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إظهار الرسائل ذات التسميات United. إظهار كافة الرسائل
إظهار الرسائل ذات التسميات United. إظهار كافة الرسائل
الأحد، 16 سبتمبر 2012
United we stand - integrating the actin cytoskeleton and cell-matrix adhesions in cellular mechanotransduction
Advance Online Publication July 13, 2012 doi: 10.1242/?jcs.093716 July 1, 2012 J Cell Sci 125, 3051-3060. Ulrich S. Schwarz1,* and Margaret L. Gardel2,*1BioQuant and Institute for Theoretical Physics, University of Heidelberg, Philosophenweg 19, 69120 Heidelberg, Germany 2Physics Department, James Franck Institute and Institute for Biophysical Dynamics, University of Chicago, Chicago, IL 60637, USA ?*Authors for correspondence (ulrich.schwarz{at}bioquant.uni-heidelberg.de; gardel{at}uchicago.edu) Many essential cellular functions in health and disease are closely linked to the ability of cells to respond to mechanical forces. In the context of cell adhesion to the extracellular matrix, the forces that are generated within the actin cytoskeleton and transmitted through integrin-based focal adhesions are essential for the cellular response to environmental clues, such as the spatial distribution of adhesive ligands or matrix stiffness. Whereas substantial progress has been made in identifying mechanosensitive molecules that can transduce mechanical force into biochemical signals, much less is known about the nature of cytoskeletal force generation and transmission that regulates the magnitude, duration and spatial distribution of forces imposed on these mechanosensitive complexes. By focusing on cell-matrix adhesion to flat elastic substrates, on which traction forces can be measured with high temporal and spatial resolution, we discuss our current understanding of the physical mechanisms that integrate a large range of molecular mechanotransduction events on cellular scales. Physical limits of stability emerge as one important element of the cellular response that complements the structural changes affected by regulatory systems in response to mechanical processes. Key words FundingU.S.S. is a member of the Heidelberg cluster of excellence CellNetworks and is supported by the BMBF MechanoSys grant [grant number 0315501C to U.S.S.]. M.L.G. is supported by a NIH Director's Pioneer Award [grant number DP10D00354], Packard Fellowship and Burroughs Wellcome Career Award. Deposited in PMC for release after 12 months. This article is part of a Minifocus on Mechanotransduction. For further reading, please see related articles: ‘Deconstructing the third dimension – how 3D culture microenvironments alter cellular cues’ by Brendon M. Baker and Christopher S. Chen (J. Cell Sci. 125, 3015-3024). ‘Finding the weakest link – exploring integrin-mediated mechanical molecular pathways’ by Pere Roca-Cusachs et al. (J. Cell Sci. 125, 3025-3038). ‘Signalling through mechanical inputs – a coordinated process’ by Huimin Zhang and Michel Labouesse (J. Cell Sci. 125, 3039-3049). ‘Mechanosensitive mechanisms in transcriptional regulation’ by Akiko Mammoto et al. (J. Cell Sci. 125, 3061-3073). ‘Molecular force transduction by ion channels – diversity and unifying principles’ by Sergei Sukharev and Frederick Sachs (J. Cell Sci. 125, 3075-3083). © 2012. Published by The Company of Biologists Ltd
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الثلاثاء، 28 أغسطس 2012
The health and development of children born to older mothers in the United Kingdom: observational study using longitudinal cohort data
The health and development of children born to older mothers in the United Kingdom: observational study using longitudinal cohort data | BMJ 
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We are open for entries! doc2doc Follow BMJ Group on Twitter BMJ Group on Facebook BMJ Group Awards Subscribe My accountOPEN ACCESS: All research articles are freely available online, with no word limit. Find out more about the BMJ's open access policy. Submit your paper. Find out how study types differ in our How to read a paper section. The truth about sports drinks: Find out more about the joint BMJ and BBC Panorama investigation into the evidence base behind many health claims. Research The health and development of children born to older mothers in the United Kingdom: observational study using longitudinal cohort data BMJ 2012; 345 doi: 10.1136/bmj.e5116 (Published 21 August 2012) Cite this as: BMJ 2012;345:e5116 Immunology (including allergy) Dentistry and oral medicine Developmental paediatrics Epidemiologic studies More topics Infectious diseases Fewer topics Article Related content Article metrics Alastair G Sutcliffe, reader in child health1, Jacqueline Barnes, professor of psychology2, Jay Belsky, professor of human development23, Julian Gardiner, senior researcher in statistics2, Edward Melhuish, professor of human development21Institute of Child Health, University College London, UK2Institute for the Study of Children, Families and Social Issues, Birkbeck, University of London, London WC1E 7HX, UK 3University of California, Davis, CA, USACorrespondence to: E Melhuish e.melhuish{at}bbk.ac.ukAccepted 11 July 2012AbstractObjective To assess relations between children’s health and development and maternal age.Design Observational study of longitudinal cohorts.Setting Millennium Cohort Study (a random sample of UK children) and the National Evaluation of Sure Start study (a random sample of children in deprived areas in England), 2001 to 2007.Participants 31?257 children at age 9 months, 24?781 children at age 3 years, and 22?504 at age 5 years.Main outcome measures Childhood unintentional injuries and hospital admissions (aged 9 months, 3 years, and 5 years), immunisations (aged 9 months and 3 years), body mass index, language development, and difficulties with social development (aged 3 and 5 years).Results Associations were independent of personal and family characteristics and parity. The risk of children having unintentional injuries requiring medical attention or being admitted to hospital both declined with increasing maternal age. For example, at three years the risk of unintentional injuries declined from 36.6% for mothers aged 20 to 28.6% for mothers aged 40 and hospital admissions declined, respectively, from 27.1% to 21.6%. Immunisation rates at nine months increased with maternal age from 94.6% for mothers aged 20 to 98.1% for mothers aged 40. At three years, immunisation rates reached a maximum, at 81.3% for mothers aged 27, being lower for younger and older mothers. This was linked to rates for the combined measles, mumps, and rubella immunisation because excluding these resulted in no significant relation with maternal age. An increase in overweight children at ages 3 and 5 years associated with increasing maternal age was eliminated once maternal body mass index was included as a covariate. Language development was associated with improvements with increasing maternal age, with scores for children of mothers aged 20 being lower than those of children of mothers aged 40 by 0.21 to 0.22 standard deviations at ages 3 and 4 years. There were fewer social and emotional difficulties associated with increasing maternal age. Children of teenage mothers had more difficulties than children of mothers aged 40 (difference 0.28 SD at age 3 and 0.16 SD at age 5).Conclusion Increasing maternal age was associated with improved health and development for children up to 5 years of age.IntroductionIn developed countries the trend towards later childbearing has been strong.1 2 3 In England and Wales the number of births to women aged 40 or more trebled from 1989 to 2009, when it reached 26?976 births.4 Similar patterns exist in almost all developed countries. In New Zealand, for example, the rate of births to women aged 35 or more almost doubled between 1995 and 2010.5 Similarly, in the United States between 1990 and 2004 birth rates increased by 43% in women aged 35-39, by 62% in women aged 40-44, and by more than 150% in women aged 45.6 Established risks associated with older maternal age include preterm labour, fetal malformation, fetal death, and increased risk of maternal cardiometabolic disease.3 Given the substantial and rapid increase in older motherhood and the known medical risks, it is important to determine whether, and how, older motherhood is linked to child health and development beyond the fetal stage. Although research documenting the deleterious consequences of young motherhood on children’s development is substantial,7 8 9 10 11 evidence of any effects, deleterious or beneficial, of older motherhood on offspring is lacking,1 perhaps owing to the recency of the trend in delayed childbearing.The few studies on the effects of older parenthood on offspring reported mixed results. In an investigation of maternal age and extremely low birth weight live births (n=14?671 children), infants born to mothers aged 40 or older were 22% more likely to survive and had a 13% decreased risk of neurodevelopmental impairment or death compared with those of mothers aged less than 20.3 In another study (n=33?437 children), advanced paternal age predicted increased externalising behaviour (that is, aggression, disobedience) and poorer cognitive ability, whereas the opposite was true of advanced maternal age.12 Advanced paternal and maternal age predicted poorer social functioning among Israeli male adolescents (n=403?486),13 and schizophrenia and autistic behaviours were more common among the children of older fathers and mothers.14 15 As there is little evidence that evaluates relations between older motherhood and children’s health and development beyond birth and the postnatal period, we tested hypotheses that maternal age would show relations with children’s health and development independent of personal and background characteristics.MethodsThe study sample consisted of children aged 9 months, 3 years, and 5 years from the Millennium Cohort Study16 and from the National Evaluation of Sure Start study.17 Eligible children for the Millennium Cohort Study were all children in England born over a period of 16 months from September 2000 and living in the 398 wards. The random sample was clustered geographically by electoral ward with some oversampling to ensure adequate representation of wards with a high number of ethnic minority populations (=30% black or Asian populations in 1991 census), and disadvantaged areas from the poorest 25% of wards based on the child poverty index,18 which is based on the proportion of children in families receiving means tested benefits. Overall there were 188 advantaged wards (not in poorest 25%), 191 disadvantaged wards, and 19 wards with a high proportion of ethnic minority families. Children were sampled from the government’s child benefit records. Child benefit is a universal provision, payable to mothers from the birth of their children. The take-up of child benefit exceeds 97%. Apart from the possibility of eligible families being too rich or too ill informed to claim, most of the children not claimed for were ineligible as the children of non-nationals with temporary or unconfirmed residence status, such as foreign armed forces, overseas students, and recent immigrants, including asylum seekers. The attained sample at nine months was 18?552 children and families (response rate 70%). Of these, 14?898 (80.3% retention rate) were seen again when the children were aged 3 and 14?678 (79.1% retention rate) when the children were aged 5. Additional children and families were recruited at three years to give 15?590 at age 3 and 15?246 at age 5.19The sample from the National Evaluation of Sure Start study was selected from areas in England chosen to receive a Sure Start local programme, all in the 20% most disadvantaged areas defined by the child poverty index—that is, all were from disadvantaged areas.18 From children born in 200 randomly chosen Sure Start areas during 29 months from January 2002, we chose a random sample of 12?705 infants aged 9 months (response rate 84.0%), again using the child benefit records as a sampling frame. Of those seen at this age, 11?118 children and families were randomly selected to be followed-up when the child was aged 3 years, 9191 (82.7%) of whom participated in data collection at the 3 years age point. When the children were aged 5 years we randomly selected to be followed-up 8000 of the children and families seen when the children were aged 3; data were collected from 7258 (response rate 90.7%). We applied no exclusion criteria except to include in analyses only the first born child when multiple births occurred. The total sample consisted of 31?257 infants aged 9 months (18?552 from the Millennium Cohort Study and 12?705 from the National Evaluation of Sure Start), 24?781 3 year olds (15?590 from the Millennium Cohort Study and 9191 from the National Evaluation of Sure Start), and 22?504 5 year olds (15?246 from the Millennium Cohort Study and 7258 from the National Evaluation of Sure Start study). In both studies we included all mothers and children. The age range of mothers was between 13 and 57 years.OutcomesWe chose the child outcomes because they have been used as indicators of child wellbeing in reports from the World Health Organization,20 applied to the total population, were likely to predict later health and development, and were reliably measured by parental report or researcher. Outcomes collected by parental report were social difficulties experienced by the children, using the strengths and difficulties questionnaire21; unintentional injuries requiring medical treatment (from nurse, general practitioner, hospital, or medical clinic) in the past year; admissions to hospital in the past year; and receipt of all recommended immunisations since the previous interview. The recommended immunisations (all free under the National Health Service) in terms of the age points in the studies were: three doses of vaccines against diphtheria, tetanus, pertussis, polio, and haemophilus influenzae type b (hib) at 9 months of age, and by 3 years of age boosters for diphtheria, tetanus, pertussis, and polio and vaccinations against measles, mumps, and rubella (typically delivered as a combined vaccine), hepatitis B, meningitis C, and pneumococcal infection. An additional immunisation outcome was included at three years, defined as complete immunisations, excluding combined measles, mumps, and rubella. Data on uptake of immunisations were obtained only from the Millennium Cohort Study. To calculate the children’s body mass index a researcher measured their weight and height at ages 3 and 5 during home visits. Data on the unintentional injury and hospital admission outcomes were available at all three ages. In addition, researchers assessed language development at ages 3 and 5 using the British ability scales naming vocabulary subscale.22We coordinated data collection across both studies, with researchers trained in common and similar procedures used to ensure collection of comparable information so that data could be combined across the studies. At each age point the parents were interviewed at home and the children measured. Personal and background information collected by parental report served as control variables in analyses.Statistical analysisWe analysed three continuous outcomes in the children: body mass index (range 6.7-63.6), naming vocabulary score (range 20-80, higher being better) from the British ability scales,22 and social difficulties score (range 0-34, lower being better) from the strengths and difficulties questionnaire.21 The four binary outcomes chosen were unintentional injury since the last survey, admission to hospital since the last survey, in receipt of all recommended immunisations, and overweight. We defined being overweight using reference data on body mass index from the US Centers for Disease Control and Prevention23 as being above the 85th percentile for the children’s sex and age. To model the continuous variables we used linear terms in the regression models.Since both samples were geographically clustered we used linear mixed effects models for the continuous outcomes and logistic regression mixed effects models for the binary outcomes, with a random effect fitted for clustering in all models. The principal independent variable was maternal age at the children’s birth, treated as a continuous variable.Regression models were fitted with the following covariates: children’s sex, children’s age, number of siblings, parity, birth weight, breast fed for at least six weeks, ethnic group, raised by single parent, paternal age, raised in workless household, family income, mother’s educational attainment, and mother’s social class (defined by regular occupation). Paternal age was grouped into 10 bands of about equal size: (<22.5 years, 22.5 to <25, 25 to <27, 27 to <29, 29 to <31, 31 to <33, 33 to <35, 35 to <37.5, 37.5 to 40, and >40); we included the father being absent as an additional category. We selected the covariates a priori to avoid any confounding of maternal age effects on outcomes. Models of the children’s body mass index and overweight were also controlled for mother’s body mass index. The covariates did not show high colinearity; only maternal and paternal age (0.66) and worklessness and income (0.57) were above 0.5.Depending on the outcome, between 78.7% and 93.7% of children had complete data on all variables. For most variables less than 10% of data was missing, with 10% exceeded only by mother’s body mass index (nine months, three years, and five years), paternal age (five years), child’s body mass index, overweight, naming vocabulary, and social difficulties score (three years). The greatest amount of missing data was for social difficulties score at three years, at 22.8%. We used the Amelia II package to impute missing data,24 with all covariate and outcome variables used in the imputations. Five imputations were generated and models fitted to each imputed dataset. Model results were consolidated using Rubin’s rules,25 with degrees of freedom ascertained using Hesterberg,26 equation 24.We selected a linear or polynomial model for maternal age. To achieve this we fitted an initial linear model then added higher order terms successively until the highest order term was no longer statistically significant, at which point we adopted the previous model as the final one. The final adjusted models were linear or quadratic for maternal age. To investigate possible confounding between maternal age and first time motherhood, we fitted additional models that included an interaction between first time motherhood and maternal age. No such interactions were significant and are not discussed further.Models were fitted in R 2.11.1. We used the linear mixed effects procedure to fit the linear mixed effects models and the generalised linear mixed models penalised quasilikelihood procedure27 to fit the logistic regression mixed effects models. We undertook analyses for the Millennium Cohort Study and the National Evaluation of Sure Start samples separately and for the combined total sample. As results were broadly similar in all cases, we present the results for the combined sample.ResultsModelsTable 1? shows the characteristics of the sample, table 2? the raw outcome data tabulated by maternal age categories, and table 3? the results of the final adjusted and unadjusted models. The figure? shows the results of the adjusted models for outcomes showing statistically significant relations with maternal age.View this table:View PopupView InlineTable 1 Descriptive statistics of participants. Values are numbers (percentages) unless stated otherwiseView this table:View PopupView InlineTable 2 Data for child outcomes stratified by maternal age. Values are numbers (percentages) of children unless stated otherwiseView this table:View PopupView InlineTable 3 Results from final models
View larger version:In a new windowDownload as PowerPoint SlideAdjusted models showing regression lines with 95% confidence intervals for outcomes by maternal age. Unintentional injury in children (quadratic model, 3 years), admission to hospital (linear models), complete immunisation (quadratic models, 9 months and 3 years), British ability scales naming vocabulary score (quadratic models), and total score on strengths and difficulties questionnaire (quadratic model, 3 years). Model coefficients, per 5 years of maternal age, are given with P values. Quadratic models are parameterised so that the coefficient for the linear component of maternal age (ß 1) gives the model slope at maternal age 30. MMR=measles, mumps, and rubella immunisation; BAS=British ability scalesUnintentional injuriesThe risk of unintentional injuries declined with increasing maternal age, with the final models for children aged 9 months, 3 years, and 5 years being linear, quadratic, and linear, respectively (figure).At nine months the risk of unintentional injuries declined across maternal age, with the risk in children of mothers aged 20 being 9.5%, decreasing to 6.1% for mothers aged 40. The model is quadratic for 3 year olds, showing a decline from 36.6% for mothers aged 20 to 28.6% for mothers aged 40.5 (where the curve reaches a minimum). The model is linear for 5 year olds, with risk decreasing from 29.1% for mothers aged 20 to 24.9% for mothers aged 40.Admissions to hospitalThe risk of children being admitted to hospital also declined with increasing maternal age, with the final models being linear in all cases (figure).At nine month, the probability of children being admitted to hospital declined from 16.0% when mothers were aged 20 to 10.7% when mothers were aged 40. For 3 year olds, the probability declined from 27.1% when mothers were aged 20 to 21.6% when mothers were 40. For 5 year olds, the change in hospital admissions with increasing maternal age was not statistically significant.ImmunisationsThe final models of complete immunisation rates by maternal age for 9 month olds and 3 year olds were quadratic (figure).At nine months the rate of complete immunisation increased with maternal age, from 94.6% when mothers were aged 20 to 98.1% when mothers were 40. At three years the maximum rate of complete immunisations was 81.3% when mothers were aged 27.3, with lower rates among younger and older mothers. Complete immunisations at three years, excluding combined measles, mumps, and rubella immunisation were also considered; the final model was linear, with no significant relation between immunisation rate and maternal age, indicating that non-linear effects are linked to take-up of the combined measles, mumps, and rubella immunisation.Children’s body mass index and overweightInitial models showed a significant positive association between the children’s body mass index and maternal age. However, once maternal body mass index was controlled for, no significant association with maternal age was found for either children’s body mass index or children’s overweight (table 2).Language development (British ability scales naming vocabulary)The final models of naming vocabulary score in relation to maternal age were quadratic, with scores increasing as maternal age increased (figure). At three years, the score for children of mothers aged 20 was 0.22 standard deviations below that for the children of mothers aged 40. At five years, the value for children of mothers aged 20 was 0.21 standard deviations below that for the children of mothers aged 40.Social and emotional difficultiesThe final models for the strengths and difficulties total problem score in relation to maternal age were quadratic at three years and linear at five years, with scores decreasing as maternal age increased, indicating better social development (figure). At three years, the score for children of mothers aged 20 was 0.28 standard deviations higher than for children of mothers aged 40, and at five years, the corresponding difference was 0.16 standard deviations.DiscussionIncreasing maternal age was associated with children having fewer hospital admissions and unintentional injuries, a greater likelihood of having had all of their immunisations by 9 months of age, better language, and fewer social and emotional difficulties. Such findings contrast with the known obstetric risks associated with older motherhood1 2 3 and serve as a counterpoint to evidence highlighting poorer health and developmental outcomes for children of younger mothers (<20 years).7 8 9 10 11 The positive findings in relation to increasing maternal age were generally consistent with the few other studies on children of older mothers, often defined as 40 years or older, which focused on other issues such as neurodevelopment, educational achievement, substance misuse, and juvenile crime12 rather than, as here, general health and development, both cognitive-linguistic and socio-emotional. There are, however, exceptions in the literature to the conclusion that older motherhood carries few risks for child functioning. One study of Israeli male adolescents showed poorer social and emotional functioning in association with both teenage mothers and mothers aged 40 or older at their child’s birth (relative to other mothers).13 Older mothers tend to be better educated, have higher family income, and be married, all factors associated with greater child wellbeing.7 28 Analyses controlled for these and other personal factors. Hence the results are indicative of associations with maternal age rather than covarying characteristics, or at least those included in this research. Also noteworthy in this study was the inclusion of children living in deprived neighbourhoods and experiencing high levels of family deprivation, as well as children representative of the general population, to increase the likelihood of identifying any adverse impacts. The fact that results were broadly similar for all outcomes when the analyses were undertaken separately for the population representative Millennium Cohort Study sample and the deprived National Evaluation of Sure Start sample indicates that these results are likely to be applicable across the spectrum of deprivation.The only health related outcome revealing any adverse relation with childbearing in older age was immunisation uptake by age 3 years, which declined with maternal age after age 33. This could have been a historical artifact, related to parental anxiety regarding now discredited claims linking the combined measles, mumps, and rubella with autism,29 which were prevalent in the media around the time of the birth and early years of the children studied here. It is possible that older mothers were more influenced by the media reports. Further analyses of immunisations excluding the combined measles, mumps, and rubella immunisation supported this interpretation as they revealed no relations between maternal age and uptake of all other immunisations.The likelihood of a child being overweight increased with increasing maternal age, but not when maternal body mass index was controlled for, indicating that this result was explained by greater maternal body mass index in older mothers, rather than by maternal age in itself. Nevertheless, this association is one that should be of concern for health practitioners.Strengths and limitations of the studyThis study has all the limitations of observational studies in attributing causality but does provide evidence relevant to an important clinical topic. Also, all covariates and some child outcomes apart from the children’s weight and height and naming vocabulary measurements were based on parental report; the fact that the data were collected in an optimal manner by direct parental interview with highly trained interviewers would seem to obviate somewhat the limitation. Although some child health information could have been extracted from medical records, that method also has pitfalls for data completeness and comparability. Error in measurement is always possible, but the measures taken were the best available and we have no reason to assume systematic mis-measurement, and if measurement error is not systematically related to a variable then measurement error decreases the likelihood of significant results. Inevitably some attrition occurred as the children aged and families withdrew or became non-contactable, but the attrition rates compare favourably with other longitudinal studies. Also, analyses were carried out in two ways, using only complete cases and using multiple imputation to include the full sample to deal with this issue. These two sets of analyses did not differ substantially. The initial hypotheses were tentative for the direction of effects owing to limited published evidence on the health of children born to older mothers. Finally, there was a shortfall in information on paternal age, as a significant proportion of children were living separately from their father. None the less, the large dataset, the representativeness of the samples, the powerful nature of the children’s outcomes, and the fact that data were collected originally for a different purpose improved the likelihood of the findings being valid. Also the fact that results were similar for all outcomes for separate analyses of the population representative Millennium Cohort Study sample and the deprived National Evaluation of Sure Start sample indicates that these results are likely to be applicable to the whole population and across the spectrum of deprivation.ConclusionIn contrast with the obstetric risks known to be associated with older motherhood these results indicate that increasing maternal age was associated with children having fewer hospital admissions and unintentional injuries, a greater likelihood of better protection from ill health through completed immunisations by age 9 months, better language development, and fewer social and emotional difficulties. The findings are noteworthy given the continuing increase in mean age of childbearing. It will be important to continue to examine relations between child outcomes and maternal age to see if the situation changes as the children age, and to explore possible mediating and moderating factors for the relations associated with maternal age. Possible mechanisms for the observed relations between child outcomes and maternal age might be environmental, such as differences in parenting, or genetic, as found for paternal age where the longer fathers and grandfathers waited to have children, the more likely it was for their offspring to live longer and healthier lives. This seemed to be due to longer telomeres of older fathers’ sperm, related to increased longevity and development, being inherited by offspring.30 Further research should explore possible mechanisms. Finally, the results of this study are relevant to concerns raised about older people seeking to use fertility treatments and possible risks posed to children delivered by older mothers.31What is already known on this topicEstablished risks associated with older maternal age (=40 years) include preterm labour, fetal malformation, fetal death, and increased risk of maternal cardiometabolic diseaseEvidence related to child health and development beyond the immediate postnatal period is lacking for the children of older mothersThere is an increased risk of deleterious consequences for children’s health and development associated with young motherhoodWhat this study addsIncreasing maternal age was associated with several beneficial effects on childrenChildren had fewer hospital admissions and unintentional injuries, a greater likelihood of being fully immunised by age 9 months, better language, and fewer social and emotional difficultiesNotesCite this as: BMJ 2012;345:e5116FootnotesWe thank the families and their children for their cooperation throughout both projects.Contributors: All authors helped to plan the research, and took part in the writing of the final article. JG undertook the statistical analyses, supervised by EM. EM had full access to all the data in the study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis. Funding: This research was funded by the Wellcome Trust through a grant entitled “Health of children born to older mothers”; the funding body had no involvement in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. All authors are independent of the funding agency.Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; and no other relationships or activities that could appear to have influenced the submitted work.Ethical approval: This study was approved for both studies by the National Health Service South West multicentre research ethics committee (reference No MREC/01/6/65). Participants in both studies gave written informed consent.Data sharing: The data from the Millennium Cohort Study and the National Evaluation of Sure Start study are available from the Economic and Social Data Service (www.esds.ac.uk/).This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.References?Sutcliffe A, Baki Y. What is known about children born to older parents? In: Bewley S, Ledger W, Nikolaou D, eds. Reproductive ageing. Royal College of Obstetricians and Gynaecologists Press, 2009:173-82.?Berryman J, Thorpe K, Windridge K. Older mothers: conception, pregnancy and birth after 35. Pandora,1995. ?Vohr BR, Tyson JE, Wright LL, Perritt, RL, Li L, Poole WK. Maternal age, multiple birth, and extremely low birth weight infants. J Pediatr2009;154:498-503.OpenUrlCrossRefMedlineWeb of Science?Office for National Statistics. Birth statistics. Series FM1 No 35. ONS, 2007.?Statistics New Zealand.Births and deaths: year ended March 2011. 2012. www.stats.govt.nz/browse_for_stats/population/births/BirthsAndDeaths_HOTPYeMar11.aspx.?Martin JA, Hamilton BE, Sutton PD, Ventura SJ, Menacker F, Kirmeyer S, Division of Vital Statistics. Births: final data for 2004. National Vital Statistics Reports 55(1). US Department of Health and Human Services, Centers for Disease Control and Prevention, 2004.?Moffitt TE, E-Risk Study Team. Teen-aged mothers in contemporary Britain. J Child Psychol Psych2002;43:727-42.OpenUrlCrossRefMedlineWeb of Science?Botting B, Rosato M, Wood R. Teenage mothers and the health of their children. Popul Trends1998;93:19-27.OpenUrlMedline?Fraser AM, Brockert JE, Ward RH. Association of young maternal age with adverse reproductive outcomes. New Engl J Med1995;332:1113-7.OpenUrlCrossRefMedlineWeb of Science?Gilbert W, Jandial D, Field N, Bigelow P, Danielsen B. Birthoutcomes in teenage pregnancies. J Matern Fetal Neonatal Med2004;16:265-70.OpenUrlCrossRefMedline?Phipps MG, Blume JD, Demonner SM. Young maternal age associated with increased risk of postnatal death. Obstet Gynaecol2002;100:481-6.OpenUrlCrossRefMedlineWeb of Science?Fergusson DM, Woodward LJ. Maternal age and educational and psychosocial outcomes in early adulthood. J Child Psychol Psych1999;40:479-89.OpenUrlCrossRefMedlineWeb of Science?Weiser M, Reichenberg A, Werbeloff N, Kleinhaus K, Lubin G, Shmushkevitch M, et al. Advanced parental age at birth is associated with poorer social functioning in adolescent males: shedding light on a core symptom of schizophrenia and autism. Schizophr Bull2008;34:1042-6.OpenUrlFREE Full Text?Zammit S, Allebeck P, Dalman C, Lundberg I, Hemmingson T, Owen MJ, et al. Paternal age and risk for schizophrenia. Br J Psychiatry2003;183:405-8.OpenUrlFREE Full Text?Croen LA, Najjar DV, Fireman B, Grether JK. Maternal and paternal age and risk of autism spectrum disorders. Arch Pediatr Adolesc Med2007;161:334-40.OpenUrlCrossRefMedline?Dex S, Joshi H. Millennium cohort study, first survey: a user’s guide to initial findings. Centre for Longitudinal Studies, University of London Institute of Education, 2004.?Melhuish E, Belsky J, Leyland AH, Barnes J, National evaluation of Sure Start team. Effects of fully-established Sure Start local programmes on 3-year-old children and their families living in England: a quasi-experimental observational study. Lancet2008;372:1641-7.OpenUrlCrossRefMedlineWeb of Science?Noble M, Smith G, Penhale B, Wright G, Dibben C, Owen T, et al. Measuring multiple deprivation at the small area level: the indices of deprivation 2000. Regeneration research summary No 37. Department of the Environment, Transport and the Regions, 2000.?Plewis I. Millennium cohort study first survey: technical report on sampling. 4th ed. Centre for Longitudinal Studies, University of London Institute of Education, 2007. ?Irwin LG, Siddiqi A, Hertzman C. Early child development: a powerful equalizer. Final report for the World Health Organization’s Commission on the Social Determinants of Health. WHO, 2007. 2011. http://whqlibdoc.who.int/hq/2007/a91213.pdf.?Goodman R. The strengths and difficulties questionnaire: a research note. J Child Psychol Psychiatry 1997;38:581-6.OpenUrlMedlineWeb of Science?Elliott CD, Smith P, McCulloch K. British ability scales. 2nd ed. NFER-Nelson, 1996.?Centers for Disease Control and Prevention. Percentile data files with LMS values. CDC, 2010. ?Honaker J, King G, Blackwell M. Amelia II (R package) 2010. http://cran.r-project.org/web/packages/Amelia/index.html.?Rubin DB. Multiple imputation for nonresponse in surveys. Wiley, 1987.?Hesterberg T. Combining multiple imputation t, chi-square, and F inferences. Research report No 75. MathSoft, 1998.?Venables WN, Ripley BD. MASS (R package). http://cran.rproject.org/web/packages/MASS/index.html.?Kiernan KE, Mensah FK. Maternal indicators in pregnancy and children’s infancy that signal future outcomes for children’s development, behaviour and health: evidence from the Millennium Cohort Study. University of York, 2009. ?Centers for Disease Control and Prevention. Measles, mumps, and rubella (MMR) vaccine and autism fact sheet. CDC, 2007.?Eisenberg DTA, Hayes MG, Kuzawa CW. Delayed paternal age of reproduction in humans is associated with longer telomeres across two generations of descendants. Proc Natl Acad Sci USA2012: published online 11 June. ?Caplan AL, Patrizio P. Are you ever too old to have a baby? The ethical challenges of older women using infertility services. Semin Reprod Med2010;28:281-6.OpenUrlCrossRefMedline Open access PDFEasy ReadPress releaseRespond to this article Tweet Services Email to friendDownload to citation managerAdd article to BMJ portfolioRequest permission Citations Find similar articles in PubMedArticles by Alastair G SutcliffeArticles by Jacqueline BarnesArticles by Jay BelskyArticles by Julian GardinerArticles by Edward MelhuishCiting articles via Web of ScienceCiting articles via Scopus Social bookmarking 
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Research The health and development of children born to older mothers in the United Kingdom: observational study using longitudinal cohort data BMJ 2012; 345 doi: 10.1136/bmj.e5116 (Published 21 August 2012) Cite this as: BMJ 2012;345:e5116 Immunology (including allergy) Dentistry and oral medicine Developmental paediatrics Epidemiologic studies More topics Infectious diseases Fewer topics Article Related content Article metrics Alastair G Sutcliffe, reader in child health1, Jacqueline Barnes, professor of psychology2, Jay Belsky, professor of human development23, Julian Gardiner, senior researcher in statistics2, Edward Melhuish, professor of human development21Institute of Child Health, University College London, UK2Institute for the Study of Children, Families and Social Issues, Birkbeck, University of London, London WC1E 7HX, UK 3University of California, Davis, CA, USACorrespondence to: E Melhuish e.melhuish{at}bbk.ac.ukAccepted 11 July 2012AbstractObjective To assess relations between children’s health and development and maternal age.Design Observational study of longitudinal cohorts.Setting Millennium Cohort Study (a random sample of UK children) and the National Evaluation of Sure Start study (a random sample of children in deprived areas in England), 2001 to 2007.Participants 31?257 children at age 9 months, 24?781 children at age 3 years, and 22?504 at age 5 years.Main outcome measures Childhood unintentional injuries and hospital admissions (aged 9 months, 3 years, and 5 years), immunisations (aged 9 months and 3 years), body mass index, language development, and difficulties with social development (aged 3 and 5 years).Results Associations were independent of personal and family characteristics and parity. The risk of children having unintentional injuries requiring medical attention or being admitted to hospital both declined with increasing maternal age. For example, at three years the risk of unintentional injuries declined from 36.6% for mothers aged 20 to 28.6% for mothers aged 40 and hospital admissions declined, respectively, from 27.1% to 21.6%. Immunisation rates at nine months increased with maternal age from 94.6% for mothers aged 20 to 98.1% for mothers aged 40. At three years, immunisation rates reached a maximum, at 81.3% for mothers aged 27, being lower for younger and older mothers. This was linked to rates for the combined measles, mumps, and rubella immunisation because excluding these resulted in no significant relation with maternal age. An increase in overweight children at ages 3 and 5 years associated with increasing maternal age was eliminated once maternal body mass index was included as a covariate. Language development was associated with improvements with increasing maternal age, with scores for children of mothers aged 20 being lower than those of children of mothers aged 40 by 0.21 to 0.22 standard deviations at ages 3 and 4 years. There were fewer social and emotional difficulties associated with increasing maternal age. Children of teenage mothers had more difficulties than children of mothers aged 40 (difference 0.28 SD at age 3 and 0.16 SD at age 5).Conclusion Increasing maternal age was associated with improved health and development for children up to 5 years of age.IntroductionIn developed countries the trend towards later childbearing has been strong.1 2 3 In England and Wales the number of births to women aged 40 or more trebled from 1989 to 2009, when it reached 26?976 births.4 Similar patterns exist in almost all developed countries. In New Zealand, for example, the rate of births to women aged 35 or more almost doubled between 1995 and 2010.5 Similarly, in the United States between 1990 and 2004 birth rates increased by 43% in women aged 35-39, by 62% in women aged 40-44, and by more than 150% in women aged 45.6 Established risks associated with older maternal age include preterm labour, fetal malformation, fetal death, and increased risk of maternal cardiometabolic disease.3 Given the substantial and rapid increase in older motherhood and the known medical risks, it is important to determine whether, and how, older motherhood is linked to child health and development beyond the fetal stage. Although research documenting the deleterious consequences of young motherhood on children’s development is substantial,7 8 9 10 11 evidence of any effects, deleterious or beneficial, of older motherhood on offspring is lacking,1 perhaps owing to the recency of the trend in delayed childbearing.The few studies on the effects of older parenthood on offspring reported mixed results. In an investigation of maternal age and extremely low birth weight live births (n=14?671 children), infants born to mothers aged 40 or older were 22% more likely to survive and had a 13% decreased risk of neurodevelopmental impairment or death compared with those of mothers aged less than 20.3 In another study (n=33?437 children), advanced paternal age predicted increased externalising behaviour (that is, aggression, disobedience) and poorer cognitive ability, whereas the opposite was true of advanced maternal age.12 Advanced paternal and maternal age predicted poorer social functioning among Israeli male adolescents (n=403?486),13 and schizophrenia and autistic behaviours were more common among the children of older fathers and mothers.14 15 As there is little evidence that evaluates relations between older motherhood and children’s health and development beyond birth and the postnatal period, we tested hypotheses that maternal age would show relations with children’s health and development independent of personal and background characteristics.MethodsThe study sample consisted of children aged 9 months, 3 years, and 5 years from the Millennium Cohort Study16 and from the National Evaluation of Sure Start study.17 Eligible children for the Millennium Cohort Study were all children in England born over a period of 16 months from September 2000 and living in the 398 wards. The random sample was clustered geographically by electoral ward with some oversampling to ensure adequate representation of wards with a high number of ethnic minority populations (=30% black or Asian populations in 1991 census), and disadvantaged areas from the poorest 25% of wards based on the child poverty index,18 which is based on the proportion of children in families receiving means tested benefits. Overall there were 188 advantaged wards (not in poorest 25%), 191 disadvantaged wards, and 19 wards with a high proportion of ethnic minority families. Children were sampled from the government’s child benefit records. Child benefit is a universal provision, payable to mothers from the birth of their children. The take-up of child benefit exceeds 97%. Apart from the possibility of eligible families being too rich or too ill informed to claim, most of the children not claimed for were ineligible as the children of non-nationals with temporary or unconfirmed residence status, such as foreign armed forces, overseas students, and recent immigrants, including asylum seekers. The attained sample at nine months was 18?552 children and families (response rate 70%). Of these, 14?898 (80.3% retention rate) were seen again when the children were aged 3 and 14?678 (79.1% retention rate) when the children were aged 5. Additional children and families were recruited at three years to give 15?590 at age 3 and 15?246 at age 5.19The sample from the National Evaluation of Sure Start study was selected from areas in England chosen to receive a Sure Start local programme, all in the 20% most disadvantaged areas defined by the child poverty index—that is, all were from disadvantaged areas.18 From children born in 200 randomly chosen Sure Start areas during 29 months from January 2002, we chose a random sample of 12?705 infants aged 9 months (response rate 84.0%), again using the child benefit records as a sampling frame. Of those seen at this age, 11?118 children and families were randomly selected to be followed-up when the child was aged 3 years, 9191 (82.7%) of whom participated in data collection at the 3 years age point. When the children were aged 5 years we randomly selected to be followed-up 8000 of the children and families seen when the children were aged 3; data were collected from 7258 (response rate 90.7%). We applied no exclusion criteria except to include in analyses only the first born child when multiple births occurred. The total sample consisted of 31?257 infants aged 9 months (18?552 from the Millennium Cohort Study and 12?705 from the National Evaluation of Sure Start), 24?781 3 year olds (15?590 from the Millennium Cohort Study and 9191 from the National Evaluation of Sure Start), and 22?504 5 year olds (15?246 from the Millennium Cohort Study and 7258 from the National Evaluation of Sure Start study). In both studies we included all mothers and children. The age range of mothers was between 13 and 57 years.OutcomesWe chose the child outcomes because they have been used as indicators of child wellbeing in reports from the World Health Organization,20 applied to the total population, were likely to predict later health and development, and were reliably measured by parental report or researcher. Outcomes collected by parental report were social difficulties experienced by the children, using the strengths and difficulties questionnaire21; unintentional injuries requiring medical treatment (from nurse, general practitioner, hospital, or medical clinic) in the past year; admissions to hospital in the past year; and receipt of all recommended immunisations since the previous interview. The recommended immunisations (all free under the National Health Service) in terms of the age points in the studies were: three doses of vaccines against diphtheria, tetanus, pertussis, polio, and haemophilus influenzae type b (hib) at 9 months of age, and by 3 years of age boosters for diphtheria, tetanus, pertussis, and polio and vaccinations against measles, mumps, and rubella (typically delivered as a combined vaccine), hepatitis B, meningitis C, and pneumococcal infection. An additional immunisation outcome was included at three years, defined as complete immunisations, excluding combined measles, mumps, and rubella. Data on uptake of immunisations were obtained only from the Millennium Cohort Study. To calculate the children’s body mass index a researcher measured their weight and height at ages 3 and 5 during home visits. Data on the unintentional injury and hospital admission outcomes were available at all three ages. In addition, researchers assessed language development at ages 3 and 5 using the British ability scales naming vocabulary subscale.22We coordinated data collection across both studies, with researchers trained in common and similar procedures used to ensure collection of comparable information so that data could be combined across the studies. At each age point the parents were interviewed at home and the children measured. Personal and background information collected by parental report served as control variables in analyses.Statistical analysisWe analysed three continuous outcomes in the children: body mass index (range 6.7-63.6), naming vocabulary score (range 20-80, higher being better) from the British ability scales,22 and social difficulties score (range 0-34, lower being better) from the strengths and difficulties questionnaire.21 The four binary outcomes chosen were unintentional injury since the last survey, admission to hospital since the last survey, in receipt of all recommended immunisations, and overweight. We defined being overweight using reference data on body mass index from the US Centers for Disease Control and Prevention23 as being above the 85th percentile for the children’s sex and age. To model the continuous variables we used linear terms in the regression models.Since both samples were geographically clustered we used linear mixed effects models for the continuous outcomes and logistic regression mixed effects models for the binary outcomes, with a random effect fitted for clustering in all models. The principal independent variable was maternal age at the children’s birth, treated as a continuous variable.Regression models were fitted with the following covariates: children’s sex, children’s age, number of siblings, parity, birth weight, breast fed for at least six weeks, ethnic group, raised by single parent, paternal age, raised in workless household, family income, mother’s educational attainment, and mother’s social class (defined by regular occupation). Paternal age was grouped into 10 bands of about equal size: (<22.5 years, 22.5 to <25, 25 to <27, 27 to <29, 29 to <31, 31 to <33, 33 to <35, 35 to <37.5, 37.5 to 40, and >40); we included the father being absent as an additional category. We selected the covariates a priori to avoid any confounding of maternal age effects on outcomes. Models of the children’s body mass index and overweight were also controlled for mother’s body mass index. The covariates did not show high colinearity; only maternal and paternal age (0.66) and worklessness and income (0.57) were above 0.5.Depending on the outcome, between 78.7% and 93.7% of children had complete data on all variables. For most variables less than 10% of data was missing, with 10% exceeded only by mother’s body mass index (nine months, three years, and five years), paternal age (five years), child’s body mass index, overweight, naming vocabulary, and social difficulties score (three years). The greatest amount of missing data was for social difficulties score at three years, at 22.8%. We used the Amelia II package to impute missing data,24 with all covariate and outcome variables used in the imputations. Five imputations were generated and models fitted to each imputed dataset. Model results were consolidated using Rubin’s rules,25 with degrees of freedom ascertained using Hesterberg,26 equation 24.We selected a linear or polynomial model for maternal age. To achieve this we fitted an initial linear model then added higher order terms successively until the highest order term was no longer statistically significant, at which point we adopted the previous model as the final one. The final adjusted models were linear or quadratic for maternal age. To investigate possible confounding between maternal age and first time motherhood, we fitted additional models that included an interaction between first time motherhood and maternal age. No such interactions were significant and are not discussed further.Models were fitted in R 2.11.1. We used the linear mixed effects procedure to fit the linear mixed effects models and the generalised linear mixed models penalised quasilikelihood procedure27 to fit the logistic regression mixed effects models. We undertook analyses for the Millennium Cohort Study and the National Evaluation of Sure Start samples separately and for the combined total sample. As results were broadly similar in all cases, we present the results for the combined sample.ResultsModelsTable 1? shows the characteristics of the sample, table 2? the raw outcome data tabulated by maternal age categories, and table 3? the results of the final adjusted and unadjusted models. The figure? shows the results of the adjusted models for outcomes showing statistically significant relations with maternal age.View this table:View PopupView InlineTable 1 Descriptive statistics of participants. Values are numbers (percentages) unless stated otherwiseView this table:View PopupView InlineTable 2 Data for child outcomes stratified by maternal age. Values are numbers (percentages) of children unless stated otherwiseView this table:View PopupView InlineTable 3 Results from final modelsView larger version:In a new windowDownload as PowerPoint SlideAdjusted models showing regression lines with 95% confidence intervals for outcomes by maternal age. Unintentional injury in children (quadratic model, 3 years), admission to hospital (linear models), complete immunisation (quadratic models, 9 months and 3 years), British ability scales naming vocabulary score (quadratic models), and total score on strengths and difficulties questionnaire (quadratic model, 3 years). Model coefficients, per 5 years of maternal age, are given with P values. Quadratic models are parameterised so that the coefficient for the linear component of maternal age (ß 1) gives the model slope at maternal age 30. MMR=measles, mumps, and rubella immunisation; BAS=British ability scalesUnintentional injuriesThe risk of unintentional injuries declined with increasing maternal age, with the final models for children aged 9 months, 3 years, and 5 years being linear, quadratic, and linear, respectively (figure).At nine months the risk of unintentional injuries declined across maternal age, with the risk in children of mothers aged 20 being 9.5%, decreasing to 6.1% for mothers aged 40. The model is quadratic for 3 year olds, showing a decline from 36.6% for mothers aged 20 to 28.6% for mothers aged 40.5 (where the curve reaches a minimum). The model is linear for 5 year olds, with risk decreasing from 29.1% for mothers aged 20 to 24.9% for mothers aged 40.Admissions to hospitalThe risk of children being admitted to hospital also declined with increasing maternal age, with the final models being linear in all cases (figure).At nine month, the probability of children being admitted to hospital declined from 16.0% when mothers were aged 20 to 10.7% when mothers were aged 40. For 3 year olds, the probability declined from 27.1% when mothers were aged 20 to 21.6% when mothers were 40. For 5 year olds, the change in hospital admissions with increasing maternal age was not statistically significant.ImmunisationsThe final models of complete immunisation rates by maternal age for 9 month olds and 3 year olds were quadratic (figure).At nine months the rate of complete immunisation increased with maternal age, from 94.6% when mothers were aged 20 to 98.1% when mothers were 40. At three years the maximum rate of complete immunisations was 81.3% when mothers were aged 27.3, with lower rates among younger and older mothers. Complete immunisations at three years, excluding combined measles, mumps, and rubella immunisation were also considered; the final model was linear, with no significant relation between immunisation rate and maternal age, indicating that non-linear effects are linked to take-up of the combined measles, mumps, and rubella immunisation.Children’s body mass index and overweightInitial models showed a significant positive association between the children’s body mass index and maternal age. However, once maternal body mass index was controlled for, no significant association with maternal age was found for either children’s body mass index or children’s overweight (table 2).Language development (British ability scales naming vocabulary)The final models of naming vocabulary score in relation to maternal age were quadratic, with scores increasing as maternal age increased (figure). At three years, the score for children of mothers aged 20 was 0.22 standard deviations below that for the children of mothers aged 40. At five years, the value for children of mothers aged 20 was 0.21 standard deviations below that for the children of mothers aged 40.Social and emotional difficultiesThe final models for the strengths and difficulties total problem score in relation to maternal age were quadratic at three years and linear at five years, with scores decreasing as maternal age increased, indicating better social development (figure). At three years, the score for children of mothers aged 20 was 0.28 standard deviations higher than for children of mothers aged 40, and at five years, the corresponding difference was 0.16 standard deviations.DiscussionIncreasing maternal age was associated with children having fewer hospital admissions and unintentional injuries, a greater likelihood of having had all of their immunisations by 9 months of age, better language, and fewer social and emotional difficulties. Such findings contrast with the known obstetric risks associated with older motherhood1 2 3 and serve as a counterpoint to evidence highlighting poorer health and developmental outcomes for children of younger mothers (<20 years).7 8 9 10 11 The positive findings in relation to increasing maternal age were generally consistent with the few other studies on children of older mothers, often defined as 40 years or older, which focused on other issues such as neurodevelopment, educational achievement, substance misuse, and juvenile crime12 rather than, as here, general health and development, both cognitive-linguistic and socio-emotional. There are, however, exceptions in the literature to the conclusion that older motherhood carries few risks for child functioning. One study of Israeli male adolescents showed poorer social and emotional functioning in association with both teenage mothers and mothers aged 40 or older at their child’s birth (relative to other mothers).13 Older mothers tend to be better educated, have higher family income, and be married, all factors associated with greater child wellbeing.7 28 Analyses controlled for these and other personal factors. Hence the results are indicative of associations with maternal age rather than covarying characteristics, or at least those included in this research. Also noteworthy in this study was the inclusion of children living in deprived neighbourhoods and experiencing high levels of family deprivation, as well as children representative of the general population, to increase the likelihood of identifying any adverse impacts. The fact that results were broadly similar for all outcomes when the analyses were undertaken separately for the population representative Millennium Cohort Study sample and the deprived National Evaluation of Sure Start sample indicates that these results are likely to be applicable across the spectrum of deprivation.The only health related outcome revealing any adverse relation with childbearing in older age was immunisation uptake by age 3 years, which declined with maternal age after age 33. This could have been a historical artifact, related to parental anxiety regarding now discredited claims linking the combined measles, mumps, and rubella with autism,29 which were prevalent in the media around the time of the birth and early years of the children studied here. It is possible that older mothers were more influenced by the media reports. Further analyses of immunisations excluding the combined measles, mumps, and rubella immunisation supported this interpretation as they revealed no relations between maternal age and uptake of all other immunisations.The likelihood of a child being overweight increased with increasing maternal age, but not when maternal body mass index was controlled for, indicating that this result was explained by greater maternal body mass index in older mothers, rather than by maternal age in itself. Nevertheless, this association is one that should be of concern for health practitioners.Strengths and limitations of the studyThis study has all the limitations of observational studies in attributing causality but does provide evidence relevant to an important clinical topic. Also, all covariates and some child outcomes apart from the children’s weight and height and naming vocabulary measurements were based on parental report; the fact that the data were collected in an optimal manner by direct parental interview with highly trained interviewers would seem to obviate somewhat the limitation. Although some child health information could have been extracted from medical records, that method also has pitfalls for data completeness and comparability. Error in measurement is always possible, but the measures taken were the best available and we have no reason to assume systematic mis-measurement, and if measurement error is not systematically related to a variable then measurement error decreases the likelihood of significant results. Inevitably some attrition occurred as the children aged and families withdrew or became non-contactable, but the attrition rates compare favourably with other longitudinal studies. Also, analyses were carried out in two ways, using only complete cases and using multiple imputation to include the full sample to deal with this issue. These two sets of analyses did not differ substantially. The initial hypotheses were tentative for the direction of effects owing to limited published evidence on the health of children born to older mothers. Finally, there was a shortfall in information on paternal age, as a significant proportion of children were living separately from their father. None the less, the large dataset, the representativeness of the samples, the powerful nature of the children’s outcomes, and the fact that data were collected originally for a different purpose improved the likelihood of the findings being valid. Also the fact that results were similar for all outcomes for separate analyses of the population representative Millennium Cohort Study sample and the deprived National Evaluation of Sure Start sample indicates that these results are likely to be applicable to the whole population and across the spectrum of deprivation.ConclusionIn contrast with the obstetric risks known to be associated with older motherhood these results indicate that increasing maternal age was associated with children having fewer hospital admissions and unintentional injuries, a greater likelihood of better protection from ill health through completed immunisations by age 9 months, better language development, and fewer social and emotional difficulties. The findings are noteworthy given the continuing increase in mean age of childbearing. It will be important to continue to examine relations between child outcomes and maternal age to see if the situation changes as the children age, and to explore possible mediating and moderating factors for the relations associated with maternal age. Possible mechanisms for the observed relations between child outcomes and maternal age might be environmental, such as differences in parenting, or genetic, as found for paternal age where the longer fathers and grandfathers waited to have children, the more likely it was for their offspring to live longer and healthier lives. This seemed to be due to longer telomeres of older fathers’ sperm, related to increased longevity and development, being inherited by offspring.30 Further research should explore possible mechanisms. Finally, the results of this study are relevant to concerns raised about older people seeking to use fertility treatments and possible risks posed to children delivered by older mothers.31What is already known on this topicEstablished risks associated with older maternal age (=40 years) include preterm labour, fetal malformation, fetal death, and increased risk of maternal cardiometabolic diseaseEvidence related to child health and development beyond the immediate postnatal period is lacking for the children of older mothersThere is an increased risk of deleterious consequences for children’s health and development associated with young motherhoodWhat this study addsIncreasing maternal age was associated with several beneficial effects on childrenChildren had fewer hospital admissions and unintentional injuries, a greater likelihood of being fully immunised by age 9 months, better language, and fewer social and emotional difficultiesNotesCite this as: BMJ 2012;345:e5116FootnotesWe thank the families and their children for their cooperation throughout both projects.Contributors: All authors helped to plan the research, and took part in the writing of the final article. JG undertook the statistical analyses, supervised by EM. EM had full access to all the data in the study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis. Funding: This research was funded by the Wellcome Trust through a grant entitled “Health of children born to older mothers”; the funding body had no involvement in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. All authors are independent of the funding agency.Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; and no other relationships or activities that could appear to have influenced the submitted work.Ethical approval: This study was approved for both studies by the National Health Service South West multicentre research ethics committee (reference No MREC/01/6/65). Participants in both studies gave written informed consent.Data sharing: The data from the Millennium Cohort Study and the National Evaluation of Sure Start study are available from the Economic and Social Data Service (www.esds.ac.uk/).This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.References?Sutcliffe A, Baki Y. What is known about children born to older parents? In: Bewley S, Ledger W, Nikolaou D, eds. Reproductive ageing. Royal College of Obstetricians and Gynaecologists Press, 2009:173-82.?Berryman J, Thorpe K, Windridge K. Older mothers: conception, pregnancy and birth after 35. Pandora,1995. ?Vohr BR, Tyson JE, Wright LL, Perritt, RL, Li L, Poole WK. Maternal age, multiple birth, and extremely low birth weight infants. J Pediatr2009;154:498-503.OpenUrlCrossRefMedlineWeb of Science?Office for National Statistics. Birth statistics. Series FM1 No 35. ONS, 2007.?Statistics New Zealand.Births and deaths: year ended March 2011. 2012. www.stats.govt.nz/browse_for_stats/population/births/BirthsAndDeaths_HOTPYeMar11.aspx.?Martin JA, Hamilton BE, Sutton PD, Ventura SJ, Menacker F, Kirmeyer S, Division of Vital Statistics. Births: final data for 2004. National Vital Statistics Reports 55(1). US Department of Health and Human Services, Centers for Disease Control and Prevention, 2004.?Moffitt TE, E-Risk Study Team. Teen-aged mothers in contemporary Britain. J Child Psychol Psych2002;43:727-42.OpenUrlCrossRefMedlineWeb of Science?Botting B, Rosato M, Wood R. Teenage mothers and the health of their children. Popul Trends1998;93:19-27.OpenUrlMedline?Fraser AM, Brockert JE, Ward RH. Association of young maternal age with adverse reproductive outcomes. New Engl J Med1995;332:1113-7.OpenUrlCrossRefMedlineWeb of Science?Gilbert W, Jandial D, Field N, Bigelow P, Danielsen B. Birthoutcomes in teenage pregnancies. J Matern Fetal Neonatal Med2004;16:265-70.OpenUrlCrossRefMedline?Phipps MG, Blume JD, Demonner SM. Young maternal age associated with increased risk of postnatal death. Obstet Gynaecol2002;100:481-6.OpenUrlCrossRefMedlineWeb of Science?Fergusson DM, Woodward LJ. Maternal age and educational and psychosocial outcomes in early adulthood. J Child Psychol Psych1999;40:479-89.OpenUrlCrossRefMedlineWeb of Science?Weiser M, Reichenberg A, Werbeloff N, Kleinhaus K, Lubin G, Shmushkevitch M, et al. Advanced parental age at birth is associated with poorer social functioning in adolescent males: shedding light on a core symptom of schizophrenia and autism. Schizophr Bull2008;34:1042-6.OpenUrlFREE Full Text?Zammit S, Allebeck P, Dalman C, Lundberg I, Hemmingson T, Owen MJ, et al. Paternal age and risk for schizophrenia. Br J Psychiatry2003;183:405-8.OpenUrlFREE Full Text?Croen LA, Najjar DV, Fireman B, Grether JK. Maternal and paternal age and risk of autism spectrum disorders. Arch Pediatr Adolesc Med2007;161:334-40.OpenUrlCrossRefMedline?Dex S, Joshi H. Millennium cohort study, first survey: a user’s guide to initial findings. Centre for Longitudinal Studies, University of London Institute of Education, 2004.?Melhuish E, Belsky J, Leyland AH, Barnes J, National evaluation of Sure Start team. Effects of fully-established Sure Start local programmes on 3-year-old children and their families living in England: a quasi-experimental observational study. Lancet2008;372:1641-7.OpenUrlCrossRefMedlineWeb of Science?Noble M, Smith G, Penhale B, Wright G, Dibben C, Owen T, et al. Measuring multiple deprivation at the small area level: the indices of deprivation 2000. Regeneration research summary No 37. Department of the Environment, Transport and the Regions, 2000.?Plewis I. Millennium cohort study first survey: technical report on sampling. 4th ed. Centre for Longitudinal Studies, University of London Institute of Education, 2007. ?Irwin LG, Siddiqi A, Hertzman C. Early child development: a powerful equalizer. Final report for the World Health Organization’s Commission on the Social Determinants of Health. WHO, 2007. 2011. http://whqlibdoc.who.int/hq/2007/a91213.pdf.?Goodman R. The strengths and difficulties questionnaire: a research note. J Child Psychol Psychiatry 1997;38:581-6.OpenUrlMedlineWeb of Science?Elliott CD, Smith P, McCulloch K. British ability scales. 2nd ed. NFER-Nelson, 1996.?Centers for Disease Control and Prevention. Percentile data files with LMS values. CDC, 2010. ?Honaker J, King G, Blackwell M. Amelia II (R package) 2010. http://cran.r-project.org/web/packages/Amelia/index.html.?Rubin DB. Multiple imputation for nonresponse in surveys. Wiley, 1987.?Hesterberg T. Combining multiple imputation t, chi-square, and F inferences. Research report No 75. MathSoft, 1998.?Venables WN, Ripley BD. MASS (R package). http://cran.rproject.org/web/packages/MASS/index.html.?Kiernan KE, Mensah FK. Maternal indicators in pregnancy and children’s infancy that signal future outcomes for children’s development, behaviour and health: evidence from the Millennium Cohort Study. University of York, 2009. ?Centers for Disease Control and Prevention. Measles, mumps, and rubella (MMR) vaccine and autism fact sheet. CDC, 2007.?Eisenberg DTA, Hayes MG, Kuzawa CW. Delayed paternal age of reproduction in humans is associated with longer telomeres across two generations of descendants. Proc Natl Acad Sci USA2012: published online 11 June. ?Caplan AL, Patrizio P. Are you ever too old to have a baby? 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Time trends in drug resistant HIV-1 infections in the United Kingdom up to 2009: multicentre observational study
Time trends in drug resistant HIV-1 infections in the United Kingdom up to 2009: multicentre observational study | BMJ Search BMJ Group Search BMJ Group Journals Jobs Education Decision support Quality improvement Community ... by doctors, for doctors, for patients About BMJ Group Customer Service Subscriptions & Sales Working for BMJ Group BMJ Media Centre BMJ Group Awards Advertising & Sponsorship Rights & Licensing Affinity & Society Publishing Online learning BMJ Learning High-quality CME / CPD for doctors and other healthcare professionals. BMJ Learning features hundreds of accredited, peer-reviewed learning modules in text, video, and audio formats. Find out more Courses and Qualifications BMJ Masterclasses BMJ Masterclasses, led by experts, help clinicians to use the latest evidence and recent guidelines in practice and meet their CPD/CME requirements. Find out more Exam Preparation The leading provider of online exam preparation, helping over 167,000 healthcare professionals to pass their exams. Find out more BMJ Learning BMJ Portfolio BMJ Masterclasses Clinical Leadership Programme Diabetes Qualifications and Courses onExamination Decision support and clinical reference BMJ Evidence Centre Informatica Systems Informatica Systems delivers performance management systems and innovative software solutions to primary care. Learn more Audit + Contract + Health Checks FrontDesk BMJ Quality The 2013 event will take place in London from 16th- 19th April 2013. Find out more BMJ Quality BMJ Quality and Safety International Forum on Quality and Safety in Healthcare BMJ BMJ Journals studentBMJ BMJ Careers 19-20 October 2012 at the Business Design Centre in Islington, London. Register here BMJ Careers Jobs and vacancies at BMJ Group BMJ Careers Fair Community ... by doctors, for doctors, for patients
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Research Time trends in drug resistant HIV-1 infections in the United Kingdom up to 2009: multicentre observational study BMJ 2012; 345 doi: 10.1136/bmj.e5253 (Published 21 August 2012) Cite this as: BMJ 2012;345:e5253 Immunology (including allergy) Infectious diseases Sexual health Article Related content Article metrics UK Collaborative Group on HIV Drug ResistanceCorrespondence to: D Dolling, UK HIV Drug Resistance Database, MRC Clinical Trials Unit, London WC2B 6NH, UK David.dolling{at}ctu.mrc.ac.ukAccepted 14 July 2012AbstractObjective To evaluate whether the prevalence of HIV-1 transmitted drug resistance has continued to decline in infections probably acquired within the United Kingdom.Design Multicentre observational study.Setting All UK public laboratories conducting tests for genotypic HIV resistance as a part of routine care.Participants 14?584 patients infected with HIV-1 subtype B virus, who were first tested for resistance before receiving antiretroviral therapy between January 2002 and December 2009.Main outcome measure Prevalence of transmitted drug resistance, defined as one or more resistance mutations from the surveillance list recommended by the World Health Organization.Results 1654 (11.3%, 95% confidence interval 10.8% to 11.9%) patients had one or more mutations associated with transmitted HIV-1 drug resistance; prevalence was found to decline from 15.5% in 2002 to 9.6% in 2007, followed by a slight increase to 10.9% in 2009 (P=0.21). This later rise was mainly a result of increases in resistance to nucleos(t)ide reverse transcriptase inhibitors (from 5.4% in 2007 to 6.6% in 2009, P=0.24) and protease inhibitors (1.5% to 2.1%, P=0.12). Thymidine analogue mutations, including T215 revertants, remained the most frequent mutations associated with nucleos(t)ide reverse transcriptase inhibitors, despite a considerable fall in stavudine and zidovudine use between 2002 and 2009 (from 29.4% of drug regimens in 2002 to 0.8% in 2009, from 47.9% to 8.8%, respectively).Conclusions The previously observed decline in the prevalence of transmitted drug resistance in HIV-1 infections probably acquired in the UK seems to have stabilised. The continued high prevalence of thymidine analogue mutations suggests that the source of this resistance may be increasingly from patients who have not undergone antiretroviral therapy and who harbour resistant viruses. Testing of all newly diagnosed HIV-1 positive people should be continued.IntroductionCombination antiretroviral therapy continues to be highly effective in treating HIV-1, and the introduction of new drugs and antiretroviral drug classes has notably improved patient prognosis. Nevertheless, resistance to antiretroviral drugs can develop in people on therapy and is associated with treatment failure.1 Against this backdrop, the rate of new HIV-1 infections within the United Kingdom continues to rise.2 In such new infections, transmitted drug resistance is sometimes assumed to reflect only direct infection from patients already receiving antiretroviral therapy. Concerns about an adverse effect of transmitted HIV-1 drug resistance on the success of antiretroviral therapy have led to national and international guidelines recommending that all newly diagnosed patients have resistance tests conducted to aid selection of first line regimens.3 4A previous study showed a sharp decline in the prevalence of transmitted HIV-1 drug resistance in the UK between 2002 and 2005,5 which was mainly attributed to changes in testing guidelines and the wider use of regimens that suppress viral concentrations to below infectious levels. Since then, more potent and better tolerated antiretroviral drugs have been introduced, and the proportion of patients achieving viral suppression has continued to increase.6 This suggests that transmitted HIV-1 drug resistance may have declined even further and could eventually fall below levels in which universal testing before antiretroviral therapy is cost effective,7 an important issue at a time when the cost of HIV-1 management is being scrutinised.8 This paper examines recent time trends in transmitted drug resistance in HIV-1 infections probably acquired in the UK.MethodsResistance dataThe UK HIV Drug Resistance Database, described in detail elsewhere,9 was established in 2001 and collects the majority of genotypic resistance tests done within the UK as part of routine clinical care. The resistance tests analysed in this study used bulk sequencing of the pol gene, encoding at least codons 4-99 of the protease gene and 34-234 of the reverse transcriptase gene, using a variety of inhouse and commercial testing systems. Subtype was assigned centrally using the Rega algorithm.10Clinical dataWe acquired demographic and clinical information by linkage (using pseudonymised identifiers) to the UK Collaborative HIV Cohort Study (UK CHIC),9 which includes patients from 13 of the largest clinics within the UK, and to the HIV and AIDS Patient and the Survey of Prevalent HIV Infections Diagnosed databases, which are coordinated by the Health Protection Agency. When possible, we linked resistance tests done after 2007 to samples on which a recent infection testing algorithm had been conducted as part of a national health surveillance programme11; these tests use antibody avidity assays to classify infections as either recent (probably occurring in the previous five months) or non-recent;12 and clinical, laboratory, or historical information to reduce the false recent rate.Tests included in analysis and definition of drug resistanceThe UK has several parallel and largely non-overlapping HIV-1 epidemics with different levels and patterns of resistance to antiretroviral therapy.13 14 To simplify the understanding of temporal trends, we limited this analysis to subtype B viral infections, so it is not intended to generate nationally representative results. This epidemic was seeded by around six introductions to the UK in the early to mid-1980s15 and is largely confined to men infected through homosexual exposure, of whom 83% are estimated to have acquired infection within the UK.16We identified the first resistance test for all patients older than 16 years who had not yet received antiretroviral therapy at the time of sampling, up to the end of 2009. Patients with an undetectable viral load (<50 copies/mL) were excluded; such levels may indicate unrecorded treatment use. Since guidelines in 2001 first recommended that resistance tests be performed for all patients who had not received antiretroviral therapy,3 we excluded tests conducted before 2002.Transmitted HIV-1 drug resistance was defined as one or more mutations from the surveillance list recommended by the World Health Organization.17 We used the Stanford HIVdb algorithm 6.0.11 (29 Mar 2011) to examine susceptibility to antiretroviral drugs, and reported low level resistance or greater. Intermediate or high level resistance was considered to reflect a substantial loss in susceptibility. We assessed temporal trends in terms of the date of the resistance test sample rather than the date of the patient’s infection, which is generally not known.Statistical methodsConfidence intervals for proportions were calculated using a 95% Wilson confidence interval for binomially distributed data. We analysed the patterns of trends over continuous time using both linear and piecewise linear logistic regression with a flexible choice of a single inflexion point calculated using least squares optimisation; we selected the model with the best fit according to Akaike’s information criterion. The trends for codons were reported if a mutation had an overall prevalence of more than 0.3% as well as other mutations for nucleos(t)ide reverse transcriptase with a strong effect on phenotype (K65R, K70E, L74I/V, Y115F). We examined differences between the prevalence of resistance in recent and non-recent infections tests using the ?2 test. All statistical analyses were conducted in Stata/IC 11.2 software.ResultsPopulation characteristicsWe analysed 14?583 patients who were antiretroviral therapy naive, infected with a subtype B virus, and whose first drug resistance test was conducted between January 2002 and December 2009. Of these patients, 10?173 (70%) were white, 995 (7%) were black, 711 (5%) had a known other ethnicity, and 2704 (19%) had an unknown ethnicity. The median age at diagnosis was 36 years (interquartile range 30 to 42). Of the patients analysed, 10?288 (71%) were men who had sex with men, 1275 (9%) had a heterosexual exposure source, 313 (2%) had a known other exposure source, and 2707 (19%) had an unknown exposure source. The median number of days between HIV-1 diagnosis and resistance sample was 22 (interquartile range five to 358). The median CD4 count at the time of testing, available from 11?219 (76.9%) patients, was 408×106 cells/L (interquartile range 271×106 to 560×106).The number of resistance tests conducted per year increased over time, reflecting a rise in the number of new diagnoses among men who have sex with men (fig 1?). The decline between 2008 and 2009 is mainly due to an increase in the proportion of tests where the patient’s status regarding antiretroviral therapy exposure was uncertain (15.6% in 2008 to 32.0% in 2009), probably as a result of a reporting lag with demographic and clinical datasets.View larger version:In a new windowDownload as PowerPoint SlideFig 1 Prevalence of transmitted drug resistance over time, by antiretroviral drug class. Bar=95% confidence intervalTrends in transmitted drug resistanceSamples from 1654 (11.3%, 95% confidence interval 10.8% to 11.9%) patients had one or more mutations associated with transmitted HIV-1 drug resistance. Of these samples, 1009 (6.9%, 6.5% to 7.3%), 604 (4.1%, 3.8% to 4.5%), and 319 (2.2%, 2.0% to 2.4%) had one or more mutations associated with nucleos(t)ide reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitors, and protease inhibitors, respectively. Of these samples, 1426 (9.8%, 9.3% to 10.3%) had single class resistance, 175 (1.2%, 1.0% to 1.4%) had dual class resistance, and 52 (0.4%, 0.3% to 0.5%) had triple class resistance; dual and triple class resistance have remained at a similar prevalence since 2005.The previously reported5 decline in the prevalence of transmitted HIV-1 drug resistance for any class of antiretroviral drug was observed to continue from 15.5% in 2002 until around January 2007 (95% confidence interval January 2006 to February 2008; odds ratio 0.88 per year (95% confidence interval 0.84 to 0.92); fig 1). However, between 2007 and 2009, we saw a non-significant increase from 9.6% to 10.9% (odds ratio 1.06, 0.97 to 1.17; P=0.21). We saw non-significant increases in resistance to nucleos(t)ide reverse transcriptase inhibitors (from 5.4% in 2007 to 6.6% in 2009; P=0.24) and protease inhibitors (1.5% to 2.1%; P=0.12). Inflection points were also identified, which gave a significantly better fit for mutations associated with nucleos(t)ide reverse transcriptase inhibitors (February 2007 (January 2006 to March 2008); second piece odds ratio 1.08 (0.95 to 1.22)) and protease inhibitors (June 2008 (August 2007 to June 2009); 1.69 (0.86 to 3.31)). The prevalence of mutations associated with non-nucleoside reverse transcriptase inhibitors remained stable at around 3.6% with no evidence of non-linearity (odds ratio 0.96 (0.92 to 1.00)).The table? displays the resistance trends over time for key individual codons with prevalence greater than 0.3%. The trends in HIV-1 drug resistance associated with nucleos(t)ide reverse transcriptase inhibitors largely reflect the most common mutations within this drug class, namely, the T215 revertant18 mutations (I/S/C/D/V/E), K219Q/E/N/R, and M41L. These mutations are either reversions or a subset of the thymidine analogue mutations that develop under regimens containing either stavudine or zidovudine. The finding of a levelling off in the prevalence of thymidine analogue mutations is paradoxical, in the light of the dramatic fall in the use of stavudine and zidovudine. For example, in the UK CHIC study, the proportion of treatment regimens that included stavudine dropped from 29.4% to 0.8% between 2002 and 2009, while the proportion that included zidovudine dropped from 47.9% to 8.8% over the same period. Stavudine and zidovudine have largely been replaced by tenofovir and abacavir,3 but signature mutations for these drugs (such as K65R) are still rare in patients with HIV-1. L90M mutations have increased in prevalence since 2007, despite the near cessation in the use of saquinavir, nelfinavir, and indinavir, which are first generation protease inhibitors that select for this mutation (their use in the UK as part of a drug regimen fell from 20.6% in 2002 to 4.6% in 2009), although L90M has broad cross resistance effects to the protease inhibitor class.19View this table:View PopupView InlineTime trends of selected mutations with prevalence greater than 0.3%. Data are no (%) of samples with mutation unless stated otherwiseDrug susceptibilityFigure 2? shows the predicted susceptibility of HIV-1 from samples collected in 2009 to currently recommended first line antiretroviral drugs3 and second generation antiretroviral drugs darunavir and etravirine. The association between genotypic mutations and the phenotypic susceptibility to antiretroviral drugs is complex, so although T215 revertants are considered by the Stanford HIVdb algorithm to result in only low level resistance to nucleos(t)ide reverse transcriptase inhibitors, they do confer a low genetic barrier to the development of high level resistance to this drug class.
View larger version:In a new windowDownload as PowerPoint SlideFig 2 Predicted susceptibility to antiretroviral drugsThe prevalence of intermediate or high level resistance to nucleos(t)ide reverse transcriptase inhibitors other than zidovudine was less than 0.9%, reflecting the low frequency of multiple mutations in thymidine analogues. There was a comparatively high level of reduced susceptibility to recommended first line regimens of the non-nucleoside reverse transcriptase inhibitors efavirenz and nevirapine (3.7%). However, etravirine, a non-nucleoside reverse transcriptase inhibitor, displayed very low levels of resistance (0.4%), reflecting the lack of predicted effect of K103N. The potency of modern drugs from the protease inhibitor class was high (only 25 (1.1%) patients had substantially reduced susceptibility to any protease inhibitor), owing to the rarity of multiple protease mutations. These findings indicate that potent first line regimens can still be constructed from the original three drug classes for almost all patients.Prevalence of resistance in recently infected patientsBetween 2007 and 2009, 742 samples were linked to a recent infection test result, of which 171 (23.0%) were classified as recent. The overall prevalence of resistance did not differ significantly (P=0.66) between recent samples (20, 11.7%) or non-recent samples (60/571, 10.5%). Furthermore, we detected no significant differences in the prevalence of resistance by individual drug class (results not shown).DiscussionInterpretationThe previously observed decline in the prevalence of transmitted drug resistance in subtype B viruses seems to have been reversed for mutations associated with nucleos(t)ide reverse transcriptase inhibitors and protease inhibitors, despite an increase in the proportion of patients on antiretroviral therapy who are virologically suppressed (from 62% in 2000 to 84% in 20076). The most frequent mutations were T215 revertants, which may be transmitted as such or evolve from a virus harbouring a T215F or T215Y mutation.19 These and other mutations are associated with a significantly higher risk of virological failure than wild type genotypes.1 17 The prevalence of transmitted thymidine analogue mutations remained moderately high, despite a marked shift away from the prescribing in the UK of drugs that select for these mutations.One plausible explanation for this paradox is the onward transmission of resistant viruses from people who have not received antiretroviral therapy before and who were themselves infected with a resistant virus. This hypothesis is supported by a previous phylogenetic analysis of UK subtype B sequences, which described five transmission clusters comprised exclusively of patients with resistance mutations who are antiretroviral therapy naive.20 It was postulated that an increasingly greater proportion of transmitted HIV-1 drug resistance could originate from antiretroviral therapy naive lineages, and that there could ultimately be a limit in the decline of transmitted HIV-1 drug resistance.The first factor contributing to this possible limit is that people with undiagnosed HIV could disproportionally21 spread the epidemic, since they are more infectious in the period immediately after infection.22 Some studies23 24 (but not all25) have also shown that undiagnosed patients with HIV have more sexual partners than diagnosed patients. Furthermore, evidence is emerging that transmitted resistant viruses are more persistent than originally thought. The fitness cost (relative to wild type virus) of certain mutations, such as the T215 revertants and K103N, has been shown to be marginal in laboratory studies.26 27 Jain and colleagues provided clinical confirmation of this finding in a series of patients infected with resistant virus and who had two or more resistance tests before they started antiretroviral therapy.28 With the exception of the M184V mutation, which is highly replicatively deficient, all groups of transmitted mutations persisted beyond at least three years in the majority of patients.A second possible explanation for the continued prevalence of thymidine analogue mutations is that the use of tenofovir and abacavir are maintaining the prevalence of such mutations in patients who have received antiretroviral therapy despite the decline in the use of zidovudine and stavudine. Further phylogenetic research could shed light on the transmission dynamics of these mutations.Comparison with other studiesTwo recent studies have reported on time trends in transmitted HIV drug resistance. In a study conducted in 20 European countries between 2002 and 2006,29 Vercauteren and colleagues found a small, linear decline in levels of nucleos(t)ide reverse transcriptase inhibitor and protease inhibitor resistance; non-nucleoside reverse transcriptase inhibitor resistance was observed to increase followed by a decrease between 2004 and 2006. Bartmeyer and colleagues30 performed a similar analysis of a German seroconverter cohort between 1996 and 2007. In more recent years, resistance to nucleos(t)ide reverse transcriptase inhibitors seemed to be stable and resistance to non-nucleoside reverse transcriptase inhibitors seemed to increase, although clear patterns are difficult to discern due to the relatively small sample size.Study limitationsOur study has several limitations. Firstly, the analysis is based on resistance found at date of sample rather than date of infection. Since viral quasi-species harbouring resistance mutations may revert to, or be overgrown by, virus without the mutations,19 29 the true level of transmitted HIV drug resistance may have been under-estimated in this analysis. Also, the degree of this bias will be affected by the average time between HIV-1 infection and diagnosis, which may have changed over time. Furthermore, the diagnosis delay could mask the underlying trend in the prevalence of transmitted resistance by date of infection. However, we found no difference in the prevalence of resistance between recent and non-recent infections, in the subset of patients in which this analysis was possible. Secondly, the genotypic data analysed were generated by population sequencing with a limit of sensitivity of approximately 15 to 25%.31 32 Our estimates of the prevalence of transmitted HIV drug resistance may therefore be biased downwards. This will have also biased the type of mutations observed, with persistent mutations appearing to be more prevalent than those which rapidly become undetectable such as K65R or M184V. We do not consider this limitation to be a major concern, because the main objective of our study was to examine changes in the prevalence of transmitted HIV drug resistance over time. Another limitation was that our method of classifying treatment status could have resulted in misclassification bias if some patients who had received antiretroviral therapy were included in the analysis. Previous research by the UK HIV Drug Resistance Database has suggested that this effect could distort trends if there is misclassification in 4% or more of the samples analysed.33 Finally, as the prevalence of transmitted HIV drug resistance in subtype B viruses, the focus of this analysis, is known to be higher than that observed in other subtypes,34 our findings are not generalisable to the UK epidemic as a whole, although coverage of patients infected with subtype B virus is high.Conclusions and policy implicationsFinally, we consider the clinical implications of our main conclusion that resistant lineages may have become fixed in the circulating viral pool. This concept, if confirmed to be correct, will apply universally, particularly in countries where first generation nucleos(t)ide reverse transcriptase inhibitors continue to be used, and underscores the importance of sentinel surveillance. In terms of the UK (and probably other well resourced countries), the detectable mutations that tend to be transmitted should have little effect on nucleos(t)ide reverse transcriptase inhibitors currently used in first line regimens, that is, abacavir, tenofovir, lamivudine, and emtricitabine. Non-nucleoside reverse transcriptase inhibitors used in first line regimens are of greater concern, with approximately 4% of patients being infected with viruses with reduced susceptibility to efavirenz and nevirapine. Previous models7 have suggested that baseline resistance testing remains cost effective at the levels observed in this study. Therefore, our findings argue that testing at HIV diagnosis and continued monitoring should remain the standard of care.What is already known on this topicTransmitted HIV drug resistance can affect therapy successSome resistance mutations could persist more than others in the absence of selective drug pressure A 2007 paper has shown a reduction in transmitted drug resistance in the UK since 2005What this study addsTransmitted drug resistance is no longer declining in UK, and evidence suggests a sustained epidemic that is resistant to nucleos(t)ide reverse transcriptase inhibitors, irrespective of previous antiretroviral therapy useSusceptibility to antiretroviral therapy remains relatively high, and potent first line regimens can still be constructed from the original three drug classes for almost all patientsNotesCite this as: BMJ 2012;345:e5253FootnotesThe UK Collaborative Group on HIV Drug Resistance is a collaboration between the UK HIV Drug Resistance Database; UK CHIC; Health Protection Agency HARS; and participating academic centres, clinics, and laboratories.Analysis/writing group: David Dolling, Caroline Sabin, Valerie Delpech, Erasmus Smit, Anton Pozniak, David Asboe, Andrew Leigh Brown, Duncan Churchill, Ian Williams, Anna Maria Geretti, Andrew Phillips, Nicola Mackie, Gary Murphy, Hannah Castro, Deenan Pillay, Patricia Cane, David Dunn. David Dolling is the guarantor.Steering Committee: Celia Aitken, Gartnavel General Hospital, Glasgow; David Asboe, Anton Pozniak, Chelsea and Westminster Hospital, London; Clare Booth, Royal Free NHS Trust, London; Patricia Cane, Health Protection Agency, Porton Down; Hannah Castro, Jonathan Crofts, David Dunn (co-chair), David Dolling, Esther Fearnhill, Kholoud Porter, MRC Clinical Trials Unit, London; David Chadwick, South Tees Hospitals NHS Trust, Middlesbrough; Duncan Churchill, Brighton and Sussex University Hospitals NHS Trust; Duncan Clark, St Bartholomew’s and The London NHS Trust; Simon Collins, HIV i-Base, London; Valerie Delpech, Health Protection Agency, Centre for Infections, London; Anna Maria Geretti, University of Liverpool; David Goldberg, Health Protection Scotland, Glasgow; Antony Hale, Leeds Teaching Hospitals NHS Trust; Stéphane Hué, University College London; Steve Kaye, Imperial College London; Paul Kellam, Wellcome Trust Sanger Institute and UCL Medical School; Linda Lazarus, Expert Advisory Group on AIDS Secretariat, Health Protection Agency, London; Andrew Leigh-Brown, University of Edinburgh; Nicola Mackie, Imperial NHS Trust; Chloe Orkin, St. Bartholomew’s Hospital, London; Philip Rice, St George’s Healthcare Trust, London; Deenan Pillay (co-chair), Andrew Phillips, Caroline Sabin, University College London Medical School; Erasmus Smit, Health Protection Agency, Birmingham Heartlands Hospital; Kate Templeton, Royal Infirmary of Edinburgh; Peter Tilston, Manchester Royal Infirmary; William Tong, Guy’s and St. Thomas’ NHS Foundation Trust, London; Ian Williams, Mortimer Market Centre, London; Hongyi Zhang, Addenbrooke’s Hospital, Cambridge; Mark Zuckerman, King’s College Hospital, London.Centres contributing data: Clinical Microbiology and Public Health Laboratory, Addenbrooke’s Hospital, Cambridge (Jane Greatorex); HIV/GUM Research Laboratory, Chelsea and Westminster Hospital, London (Adrian Wildfire); Guy’s and St. Thomas’ NHS Foundation Trust, London (Siobhan O’Shea, Jane Mullen); HPA – Public Health Laboratory, Birmingham Heartlands Hospital, Birmingham (Erasmus Smit); HPA London (Tamyo Mbisa); Imperial College Health NHS Trust, London (Alison Cox); King’s College Hospital, London (Richard Tandy); Medical Microbiology Laboratory, Leeds Teaching Hospitals NHS Trust (Tony Hale, Tracy Fawcett); Specialist Virology Centre, Liverpool (Mark Hopkins, Lynn Ashton); Department of Clinical Virology, Manchester Royal Infirmary, Manchester (Peter Tilston); Department of Virology, Royal Free Hospital, London (Clare Booth, Ana Garcia-Diaz); Edinburgh Specialist Virology Centre, Royal Infirmary of Edinburgh (Jill Shepherd); Department of Infection and Tropical Medicine, Royal Victoria Infirmary, Newcastle (Matthias L Schmid, Brendan Payne); South Tees Hospitals NHS Trust, Middlesbrough (David Chadwick); St George’s Hospital, London (Phillip Hay, Phillip Rice, Mary Paynter); Department of Virology, St Bartholomew’s and The London NHS Trust (Duncan Clark, David Bibby); Molecular Diagnostic Unit, Imperial College, London (Steve Kaye); University College London Hospitals (Stuart Kirk); West of Scotland Specialist Virology Lab Gartnavel, Glasgow (Alasdair MacLean, Celia Aitken, Rory Gunson).Coordinating centre: Medical Research Council Clinical Trials Unit, London (Kate Coughlin, Jonathan Crofts, David Dolling, David Dunn, Esther Fearnhill, Kholoud Porter). Funding: This work was supported by the UK Medical Research Council (grant G0900274) and the European Community’s 7th framework programme (FP7/2007-2013) under the Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN; project 223131).Competing interests: All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: support from the UK Medical Research Council and the European Community’s 7th framework programme; no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; and no other relationships or activities that could appear to have influenced the submitted work.Ethical approval: This study was approved by the UK multicentre research ethics committee and relevant local research ethic committees.Data sharing: No additional data available. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.References?Wittkop L, Günthard HF, de Wolf F, Dunn D, Cozzi-Lepri A, de Luca A, et al. Effect of transmitted drug resistance on virological and immunological response to initial combination antiretroviral therapy for HIV (EuroCoord-CHAIN joint project): a European multicohort study. Lancet Infect Dis2011;11:363-71.OpenUrlCrossRefMedline?Health Protection Agency. HIV in the United Kingdom: 2010 report. Health Protection Report2010;4:47.OpenUrl?Gazzard BG on behalf of the BHIVA Treatment Guidelines Writing Group. 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A cost-effectiveness analysis. Clin Infect Dis2005;41:1316-23.OpenUrlFREE Full Text?Cairns G. UK HIV treatment guidelines: “thrifty changes.”. 2011. www.natap.org/2011/newsUpdates/052611_06.htm.?The UK Collaborative HIV Cohort (UK CHIC) Study Group. The creation of a large UK-based multicentre cohort of HIV-infected individuals: the UK Collaborative HIV Cohort (UK CHIC) study. HIV Med2004;5:115-24.OpenUrlCrossRefMedlineWeb of Science?De Oliveira T, Deforche K, Cassol S, Salminen M, Paraskevis D, et al. An automated genotyping system for analysis of HIV-1 and other microbial sequences. Bioinformatics2005;21:3797-800.OpenUrlFREE Full Text?Health Protection Agency. Recent Infection Testing Algorithm (RITA)/HIV incidence. 2012. www.hpa.org.uk/Topics/InfectiousDiseases/InfectionsAZ/HIV/HIVIncidence/.?Murphy G, Parry JV. Assays for the detection of recent infection with human immunodeficiency virus type 1. Eurosurveillance2008;13:4-10.OpenUrl?Presanis AM, Gill ON, Chadborn TR, Hill C, Hope V, Logan L, et al. Insights into the rise in HIV infections, 2001 to 2008: a Bayesian synthesis of prevalence evidence. AIDS2010;24:2849-58.OpenUrlCrossRefMedlineWeb of Science?Chilton DN, Castro H, Lattimore S, Harrison LJ, Fearnhill E. HIV type-1 drug resistance in antiretroviral treatment-naive adults infected with non-B subtype virus in the United Kingdom. Antiviral Therapy2010;15:985-91.OpenUrlCrossRefMedline?Hué S, Pillay D, Clewley JP, Pybus OG. Genetic analysis reveals the complex structure of HIV-1 transmission within defined risk groups. Proc Natl Acad Sci U S A2005;102;4425-9.?Health Protection Agency. Testing times; HIV and other sexually transmitted infections in the United Kingdom. Health Protection Agency, 2007.?Bennett DE, Camacho RJ, Otelea D, Kuritzkes DR, Fleury H, Kiuchi M, et al. Drug resistance mutations for surveillance of transmitted HIV-1 drug-resistance: 2009 update. PLoS ONE2009;4:e4724.OpenUrlCrossRefMedline?Pingen M, Nijhuis M, de Bruijn JA, Boucher CAB, Wensing AMJ. Evolutionary pathways of transmitted drug-resistant HIV-1. J Antimicrob Chemother2011;66:1467-80.OpenUrlFREE Full Text?Hertogs K, Bloor S, Kemp SD, Van den Eynde C, Alcorn TM, Pauwels R, et al. Phenotypic and genotypic analysis of clinical HIV-1 isolates reveals extensive protease inhibitor cross-resistance: a survey of over 6000 samples. AIDS2000;14:1203-10.OpenUrlCrossRefMedlineWeb of Science?Hué S, Gifford R, Dunn D, Fearnhill E, Pillay D. Demonstration of sustained drug-resistant HIV-1 lineages circulating amongst treatment-naïve individuals. J Virol2009;83:2645-54.OpenUrlFREE Full Text?Brenner BG, Roger M, Moisi DD, Oliveira M, Hardy I, Turgel R, et al. Transmission networks of drug resistance acquired in primary/early stage HIV infection. AIDS2008;22:2509-15.OpenUrlCrossRefMedlineWeb of Science?Pilcher CD, Joaki G, Hoffman IF, Martinson FEA, Mapanje C, Stewart PW, et al. Amplified transmission of HIV-1: comparison of HIV-1 concentrations in semen and blood during acute and chronic infection. AIDS2007;21:1723-30.OpenUrlCrossRefMedlineWeb of Science?Heijman RLJ, Geskus G, Davidovich U, Coutinho R, Prins M, Stolte I. Changes in sexual behaviour after HIV diagnosis among MSM who seroconverted before and after the introduction of ART. Eighteenth Conference on Retroviruses and Opportunistic Infections, 2011.?Vallabhaneni S, Loeb L, Bragg L, McConnell J, Hartogensis W, Grant R, et al. Seroadaptive tactics adopted by HIV-positive MSM can contribute to profound and sustained reductions in HIV transmission risk following HIV diagnosis. Eighteenth Conference on Retroviruses and Opportunistic Infections, 2011.?Williamson LM, Dodds JP, Mercey DE, Hart GJ, Johnson AM. Sexual risk behaviour and knowledge of HIV status among community samples of gay men in the UK. AIDS2008;22:1063-70.OpenUrlCrossRefMedlineWeb of Science?García-Lerma JG, Nidtha S, Blumoff K, Weinstock H, Heneine W. Increased ability for selection of zidovudine resistance in a distinct class of wild-type HIV-1 from drug-naive persons. PNAS2001;98:13907-12.OpenUrlFREE Full Text?Kulkarni R, Miller MD, White K. The contribution of HIV-1 reverse transcriptase resistance mutations to antiviral synergy and replication fitness in vitro. Sixth Conference on HIV Pathogenesis, Treatment and Prevention, 2011. http://pag.ias2011.org/Abstracts.aspx?AID=4454. ?Jain V, Sucupira MC, Bacchetti P, Hartogensis W, Diaz RS, Kallas EG. Differential persistence of transmitted HIV-1 drug resistance mutation classes. J Infect Dis2011;203:1174-81.OpenUrlFREE Full Text?Vercauteren J, Wensing AM, van de Vijver DA, Albert J, Barlotta C, et al. Transmission of drug-resistant HIV-1 is stabilizing in Europe. J Infect Dis2009;200:1503-8.OpenUrlFREE Full Text?Bartmeyer B, Kuecherer C, Houareau C, Werning J, Keeren K, Somogyi S, et al. Prevalence of transmitted drug resistance and impact of transmitted resistance on treatment success in the German HIV-1 seroconverter cohort. PLoS ONE2010;5:e12718.OpenUrlCrossRefMedline?Grant RM, Kuritzkes DR, Johnson VA, Mellors JW, Sullivan JL, Swanstrom R, et al. Accuracy of the TRUGENE HIV-1 genotyping kit. J Clin Microbiol2003;41:1586-93.OpenUrlFREE Full Text?Schuurman R, Brambilla D, de Groot T, Huang D, Land S, Bremer J, et al. Underestimation of HIV type 1 drug resistance mutations: results from the ENVA-2 genotyping proficiency program. AIDS Res Hum Retrovir2002;18:243-8.OpenUrlCrossRefMedlineWeb of Science?Castro H, Pillay D, Sabin C, Dunn DT. Effect of misclassification of antiretroviral treatment status on the prevalence of transmitted HIV-1 drug resistance. BMC Med Res Methodol2012 [forthcoming]. ?Chilton DN, Castro H, Lattimore S, Harrison LJ, Fearnhill E. HIV type-1 drug resistance in antiretroviral treatment-naive adults infected with non-B subtype virus in the United Kingdom. Antivir Ther2010;15:985-91.OpenUrlCrossRefMedline Open access PDFEasy ReadRespond to this article Tweet Services Email to friendDownload to citation managerAdd article to BMJ portfolioRequest permission Citations Find similar articles in PubMedArticles by UK Collaborative Group on HIV Drug ResistanceCiting articles via Web of ScienceCiting articles via Scopus Social bookmarking CiteULike Connotea Del.icio.us Digg Facebook Mendeley Reddit![Add to Del.icio.us]( "Del.icio.us")
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Read all articles published in the last 7 days. You can use bmj.com to help you with your continuing medical education. Find out about CME/CPD credits for BMJ articles Keep up to date with cardiology: Access the latest cardiovascular medicine resources from across BMJ Group.OPEN ACCESS: All research articles are freely available online, with no word limit. Find out more about the BMJ's open access policy. Submit your paper. Find out how study types differ in our How to read a paper section. The truth about sports drinks: Find out more about the joint BMJ and BBC Panorama investigation into the evidence base behind many health claims. Research Time trends in drug resistant HIV-1 infections in the United Kingdom up to 2009: multicentre observational study BMJ 2012; 345 doi: 10.1136/bmj.e5253 (Published 21 August 2012) Cite this as: BMJ 2012;345:e5253 Immunology (including allergy) Infectious diseases Sexual health Article Related content Article metrics UK Collaborative Group on HIV Drug ResistanceCorrespondence to: D Dolling, UK HIV Drug Resistance Database, MRC Clinical Trials Unit, London WC2B 6NH, UK David.dolling{at}ctu.mrc.ac.ukAccepted 14 July 2012AbstractObjective To evaluate whether the prevalence of HIV-1 transmitted drug resistance has continued to decline in infections probably acquired within the United Kingdom.Design Multicentre observational study.Setting All UK public laboratories conducting tests for genotypic HIV resistance as a part of routine care.Participants 14?584 patients infected with HIV-1 subtype B virus, who were first tested for resistance before receiving antiretroviral therapy between January 2002 and December 2009.Main outcome measure Prevalence of transmitted drug resistance, defined as one or more resistance mutations from the surveillance list recommended by the World Health Organization.Results 1654 (11.3%, 95% confidence interval 10.8% to 11.9%) patients had one or more mutations associated with transmitted HIV-1 drug resistance; prevalence was found to decline from 15.5% in 2002 to 9.6% in 2007, followed by a slight increase to 10.9% in 2009 (P=0.21). This later rise was mainly a result of increases in resistance to nucleos(t)ide reverse transcriptase inhibitors (from 5.4% in 2007 to 6.6% in 2009, P=0.24) and protease inhibitors (1.5% to 2.1%, P=0.12). Thymidine analogue mutations, including T215 revertants, remained the most frequent mutations associated with nucleos(t)ide reverse transcriptase inhibitors, despite a considerable fall in stavudine and zidovudine use between 2002 and 2009 (from 29.4% of drug regimens in 2002 to 0.8% in 2009, from 47.9% to 8.8%, respectively).Conclusions The previously observed decline in the prevalence of transmitted drug resistance in HIV-1 infections probably acquired in the UK seems to have stabilised. The continued high prevalence of thymidine analogue mutations suggests that the source of this resistance may be increasingly from patients who have not undergone antiretroviral therapy and who harbour resistant viruses. Testing of all newly diagnosed HIV-1 positive people should be continued.IntroductionCombination antiretroviral therapy continues to be highly effective in treating HIV-1, and the introduction of new drugs and antiretroviral drug classes has notably improved patient prognosis. Nevertheless, resistance to antiretroviral drugs can develop in people on therapy and is associated with treatment failure.1 Against this backdrop, the rate of new HIV-1 infections within the United Kingdom continues to rise.2 In such new infections, transmitted drug resistance is sometimes assumed to reflect only direct infection from patients already receiving antiretroviral therapy. Concerns about an adverse effect of transmitted HIV-1 drug resistance on the success of antiretroviral therapy have led to national and international guidelines recommending that all newly diagnosed patients have resistance tests conducted to aid selection of first line regimens.3 4A previous study showed a sharp decline in the prevalence of transmitted HIV-1 drug resistance in the UK between 2002 and 2005,5 which was mainly attributed to changes in testing guidelines and the wider use of regimens that suppress viral concentrations to below infectious levels. Since then, more potent and better tolerated antiretroviral drugs have been introduced, and the proportion of patients achieving viral suppression has continued to increase.6 This suggests that transmitted HIV-1 drug resistance may have declined even further and could eventually fall below levels in which universal testing before antiretroviral therapy is cost effective,7 an important issue at a time when the cost of HIV-1 management is being scrutinised.8 This paper examines recent time trends in transmitted drug resistance in HIV-1 infections probably acquired in the UK.MethodsResistance dataThe UK HIV Drug Resistance Database, described in detail elsewhere,9 was established in 2001 and collects the majority of genotypic resistance tests done within the UK as part of routine clinical care. The resistance tests analysed in this study used bulk sequencing of the pol gene, encoding at least codons 4-99 of the protease gene and 34-234 of the reverse transcriptase gene, using a variety of inhouse and commercial testing systems. Subtype was assigned centrally using the Rega algorithm.10Clinical dataWe acquired demographic and clinical information by linkage (using pseudonymised identifiers) to the UK Collaborative HIV Cohort Study (UK CHIC),9 which includes patients from 13 of the largest clinics within the UK, and to the HIV and AIDS Patient and the Survey of Prevalent HIV Infections Diagnosed databases, which are coordinated by the Health Protection Agency. When possible, we linked resistance tests done after 2007 to samples on which a recent infection testing algorithm had been conducted as part of a national health surveillance programme11; these tests use antibody avidity assays to classify infections as either recent (probably occurring in the previous five months) or non-recent;12 and clinical, laboratory, or historical information to reduce the false recent rate.Tests included in analysis and definition of drug resistanceThe UK has several parallel and largely non-overlapping HIV-1 epidemics with different levels and patterns of resistance to antiretroviral therapy.13 14 To simplify the understanding of temporal trends, we limited this analysis to subtype B viral infections, so it is not intended to generate nationally representative results. This epidemic was seeded by around six introductions to the UK in the early to mid-1980s15 and is largely confined to men infected through homosexual exposure, of whom 83% are estimated to have acquired infection within the UK.16We identified the first resistance test for all patients older than 16 years who had not yet received antiretroviral therapy at the time of sampling, up to the end of 2009. Patients with an undetectable viral load (<50 copies/mL) were excluded; such levels may indicate unrecorded treatment use. Since guidelines in 2001 first recommended that resistance tests be performed for all patients who had not received antiretroviral therapy,3 we excluded tests conducted before 2002.Transmitted HIV-1 drug resistance was defined as one or more mutations from the surveillance list recommended by the World Health Organization.17 We used the Stanford HIVdb algorithm 6.0.11 (29 Mar 2011) to examine susceptibility to antiretroviral drugs, and reported low level resistance or greater. Intermediate or high level resistance was considered to reflect a substantial loss in susceptibility. We assessed temporal trends in terms of the date of the resistance test sample rather than the date of the patient’s infection, which is generally not known.Statistical methodsConfidence intervals for proportions were calculated using a 95% Wilson confidence interval for binomially distributed data. We analysed the patterns of trends over continuous time using both linear and piecewise linear logistic regression with a flexible choice of a single inflexion point calculated using least squares optimisation; we selected the model with the best fit according to Akaike’s information criterion. The trends for codons were reported if a mutation had an overall prevalence of more than 0.3% as well as other mutations for nucleos(t)ide reverse transcriptase with a strong effect on phenotype (K65R, K70E, L74I/V, Y115F). We examined differences between the prevalence of resistance in recent and non-recent infections tests using the ?2 test. All statistical analyses were conducted in Stata/IC 11.2 software.ResultsPopulation characteristicsWe analysed 14?583 patients who were antiretroviral therapy naive, infected with a subtype B virus, and whose first drug resistance test was conducted between January 2002 and December 2009. Of these patients, 10?173 (70%) were white, 995 (7%) were black, 711 (5%) had a known other ethnicity, and 2704 (19%) had an unknown ethnicity. The median age at diagnosis was 36 years (interquartile range 30 to 42). Of the patients analysed, 10?288 (71%) were men who had sex with men, 1275 (9%) had a heterosexual exposure source, 313 (2%) had a known other exposure source, and 2707 (19%) had an unknown exposure source. The median number of days between HIV-1 diagnosis and resistance sample was 22 (interquartile range five to 358). The median CD4 count at the time of testing, available from 11?219 (76.9%) patients, was 408×106 cells/L (interquartile range 271×106 to 560×106).The number of resistance tests conducted per year increased over time, reflecting a rise in the number of new diagnoses among men who have sex with men (fig 1?). The decline between 2008 and 2009 is mainly due to an increase in the proportion of tests where the patient’s status regarding antiretroviral therapy exposure was uncertain (15.6% in 2008 to 32.0% in 2009), probably as a result of a reporting lag with demographic and clinical datasets.View larger version:In a new windowDownload as PowerPoint SlideFig 1 Prevalence of transmitted drug resistance over time, by antiretroviral drug class. Bar=95% confidence intervalTrends in transmitted drug resistanceSamples from 1654 (11.3%, 95% confidence interval 10.8% to 11.9%) patients had one or more mutations associated with transmitted HIV-1 drug resistance. Of these samples, 1009 (6.9%, 6.5% to 7.3%), 604 (4.1%, 3.8% to 4.5%), and 319 (2.2%, 2.0% to 2.4%) had one or more mutations associated with nucleos(t)ide reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitors, and protease inhibitors, respectively. Of these samples, 1426 (9.8%, 9.3% to 10.3%) had single class resistance, 175 (1.2%, 1.0% to 1.4%) had dual class resistance, and 52 (0.4%, 0.3% to 0.5%) had triple class resistance; dual and triple class resistance have remained at a similar prevalence since 2005.The previously reported5 decline in the prevalence of transmitted HIV-1 drug resistance for any class of antiretroviral drug was observed to continue from 15.5% in 2002 until around January 2007 (95% confidence interval January 2006 to February 2008; odds ratio 0.88 per year (95% confidence interval 0.84 to 0.92); fig 1). However, between 2007 and 2009, we saw a non-significant increase from 9.6% to 10.9% (odds ratio 1.06, 0.97 to 1.17; P=0.21). We saw non-significant increases in resistance to nucleos(t)ide reverse transcriptase inhibitors (from 5.4% in 2007 to 6.6% in 2009; P=0.24) and protease inhibitors (1.5% to 2.1%; P=0.12). Inflection points were also identified, which gave a significantly better fit for mutations associated with nucleos(t)ide reverse transcriptase inhibitors (February 2007 (January 2006 to March 2008); second piece odds ratio 1.08 (0.95 to 1.22)) and protease inhibitors (June 2008 (August 2007 to June 2009); 1.69 (0.86 to 3.31)). The prevalence of mutations associated with non-nucleoside reverse transcriptase inhibitors remained stable at around 3.6% with no evidence of non-linearity (odds ratio 0.96 (0.92 to 1.00)).The table? displays the resistance trends over time for key individual codons with prevalence greater than 0.3%. The trends in HIV-1 drug resistance associated with nucleos(t)ide reverse transcriptase inhibitors largely reflect the most common mutations within this drug class, namely, the T215 revertant18 mutations (I/S/C/D/V/E), K219Q/E/N/R, and M41L. These mutations are either reversions or a subset of the thymidine analogue mutations that develop under regimens containing either stavudine or zidovudine. The finding of a levelling off in the prevalence of thymidine analogue mutations is paradoxical, in the light of the dramatic fall in the use of stavudine and zidovudine. For example, in the UK CHIC study, the proportion of treatment regimens that included stavudine dropped from 29.4% to 0.8% between 2002 and 2009, while the proportion that included zidovudine dropped from 47.9% to 8.8% over the same period. Stavudine and zidovudine have largely been replaced by tenofovir and abacavir,3 but signature mutations for these drugs (such as K65R) are still rare in patients with HIV-1. L90M mutations have increased in prevalence since 2007, despite the near cessation in the use of saquinavir, nelfinavir, and indinavir, which are first generation protease inhibitors that select for this mutation (their use in the UK as part of a drug regimen fell from 20.6% in 2002 to 4.6% in 2009), although L90M has broad cross resistance effects to the protease inhibitor class.19View this table:View PopupView InlineTime trends of selected mutations with prevalence greater than 0.3%. Data are no (%) of samples with mutation unless stated otherwiseDrug susceptibilityFigure 2? shows the predicted susceptibility of HIV-1 from samples collected in 2009 to currently recommended first line antiretroviral drugs3 and second generation antiretroviral drugs darunavir and etravirine. The association between genotypic mutations and the phenotypic susceptibility to antiretroviral drugs is complex, so although T215 revertants are considered by the Stanford HIVdb algorithm to result in only low level resistance to nucleos(t)ide reverse transcriptase inhibitors, they do confer a low genetic barrier to the development of high level resistance to this drug class.View larger version:In a new windowDownload as PowerPoint SlideFig 2 Predicted susceptibility to antiretroviral drugsThe prevalence of intermediate or high level resistance to nucleos(t)ide reverse transcriptase inhibitors other than zidovudine was less than 0.9%, reflecting the low frequency of multiple mutations in thymidine analogues. There was a comparatively high level of reduced susceptibility to recommended first line regimens of the non-nucleoside reverse transcriptase inhibitors efavirenz and nevirapine (3.7%). However, etravirine, a non-nucleoside reverse transcriptase inhibitor, displayed very low levels of resistance (0.4%), reflecting the lack of predicted effect of K103N. The potency of modern drugs from the protease inhibitor class was high (only 25 (1.1%) patients had substantially reduced susceptibility to any protease inhibitor), owing to the rarity of multiple protease mutations. These findings indicate that potent first line regimens can still be constructed from the original three drug classes for almost all patients.Prevalence of resistance in recently infected patientsBetween 2007 and 2009, 742 samples were linked to a recent infection test result, of which 171 (23.0%) were classified as recent. The overall prevalence of resistance did not differ significantly (P=0.66) between recent samples (20, 11.7%) or non-recent samples (60/571, 10.5%). Furthermore, we detected no significant differences in the prevalence of resistance by individual drug class (results not shown).DiscussionInterpretationThe previously observed decline in the prevalence of transmitted drug resistance in subtype B viruses seems to have been reversed for mutations associated with nucleos(t)ide reverse transcriptase inhibitors and protease inhibitors, despite an increase in the proportion of patients on antiretroviral therapy who are virologically suppressed (from 62% in 2000 to 84% in 20076). The most frequent mutations were T215 revertants, which may be transmitted as such or evolve from a virus harbouring a T215F or T215Y mutation.19 These and other mutations are associated with a significantly higher risk of virological failure than wild type genotypes.1 17 The prevalence of transmitted thymidine analogue mutations remained moderately high, despite a marked shift away from the prescribing in the UK of drugs that select for these mutations.One plausible explanation for this paradox is the onward transmission of resistant viruses from people who have not received antiretroviral therapy before and who were themselves infected with a resistant virus. This hypothesis is supported by a previous phylogenetic analysis of UK subtype B sequences, which described five transmission clusters comprised exclusively of patients with resistance mutations who are antiretroviral therapy naive.20 It was postulated that an increasingly greater proportion of transmitted HIV-1 drug resistance could originate from antiretroviral therapy naive lineages, and that there could ultimately be a limit in the decline of transmitted HIV-1 drug resistance.The first factor contributing to this possible limit is that people with undiagnosed HIV could disproportionally21 spread the epidemic, since they are more infectious in the period immediately after infection.22 Some studies23 24 (but not all25) have also shown that undiagnosed patients with HIV have more sexual partners than diagnosed patients. Furthermore, evidence is emerging that transmitted resistant viruses are more persistent than originally thought. The fitness cost (relative to wild type virus) of certain mutations, such as the T215 revertants and K103N, has been shown to be marginal in laboratory studies.26 27 Jain and colleagues provided clinical confirmation of this finding in a series of patients infected with resistant virus and who had two or more resistance tests before they started antiretroviral therapy.28 With the exception of the M184V mutation, which is highly replicatively deficient, all groups of transmitted mutations persisted beyond at least three years in the majority of patients.A second possible explanation for the continued prevalence of thymidine analogue mutations is that the use of tenofovir and abacavir are maintaining the prevalence of such mutations in patients who have received antiretroviral therapy despite the decline in the use of zidovudine and stavudine. Further phylogenetic research could shed light on the transmission dynamics of these mutations.Comparison with other studiesTwo recent studies have reported on time trends in transmitted HIV drug resistance. In a study conducted in 20 European countries between 2002 and 2006,29 Vercauteren and colleagues found a small, linear decline in levels of nucleos(t)ide reverse transcriptase inhibitor and protease inhibitor resistance; non-nucleoside reverse transcriptase inhibitor resistance was observed to increase followed by a decrease between 2004 and 2006. Bartmeyer and colleagues30 performed a similar analysis of a German seroconverter cohort between 1996 and 2007. In more recent years, resistance to nucleos(t)ide reverse transcriptase inhibitors seemed to be stable and resistance to non-nucleoside reverse transcriptase inhibitors seemed to increase, although clear patterns are difficult to discern due to the relatively small sample size.Study limitationsOur study has several limitations. Firstly, the analysis is based on resistance found at date of sample rather than date of infection. Since viral quasi-species harbouring resistance mutations may revert to, or be overgrown by, virus without the mutations,19 29 the true level of transmitted HIV drug resistance may have been under-estimated in this analysis. Also, the degree of this bias will be affected by the average time between HIV-1 infection and diagnosis, which may have changed over time. Furthermore, the diagnosis delay could mask the underlying trend in the prevalence of transmitted resistance by date of infection. However, we found no difference in the prevalence of resistance between recent and non-recent infections, in the subset of patients in which this analysis was possible. Secondly, the genotypic data analysed were generated by population sequencing with a limit of sensitivity of approximately 15 to 25%.31 32 Our estimates of the prevalence of transmitted HIV drug resistance may therefore be biased downwards. This will have also biased the type of mutations observed, with persistent mutations appearing to be more prevalent than those which rapidly become undetectable such as K65R or M184V. We do not consider this limitation to be a major concern, because the main objective of our study was to examine changes in the prevalence of transmitted HIV drug resistance over time. Another limitation was that our method of classifying treatment status could have resulted in misclassification bias if some patients who had received antiretroviral therapy were included in the analysis. Previous research by the UK HIV Drug Resistance Database has suggested that this effect could distort trends if there is misclassification in 4% or more of the samples analysed.33 Finally, as the prevalence of transmitted HIV drug resistance in subtype B viruses, the focus of this analysis, is known to be higher than that observed in other subtypes,34 our findings are not generalisable to the UK epidemic as a whole, although coverage of patients infected with subtype B virus is high.Conclusions and policy implicationsFinally, we consider the clinical implications of our main conclusion that resistant lineages may have become fixed in the circulating viral pool. This concept, if confirmed to be correct, will apply universally, particularly in countries where first generation nucleos(t)ide reverse transcriptase inhibitors continue to be used, and underscores the importance of sentinel surveillance. In terms of the UK (and probably other well resourced countries), the detectable mutations that tend to be transmitted should have little effect on nucleos(t)ide reverse transcriptase inhibitors currently used in first line regimens, that is, abacavir, tenofovir, lamivudine, and emtricitabine. Non-nucleoside reverse transcriptase inhibitors used in first line regimens are of greater concern, with approximately 4% of patients being infected with viruses with reduced susceptibility to efavirenz and nevirapine. Previous models7 have suggested that baseline resistance testing remains cost effective at the levels observed in this study. Therefore, our findings argue that testing at HIV diagnosis and continued monitoring should remain the standard of care.What is already known on this topicTransmitted HIV drug resistance can affect therapy successSome resistance mutations could persist more than others in the absence of selective drug pressure A 2007 paper has shown a reduction in transmitted drug resistance in the UK since 2005What this study addsTransmitted drug resistance is no longer declining in UK, and evidence suggests a sustained epidemic that is resistant to nucleos(t)ide reverse transcriptase inhibitors, irrespective of previous antiretroviral therapy useSusceptibility to antiretroviral therapy remains relatively high, and potent first line regimens can still be constructed from the original three drug classes for almost all patientsNotesCite this as: BMJ 2012;345:e5253FootnotesThe UK Collaborative Group on HIV Drug Resistance is a collaboration between the UK HIV Drug Resistance Database; UK CHIC; Health Protection Agency HARS; and participating academic centres, clinics, and laboratories.Analysis/writing group: David Dolling, Caroline Sabin, Valerie Delpech, Erasmus Smit, Anton Pozniak, David Asboe, Andrew Leigh Brown, Duncan Churchill, Ian Williams, Anna Maria Geretti, Andrew Phillips, Nicola Mackie, Gary Murphy, Hannah Castro, Deenan Pillay, Patricia Cane, David Dunn. David Dolling is the guarantor.Steering Committee: Celia Aitken, Gartnavel General Hospital, Glasgow; David Asboe, Anton Pozniak, Chelsea and Westminster Hospital, London; Clare Booth, Royal Free NHS Trust, London; Patricia Cane, Health Protection Agency, Porton Down; Hannah Castro, Jonathan Crofts, David Dunn (co-chair), David Dolling, Esther Fearnhill, Kholoud Porter, MRC Clinical Trials Unit, London; David Chadwick, South Tees Hospitals NHS Trust, Middlesbrough; Duncan Churchill, Brighton and Sussex University Hospitals NHS Trust; Duncan Clark, St Bartholomew’s and The London NHS Trust; Simon Collins, HIV i-Base, London; Valerie Delpech, Health Protection Agency, Centre for Infections, London; Anna Maria Geretti, University of Liverpool; David Goldberg, Health Protection Scotland, Glasgow; Antony Hale, Leeds Teaching Hospitals NHS Trust; Stéphane Hué, University College London; Steve Kaye, Imperial College London; Paul Kellam, Wellcome Trust Sanger Institute and UCL Medical School; Linda Lazarus, Expert Advisory Group on AIDS Secretariat, Health Protection Agency, London; Andrew Leigh-Brown, University of Edinburgh; Nicola Mackie, Imperial NHS Trust; Chloe Orkin, St. Bartholomew’s Hospital, London; Philip Rice, St George’s Healthcare Trust, London; Deenan Pillay (co-chair), Andrew Phillips, Caroline Sabin, University College London Medical School; Erasmus Smit, Health Protection Agency, Birmingham Heartlands Hospital; Kate Templeton, Royal Infirmary of Edinburgh; Peter Tilston, Manchester Royal Infirmary; William Tong, Guy’s and St. Thomas’ NHS Foundation Trust, London; Ian Williams, Mortimer Market Centre, London; Hongyi Zhang, Addenbrooke’s Hospital, Cambridge; Mark Zuckerman, King’s College Hospital, London.Centres contributing data: Clinical Microbiology and Public Health Laboratory, Addenbrooke’s Hospital, Cambridge (Jane Greatorex); HIV/GUM Research Laboratory, Chelsea and Westminster Hospital, London (Adrian Wildfire); Guy’s and St. Thomas’ NHS Foundation Trust, London (Siobhan O’Shea, Jane Mullen); HPA – Public Health Laboratory, Birmingham Heartlands Hospital, Birmingham (Erasmus Smit); HPA London (Tamyo Mbisa); Imperial College Health NHS Trust, London (Alison Cox); King’s College Hospital, London (Richard Tandy); Medical Microbiology Laboratory, Leeds Teaching Hospitals NHS Trust (Tony Hale, Tracy Fawcett); Specialist Virology Centre, Liverpool (Mark Hopkins, Lynn Ashton); Department of Clinical Virology, Manchester Royal Infirmary, Manchester (Peter Tilston); Department of Virology, Royal Free Hospital, London (Clare Booth, Ana Garcia-Diaz); Edinburgh Specialist Virology Centre, Royal Infirmary of Edinburgh (Jill Shepherd); Department of Infection and Tropical Medicine, Royal Victoria Infirmary, Newcastle (Matthias L Schmid, Brendan Payne); South Tees Hospitals NHS Trust, Middlesbrough (David Chadwick); St George’s Hospital, London (Phillip Hay, Phillip Rice, Mary Paynter); Department of Virology, St Bartholomew’s and The London NHS Trust (Duncan Clark, David Bibby); Molecular Diagnostic Unit, Imperial College, London (Steve Kaye); University College London Hospitals (Stuart Kirk); West of Scotland Specialist Virology Lab Gartnavel, Glasgow (Alasdair MacLean, Celia Aitken, Rory Gunson).Coordinating centre: Medical Research Council Clinical Trials Unit, London (Kate Coughlin, Jonathan Crofts, David Dolling, David Dunn, Esther Fearnhill, Kholoud Porter). Funding: This work was supported by the UK Medical Research Council (grant G0900274) and the European Community’s 7th framework programme (FP7/2007-2013) under the Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN; project 223131).Competing interests: All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: support from the UK Medical Research Council and the European Community’s 7th framework programme; no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; and no other relationships or activities that could appear to have influenced the submitted work.Ethical approval: This study was approved by the UK multicentre research ethics committee and relevant local research ethic committees.Data sharing: No additional data available. 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BMC Med Res Methodol2012 [forthcoming]. ?Chilton DN, Castro H, Lattimore S, Harrison LJ, Fearnhill E. HIV type-1 drug resistance in antiretroviral treatment-naive adults infected with non-B subtype virus in the United Kingdom. Antivir Ther2010;15:985-91.OpenUrlCrossRefMedline Open access PDFEasy ReadRespond to this article Tweet Services Email to friendDownload to citation managerAdd article to BMJ portfolioRequest permission Citations Find similar articles in PubMedArticles by UK Collaborative Group on HIV Drug ResistanceCiting articles via Web of ScienceCiting articles via Scopus Social bookmarking CiteULike Connotea Del.icio.us Digg Facebook Mendeley Reddit Twitter Stumbleupon Latest jobsUK jobsInternational jobsUK jobs AXESS LTD, EU MEDICAL ADVISER. 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