Specialized Hospital

Inter-quartile Range

Lubeck Medication Satisfaction Questionnaire

Numerical Rating Scale

Pain Management Index

Standard Deviation

Tibebe-Ghion Compressive and Specialized Hospital

University of Gondar Compressive and Specialized Hospital

World health organization

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Acknowledgements

We would like to acknowledge the University of Gondar Comprehensive Specialized Hospital, Tibebe-Ghion Comprehensive Specialized Hospital, Felege-Hiwot Comprehensive Specialized Hospital, Dessie Comprehensive Specialized Hospital. We would also want to acknowledge Ludwig Matrisch from the Department of Rheumatology and Clinical Immunology, Universität zu Lübeck, 23562 Lübeck, Germany for supporting us on the utilization of the Lübeck Medication Satisfaction Questionnaire (LMSQ) [email protected],

This study was supported by University of Gondar and Debre Birhan University with no conflict of interest. The support did not include publication charges.

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Molla Amsalu

Department of Anaesthesia, University of Gondar, Gondar, Ethiopia

Henos Enyew Ashagrie, Amare Belete Getahun & Yophtahe Woldegerima Berhe

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‘’M.A. has conceptualized the study and objectives; and developed the proposal. Y.W.B., H.E.A., and A.B.G. criticized the proposal. All authors had participated in the data management and statistical analyses. Y.W.B, M.A., and H.E.A. have prepared the final manuscript. All authors read and approved the final manuscript.‘’.

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Ethical approval was obtained from the Ethical Review Committee of the School of Medicine, at the University of Gondar ( Reference number: CMHS/SM/06/01/4097/2015, Date: March 24, 2023 ). Permission support letters were obtained from FHCSH, TGCSH, and DCSH. Written informed consent was obtained from each participant after detailed explanations about the study. Informed consent with a fingerprint signature was obtained from patients who could not read or write after detailed explanations by the data collectors as approved by the Ethical Review Committee of the School of Medicine, at the University of Gondar.

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Amsalu, M., Ashagrie, H.E., Getahun, A.B. et al. Patients’ satisfaction with cancer pain treatment at adult oncologic centers in Northern Ethiopia; a multi-center cross-sectional study. BMC Cancer 24 , 647 (2024). https://doi.org/10.1186/s12885-024-12359-7

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Received : 17 October 2023

Accepted : 08 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1186/s12885-024-12359-7

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