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Dear Professor Cullum
Thank you for your comments regarding the of Cullum et al (2016) and Madden & Morley (2016). These two publications were identified through our search strategy, however, they did not meet our inclusion criteria. The a priory criteria set for inclusion and exclusion of primary studies are listed below:
Inclusion criteria: All steps from James Lind Alliance, list of Top 10 priorities, adults (aged > 18 years or older)
Exclusion criteria: Unpublished literature, articles not written in English, priority setting partnership without James Lind Alliance, James Lind Alliance without priority setting partnership, protocols, errata, editorial, thesis, comments, review, guidelines, randomized controlled trials.
1] Cullum N, Buckley H, Dumville J, Hall J, Lamb K, Madden M, Morley R, O'Meara S, Goncalves PS, Soares M, Stubbs N. Wounds research for patient benefit: a 5 year programme of research. NIHR Journals Library; 2016. This report does not describe prioritized the Top 10 list.
2] Madden M, Morley R. (2016). Exploring the challenge of health research priority setting in partnership: reflections on the methodology used by the James Lind Alliance Pressure Ulcer Priority Setting Partnership. This article does not describe prioritized the Top 10 list.
In England I have used the output area classification with success for over 10 years to identify social groups with higher rates of admission. I have found deprivation to be a very crude measure. Alas much of this is not published. but several studies regarding admission to the CCU. Also a couple of studies looking at outbreaks of a mystery disease. See below.
Hope this helps.
Beeknoo N, Jones R. Using Social Groups to Locate Areas with High Emergency Department Attendance, Subsequent Inpatient Admission and Need for Critical Care. British Journal of Medicine and Medical Research 2016; 18(6): 1-23. doi: 10.9734/BJMMR/2016/29208
Beeknoo N, Jones R. Using social groups to locate areas of high utilization of critical care. Brit J Healthcare Manage 2016; 22(11): 551-560.
Jones R. Role of social group and gender in outbreaks of a novel agent leading to increased deaths, with insights into higher international deaths in 2015. Fractal Geometry and Nonlinear Analysis in Medicine and Biology 2017; 3(1): 1-7. doi: 10.15761/FGNAMB.1000146
In England I have used the output area classification with success for over 10 years to identify social groups with higher rates of admission. I have found deprivation to be a very crude measure. Alas much of this is not published. but several studies regarding admission to the CCU. Also a couple of studies looking at outbreaks of a mystery disease. See below.
Hope this helps.
Beeknoo N, Jones R. Using Social Groups to Locate Areas with High Emergency Department Attendance, Subsequent Inpatient Admission and Need for Critical Care. British Journal of Medicine and Medical Research 2016; 18(6): 1-23. doi: 10.9734/BJMMR/2016/29208
Beeknoo N, Jones R. Using social groups to locate areas of high utilization of critical care. Brit J Healthcare Manage 2016; 22(11): 551-560.
Jones R. Role of social group and gender in outbreaks of a novel agent leading to increased deaths, with insights into higher international deaths in 2015. Fractal Geometry and Nonlinear Analysis in Medicine and Biology 2017; 3(1): 1-7. doi: 10.15761/FGNAMB.1000146
Jones R. Different patterns of male and female deaths in 2015 in English and Welsh local authorities question the role of austerity as the primary force behind higher deaths. Fractal Geometry and Nonlinear Analysis in Medicine and Biology 2017; 3(1): 1-4. doi: 10.15761/FGNAMB.1000145
Saez, Vidiella-Martin, and López Casasnovas1 investigate the impact of the Great Recession on self-assessed health in Spain analyzing data from four waves—2005, 2008, 2011 and 2014—of a Survey of Household Finances by the Bank of Spain. The surveys included repeated observations of self-assessed health and other variables measured in the same individuals.
The statistical model of Saez et al. is a mixed logistic regression in which the log of the odds of poor health (log [P / (1 - P)], where P is the probability of declaring poor health) is computed as a linear combination of a random effect for the year of the survey, random effects for individuals and families, and a large set of control variables at both family level (gross wealth, total debt, family income, savings rate, family size, number of family members who work, and type of family residence—owned or rented) and individual level (sex, age—stratified in six groups—, educational level, occupation, and marital status). Saez et al. computed the model with and without adjustment for the control variables and both for the whole sample and for 12 subsamples stratified by sex and age. The observations were weighted, to compensate for the fact that the original survey oversampled the wealthiest households, and the sample was trimmed to eliminate outliers.
Saez et al. found a downward change in self-perceived health during the third wave of the survey, i.e., that of 2011, which coincides with the most severe per...
Saez, Vidiella-Martin, and López Casasnovas1 investigate the impact of the Great Recession on self-assessed health in Spain analyzing data from four waves—2005, 2008, 2011 and 2014—of a Survey of Household Finances by the Bank of Spain. The surveys included repeated observations of self-assessed health and other variables measured in the same individuals.
The statistical model of Saez et al. is a mixed logistic regression in which the log of the odds of poor health (log [P / (1 - P)], where P is the probability of declaring poor health) is computed as a linear combination of a random effect for the year of the survey, random effects for individuals and families, and a large set of control variables at both family level (gross wealth, total debt, family income, savings rate, family size, number of family members who work, and type of family residence—owned or rented) and individual level (sex, age—stratified in six groups—, educational level, occupation, and marital status). Saez et al. computed the model with and without adjustment for the control variables and both for the whole sample and for 12 subsamples stratified by sex and age. The observations were weighted, to compensate for the fact that the original survey oversampled the wealthiest households, and the sample was trimmed to eliminate outliers.
Saez et al. found a downward change in self-perceived health during the third wave of the survey, i.e., that of 2011, which coincides with the most severe period of the Great Recession in Spain (2009–2011, according to Saez et al.). However, they also found significant differences when they stratified the analysis by gender and age groups. According to Figure 1 of the paper, the unadjusted odds of declaring poor health dropped in 2008 to 0.8 and rose in 2011 to about 1.2, but the adjusted odds changed much less. This is interpreted by Saez et al. as an indication that the variation in health status can be captured by either demographic or socioeconomic controls. Figure 2 of the paper reveals extreme heterogeneity in the evolution of the unadjusted odds of declaring poor health in the year 2011. For example, the odds of reporting poor health in 2011, that is, at the worst time of the economic crisis, are about 5 for females aged 35-44 while just slightly above 1.0 for males in that age group. However, when the model includes the control variables the adjusted odds for the year 2011 dramatically drop down to about 1.0 for females and 1.2 for males, as shown in Figure 3. Comparing Figures 2 and 3 of the paper reveals major differences in the profiles of adjusted and unadjusted odds for specific groups of individuals.
Overall, it is hard to make sense of the large heterogeneities found by Saez et al. in this investigation. In the long discussion section of the paper the authors comment on characteristics of the Spanish social security system, on the impact of the Great Recession on income and wealth of pensioners, and other issues, but no clear rationale to explain the extreme heterogeneity of results is presented. It is known that self-assessed health is to some extent a measure of psychological wellbeing and thus it is not surprising that it deteriorated during the Great Recession, as indicators of mental health have been repeatedly found deteriorating during recessions. However, that does not explain the extreme heterogeneity of the results found by Saez et al. To this respect, it may be perhaps important to consider sample size. The investigated sample included 28,678 individuals which Saez et al. claim to be representative of a population of some 20 million Spanish individuals. Now, a general sample of some 29,000 individuals, when stratified in 12 subsamples might yield some very small subsamples in which almost any result is possible. Unfortunately, the sizes of the stratified subsamples are not reported in the paper. At any rate, this investigation tends to reinforce the view that self-assessed health is an indicator quite hard to interpret. For purposes of assessing population health, it would not resist a comparison for instance with mortality indicators.
Saez et al. assert that “it is widely acknowledged that the Great Recession (…) had an adverse impact on health.” This is a very arguable statement. Evidence has piled up that during recessions mortality tends to decline faster than during economic expansions and during the Great Recession in particular, mortality accelerated its decline with respect to the previous years of expansion, both in Europe in general2,3 and in Spain in particular.4,5 As shown in Table 1 here, life expectancy at birth for the Spanish population either grew or kept steady during 2009-2011, that is, the years which Saez et al. consider to be the worst of the Great Recession; that indicates that mortality continued declining during those years. Contrarily, both before the crisis, in the expansion years of 2003, 2005 and 2007, and in 2015, when the recession was finished and the Spanish GDP grew at 3.4%, life expectancy at birth declined indicating increasing mortality. There is now plenty of evidence provided by population statistics rather than survey samples, that health tends to deteriorate in economic expansions and improve in recessions.2-7 However, based perhaps on the flimsy evidence provided by Harvey Brenner in the 1980s, preconceptions about a presumable harmful effect of recessions on health are common. Probably supported by peer reviewers with these preconceptions, a paper was published in 2018 “demonstrating” that in terms of mortality, the Great Recession in Spain had been even more damaging that the Spanish civil war, as it had caused more than a half a million deaths.8 This report has been recently retracted,9 as it was fully based in wrong data.10 The pooled evidence clearly suggests that in the experience of the present and the past century, recessions, on average, have beneficial short‐term effects on mortality, and therefore, on health, of the general population.2-7, 10 There is little evidence either that they might increase health inequalities.4,11
REFERENCES
1. Saez M, Vidiella-Martin J, Casasnovas GL. Impact of the great recession on self-perceived health in Spain: a longitudinal study with individual data. BMJ Open 2019;9:e023258. doi: 10.1136/bmjopen-2018-023258
2. Toffolutti V, Suhrcke M. Assessing the short term health impact of the Great Recession in the European Union: A cross‐country panel analysis. Preventive Medicine 2014, 64(7):54– 62.
3. Tapia Granados JA , Ionides EL. Population health and the economy: mortality and the great recession in Europe. Health Econ 2017;26:e219235.doi:10.1002/hec.3495.
4. Regidor E, Vallejo F, Tapia Granados JA, Viciana-Fernández FJ, de la Fuente L, Barrio G. Faster mortality decline in low socioeconomic groups during the economic crisis in Spain: a cohort study of 36 million people. Lancet 388: 2642–52.
5. Regidor E, Ronda E, Tapia Granados JA, Pulido J, de la Fuente L, Barrio G. Reversal of upward trends in mortality during the Great Recession in employed and unemployed individuals at baseline in a national longitudinal study. Am J Epidemiol 2019; 188(11):2004–2012, doi.org/10.1093/aje/kwz150.
6. Tapia Granados JA, Ionides EL. Statistical evidence shows that mortality tends to fall during recessions: a rebuttal to Catalano and Bruckner. Int J Epidemiol 2016, 45(5):1683–1686, doi.org/10.1093/ije/dyw206.
7. Tapia Granados JA, House JS, Ionides EL, Burgard S, Schoeni RS. Individual joblessness, contextual unemployment, and mortality risk. Am J Epidemiol 2014; 180(3):280-7, doi: 10.1093/aje/kwu128.
8. Cabrera de León A, Rodríguez IM et al. Austerity policies and mortality in Spain after the financial crisis of 2008. Am J Public Health 2018;108(8):1091-1098, doi: 10.2105/AJPH.2018.304346.
9. Morabia A. Notice of Retraction of “Austerity Policies and Mortality in Spain After the Financial Crisis of 2008”. Am J Public Health 2019, 109(7):e17. doi:0.2105/AJPH.2019.305147.
10. Regidor E, Mateo A, Barrio G, de la Fuente L. Mortality in Spain in the Context of the Economic Crisis and Austerity Policies. Am J Public Health Published Online: June 05, 2019, ajph.aphapublications.org/doi/10.2105/AJPH.2019.305075
11. Bartoll X, Palència L, Malmusi D, Suhrcke D, Borrell C. The evolution of mental health in Spain during the economic crisis. Eur J Public Health 2014, 24(3):415–418.
Table 1. Life expectancy at birth (years) for males and females and the whole population in Spain, 2000-2015
Annual change
Year All Females Males All Females Males
2000 79.5 83.0 76.0 0.7 0.7 0.7
2001 79.8 83.3 76.3 0.3 0.3 0.3
2002 79.9 83.4 76.4 0.1 0.0 0.1
2003 79.8 83.1 76.4 -0.1 -0.2 0.0
2004 80.5 83.9 77.0 0.7 0.7 0.6
2005 80.4 83.8 77.1 0.0 -0.1 0.0
2006 81.2 84.5 77.9 0.8 0.7 0.8
2007 81.2 84.5 77.9 0.0 -0.1 0.0
2008 81.5 84.7 78.3 0.3 0.2 0.4
2009 81.9 85.0 78.8 0.4 0.3 0.4
2010 82.3 85.4 79.2 0.4 0.4 0.4
2011 82.4 85.5 79.4 0.1 0.0 0.2
2012 82.6 85.5 79.5 0.1 0.1 0.2
2013 83.2 86.2 80.2 0.7 0.6 0.7
2014 83.3 86.2 80.4 0.1 0.1 0.2
2015 83.0 85.8 80.1 -0.4 -0.5 -0.3
Data source: HFA database of the European Regional Office of the World Health Organization. Shaded cells correspond to the years considered by Saez et al. as the worst of the economic crisis.
We thank Dr Pollock and colleagues for their interest in our recent study, which suggested that among patients with non-valvular atrial fibrillation in the UK, inappropriate underdosing was more than twice as common among patients starting on apixaban than those starting on dabigatran or rivaroxaban.
In our analyses, we assumed that patients with missing data on renal function were likely to have unimpaired renal function. Pollock et al expressed their concern regarding the possibility of significant bias resulting from misclassification of renal function among these patients, which could have resulted in the percentage of patients inappropriately prescribed a reduced dose NOAC being overestimated. Among our study population of 30,467 patients, 3856 (12.7%) had missing data on renal function (eGFR values).
Pollock and colleagues also queried the absence of bodyweight data in our results. We acknowledge that it may have been useful for the reader to see these data, although we presented data on BMI in Table 1 of our article as a proxy measure. Nevertheless, we can confirm that in our dataset very few patients had missing data on bodyweight (2.6% of patients starting on apixaban, 3.0% of those starting on dabigatran and 2.3% of those starting on rivaroxaban). Among patients with a recorded bodyweight, the mean bodyweight was 81.4 kg for patients starting on apixaban, 82.6 kg for those starting on dabigatran, and 82.0 kg for those starting on rivaroxaban. While...
We thank Dr Pollock and colleagues for their interest in our recent study, which suggested that among patients with non-valvular atrial fibrillation in the UK, inappropriate underdosing was more than twice as common among patients starting on apixaban than those starting on dabigatran or rivaroxaban.
In our analyses, we assumed that patients with missing data on renal function were likely to have unimpaired renal function. Pollock et al expressed their concern regarding the possibility of significant bias resulting from misclassification of renal function among these patients, which could have resulted in the percentage of patients inappropriately prescribed a reduced dose NOAC being overestimated. Among our study population of 30,467 patients, 3856 (12.7%) had missing data on renal function (eGFR values).
Pollock and colleagues also queried the absence of bodyweight data in our results. We acknowledge that it may have been useful for the reader to see these data, although we presented data on BMI in Table 1 of our article as a proxy measure. Nevertheless, we can confirm that in our dataset very few patients had missing data on bodyweight (2.6% of patients starting on apixaban, 3.0% of those starting on dabigatran and 2.3% of those starting on rivaroxaban). Among patients with a recorded bodyweight, the mean bodyweight was 81.4 kg for patients starting on apixaban, 82.6 kg for those starting on dabigatran, and 82.0 kg for those starting on rivaroxaban. While it may be true that, among the general population as a whole, weight may not be recorded in many patient records in primary care, this is unlikely to be the case for specific patient populations, such as patients with NVAF, who will be having more regular contact with their primary care practitioner.
In response to these concerns, and to validate our assumption that patients with missing data on renal function were likely to have unimpaired renal function, we have performed a sensitivity analysis excluding patients with missing data on renal function and/or bodyweight (4418 patients; 14.5% of our study cohort). The results of the sensitivity analysis are shown in the Table and Figures below. Only very minimal differences were seen for apixaban (21.6% were inappropriately underdosed in the main analysis vs. 21.4% in the sensitivity analysis), for dabigatran (8.3% in the main analysis vs. 8.2% in the sensitivity analysis), and for rivaroxaban (9.1% in the main analysis vs. 8.4% in the sensitivity analysis)(Table and Figure 1). When dosing was evaluated according to degree of renal function (Figure 2), the data still showed that reduced doses were prescribed to patients with no evidence of renal impairment, with minimal or no difference seen between results of the main analysis and the sensitivity analysis: 26.7% vs. 26.6% for apixaban, 51.1% vs. 50.5% for dabigatran, and 10.3% in both analyses for rivaroxaban. Furthermore, in another ongoing study conducted by our team we have found that when we looked at eGFR values recorded further back in patients’ medical records (further than within the 1 year before the index date), among patients with no recorded value within the year before the index date, about 10% of these had an eGFR value indicating suboptimal renal function. This would translate into about 1% of patients in our current study being misclassified as having normal renal function based on this older eGFR reading. Therefore, we do not consider potential misclassification of renal function, or the inclusion of patients with missing data on bodyweight, to be significant sources of bias, and further analysis is highly unlikely to alter the conclusions of the study in this respect.
In response to the final point raised by Pollock et al, we would like to clarify that the aim of our study was not to ascertain whether a reduced dose such as 2.5 mg/day apixaban was the correct dose for an individual patient, but whether patients were prescribed an appropriate dose as specified on the EU drug label, based on the data recorded in THIN and CPRD databases. We provided a clear definition of appropriate and inappropriate dosing with respect to the EU label in our article.
Considering this further evidence from our study – that any effect of bias due to misclassification of renal function or bodyweight is likely to be very minimal – we are of the opinion that the inferences made in our article remain valid and justifiable.
Thank you for this critical note about vitamin D and B12 testing. The aim of our study was to explore the barriers and facilitators for reducing the number of unnecessary ordered vitamin D and B12 laboratory tests. We found that GPs experienced difficulty to request laborotory tests only for evidenced based indications; often vitamin testing was performed to satisfy patients' requests. We acknowledge the presence of certain medical indications to test vitamin D or B12 bloodlevels and we also performed a training for participating GPs of our study on vitamin D and B12 deficiency and people at risk of such deficiency. The purpose of our study was not to reduce the number of vitamin D and B12 tests to zero, but to explore the barriers and facilitators related to vitamin D and B12 testing in order to improve properly indicated vitamin testing in general practice.
Re. Diagnosed prevalence of Ehlers-Danlos syndrome and hypermobility spectrum disorder in Wales, UK: a national electronic cohort study and case-control comparison.
Demmler J C, Atkinson M D, Reinhold E, Choy E, Lyons R A, Brophy S T
BMJ Open 2019;9:e031365
We write concerning the paper by Demmler et al., published in BMJ Open. We wish to raise the following concerns:
1. With regard to combining the Joint Hypermobility Syndrome (JHS) and Ehlers-Danlos syndromes (EDS) populations for analysis.
If one combines data from a cohort that is found to be ‘common’ (in this case ‘diagnosed JHS’) with one that is found to be ‘rare’ (in this case ‘diagnosed EDS’), the new combined cohort (i.e. diagnosed JHS/EDS) will be common. To then consider the rare cohort common is a fallacy.
Also, although individuals in a population with a previous diagnosis of JHS (i.e. prior to the 2017 international classification (1,2)) might have Hypermobile EDS (hEDS) by the current classification, it is not known how JHS segregates into Hypermobility Spectrum Disorder (HSD) and hEDS. A JHS population would need to be reassessed to confirm this, or modelling assumptions of the data would need to be applied.
In addition, it is not known what proportion of the EDS cohort have hEDS versus the rare Mendelian types of EDS. As such, there is no way of knowing whether or by what proportion the two cohorts represent the same or similar or dif...
Re. Diagnosed prevalence of Ehlers-Danlos syndrome and hypermobility spectrum disorder in Wales, UK: a national electronic cohort study and case-control comparison.
Demmler J C, Atkinson M D, Reinhold E, Choy E, Lyons R A, Brophy S T
BMJ Open 2019;9:e031365
We write concerning the paper by Demmler et al., published in BMJ Open. We wish to raise the following concerns:
1. With regard to combining the Joint Hypermobility Syndrome (JHS) and Ehlers-Danlos syndromes (EDS) populations for analysis.
If one combines data from a cohort that is found to be ‘common’ (in this case ‘diagnosed JHS’) with one that is found to be ‘rare’ (in this case ‘diagnosed EDS’), the new combined cohort (i.e. diagnosed JHS/EDS) will be common. To then consider the rare cohort common is a fallacy.
Also, although individuals in a population with a previous diagnosis of JHS (i.e. prior to the 2017 international classification (1,2)) might have Hypermobile EDS (hEDS) by the current classification, it is not known how JHS segregates into Hypermobility Spectrum Disorder (HSD) and hEDS. A JHS population would need to be reassessed to confirm this, or modelling assumptions of the data would need to be applied.
In addition, it is not known what proportion of the EDS cohort have hEDS versus the rare Mendelian types of EDS. As such, there is no way of knowing whether or by what proportion the two cohorts represent the same or similar or different conditions.
Given the distribution of JHS to EDS in this study is approximately 6 to 1, the combined cohort would likely share the characteristics of the JHS cohort in a case-control analysis.
By combining the two populations to explore association with other aspects of health and wellbeing the opportunity to compare differences between the two groups is lost. Do the populations in this study differ? What evidence is there that JHS and EDS are behaving statistically in the same way or differently with regard to the parameters collected?
We surmise that the authors in part combined the two populations to identify a larger absolute value for prevalence that informs the need for education, training and healthcare resources. It is increasingly recognized that complex combinations of health concerns arise in HSD and hEDS alike. We recognise why HSD and hEDS would be considered together in this context.
However, by their nature the rare types of EDS will have different specific concerns that influence different education and training needs, and healthcare resource allocation. Rare disease status has significant impact on the way healthcare services plan care (e.g. direct commissioning of specialised services in the UK); or the way organisations support research ( e.g. The Office of Rare Diseases, The National Center for Advancing Translational Sciences, USA), for example.
Regardless of combining the prevalence, it remains necessary that we understand the size effect for each of the different patient populations, as specific needs may differ.
2. With regard to interchanging the term JHS with HSD and hEDS, and determining the prevalence of EDS:
The authors switch from the term JHS to a combination of HSD and hEDS. We recognize this inference. Nevertheless, at this time there is no evidence to inform how JHS segregates into HSD or hEDS, and this uncertainty was not clarified in the paper. There is no modelling of the data that might support inferences with regard to such segregation.
What proportion of the JHS group might add to the overall prevalence of hEDS? How does that modelling affect the prevalence of EDS over all when added to the EDS cohort data?
For example, by our estimate approximately 15% of the JHS cohort would need to have hEDS for this prevalence figure plus that from the EDS cohort to reach a population size of 50 per 100000 for EDS as a whole in this study. Even then that would be 1 in 2000 of the general population; and given that some of the EDS cohort would not have hEDS this would also infer that hEDS per se is rare as it would be a proportion of that EDS population.
If none of the JHS population had hEDS the prevalence calculation for EDS using only the EDS cohort data demonstrates EDS to be rare.
There are other ways to model the data, but we hope that this demonstrates our point.
Until reasonable evidence is available as to the segregation of JHS into hEDS and HSD, we believe studies such as that of Demmler et al. should present the data as found for JHS, or define the assumptions and models they are using to reach their conclusions. In addition, studies should avoid clustering JHS and EDS. The term EDS is a pleural (i.e. Ehlers-Danlos syndromes), grouping together disorders under the title.
Further studies are required to determine how common HSD, hEDS, and other forms of EDS are. Shared knowledge among members of the international medical and scientific community suggest that HSD is likely common; that more work is required to determine if hEDS is common or not; and, that other Mendelian types of EDS are rare or ultra-rare.
3. Concerning the combining the populations and the paper's Conclusion.
The authors conclude that “EDS/HSD are not rare conditions…”. This is asserted because the authors have combined the JHS and EDS populations. We question the reasoning for combining the populations above, but wish, here, to raise further concern about interpretation.
We are aware that this publication is being cited in public forums as saying ‘EDS is common’. At this time, from the data presented, there is no evidence to support that statement, for either hEDS or the rarer types of EDS. Even were it found that hEDS is not rare, it is that observation that would make most sense to assert given many forms of EDS are between rare and ultra-rare.
While there is no direct statement from the authors that ‘EDS is common’, we suggest that the opinion has arisen because of the way the whole paper, including the conclusion has been presented.
We respectfully request that the editors of BMJ Open and the paper’s authors consider the above.
Yours faithfully,
Dr. Alan J Hakim, Adjunct Associate Professor of Medicine, College of Medicine, PennState University, USA, & Consultant Rheumatologist, London
Dr Clair A. Francomano, Professor of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, USA
Dr. Marco Castori, Consultant and Director, Division of Medical Genetics, IRCCS Casa Sollievo della Sofferenza, Foggia, Italy
Prof. Fransiska Malfait, Associate Professor, Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium.
References:
Malfait F, Francomano C, Byers P, et al. 2017. The 2017 international classification of the Ehlers–Danlos syndromes. Am J Med Genet Part C Semin Med Genet 175C:8–26.
Castori M, Tinkle B, Levy H, Grahame R, Malfait F, Hakim A. 2017. A framework for the classification of joint hypermobility and related conditions. Am J Med Genet C Semin Med Genet 175C:148-157.
I believe that bipolar/mental disorders could be related to coeliac disease. Any study in relation to diet / supplements etc could be affected by this as undiagnosed coeliacs who are continuing to eat gluten do not digest foods properly and become deficient in minerals and vitamins as they CANNOT ABSORB them. I do feel that more research and studies need to be done with this in mind. Doctors should be testing more people. In Australia the AVERAGE time it takes for a coeliac to be diagnosed is 9 YEARS. A blood test is not reliable as often it comes back a false negative. Meanwhile they get diagnosed with bipolar and other illnesses caused by mineral and vitamin deficiencies. I have a father in law who was diagnosed bipolar BEFORE being diagnosed gluten intolerant (he has Dermatitis Herpetiformis which is related to coeliac disease). I don’t believe he is bipolar. My husband also was misdiagnosed with bipolar instead of coeliac disease. Brain cells recover after going on a gluten free diet!
All people diagnosed with bipolar should be tested for Coeliac Disease (or Dermatitis Herpetiformis if they have any kind of rash). And any study for treating bipolar disease with nutritional supplements should be done after the test and/or on people who have excluded gluten from their diet.
Mark L Levy,1,9 Darragh Murnane2, Peter J Barnes,3,9 Mark Sanders,4 Louise Fleming,5 Jane Scullion,6,9 Chris Corrigan,7,9 Omar S Usmani8,9
1. Locum general practitioner, Clinical Lead NRAD (2011-2014)
2. King’s College London Faculty of Life Sciences & Medicine, School of Immunology & Microbial Sciences ; School of Life and Medical Sciences, University of Hertfordshire, Hatfield, Hertfordshire
3. National Heart & Lung Institute, Imperial College, London
4. Clement Clarke international Ltd (CCI) and founder of online museum of inhaler devices, www.inhalatorium.com.
5. Imperial College, London and the Royal Brompton and Harefield, NHS Foundation Trust
6. University Hospitals of Leicester
7. King’s College London Faculty of Life Sciences & Medicine, School of Immunology & Microbial Sciences
8. Imperial College London & Royal Brompton Hospital
9. Aerosol Drug ManagementImprovement Team (ADMIT), www.inhalers4u.org
In an attempt to address issues related to global warming contributed to by the use of pressurised, metered-dose inhalers (pMDIs), Wilkinson et al (1) have succeeded in generating a great deal of negative, potentially harmful media interest for patients who currently rely on these devices. They analysed the potential impact of switching therapy from pMDIs to dry powder inhalers (DPIs) in terms of both c...
Mark L Levy,1,9 Darragh Murnane2, Peter J Barnes,3,9 Mark Sanders,4 Louise Fleming,5 Jane Scullion,6,9 Chris Corrigan,7,9 Omar S Usmani8,9
1. Locum general practitioner, Clinical Lead NRAD (2011-2014)
2. King’s College London Faculty of Life Sciences & Medicine, School of Immunology & Microbial Sciences ; School of Life and Medical Sciences, University of Hertfordshire, Hatfield, Hertfordshire
3. National Heart & Lung Institute, Imperial College, London
4. Clement Clarke international Ltd (CCI) and founder of online museum of inhaler devices, www.inhalatorium.com.
5. Imperial College, London and the Royal Brompton and Harefield, NHS Foundation Trust
6. University Hospitals of Leicester
7. King’s College London Faculty of Life Sciences & Medicine, School of Immunology & Microbial Sciences
8. Imperial College London & Royal Brompton Hospital
9. Aerosol Drug ManagementImprovement Team (ADMIT), www.inhalers4u.org
In an attempt to address issues related to global warming contributed to by the use of pressurised, metered-dose inhalers (pMDIs), Wilkinson et al (1) have succeeded in generating a great deal of negative, potentially harmful media interest for patients who currently rely on these devices. They analysed the potential impact of switching therapy from pMDIs to dry powder inhalers (DPIs) in terms of both changes in greenhouse gas emissions and costs to the UK National Health Service based on prescribing information in England alone. This strategy was apparently devised in a vacuum, without regard to implications for threats to patient wellbeing and safety as a consequence of their being deprived of access to pMDI therapy for obstructive airways disease (asthma and COPD). Physicians are obliged to consider many factors when selecting the most appropriate inhaler device for patients other than the cost to the health care system if they wish the treatment to be effective; (2) these issues were not mentioned in this paper which, in our view, was heavily biased in favour of DPIs, which are seldom appropriate for use in young children, children, the elderly and infirm and those with considerable, irreversible airways obstruction. (3) In addition many patients rely on pMDIs for rapid relief of symptoms.
Their study addressed two possible switching scenarios in the United Kingdom (UK): (i) from pMDIs to currently prescribed DPIs, and (ii) to the cheapest available ‘equivalent’ DPIs based on drug content but not the instrinsic characteristics of the devices. While we agree with one of their conclusions that, with immediate effect, smaller volume HFA134a inhalers (e.g. Salamol) should be prioritised over larger volume or HFA227ea-containing inhalers (e.g. Ventolin) we have major reservations about their remaining assertions, and in particular their conclusion that switching patients’ medication from pMDIs to the cheapest available equivalent DPIs would result in large carbon savings, while ignoring the possible consequences for loss of disease control and consequent morbidity and mortality from obstructive airways diseases such as asthma, which might result in considerable patient harm.
In addition to potential harm from loss of disease control, the burden of suffering and anxiety to patients from this sort of approach to reporting is potentially further amplified by the “guilt factor” already instilled by recent headlines in the media prompted by the report, such as “Asthma inhalers are as bad for the environment as a 180-mile car journey”, “Asthma sufferers should switch to “green” inhalers to help the environment and save millions for the NHS” and “Some asthma inhalers are as bad for the environment as eating meat”. These messages clearly have the potential to stigmatise patients with asthma and COPD for taking their essential medication, and there are anecdotal reports of children, many of whom benefit particularly from pMDI therapy, not wanting to be seen using their inhalers in public. We know of no other situation in the National Health Service where patients are stigmatised for using licensed and approved medications, and the situation will doubtless further impact on the issue of compliance with therapy, which if undermined is another potential threat to their well-being, as well as impacting on morbidity and mortality. (4) Conversely, there was no mention in this commentary, or indeed in the media, that correct delivery of inhaled drugs by an inhaler device which can be used efficiently and reliably by individual patients improves symptoms and quality of life and reduces morbidity, mortality and hospital acute care costs. (5, 6) (3, 7-9)
Further to this last issue, there was correspondingly no mention or consideration of the potential consequences where switching “goes wrong”, resulting in disease destabilisation and the associated “carbon cost” of unnecessary emergency and hospital inpatient management (10); Goulet et al (11) estimated that the carbon footprint of a single bronchodilator dose administered with an electric nebuliser is a considerable 0.0294-0.0477 kg CO¬2-eq. Moorfields Eye Hospital estimate that the carbon footprint of a single patient visit is between 8-10 kg CO2-eq per patient, per visit. It is difficult to assess the current carbon footprint of medical air/oxygen supply, however the European Industry Gas Association lifecycle appraisal framework (document 167/11) identified that the majority of the carbon footprint for liquid gases arises during production and distribution, not during the use of those gases by the end-user. Therefore, unlike the propellant in pMDIs, it is difficult to reduce the carbon footprint of therapeutic gases simply by altering the in-use conditions. (12) One supplier of compressed gases, Linde, in their Corporate Responsibility report of 2017, reported that 5.7 million tonnes of CO2 were produced during liquid gas production, or 52% of their Scope 1 Direct emissions. So the footprint for air therapies is also likely to be significant.
Finally, Wilkinson’s comparison between the UK (70%) and Sweden (10%) pMDI use fails to account for potential differences in disease therapy indications in both countries. For example, both NICE (13) and SIGN/BTS (14) guidelines advocate the use of short-acting, beta-agonist (SABA) use as first line therapy for asthma, which may account for the high levels of prescribing in England. While they acknowledge that they have no idea what disease(s) are being treated in their analysis of prescribing in England, Wilkinson and colleagues have assumed that all patients can be summarily switched from pMDIs to DPIs irrespective of their age, the nature and severity of their disease, their ability to use DPIs efficiently (not all patients can use DPIs efficiently whereas all patients can use pMDIs efficiently if well instructed, with a spacer device), or their satisfaction or preference for the switched device which is a another known factor influencing therapeutic outcome.(15)They have also failed to discuss the fact that not all DPI drugs have clinically equivalent efficacy compared with that of the pMDIs currently in use. It is well known that poor inhaler technique and poor disease outcome are closely related and both NICE (13) and SIGN/BTS (14) guidelines emphasise the necessity of and carefully verifying the ability of individual patients to use inhalers efficiently before prescribing, yet the authors have omitted to address the cost effectiveness and possible carbon footprint of the workload involved in switching and training patients. Furthermore, there was no mention of the chaos familiar to many GPs when patients’ inhalers are switched to “cheaper equivalents” by Clinical Commissioning Group (CCG) employed pharmacists without face to face education and checking technique. Patients become confused when given an unfamiliar device, typically stop using it and in some cases require urgent health care as a consequence. What then often happens is that their GPs will reinstate the original medication. Perhaps the authors could have cited some of the disadvantages arising from switches of medication on non-medical grounds without the patient’s consent, with deterioration of disease control and increased health care utilization. (16) (5) Indeed, data show that stable patients on pMDI maintenance treatment for asthma (17) and COPD (18) achieve better healthcare outcomes than the same drug in a DPI.
While the authors do mention the use of maintenance and reliever regimes (MART and SMART) they do not emphasise the implications, for patient welfare or global warming, of the concerns raised by over usage of short-acting beta-agonists (SABA) as a result of poor education and/or poor overall disease control in patients with asthma. (19, 20) Clearly, simply switching from one SABA to another in this situation will not alter the considerable risk of poor health outcomes, including asthma deaths. (21-23) A strategy is required to eliminate the over usage of SABA by every patient. Combinations of inhaled corticosteroids and long-acting beta-agonists such as formoterol taken “as needed” for the management of mild/moderate asthma are now licensed in five countries based on evidence that they are safer than SABA, (24-27) and it seems very likely that this strategy will be implemented worldwide in the near future, and that it will prove safer and clinically more cost effective. As most of these combination drugs are currently delivered in DPIs this will have an impact on global warming and manufacturers will need to focus on reducing the effect of these on the carbon footprint.
There are considerable shortcomings in carbon footprint modelling in the Wilkinson et al paper. There is relatively scant justification for the carbon footprint of the DPI inhalers included as comparators in this study: in fact, all of the DPIs included in their modelling are assumed to possess the same global warming potential (GWP) as the Ellipta and Diskus devices of ~1kg CO2-eq per device. This is despite the widely different structures and contents of many DPI devices, in terms of parts and plastics involved in their manufacture. In their evidence and subsequent discussion on usage, Wilkinson et al also failed to mention the differential effects on the environment related to the stages of manufacture of pMDIs and DPIs. For example, in the case of GlaxoSmithKline products, the majority of the CO2 emissions from this company’s DPI devices arise from the production of the plastic container and the active pharmaceutical ingredients (APIs) fluticasone propionate (FP) and salmeterol xinafoate (SX), whereas the primary contributors with pMDIs are emissions resulting from actual usage of the devices, and at the end of the life of the device when it is disposed of. (28)
Furthermore it is not apparent that the lactose monohydrate included in DPI formulations is ever included in the Wilkinson or other models of GWP. Lactose monohydrate is a product of the dairy industry contributing considerably to the carbon footprint: for example one study (29) reported that the total carbon required to produce Whey Powder from raw milk is 13.1 kg CO2-eq per kg of Whey Powder product, a value apparently similar to other, international GWP estimates of Whey Powder production (in the USA). Dairy farming is well known to be carbon intensive, not least due to methane production by the herd, but also from the transport and processing of the Whey Powder products
Wilkinson’s approach of simply addressing the GWP of Inhaled Products is misleading; preferably the methodology of Jolliet et al(30) of a holistic Life Cycle Assessment should also be made. This was performed by Jeswani and Azapagic (31)for pMDIs made with HFA134, HFA227, HFA152 and a GSK dry powder inhaler Diskus device. Although the DPI outperformed the HFA134 and HFA227 inhaled devices for Global Warming Potential as expected, however human toxicity, marine eutrophication and fossil depletion are all worse for DPIs than HFA-based pMDIs, when the holistic life-cycle analysis is undertaken. Thus it may be the case that GWP is better for DPIs, but the full long-term environmental effects were actually worse for eight out of fourteen environmental impact metrics for DPIs than pMDIs. It was also noted by the authors (31) that once HFA152a switchover has been made, that pMDIs will have an equivalent carbon footprint to DPIs, but have improved environmental impact profile than DPIs.
While we are concerned about the environment and the effect humans are having on global warming, we are concerned that this article lacks balance in its discussion and conclusions and puts patients with asthma at risk of an attack through inappropriately stopping or switching inhalers. In our view, asthma and COPD management should focus on prescribing appropriate inhaler devices and ancillary equipment such as spacers that individual patients are able to use efficiently after appropriate education and in the context of management plans agreed with appropriately trained health care professionals. These factors should take priority. (32) There may be suitable opportunities to consider the “greenest” alternatives when patients commence therapy or alter it for reasons of poor disease control or inadequate inhaler technique.
Bibliography
1. Wilkinson AJK, Braggins R, Steinbach I, Smith J. Costs of switching to low global warming potential inhalers. An economic and carbon footprint analysis of NHS prescription data in England. BMJ Open. 2019;9.
2. Bjermer L. The Importance of Continuity in Inhaler Device Choice for Asthma and Chronic Obstructive Pulmonary Disease. Respiration. 2014;88(4):346-52.
3. Giraud V, Roche N. Misuse of corticosteroid metered-dose inhaler is associated with decreased asthma stability. European respiratory Journal. 2002;19(2):246-51.
4. Why asthma still kills: the National Review of Asthma Deaths (NRAD) Confidential Enquiry report: Royal College of Physicians. London; 2014 [Available from: http://www.rcplondon.ac.uk/sites/default/files/why-asthma-still-kills-fu....
5. Melani AS, Paleari D. Maintaining Control of Chronic Obstructive Airway Disease: Adherence to Inhaled Therapy and Risks and Benefits of Switching Devices. COPD: Journal of Chronic Obstructive Pulmonary Disease. 2016;13(2):241-50.
6. Melani AS, Bonavia M, Cilenti V, Cinti C, Lodi M, Martucci P, et al. Inhaler mishandling remains common in real life and is associated with reduced disease control. Respiratory Medicine. 2011;105(6):930-8.
7. Giraud V, Allaert FA. Improved asthma control with breath-actuated pressurized Metered Dose Inhaler (pMDI): The SYSTER survey. European Review for Medical and Pharmacological Sciences. 2009;13(5):323-30.
8. Molimard M, Gros VL. Impact of patient-related factors on asthma control. Journal of Asthma. 2008;45(2):109-13.
9. Haughney J, Price D, Kaplan A, Chrystyn H, Horne R, May N, et al. Achieving asthma control in practice: Understanding the reasons for poor control. Respiratory Medicine. 2008;102(12):1681-93.
10. Moorfields Hospital Foundation Trust. Sustainable Development Management Plan 2017 [Available from: https://www.moorfields.nhs.uk/sites/default/files/Item%2009%20Sustainabl....
11. Goulet B, Olson L, Mayer BK. A Comparative Life Cycle Assessment between a Metered Dose Inhaler and Electric Nebulizer. Sustainability. 2017;9(10):1725.
12. EUROPEAN INDUSTRIAL GASES ASSOCIATION AISBL. METHODOLOGY TO ESTABLISH A “PRODUCT CARBON FOOTPRINT: IGC Doc 167/11/E 2007 [Available from: https://www.eiga.eu/index.php?eID=dumpFile&t=f&f=2580&token=c87996bc2e26....
13. Commissioned by the National Institute for Health and Care Excellence (NICE). Asthma: diagnosis, monitoring and chronic asthma management. NICE guideline [NG80] 2017 [Available from: https://www.nice.org.uk/guidance/ng80
14. Scottish Intercollegiate Guideline Network (SIGN), the British Thoracic society (BTS). British guideline on the management of asthma 2019 [Available from: https://www.sign.ac.uk/sign-158-british-guideline-on-the-management-of-a....
15. Plaza V, Giner J, Calle M, Rytila P, Campo C, Ribo P, et al. Impact of patient satisfaction with his or her inhaler on adherence and asthma control. Allergy Asthma Proc. 2018;39(6):437-44.
16. Björnsdóttir US, Gizurarson S, Sabale U. Potential negative consequences of non-consented switch of inhaled medications and devices in asthma patients. International Journal of Clinical Practice. 2013;67(9):904-10.
17. Price D, Roche N, Christian Virchow J, Burden A, Ali M, Chisholm A, et al. Device type and real-world effectiveness of asthma combination therapy: An observational study. Respiratory Medicine. 2011;105(10):1457-66.
18. Jones R, Martin J, Thomas V, Skinner D, Marshall J, Stagno d'Alcontres M, et al. The comparative effectiveness of initiating fluticasone/salmeterol combination therapy via pMDI versus DPI in reducing exacerbations and treatment escalation in COPD: a UK database study. Int J Chron Obstruct Pulmon Dis. 2017;12:2445-54.
19. The Global Strategy for Asthma Management and Prevention, Global Initiative for Asthma (GINA).2019. Available from: http://www.ginasthma.org.
20. Reddel HK, FitzGerald JM, Bateman ED, Bacharier LB, Becker A, Brusselle G, et al. GINA 2019: a fundamental change in asthma management: Treatment of asthma with short-acting bronchodilators alone is no longer recommended for adults and adolescents 2019 [updated Jun. 2019/06/30:[Available from: https://erj.ersjournals.com/content/53/6/1901046.long.
21. Suissa S, Ernst P, Boivin JF, Horwitz RI, Habbick B, Cockroft D, et al. A cohort analysis of excess mortality in asthma and the use of inhaled beta-agonists. Am J Respir Crit Care Med. 1994;149(3 Pt 1):604-10.
22. Suissa S, Blais L, Ernst P. Patterns of increasing beta-2-agonist use and the risk of fatal or near-fatal asthma. European Respiratory Journal. 1994;7(9):1602-9.
23. Reddel HK, Ampon RD, Sawyer SM, Peters MJ. Risks associated with managing asthma without a preventer: urgent healthcare, poor asthma control and over-the-counter reliever use in a cross-sectional population survey. BMJ Open. 2017;7.
24. Beasley R, Holliday M, Reddel HK, Braithwaite I, Ebmeier S, Hancox RJ, et al. Controlled Trial of Budesonide-Formoterol as Needed for Mild Asthma. N Engl J Med. 2019;380(21):2020-30.
25. Hardy J, Baggott C, Fingleton J, Reddel HK, Hancox RJ, Harwood M, et al. Budesonide-formoterol reliever therapy versus maintenance budesonide plus terbutaline reliever therapy in adults with mild to moderate asthma (PRACTICAL): a 52-week, open-label, multicentre, superiority, randomised controlled trial. The Lancet. 2019;394(10202):919-28.
26. O’Byrne PM, FitzGerald JM, Bateman ED, Barnes PJ, Zhong N, Keen C, et al. Inhaled Combined Budesonide–Formoterol as Needed in Mild Asthma. New England Journal of Medicine. 2018;378(20):1865-76.
27. Bateman ED, Reddel HK, O’Byrne PM, Barnes PJ, Zhong N, Keen C, et al. As-Needed Budesonide–Formoterol versus Maintenance Budesonide in Mild Asthma. New England Journal of Medicine. 2018;378(20):1877-87.
28. Carbon Trust. GlaxoSmithKline PLC. Product Carbon Footprint Certification Summary Report 2014 [Available from: https://networks.sustainablehealthcare.org.uk/sites/default/files/media/....
29. Finnegan W, Goggins J, Zhan X. Assessing the environmental impact of the dairy processing industry in the Republic of Ireland. Journal of Dairy Research. 2018;85:1-4.
30. Jolliet O, Margni M, Charles R, Humbert S, Payet J, Rebitzer G, et al. IMPACT 2002+: a new life cycle assessment methodology. Int J Life Cycle Assess 8:324-330. The International Journal of Life Cycle Assessment. 2003;8:324-30.
31. Jeswani HK, Azapagic A. Life cycle environmental impacts of inhalers. Journal of Cleaner Production. 2019;237:117733.
32. Usmani OS, Scullion J, Keeley D. Our planet or our patients-is the sky the limit for inhaler choice? Lancet Respir Med. 2019;7(1):11-3.
We appreciate the response to our study.
The response assumes that a restriction in the population under study also limited the bias in two previous studies (1;2). In the two previous studies only individuals, who had two diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines at 11 months of age were included.
Therefore, we found it relevant to apply the same restriction to our study population and present the corresponding estimates adjusted for the confounders included in our study (3) (Table 1 - https://blogs.bmj.com/bmjopen/files/2019/11/Jenson-et-al-table.jpg).
In Table 1 it can be seen that the restriction of the analysis to include only individuals with two diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines at 11 months of age had little impact on the estimates. Importantly, the associations showing a reduced risk of hospitalisation for accidents among children with two or three diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines and the measles-mumps-rubella vaccine were essentially unchanged when we restricted the analysis to include individuals with two diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines at 11 months of age only.
Reference List
(1) Sorup S, Benn CS, Poulsen A, Krause TG, Aaby P, Ravn H. Live vaccine against measles, mumps, and rubella and the risk of hospital admissions for no...
We appreciate the response to our study.
The response assumes that a restriction in the population under study also limited the bias in two previous studies (1;2). In the two previous studies only individuals, who had two diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines at 11 months of age were included.
Therefore, we found it relevant to apply the same restriction to our study population and present the corresponding estimates adjusted for the confounders included in our study (3) (Table 1 - https://blogs.bmj.com/bmjopen/files/2019/11/Jenson-et-al-table.jpg).
In Table 1 it can be seen that the restriction of the analysis to include only individuals with two diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines at 11 months of age had little impact on the estimates. Importantly, the associations showing a reduced risk of hospitalisation for accidents among children with two or three diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines and the measles-mumps-rubella vaccine were essentially unchanged when we restricted the analysis to include individuals with two diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines at 11 months of age only.
Reference List
(1) Sorup S, Benn CS, Poulsen A, Krause TG, Aaby P, Ravn H. Live vaccine against measles, mumps, and rubella and the risk of hospital admissions for nontargeted infections. JAMA 2014 Feb 26;311(8):826-35.
(2) Sorup S, Benn CS, Stensballe LG, Aaby P, Ravn H. Measles-mumps-rubella vaccination and respiratory syncytial virus-associated hospital contact. Vaccine 2015 Jan 1;33(1):237-45.
(3) Jensen A, Andersen PK, Stensballe LG. Early childhood vaccination and subsequent mortality or morbidity: are observational studies hampered by residual confounding? A Danish register-based cohort study. BMJ Open 2019 Sep 18;9(9):e029794.
Dear Professor Cullum
Thank you for your comments regarding the of Cullum et al (2016) and Madden & Morley (2016). These two publications were identified through our search strategy, however, they did not meet our inclusion criteria. The a priory criteria set for inclusion and exclusion of primary studies are listed below:
Inclusion criteria: All steps from James Lind Alliance, list of Top 10 priorities, adults (aged > 18 years or older)
Exclusion criteria: Unpublished literature, articles not written in English, priority setting partnership without James Lind Alliance, James Lind Alliance without priority setting partnership, protocols, errata, editorial, thesis, comments, review, guidelines, randomized controlled trials.
1] Cullum N, Buckley H, Dumville J, Hall J, Lamb K, Madden M, Morley R, O'Meara S, Goncalves PS, Soares M, Stubbs N. Wounds research for patient benefit: a 5 year programme of research. NIHR Journals Library; 2016. This report does not describe prioritized the Top 10 list.
2] Madden M, Morley R. (2016). Exploring the challenge of health research priority setting in partnership: reflections on the methodology used by the James Lind Alliance Pressure Ulcer Priority Setting Partnership. This article does not describe prioritized the Top 10 list.
In England I have used the output area classification with success for over 10 years to identify social groups with higher rates of admission. I have found deprivation to be a very crude measure. Alas much of this is not published. but several studies regarding admission to the CCU. Also a couple of studies looking at outbreaks of a mystery disease. See below.
Hope this helps.
Beeknoo N, Jones R. Using Social Groups to Locate Areas with High Emergency Department Attendance, Subsequent Inpatient Admission and Need for Critical Care. British Journal of Medicine and Medical Research 2016; 18(6): 1-23. doi: 10.9734/BJMMR/2016/29208
Beeknoo N, Jones R. Using social groups to locate areas of high utilization of critical care. Brit J Healthcare Manage 2016; 22(11): 551-560.
Jones R. Year-to-year variation in deaths in English Output Areas (OA), and the interaction between a presumed infectious agent and influenza in 2015. SMU Medical Journal 2017; 4(2): 37-69.
http://smu.edu.in/content/dam/manipal/smu/smims/Volume4No2July2017/SMU%2...(July%202017)%20-%204.pdf
Jones R. Role of social group and gender in outbreaks of a novel agent leading to increased deaths, with insights into higher international deaths in 2015. Fractal Geometry and Nonlinear Analysis in Medicine and Biology 2017; 3(1): 1-7. doi: 10.15761/FGNAMB.1000146
Jones R. Differ...
Show MoreSaez, Vidiella-Martin, and López Casasnovas1 investigate the impact of the Great Recession on self-assessed health in Spain analyzing data from four waves—2005, 2008, 2011 and 2014—of a Survey of Household Finances by the Bank of Spain. The surveys included repeated observations of self-assessed health and other variables measured in the same individuals.
Show MoreThe statistical model of Saez et al. is a mixed logistic regression in which the log of the odds of poor health (log [P / (1 - P)], where P is the probability of declaring poor health) is computed as a linear combination of a random effect for the year of the survey, random effects for individuals and families, and a large set of control variables at both family level (gross wealth, total debt, family income, savings rate, family size, number of family members who work, and type of family residence—owned or rented) and individual level (sex, age—stratified in six groups—, educational level, occupation, and marital status). Saez et al. computed the model with and without adjustment for the control variables and both for the whole sample and for 12 subsamples stratified by sex and age. The observations were weighted, to compensate for the fact that the original survey oversampled the wealthiest households, and the sample was trimmed to eliminate outliers.
Saez et al. found a downward change in self-perceived health during the third wave of the survey, i.e., that of 2011, which coincides with the most severe per...
We thank Dr Pollock and colleagues for their interest in our recent study, which suggested that among patients with non-valvular atrial fibrillation in the UK, inappropriate underdosing was more than twice as common among patients starting on apixaban than those starting on dabigatran or rivaroxaban.
In our analyses, we assumed that patients with missing data on renal function were likely to have unimpaired renal function. Pollock et al expressed their concern regarding the possibility of significant bias resulting from misclassification of renal function among these patients, which could have resulted in the percentage of patients inappropriately prescribed a reduced dose NOAC being overestimated. Among our study population of 30,467 patients, 3856 (12.7%) had missing data on renal function (eGFR values).
Pollock and colleagues also queried the absence of bodyweight data in our results. We acknowledge that it may have been useful for the reader to see these data, although we presented data on BMI in Table 1 of our article as a proxy measure. Nevertheless, we can confirm that in our dataset very few patients had missing data on bodyweight (2.6% of patients starting on apixaban, 3.0% of those starting on dabigatran and 2.3% of those starting on rivaroxaban). Among patients with a recorded bodyweight, the mean bodyweight was 81.4 kg for patients starting on apixaban, 82.6 kg for those starting on dabigatran, and 82.0 kg for those starting on rivaroxaban. While...
Show MoreThank you for this critical note about vitamin D and B12 testing. The aim of our study was to explore the barriers and facilitators for reducing the number of unnecessary ordered vitamin D and B12 laboratory tests. We found that GPs experienced difficulty to request laborotory tests only for evidenced based indications; often vitamin testing was performed to satisfy patients' requests. We acknowledge the presence of certain medical indications to test vitamin D or B12 bloodlevels and we also performed a training for participating GPs of our study on vitamin D and B12 deficiency and people at risk of such deficiency. The purpose of our study was not to reduce the number of vitamin D and B12 tests to zero, but to explore the barriers and facilitators related to vitamin D and B12 testing in order to improve properly indicated vitamin testing in general practice.
Dear Sir or Madam
Re. Diagnosed prevalence of Ehlers-Danlos syndrome and hypermobility spectrum disorder in Wales, UK: a national electronic cohort study and case-control comparison.
Demmler J C, Atkinson M D, Reinhold E, Choy E, Lyons R A, Brophy S T
BMJ Open 2019;9:e031365
We write concerning the paper by Demmler et al., published in BMJ Open. We wish to raise the following concerns:
1. With regard to combining the Joint Hypermobility Syndrome (JHS) and Ehlers-Danlos syndromes (EDS) populations for analysis.
If one combines data from a cohort that is found to be ‘common’ (in this case ‘diagnosed JHS’) with one that is found to be ‘rare’ (in this case ‘diagnosed EDS’), the new combined cohort (i.e. diagnosed JHS/EDS) will be common. To then consider the rare cohort common is a fallacy.
Also, although individuals in a population with a previous diagnosis of JHS (i.e. prior to the 2017 international classification (1,2)) might have Hypermobile EDS (hEDS) by the current classification, it is not known how JHS segregates into Hypermobility Spectrum Disorder (HSD) and hEDS. A JHS population would need to be reassessed to confirm this, or modelling assumptions of the data would need to be applied.
Show MoreIn addition, it is not known what proportion of the EDS cohort have hEDS versus the rare Mendelian types of EDS. As such, there is no way of knowing whether or by what proportion the two cohorts represent the same or similar or dif...
I believe that bipolar/mental disorders could be related to coeliac disease. Any study in relation to diet / supplements etc could be affected by this as undiagnosed coeliacs who are continuing to eat gluten do not digest foods properly and become deficient in minerals and vitamins as they CANNOT ABSORB them. I do feel that more research and studies need to be done with this in mind. Doctors should be testing more people. In Australia the AVERAGE time it takes for a coeliac to be diagnosed is 9 YEARS. A blood test is not reliable as often it comes back a false negative. Meanwhile they get diagnosed with bipolar and other illnesses caused by mineral and vitamin deficiencies. I have a father in law who was diagnosed bipolar BEFORE being diagnosed gluten intolerant (he has Dermatitis Herpetiformis which is related to coeliac disease). I don’t believe he is bipolar. My husband also was misdiagnosed with bipolar instead of coeliac disease. Brain cells recover after going on a gluten free diet!
All people diagnosed with bipolar should be tested for Coeliac Disease (or Dermatitis Herpetiformis if they have any kind of rash). And any study for treating bipolar disease with nutritional supplements should be done after the test and/or on people who have excluded gluten from their diet.
Mark L Levy,1,9 Darragh Murnane2, Peter J Barnes,3,9 Mark Sanders,4 Louise Fleming,5 Jane Scullion,6,9 Chris Corrigan,7,9 Omar S Usmani8,9
1. Locum general practitioner, Clinical Lead NRAD (2011-2014)
2. King’s College London Faculty of Life Sciences & Medicine, School of Immunology & Microbial Sciences ; School of Life and Medical Sciences, University of Hertfordshire, Hatfield, Hertfordshire
3. National Heart & Lung Institute, Imperial College, London
4. Clement Clarke international Ltd (CCI) and founder of online museum of inhaler devices, www.inhalatorium.com.
5. Imperial College, London and the Royal Brompton and Harefield, NHS Foundation Trust
6. University Hospitals of Leicester
7. King’s College London Faculty of Life Sciences & Medicine, School of Immunology & Microbial Sciences
8. Imperial College London & Royal Brompton Hospital
9. Aerosol Drug ManagementImprovement Team (ADMIT), www.inhalers4u.org
In an attempt to address issues related to global warming contributed to by the use of pressurised, metered-dose inhalers (pMDIs), Wilkinson et al (1) have succeeded in generating a great deal of negative, potentially harmful media interest for patients who currently rely on these devices. They analysed the potential impact of switching therapy from pMDIs to dry powder inhalers (DPIs) in terms of both c...
Show MoreWe appreciate the response to our study.
The response assumes that a restriction in the population under study also limited the bias in two previous studies (1;2). In the two previous studies only individuals, who had two diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines at 11 months of age were included.
Therefore, we found it relevant to apply the same restriction to our study population and present the corresponding estimates adjusted for the confounders included in our study (3) (Table 1 - https://blogs.bmj.com/bmjopen/files/2019/11/Jenson-et-al-table.jpg).
In Table 1 it can be seen that the restriction of the analysis to include only individuals with two diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines at 11 months of age had little impact on the estimates. Importantly, the associations showing a reduced risk of hospitalisation for accidents among children with two or three diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines and the measles-mumps-rubella vaccine were essentially unchanged when we restricted the analysis to include individuals with two diphtheria–tetanus–pertussis–polio–H. influenzae type b-vaccines at 11 months of age only.
Reference List
(1) Sorup S, Benn CS, Poulsen A, Krause TG, Aaby P, Ravn H. Live vaccine against measles, mumps, and rubella and the risk of hospital admissions for no...
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