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The most commonly endorsed barriers amongst non-attending women responding to our survey were difficulties making a convenient appointment close enough to home, worry about and/or previous experience of pain, concern that a man would do the test and having too many other things to worry about. In particular, pain (worry about pain and/or previous experience of pain) was a markedly prominent barrier, endorsed by nearly 40% of participants. These barriers are similar to those identified in the recent Cancer Awareness Measure (CAM) survey [4] where appointment convenience and locality, pain, concerns about a man doing the screening test and having too many other things to worry about also featured within the most endorsed barriers among screening attenders as well as non-attenders. Endorsement of the barriers by non-attenders (N = 91) in the CAM survey ranged between 9 and 19% which was lower than for this study and may reflect the different sampling methods (direct contact with non-attenders vs. a population-based survey including only a small proportion of non-attenders).

Exploration of the free text responses provided additional insight into barriers faced by participants. The high volume of free text responses was unanticipated, and it was interesting that women used this space as a forum to express their concerns and experiences about screening in more detail. A substantial proportion elaborated on experiences or fears about pain. This corresponds with both the findings of this study and existing quantitative and qualitative research illustrating that pain represents a particularly powerful barrier to different types of cancer screening [16,17,18]. The free text responses also revealed additional barriers, the most common of which were work commitments, difficulties with appointment communication and previous negative experience of screening. These are closely aligned with the pre-defined barriers of appointment convenience, pain and embarrassment and highlight the salience of issues around accessibility and acceptability.

Most women reported multiple barriers to breast screening which often encompassed facets of individual belief (e.g. worry about screening being painful) and motivation to attend screening (e.g. beliefs about having other more important things to do) as well as wider organisational and opportunistic barriers s (e.g. not being able to find a local appointment). These findings highlight the multifaceted causation of screening non-attendance and are consistent with the COM-B (Capability Opportunity Motivation– Behaviour) framework of health behaviour [19] which conceptualises screening behaviour as the product of an individual’s capability, motivation and opportunity to attend screening. This suggests that multi-component interventions that tackle a variety of barriers spanning individual beliefs around capability and motivation as well as addressing service-level barriers facilitating the opportunity to attend may be most useful to increase participation.

Exploratory analysis indicated variations in barrier endorsement according to several demographic factors. Age was found to have a significant influence on barrier endorsement. Women aged ≤ 64 reported greater difficulties getting a convenient appointment which may reflect greater demands on their time particularly from work. Older women were more likely to report previous experience of pain as a barrier, consistent with their greater breast screening experience. Prior experience of pain during breast screening is a significant predictor of expectation of pain at future breast screening and is significantly associated with reduced reattendance [18].

Age was also associated with concern that a man may conduct the mammogram with women aged between 55 and 64 more likely to endorse this barrier than younger or older women. There is no clear explanation for this finding. It may be spurious due to multiple testing in exploratory analysis but merits further research. Women from minoritised ethnic backgrounds and those who reported mental health problems were also more likely to be concerned about a man doing the mammogram​. Mammography is the only health procedure conducted exclusively by women practitioners with employers allowed to restrict mammography roles to females only within current equality legislation. The NHS breast screening invitation states that “Female staff will take your mammograms” and the accompanying leaflet states that “Mammograms are carried out by women called mammographers” and uses she/her pronouns to reference the mammographer as well as using a picture of a female mammographer. However, the findings of our survey and other research suggest this message has not reached all screening invitees [20, 21]. Our finding that women from minoritised ethnic backgrounds were more likely to endorse concern about a man doing the screening test has also been identified in other research and may reflect cultural and religious differences in modesty, acceptability of nudity and perceptions of indecency [20].

In relation to mental health status, research has identified that women with mental health problems or mental illness are less likely to attend breast screening, have poorer cancer outcomes and may have less awareness and understanding of the specifics of the screening process, and may be more likely to avoid medical examinations in general [13, 22,23,24]. It is possible that reduced awareness and understanding may increase vulnerability to the misperception that a man may conduct the mammography. It is important to note that our self-reported measure did not gather specific information about the type or severity of mental health problems, so it is difficult to draw specific conclusions; however, the overall findings suggest that the misperception that a man may conduct the screening is more prevalent amongst women with mental health problems.

The appointment being too far away was more likely to be endorsed by those reporting a physical disability. This finding fits in with previous research illustrating that physical barriers to healthcare including distance to appointments and lack of transport are particularly salient for individuals with disability [25, 26].

It is noteworthy that we did not identify any variation in barrier endorsement according to educational level suggesting that the nature of barriers faced by individuals with different educational backgrounds may not be significantly different. In the context of the robust association between lower educational attainment and lower screening uptake [9], it is possible that this association may be driven more by the additive impact of multiple barriers and lack of resources to overcome those barriers facing those from lower educational backgrounds rather than from variation in the type of barriers faced.

Most respondents reported being aware of breast screening and receiving invitations, however, respondents from minoritised ethnic backgrounds were more likely to indicate lack of awareness of breast screening or report not ever receiving an invitation. This finding aligns with previous research that has identified lower levels of cervical screening awareness and engagement as barriers to uptake amongst minoritised ethnic populations [27]. It is particularly notable that this was the case even in a sample of women who had recently been invited for screening, according to NBSS.

There are several limitations to the survey results. As anticipated, the response rate was very low with only 5.1% of those identified as not attending breast screening entering the survey which limits generalisability of our findings. The low response rate was partially due to the inability to contact over a third of identified participants due to the lack of a recorded mobile phone number. This is an important limitation as the lack of valid mobile telephone numbers recorded in NBSS is not random. Those with no recorded number are likely to be women who have limited contact with their GP, are unregistered with a GP or have no mobile phone access. These women may experience different barriers to the women with recorded mobile telephone numbers and merit further research. The high proportion of women without a mobile number recorded also has implications for the planned shift to digital app-based invitations [28]. Similarly, it was not possible to determine the type of breast screening invitation (timed or open appointment) that participants had received, and it may be that barriers vary by invitation type.

The majority of respondents were aged < 64 which limits generalisability of our findings to older populations whose barriers may differ; however, as attendance has been found to increase with increasing age, this may be a less significant limitation [4]. Furthermore, due to the anonymous nature of the survey, we were not able to use information from screening records in our analysis and subsequently were not able to determine individual screening histories (for example, how many previous appointments had been missed or attended) nor previous screening results or breast cancer diagnoses. Similarly, we asked participants to report whether they had mental health conditions and/or disability using a binary response format, however, we acknowledge that these questions are simplistic and do not capture the different types of mental health conditions and disability that may differentially impact access to screening. More nuanced analysis exploring the barriers faced by individuals with different screening histories, mental health conditions and types of disabilities would provide more specific insight into the barriers encountered by these populations, however, our analysis aimed to provide an overview of the barriers faced by non-attenders as well as exploration of variation in barrier endorsement according to broad demographic variables as a first step to identifying targets for intervention.

Generalisability to all non-attenders is further limited by possible self-selection bias whereby those opting to complete the survey may differ from those who chose not to complete it. Similarly, the survey required participants to recall the barriers preventing them from attending screening and these recollections may be inaccurate and vulnerable to recall bias. We aimed to reduce this risk by only including non-attenders who had been invited for screening within the last six months. In addition, the study was carried out over a 6-month period which means we cannot rule out the possibility that we did not capture seasonal barriers affecting attendance at different times of the year. However, exploration of the free text responses revealed very little mention of seasonal issues (coughs/colds, seasonal busyness or holidays) suggesting that these issues were not significant barriers.

A significant proportion of respondents from minoritised ethnic backgrounds were routed out of the barriers part of the survey because, on entering the survey, they indicated they were either unaware of screening or reported not receiving an invitation. This, combined with the overall small numbers initiating the survey, means that relatively few women from minoritised ethnic backgrounds completed the barriers items, again limiting generalisability. The survey was administered in English only, which limited accessibility for non-English speaking participants. The survey did includ assessment of language and ease of understanding of the invitation which indicated only a small proportion of participants did not speak English and very few reported trouble understanding the invitation. However, respondents with limited English may have found it difficult to complete the survey or may have been routed around this question by indicating lack of screening awareness or invitation receipt at the start of the survey. Older women from minoritised ethnic backgrounds have also been found to have less access to mobile phones and the internet which would have further limited the accessibility of the survey [29]. In the context that screening adherence is typically lower in minoritised ethnic populations [8, 30] further research using more accessible methods is required to clarify the barriers that are most prevalent in these populations.

Despite the low response rate and limited ethnic diversity, the sample size was considerably larger than that included in the CAM survey and provides insight into the barriers faced by an under-researched population.

Our findings have three key implications for increasing breast screening uptake. Firstly, there is a need for appointment booking modernisation and flexibility as the most common barrier to attendance was difficulty getting a convenient appointment. The difficulties associated with competing priorities, locality of appointment and communication around the appointment were also highly endorsed or referred to within the free text responses. Ensuring flexible appointments in local areas that are easy to access and schedule as well as simple to cancel and reschedule would help to overcome this barrier. Secondly, there is a need to communicate even more clearly to women that men will not be conducting their breast screening to reduce this misperception. This could involve adding a direct statement clarifying the female only nature of mammography in the leaflet accompanying the breast screening invitation or may entail exploring different modes of communication from traditional written information, for example, greater use of emphasis and visual images to more clearly disseminate this message. This is a particularly important for equitable access as our findings suggest that this barrier disproportionately impeded women from minoritised ethnic backgrounds and those reporting mental health problems who have lower screening uptake and poorer breast cancer outcomes. It is timely to note here that current shortfalls in mammographer recruitment have led to recent calls to allow men to practice as mammographers; however, our findings suggest that this should be approached with caution and more research is required to determine ways in which this could be addressed sensitively, for example, the use of chaperones or offering choice of practitioner gender. Our findings correspond with the results of recent commissioned work exploring the potential impact of the introduction of male mammographers into the NHS breast screening programme [31].

Thirdly, our finding that both previous experience of pain and fear of pain were significantly endorsed barriers as well as the high volume of free text responses describing painful experiences or significant fear of pain, suggests that tackling pain during breast screening is important to increasing uptake. Pain has been identified as a consistent barrier to screening and has been found to reduce attendance [18, 32]. Little is currently known about ways to prevent or reduce pain during screening and there is a need to address this gap within the research to identify effective ways to make screening more comfortable for women [33].

Bad news for sausage fans: According to a new study, even small amounts of processed meat can increase the risk of chronic diseases. A ham sandwich here, a snack salami there–what sounds harmless can lead to diabetes, coronary heart disease, or colon cancer. FITBOOK nutrition expert presents the study results to you.

Processed meat products have long been suspected of promoting chronic diseases. However, the strength of the connection–and whether even small amounts are harmful–remained unclear until now.

A research team led by Demewoz Haile from the Institute for Health Metrics and Evaluation (IHME) at the University of Washington aimed to find out exactly that. The goal was to systematically quantify the impact of common food groups on the development of chronic diseases. Given that chronic diseases like diabetes, coronary heart disease (CHD), and colon cancer cause a significant disease burden worldwide, precise insights into diet-related risk factors are of great importance for public health.¹

How the Researchers Proceeded

The study was conducted as a so-called “Burden of Proof” analysis–an advanced method based on systematic reviews (meta-analyses) and statistical modeling. The unique aspect: With this special method, the researchers were able to calculate dose-response relationships–that is, how much the risk increases with different consumption levels. They deliberately calculated conservatively to avoid overestimating effects. They used only existing data from large observational studies.

What is the “Burden of Proof” Method?

The “Burden of Proof” studies are a series of particularly elaborate meta-analyses developed by the IHME. They compile data from numerous observational studies and assess how strong and reliable the connection is between a risk factor (e.g., processed meat) and a disease (e.g., type 2 diabetes). The result is presented with a star system–from weak (one star) to strong evidence (five stars). The aim is to reduce scientific uncertainties and enable reliable statements for dietary recommendations.

For the present study, data from a variety of prospective cohort studies and case-control studies were included, such as:

• 15 studies with over 1.1 million participants on the link between processed meat and type 2 diabetes

• 11 studies with over 1.17 million participants on processed meat and CHD

• 18 studies with over 2.67 million participants on processed meat and colon cancer

Also interesting: The less meat in the diet, the lower …

Even One Sausage a Day Is Too Much

The analysis showed that even the smallest daily amounts of processed meat are associated with a measurable increase in disease risk–in all areas studied.

The risk of developing type 2 diabetes increased by an average of 11 percent with a daily intake of 0.6 to 57 grams. Assuming a person eats 50 grams per day (equivalent to two to three slices of cold cuts or a Vienna sausage), the risk increased by a whopping 30 percent. The results for colon cancer were similarly unfavorable. Here, a daily intake of 0.78 to 55 grams resulted in an average risk increase of seven percent. Those who eat 50 grams of processed meat daily have a 26 percent higher risk of colon cancer. For CHD, a daily 50-gram portion led to a 15 percent increase in risk.

The study authors noted: The risk increased continuously with the amount–but particularly strongly at low, everyday amounts.

Sugary Drinks and Trans Fats Also Showed a Negative Effect

The authors also considered two other common food groups that showed a negative effect on the development of chronic diseases: sugary drinks and trans fats. The analysis included

• 19 studies on sugar-sweetened beverages and diabetes (563,444 participants), eight studies on CHD (961,176 participants), and

• 6 studies with 226,509 people on trans fats and CHD.

The result: Consuming sugar-sweetened beverages (e.g., soft drinks) increased the risk of diabetes by 20 percent and CHD by seven percent when 250 grams were consumed daily. If trans fats (e.g., in croissants, fries, ready meals) made up one percent of daily energy intake, the risk of chronic diseases increased by 11 percent.

Significance of the Results

Even the smallest amounts of sausage, soft drinks, and trans fats can increase the risk of three of the most common chronic diseases worldwide. Particularly critical: The steepest increase in risk occurred at low daily intake levels–areas that many people regularly consume. For everyday life, this means: Even those who “moderately” indulge in processed meat or soft drinks may potentially expose themselves to an increased risk of disease. This result is also reflected in the 2024 updated recommendations of the German Nutrition Society. Instead of 600 grams, only 300 grams of meat and sausage can be consumed per week–if one wants to eat these foods at all. The DGE emphasizes: “Even with a consumption of no or less than 300 grams of meat per week, the nutritional goals can be achieved.”²

For research, the study underscores the need to precisely capture dose-response relationships–not least to be able to provide realistic and effective dietary recommendations.

Context and Possible Limitations

The study uses an advanced methodology with systematic literature search and precise dose-response modeling. The “Burden of Proof” method is among the most demanding approaches in nutritional epidemiology today. Also noteworthy is the transparency of the work: The data, methods, and codes are publicly accessible, and conflicts of interest have been disclosed and excluded. The work was peer-reviewed and published in “Nature Medicine,” a renowned journal.

However, there are limitations: The results are based on observational studies–studies that cannot prove cause-and-effect relationships but only show associations. These were each internally rated with only two stars, “indicating weak associations or conflicting evidence and underscoring both the need for further research and–given the high burden of these chronic diseases–the advisability of continuing to limit the consumption of these foods,” the study authors conclude.

Less Is More

Small amounts of processed meat seem harmless, but they are not. The current study shows that small amounts of sausage and the like are enough to significantly increase the risk of chronic diseases such as diabetes, CHD, and colon cancer. Particularly insidious: The greatest risk increase occurs with small, everyday portions. So, if you want to do something good for your health in the long term, you might want to opt for the plant-based alternative at your next snack. Less is clearly more in this case–and sometimes even life-extending.

The post Even a single slice of sausage a day can increase the risk of chronic diseases. appeared first on FITBOOK.

This study used a cross-sectional design analyzing real word electronic health record data. The study leverages data from the TriNetX Research Network, encompassing electronic medical records from approximately 115 million patients across 83 de-identified healthcare organizations [17]. The data used in this study was collected on November 29th, 2023. The study design is a cross-sectional analysis focusing on patients aged 45 and above who had healthcare visits between January 1, 2017, and December 31, 2018. This timeframe was chosen to avoid the confounding impact of the COVID-19 pandemic.

Ethical considerations

This TriNetX study is exempt from requiring informed consent because it is a retrospective study that involves secondary analysis of existing, de-identified data viewed only in the aggregate. This analysis does not involve direct intervention or interaction with human subjects. The de-identification of the data adheres to the standards defined in Section § 164.514(a) of the HIPAA Privacy Rule.

Terms used in this manuscript

We use person-first language throughout this manuscript, including people with diabetes and people with visual impairment.

Classification of visual impairment

In the absence of either objective visual assessment data or self-reported visual disability status (neither of which is available in Trinetx), we used the presence of visual disability-related ICD-10 codes (here termed VDRC) to define four different categories of visual impairment: None, Unqualified, Low Vision, and Blindness. We identify people with visual impairment if they have a diagnosis code in the latter three categories. Supplemental Table 1 identifies the ICD-10 codes associated with each.

Inclusion criteria

To compare rates of diabetes across race and visual disability status, we developed a cohort of participants aged 45 or older who had a healthcare visit between January 1, 2017, and December 31, 2018. The cohort was limited to those with a recorded race value in the categories of White, African American, or Asian, as these were the only groups with a sufficiently large sample of patients with a visual disability to allow for race-stratified analysis.

For our analysis of people with diabetes, we included those aged 45 or older with a diabetes-related healthcare visit in the same period (January 1, 2017 to December 31, 2018). They must have had at least one visit post-2019, ensuring they were present throughout all of 2019 and, therefore, able to have at least three ambulatory visits. This question is examined across two cohorts: Cohort 1, which includes individuals without a VDRC and with diabetes (excluding those with ophthalmic complications in the baseline period); and Cohort 2, comprising those without a VDRC (excluding those with diabetes-related ophthalmology complications).

Outcome variables

To explore disparities in disease burden, examine a selected set of comorbidities related to chronic pulmonary conditions identified by the Charlson Comorbidity Index (CCI) [18]. We focused on this set of conditions as people with disability have an increased risk of these diseases as well as an increased risk of mortality for asthma-associated hospitalization [19]. Furthermore, to determine if racial minorities experience poorer diabetes outcomes, we identify the prevalence of chronic kidney disease and the rate of uncontrolled diabetes (A1C > 9.0%) in the follow-up year. These outcome variables are crucial in understanding and addressing healthcare disparities among different racial groups.

Healthcare access patterns

We examined several key aspects of healthcare utilization. For each of these outcomes, we identified the difference in proportion between people with visual impairment and those without. First, we identified differences in the frequency of healthcare visits between people with visual impairment and those without by identifying whether patients had at least three ambulatory visits in the follow-up year. Next, we assessed the frequency of diabetes monitoring. This part of the study specifically looked at whether patients had two A1c lab tests in the follow-up year. Finally, we analyzed kidney disease monitoring, an important aspect of diabetes management [20] by determining whether medical providers had recorded at least one glomerular filtration rate (GFR) measurement.

We assessed uncontrolled diabetes and chronic kidney disease outcomes within each healthcare utilization cohort: those with 3 + visits, those with 1 + A1C visits, and those with 1 + GFR. Additionally, we assessed comorbidities among all patients with a diabetic-related visit in the baseline period and one post-2019 visit to align the cohort with our outcomes analysis.

Statistical analysis

Differences in diabetes management between white and black people with visual impairment

We used the difference in proportion test to measure the difference in prevalence of having 3 + ambulatory visits, 1 + A1C value, and 1 + GFR value between people with visual impairment and those without. Not having at least 1 A1C measurement within a year is an indication of uncontrolled diabetes and, therefore, a good metric of diabetes management [21].

Differences in diabetes outcomes in the follow-up year

This study identified differences in the prevalence of uncontrolled diabetes between people with visual impairment and those without these conditions using risk ratios. Uncontrolled diabetes was defined by a Hemoglobin A1c level greater than 9.0% or a patient having no A1C measurement [21], in the follow-up year. This study used risk ratios to measure the strength of the association between chronic kidney disease (CKD) and visual impairment. CKD was identified by the presence of any ICD-10 code starting with N18 (chronic kidney disease) in the follow-up year.

To control for potential confounding factors for all statistical analyses, we employed propensity score matching. Controls were matched to cases at a 1:1 ratio, based on age, race, and sex. This matching process helps to ensure that the comparison between groups is as unbiased as possible by accounting for these key demographic variables.

Comparing co-morbidities related to chronic pulmonary disease

We used the difference in proportion test to measure differences in the prevalence of comorbidities between people with visual impairment and those without for each population.

Directed Acyclic Graph (DAG) inclusion

A key addition to our methods is the inclusion of a DAG to serve several purposes. The DAG expresses assumptions regarding the relationships between variables, identifying covariates and potential confounders and illustrating transitive relationships impacting diabetes care and outcomes in patients with visual disabilities. While DAGs do not empirically determine these relationships, they are a powerful tool for structuring analysis and ensuring that researchers’ assumptions are made explicit and logically consistent by confirming whether these assumed relationships are consistent with observed data.

By incorporating the perspectives of disabled researchers, including people with visual impairment, our DAG ensures that the study results’ interpretation comprehensively addresses the nuances of disability research within the context of EHR data.

DAGs also allow for the representation of missing variables, highlighting the ongoing challenge in the analysis of EHR data regarding the omission of data important to capturing disability. The DAG also allows researchers to visualize and identify collider variables and other sources of bias, creating a holistic picture that can yield insights into omissions and next steps for future research. The DAG in our study displays the pathways between having a visual impairment and being measured as having uncontrolled diabetes.