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Recent eLetters

Displaying 1-10 letters out of 299 published

  1. Nalmefene: Extrapolation, Exaggeration or Evidence Based Medicine?

    How Lundbeck's conclusion "Nalmefene can be seen as a cost-effective treatment for alcohol dependence, with substantial public health benefits" can have been published?(1) Should "Open" in BMJ Open means open to aggressive marketing from the drug industry, open not to hope but to hype, open without proper discussion despite several gross limitations.

    First, the piece apply a mathematical model to three trials that were sponsored by Lundbeck with authors who were on its payrolls. The first two trials of six months' duration (ESENSE 1 and 2) are negative, however the EuropeanMedicines Agency granted a marketing authorization on a subgroup analysis comprising only a quarter of the patients, with half of the data missing in the nalmefene group.(2) The thrid study (SENSE) is also negative in intention to treat analysis.(3) The publication has little to do with science as it associated per protocol analysis, sub group analysis, post hoc analysis, and added to 16 end points of the genuine protocol (which failed to evidence a relevant effect), a 17 and 18th end points (heavy drinking days and reduction of total alcohol consumption) at 13 months.(3) Both editors of the journal, who are on Lundbeck's payrolls, failed to respond a proposal to comment these major flaws.(personal experience) Last, Laram?e failed to included results from previous trials.(1) In 1998 Contral Pharma Ltd tried to develop nalmefene for alcohol related problems but in 2003 nalmefene didn't succeed to meet phase III clinical end points statistically significant. Lundbeck must have the data, it paid for it when buying the patent to Contral Pharma. Other previous nalmefene unsuccessful development included interstitial cystitis, schizophrenic patients ...P values must be corrected for multiple testings.

    Second, the three studies are limited to surrogate endpoints: heavy drinking days and total alcohol consumption. Although the European Medicines Agency has accepted a reduction in drinking at 6 months as an end point, the US Food and Drug Administration (FDA) does not accept it yet. FDA is right, this surrogate endpoint has not been validated. Harm reduction strategy used to minimize the personal harm and adverse societal effects of alcohol dependence is not yet evidence based.(5,6). Moreover theses surrogate end points in the nalmefene trials were obtained from patient reports, a non reliable method.(6,7) Approving an alcohol misuse drug on the basis of an increase in non-drinking days is similar to approving a weight reduction intervention on the basis of reducing the number of cakes consumed daily rather than the weight loss achieved or ideally obesity related complications or obesity related death.(4) At best, daily alcohol consumption is 5 to 9 grams lower with nalmefene than with placebo and the impact of nalmefene on the complications of alcohol dependence is not known. The crucial first step in the management of alcohol-dependent patients is to establish a relationship built on trust and to provide psychological and social support. When medication is considered, it is better to choose acamprosate or naltrexone, drugs that are only moderately effective but better-assessed.(8)

    Third, regarding ethics, the lack of effective treatment in the controlled group is a serious concern. Three treatments are validated (acamprosate, naltrexone, disulfiram) to treat alcohol use disorders. Why patients were deemed effective treatments for six months or even a year? To avoid comparisons with an effective comparator? Indeed nalmefene do not perform better than naltrexone but exhibits greater side effects.(9) This is a breach in Helsinki declaration! Nalmefene should have been studied on relevant clinical end points vs one of the usual treaments.

    Last, despite Markov is a random process usually characterized as memoryless, we must not forget Lundbeck's records. On June 19, 2013, the European Commission imposed a fine of ?93.8 million on Lundbeck and fined several producers of generic pharmaceuticals a total of ?52.2 million after Lundbeck made agreements with the other companies to delay less expensive generics of Lundbeck's branded citalopram, it best-selling product at the time, from entering the market. In return for the ability to maintain a monopoly on the drug's manufacture, Lundbeck offered payments and other kickbacks. These violated EU antitrust rules that prohibit anticompetitive agreements (Article 101 of the Treaty on the Functioning of the European Union - TFEU). Commission Vice-President Joaqu?n Almunia: "Agreements of this type directly harm patients and national health systems, which are already under tight budgetary constraints".(10) In conclusion, Markov chain Monte Carlo, named after Andrey Markov, a Russian mathematician, now evokes me the exaggeration of a russian oligarch gambling in the small tax haven of the French Riviera, not an hypothesis to be tested on robust data for the patients' benefit.

    1 Laram?e P, Brodtkorb TH, Rahhali N, et al. The cost-effectiveness and public health benefit of nalmefene added to psychosocial support for the reduction of alcohol consumption in alcohol-dependent patients with high/very high drinking risk levels: a Markov model. BMJ Open 2014;4:e005376.

    2 Braillon A.Nalmefene in alcohol misuse: junk evaluation by the European Medicines Agency. BMJ 2014;348:g2017.

    3 van den Brink W, S?rensen P, Torup L, Mann K, Gual A; for the SENSE Study Group. Long-term efficacy, tolerability and safety of nalmefene as- needed in patients with alcohol dependence: A 1-year, randomised controlled study. J Psychopharmacol 2014;28:733-744.

    4 McNulty SJ1, Williams P. Bad medicine: using surrogate markers. BMJ 2014;348:g2012.

    5 Pendery ML, Maltzman IM, West LJ. Controlled drinking by alcoholics? New findings and a reevaluation of a major affirmative study. Science 1982;217:169-75.

    6 Muckle W, Muckle J, Welch V, Tugwell P. Managed alcohol as a harm reduction intervention for alcohol addiction in populations at high risk for substance abuse. Cochrane Database Syst Rev 2012;12,CD006747

    7 Wetterling T, Dibbelt L, Wetterling G et al. Ethyl glucuronide (EtG): better than breathalyser or self-reports to detect covert short- term relapses into drinking. Alcohol Alcohol 2014;49:51-4.

    8 Editorial. Nalmefene. Alcohol dependence: no advance. Prescrie Int 2014;23:150-2.

    9 Drobes DJ, Anton RF, Thomas SE, Voronin K. A clinical laboratory paradigm for evaluating medication effects on alcohol consumption: naltrexone and nalmefene. Neuropsychopharmacology 2003;28:755-64.

    10 http://europa.eu/rapid/press-release_IP-13-563_en.htm

    Conflict of Interest:

    None declared

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  2. Why exclude adverse events possibly due to medication or to some procedure?

    This protocol is enterprising, interesting and important, but it nowhere refers to adverse events that might be effects of medication, or of failure of a medicine to work.

    Research on 'patient safety' in hospital has developed quite separately from pharmacovigilance and the elucidation of harms possibly caused by medicines and their prevention, but they are related both conceptually and in practice. They need integration, or at least to take notice of one another. This study could take a big step in that direction.

    A further valuable dimension would be added by including all events ['incidents'] that might be attributable to a medication or a diagnostic or therapeutic procedure. I very much hope this will be put into the protocol. It would in any case be very useful also to collect all reports of suspected and reported adverse drug reactions in the study cohort, with their timelines, and to examine them as fully as the other events that will be collected.

    One other aspect is not clear from the protocol: who first noticed and mentioned the 'incident', and how did the conversations about it develop? (For example: patient> nurse> doctor; pharmacist> doctor>; nurse> doctor >patient) That could influence how to set about improving detection, reporting, analysis and communication.

    Conflict of Interest:

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  3. Response to the comments published by Pruthu et al.

    Thank you for your comments on the published paper. C1) Recruitment: In Bissau the majority of TB patients are diagnosed at a laboratory at the local health centre, where the patient upon a positive smear is referred to the TB nurse at the same facility who will then start treatment immediately. Patients with smear negative TB are diagnosed at the national TB hospital upon x-ray and physician consultation, and are also from there given treatment on the same day. There is therefore no delay in this part of the process except in rare events of drug stock-outs etc, but in general day of diagnosis and day of treatment initiation will be the same.

    C2) Censoring: As it is described in the paper the patient inclusion stopped at 4th of June 2010. From then patients were followed through the treatment period. The mortality data follow up was censored at 27th of April 2011 when the final data-analysis was done. The details of this censoring could definitely have been described in more detail in the manuscript and we thank the eLetter authors for pointing this out.

    C3) Lost to follow up: In mortality studies the lost to follow up data are always important and can be essential to evaluate the data strength and weaknesses of the data collection. In our case the focus of the paper was on treatment delay, clinical severity at the diagnosis of TB and the risk factor analysis. The mortality data was included to confirm the effect of long-term anti-TB treatment for presumed smear negative TB patients. It is important to understand that the follow up data mentioned concerning effects of treatment assessed by change in clinical severity (e.g. TBscore) is taken from the six months trial clinical examinations - at the end of treatment. When it comes to treatment delay and mortality the results are inconclusive. The mortality analysis was done over the entire mortality follow up time at 24 months but because of the significant challenges with infrastructure and mobility of the population in a country like Guinea Bissau there was a substantial number of "lost to follow up" cases after 2 years. This would probably explain at least part of the missing association between treatment delay and mortality.

    C4) Beta coefficient vs. relative risk The risk factor analysis has been done on categorical independent variables and the treatment delay variable (dependent) is continuous (though logtransformed because of outliers). It is a simple linear regression as it is described in the paper. We did a search on a proper cutoff for delay and found differing information in the literature and therefore decided to use the continuous variable instead as to not "loose" too much data strength. The results are as you correctly point out given as a beta-coefficient and should have been described as such in the paper.

    C5) Observation time on smear-negative TB-cases: This is a helpful argument and we thank the authors of the E-letter. We could have pointed this out in more detail in the discussion session. It is widely accepted and well known that the diagnosis of TB-smear negative TB is a process where there is a natural and inevitable delay in the diagnosis and treatment of TB. The results of the paper only present the difference in time whether it being a delay or a natural cause of TB-smear negative diagnosis could always be discussed.

    Conflict of Interest:

    None declared

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  4. Reply to Rydahl and Clausen

    Rydahl and Clausen are right about problems with the validity of some official statistics published by central institutions or on the home page of the State Serum Institute (SSI). These processed data are, however, not the same as raw data from the Danish Birth Registry (DBR).

    Today, official statistics are typically made by data managers in severely understaffed units in our central administration, often without the possibility of knowledge sharing with clinical expertise. Please don't make us accountable for these flawed statistics and the changes made in them over time.

    Misinterpretation of DBR data is, however, not the same as unreliable DBR data. Making reliable statistics from raw diagnosis, procedural or surgical codes is indeed not simple, even with a hard end point such as stillbirth. For this reason, we have argued that such statistics should be made at least in collaboration with clinicians with research experience in handling registry data. Unfortunately, the central administrative units do not always (in contrast to good old days) have budget for this proposal to be effected.

    Some people, e.g. Rydahl and Clausen apparently do not discriminate between flawed analyses of valid registry data on the one hand, and the baseline validity of the analysed data on the other hand, leading to the rambling conclusion that all analyses based upon DBR data necessarily are flawed.

    For some years, the DBR has been provided with data from the National Health Registry, supplemented by chart data from home deliveries (which are not recorded in the National Health Registry) and by a separate feed from death certificates which are not always included in the National Health Registry. To achieve valid numbers of stillbirths demands all of these three sources of death data to be collected and analysed together. By doing so, provides in our opinion rather precise estimates of the real number of stillbirths. And our guess is that one or two of these sources of data may be missing in some of the official statistics. But that is nothing but a qualified guess.

    Considering all these challenges, it is a little hard to understand why Rydahl and Clausen get so incensed that someone now publishes carefully prepared analyses on stillbirths, since such qualified analyses according to Rydahl and Clausen have been missing.

    Conflict of Interest:

    See original paper.

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  5. Data in the National Danish Birth Register are unreliable.

    Olsen (1) has questioned the validity of the stillbirth data used in Hedegaard et als study based on The Danish Birth Register (DBR). Lidegaard replies that "We always make our own data retrieval from the raw data in the National registries, including the birth registry rather than relying on the official statistics" (2). However, we are concerned that data in DBR seems very unreliable, and perhaps even compromised.

    We check the register regularly and recently noted a dramatic retrospective change on augmentation during year 2000-2006. The incidence was suddenly lowered from 16,000 to approximately 10,000 annually. This means that 6000 oxytocin augmentations a year have been deleted from the register. No public information is available as to why this change occurred. We e-mailed DBR (Aug.15 2014) and asked for an explanation but received no answer. We sent a reminder Aug 27 and the next day the online access to data from 2000-2006 were removed from the website, again without any explanation. The Danish Health Authorities offer no public information on why these changes have occurred. Even though augmentation is not an outcome used in the study by Hedegaard et al, the changes raises strong concerns about validity of the data in DBR in general. More importantly for the validity of the data in the study by Hedegaard et al, we have recently observed changes in DBR on induction rates for the year 2010 where 1033 induction had been added. Besides these inconsistencies Olsen (1) draws attention to the discrepancy concerning the data on intra uterine death, which are the primary outcome of the study by Hedegaard et al (2).

    All these observations make us question the validity of the data in DBR and in the paper. The unexpected retrospective change in data, the lack of explanation for these changes, and the closedown of the register all fuel an uncertainty toward the data in DBR. The problem is, that nobody knows which data are correct. We urge the authors to document the validity of all data in their paper.

    1. Olsen. O. Stillbirth data seem incorrect. BMJ Open. Epub 2014 Sep 10. http://bmjopen.bmj.com/content/4/8/e005785.short/reply#bmjopen_el_8836

    2. Hedegaard M, Lidegaard O, Skovlund CW, Morch LS, Hedegaard M. Reduction in stillbirths at term after new birth induction paradigm: results of a national intervention. BMJ Open. 2014 Aug 14;4(8)

    Conflict of Interest:

    None declared

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  6. Correction to "Alcohol and drug use among adolescents: and the co-occurrence of mental health problems. Ung@hordaland, a population-based study"

    Skogen, J.C., Sivertsen, B., Lundervold, A. J., et al. "Alcohol and drug use among adolescents: and the co-occurrence of mental health problems. Ung@hordaland, a population-based study" BMJ OPEN 2014;4;e005357. The first sentence in the discussion section of the main text should read: "In sum, most adolescents aged 17-19 years had tried alcohol and about one-tenth had tried some illicit drug."

    Conflict of Interest:

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  7. BCIS in hemiarthroplasty

    We found this to be an interesting and thought-provoking article about a relatively less known condition, bone cement implantation syndrome (BCIS).(1) There are clear advantages and disadvantages to the use of cement in hemiarthroplasty, and this data illustrates the infrequent incidence of BCIS whilst emphasising the ever-present risk of serious harm to the patient. As such, it should be worth mentioning the risk of BCIS during the consenting process, as per GMC guidance,(2) in order to ensure that an informed decision is made. The article provides us with an estimated incidence that can be mentioned in the preoperative consenting process.

    Razuin et al.(3) reported post-mortem findings in BCIS of multi- organ embolisation of material from the procedure, i.e. cement and bone. It would be interesting to find out how many BCIS deaths reported on the NRLS had similar post-mortem findings, as opposed to other causes of peri- and intra-operative mortality. We wonder if a retrospective review of affected patients would help identify a subgroup that should not have a cemented procedure. It may also be worth considering further study into alternatives to cement, or the development of a new monomer.

    Anoop Jose, Medical Student, University of Manchester

    Anup Kumar Shetty, Acting consultant orthopaedic surgeon, Tameside General Hospital

    Raja Swaminathan, Consultant orthopaedic surgeon, Tameside General Hospital

    References

    1) Rutter PD, Panesar SS, Darzi A, & Donaldson LJ. (2014). What is the risk of death or severe harm due to bone cement implantation syndrome among patients undergoing hip hemiarthroplasty for fractured neck of femur? A patient safety surveillance study. BMJ open, 4:e004853 doi:10.1136/bmjopen-2014-004853

    2) General Medical Council. Consent: patients and doctors making decisions together - Guidance for doctors. GMC 2008

    3) Razuin R, Effat O, Shahidan MN et al. (2013). Bone cement implantation syndrome. Malaysian J Pathol, 35(1), 87-90

    Conflict of Interest:

    None declared

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  8. Treatment delay affects clinical severity of tuberculosis: a longitudinal cohort study

    Dear Sir, We read the article titled "Treatment delay affects clinical severity of tuberculosis: a longitudinal cohort study" published in BMJ open. The article was interesting as tuberculosis (TB) treatment delay is one of the important issues in timely case detection and management of TB in developing countries. The article was well written. However we have few comments to share which will further facilitate the understanding of the results.

    We noticed inconsistency in the information provided in abstract and main article. In abstract it was mentioned that patients were included 'at the time of diagnosis' vs 'at the start of treatment in TB treatment facilities' in the methodology section of the main article. Especially, in countries like India, we expect some delay in initiation of treatment from the time of diagnosis.(1) So the information on the stage at which the patients were recruited into the study is very important. Moreover the definition of time delay as per the article is "from start of symptom to initiation of specific anti-TB treatment". Hence the recruitment at time of diagnosis of TB is not valid, unless the program in Guinea-Bissau is such that the treatment is started for the TB patient on the same day of diagnosis. If recruitment was done at TB treatment facilities, whether all TB treatment facilities in Bissau were considered for study or any sampling of facilities was done? Time of registration for the study and place of recruitment will affect the treatment delay and hence internal validity and generalizability of study results.

    The study says all cohorts will be followed for two years duration for assessment of mortality outcome; in that case data of patient recruited on 4th June 2010 has to be followed till 4th June 2012. But it is mentioned data was analyzed during June 2010. It could have been better if authors have stated that any censoring for the above time period and the proportion of study population censored. It may be such that data pertaining to objectives- factors affecting treatment delay and effect of treatment delay on clinical severity at diagnosis may have been analyzed during June 2010, but the way it is mentioned in the article is misleading.

    Also in cohort study it is essential to comment on "lost to follow up" during course of study. In smear negative patients, the lost to follow up at 12 months (6 months after completion of treatment) was mentioned as 31.4%. Therefore we can expect more percentage of loss to follow up by end of 24 months. Similarly the information on lost to follow up among smear positive patients was not mentioned though they were the majority in the study population. The author had mentioned all-cause mortality only among smear negative patients that too over 12 months. It would have been better if the total number of tuberculosis patients died over 24 months was also mentioned in the article, as mortality is one of the outcome measures.

    . In the methodology section authors have stated that they have used multiple linear regression model to adjust for multiple risk factors in the treatment delay analysis. It is expected that the outcome variable for multiple linear regression should be a continuous variable (i.e. number of days of delay) and the association in linear regression is expressed in terms of Beta co-efficient. But in table 2 the strength of association was expressed in terms of relative risk showing risk factors related to delay in treatment, which cannot be obtained from multiple linear regression. In case if the association has to be interpreted in terms of Odds ratio or relative risk, the outcome has to be binary (either treatment was delayed or not delayed). There are studies where they have quoted predictor value of factors in terms of either Beta coefficient (2) or in terms of odds ratio after mentioning cut off for considering patient was delayed or not in his treatment (3-5). If the outcome was considered binary and relative risk was obtained using logistic or cox regression, then there should be some cut-off to define whether patient delayed his treatment or not ( Ex. Patients with more than 30 days delay are considered to be delayed in treatment or median cut off).(3-5)

    The study interpreted the smear negative TB as the risk factor for treatment delay. But as per WHO clinical criteria at least two weeks of follow up with antibiotics was recommended before considering a patient for anti- TB specific treatment. So the observed difference of 2-3 weeks in this could be due to the WHO clinical criteria for diagnosing the case and not necessarily a risk factor for treatment delay.

    References 1. Sai Babu B, Satyanarayana AVV, Venkateshwaralu G, Ramakrishna U, Vikram P, Sahu S, et al. Initial default among diagnosed sputum smear- positive pulmonary tuberculosis patients in Andhra Pradesh, India. Int J Tuberc Lung Dis Off J Int Union Tuberc Lung Dis 2008;12(9):1055-8.

    2. Ukwaja KN, Alobu I, Nweke CO, Onyenwe EC. Healthcare-seeking behavior, treatment delays and its determinants among pulmonary tuberculosis patients in rural Nigeria: a cross-sectional study. BMC Health Serv Res 2013;13:25.

    3. Ngadaya ES, Mfinanga GS, Wandwalo ER, Morkve O. Delay in Tuberculosis case detection in Pwani region, Tanzania. a cross sectional study. BMC Health Serv Res 2009;9:196.

    4. Yimer S, Bjune G, Alene G. Diagnostic and treatment delay among pulmonary tuberculosis patients in Ethiopia: a cross sectional study. BMC Infect Dis 2005;5:112.

    5. Needham DM, Foster SD, Tomlinson G, Godfrey-Faussett P. Socio- economic, gender and health services factors affecting diagnostic delay for tuberculosis patients in urban Zambia. Trop Med Int Health 2001;6(4):256-9.

    Conflict of Interest:

    None declared

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  9. Fruit and vegetables in the diet and mental well-being

    The findings of Stranges, et al (2014) that fruit and vegetable consumption was associated with mental well-being brought to mind some other findings (1). There could be numerous mechanisms to explain these results; however, a few jump readily to mind.

    It has been asserted that oxidative stress may play a role in anxiety (2). A diet containing ample amounts of fruits and vegetables, which are rich in antioxidant and anti-inflammatory agents, might promote brain- health in part by reducing oxidative stress (3).

    There is growing evidence that the gastrointestinal microbiota influence brain function (4). This might be by multiple mechanisms. One intriguing possibility is that the gut ecosystem might influence the human epigenome including that of the brain (5). It should be noted that the epigenome retains some malleability throughout the lifespan and that the epigenome appears to influence brain function (6, 7). Whole plant foods, including fruits and vegetables, are believed to promote a health gut ecosystem (8). In addition, phytochemicals found in fruits and vegetables also affect the epigenome and might plausibly shape mood by modulating gene expression (9, 10).

    About the author: www.CeliaMRoss.com

    1. Stranges S, Samaraweera PC, Taggart F, Kandala NB, Stewart-Brown S. Major health-related behaviours and mental well-being in the general population: the Health Survey for England. BMJ Open. 2014 Sep 19;4(9):e005878. 2. Bouayed J, Rammal H, Soulimani R. Oxidative stress and anxiety: relationship and cellular pathways. Oxid Med Cell Longev. 2009 Apr- Jun;2(2):63-7. 3. Lau FC, Shukitt-Hale B, Joseph JA. Nutritional intervention in brain aging: reducing the effects of inflammation and oxidative stress. Subcell Biochem. 42:299-318. 4. Tillisch K. The effects of gut microbiota on CNS function in humans. Gut Microbes. 2014 May 16;5(3). 5. Stilling RM, Dinan TG, Cryan JF. Microbial genes, brain & behaviour - epigenetic regulation of the gut-brain axis. Genes Brain Behav. 2014 Jan;13(1):69-86. 6. Fraga MF, et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proceeding of the National Academy of Science U S A. 2005 Jul 26;102(30):10604-9. 7. D'Addario C, et al. Selective DNA methylation of BDNF promoter in bipolar disorder: differences among patients with BDI and BDII. Neuropsychopharmacology. 2012 Jun;37(7):1647-55. 8. Tuohy KM, Contemo L, Gasperotti M, Viola R. Up-regulating the human intestinal microbiome using whole plant foods, polyphenols, and/or fiber. J Agric Food Chem. 2012 Sep 12;60(36):8776-82. 9. Blade C, Baselga-Escudero L, Arola-Amal A MicroRNAs as New Targets of Dietary Polyphenols. Curr Pharm Biotechnol. 2014 Jul 11. [Epub ahead of print] 10. Pan MH, Lai CS, Wu JC, Ho CT. Epigenetic and disease targets by polyphenols. Curr Pharm Des. 2013;19(34):6156-85.

    Conflict of Interest:

    I write about health issues.

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  10. Reply to Ole Olsen

    Thanks to Ole Olsen for calling attention to a possible inconsistency between the data included in our paper and official stillbirth statistics.

    We always make our own data retrieval from the raw data in the National registries, including the birth registry rather than relying on the official statistics.

    First the official statistics often and also in this case are inconsistent. According to the official publication from National Health Board covering the years 2009-2011 the number of stillbirths in 2010 and 2011 were 255 and 246 respectively (1) whereas according to the official 2013 publication by Statens Serum Institute, the numbers were 270 and 274 in the same two years(2), and finally Statistics of Denmark reports 269 and 262 respectively (3). So what Ole Olsen considers as the truth actually differs between different official bodies.

    Secondly, for scientific purposes it is mandatory to know exactly which conditions are applied for your data, which data sources you get your information from, restriction periods for repeated non fatal events etc.

    Our focus was on stillbirths from 37 weeks. Some differences between dataset may arise from different basic requirements to the dataset. E.g. we only included women delivering in Denmark with a Danish PIN-code. Thus women in transit from other countries, but delivering in Denmark, are not included in our data. Such conditions may explain small differences in different dataset. But according to the latest official stillbirth data including the online statistics which Ole Olsen refer to, the number of stillbirths from 37 weeks was in 2009-2010 247 and in 2011-2012 152, a decrease of 95 (2). According to our data the number of stillbirths from 37 weeks fell from 198 to 148 or with 50 events from 2009-2010 to 2011-2012. In other words, the official statistics suggest a fall which is almost twice the fall we suggested. So if anything, our data were more conservative than the official statistics.

    The reason of focusing on stillbirths from 37 weeks is that a change in induction practice around and after term is not expected to influence stillbirths before 37 weeks. Contrary, inclusion of stillbirths before 37 weeks may actually hide the impressing and welcome decrease in stillbirths from 37 weeks in Denmark through recent years.

    We have delivered detailed figures on our data and the conditions and anticipations we made. We still think we have valid data, which except for year 2010 are actually very close to the latest official statistics (2). We have checked data for year 2010, and still find the results that we published.

    Thus, we see no reason to doubt our results and even less to withdraw our publication.

    ?jvind Lidegaard

    1) Sundhedsstyrelsen. F?dselsstatistikken 2011. Copenhagen, Sundhedsstyrelsen, 2012.

    2) Sundhedsstyrelsen. F?dselsstatistikken 2012. Copenhagen, Statens Serum Institut, 2013.

    3) http://www.statistikbanken.dk/statbank5a/default.asp?w=1280 Visited September 14, 2014.

    Conflict of Interest:

    See original paper

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