A Systematic Review of Methodologies Used in Models of the Treatment of Diabetes Mellitus.

BACKGROUND: Diabetes mellitus is a chronic and complex disease, increasing in prevalence and consequent health expenditure. Cost-effectiveness models with long time horizons are commonly used to perform economic evaluations of diabetes' treatments. As such, prediction accuracy and structural uncertainty are important features in cost-effectiveness models of chronic conditions. OBJECTIVES: The aim of this systematic review is to identify and review published cost-effectiveness models of diabetes treatments developed between 2011 and 2022 regarding their methodological characteristics. Further, it also appraises the quality of the methods used, and discusses opportunities for further methodological research. METHODS: A systematic literature review was conducted in MEDLINE and Embase to identify peer-reviewed papers reporting cost-effectiveness models of diabetes treatments, with time horizons of more than 5 years, published in English between 1 January 2011 and 31 of December 2022. Screening, full-text inclusion, data extraction, quality assessment and data synthesis using narrative synthesis were performed. The Philips checklist was used for quality assessment of the included studies. The study was registered in PROSPERO (CRD42021248999). RESULTS: The literature search identified 30 studies presenting 29 unique cost-effectiveness models of type 1 and/or type 2 diabetes treatments. The review identified 26 type 2 diabetes mellitus (T2DM) models, 3 type 1 DM (T1DM) models and one model for both types of diabetes. Fifteen models were patient-level models, whereas 14 were at cohort level. Parameter uncertainty was assessed thoroughly in most of the models, whereas structural uncertainty was seldom addressed. All the models where validation was conducted performed well. The methodological quality of the models with respect to structure was high, whereas with respect to data modelling it was moderate. CONCLUSIONS: Models developed in the past 12 years for health economic evaluations of diabetes treatments are of high-quality and make use of advanced methods. However, further developments are needed to improve the statistical modelling component of cost-effectiveness models and to provide better assessment of structural uncertainty.

Baseline and Peak Cortisol Response to the Low-Dose Short Synacthen Test Relates to Indication for Testing, Age, and Sex.

Context: Meta-analyses report that the low dose short Synacthen test (LDSST) is more sensitive but less specific than the standard dose test for the diagnosis of adrenal insufficiency, and there are concerns regarding the accuracy of dosing in the LDSST. Objective: Perform a retrospective, observational study to review the outcomes of LDSSTs performed in a tertiary endocrine service from 2008 to 2014 (N = 335) and 2016 to 2020 (N = 160), and examine for relationships between cortisol measurements and indication for testing, age and sex. Methods: LDSST were performed by endocrine nurses. Synacthen 500 ng/1.73m2 administered as IV bolus, sampling at 0, 15, 25, and 35 minutes. Results: Mean (± 1SD) baseline cortisol was 221 ± 120 nmol/L, peak 510 ± 166 nmol/L and increment 210 ± 116 nmol/L. 336 (70%) patients had a normal response (baseline cortisol >100 nmol/L, peak >450 nmol/L), 78 (16%) a suboptimal response (peak cortisol 350-450 nmol/L) and were prescribed hydrocortisone to during periods of stress only, 67 (14%) an abnormal response (baseline <100nmol/L or peak <350nmol/L) and were prescribed daily hydrocortisone. Basal, peak, and incremental increases in cortisol were higher in females (P = .03, P < .001, P = .03, respectively). Abnormal results occurred most frequently in patients treated previously with pharmacological doses of glucocorticoids or structural brain abnormalities (P < .001). Conclusion: The low prevalence and strong association of abnormal results with indication for testing, suggests that over diagnosis occurred infrequently in this clinical setting.

General tests of the Markov property in multi-state models.

Multi-state models for event history analysis most commonly assume the process is Markov. This article considers tests of the Markov assumption that are applicable to general multi-state models. Two approaches using existing methodology are considered; a simple method based on including time of entry into each state as a covariate in Cox models for the transition intensities and a method involving detecting a shared frailty through a stratified Commenges-Andersen test. In addition, using the principle that under a Markov process the future rate of transitions of the process at times $t > s$ should not be influenced by the state occupied at time $s$, a new class of general tests is developed by considering summaries from families of log-rank statistics where patients are grouped by the state occupied at varying initial time $s$. An extended form of the test applicable to models that are Markov conditional on observed covariates is also derived. The null distribution of the proposed test statistics are approximated by using wild bootstrap sampling. The approaches are compared in simulation and applied to a dataset on sleeping behavior. The most powerful test depends on the particular departure from a Markov process, although the Cox-based method maintained good power in a wide range of scenarios. The proposed class of log-rank statistic based tests are most useful in situations where the non-Markov behavior does not persist, or is not uniform in nature across patient time.

Incidence and risk factors of long COVID in the UK: a single-centre observational study.

BACKGROUND: Studies to evaluate long COVID symptoms and their risk factors are limited. We evaluated the presence of long COVID and its risk factors in patients discharged from a hospital with COVID-19 illness. METHODS: This observational study included 271 COVID-19 patients admitted between February and July 2020 in a hospital in the UK. The primary outcome measure was to assess the duration and severity of long COVID and its predictors at 3, 6 and 9 months. Logistic regression was performed to assess the potential risk factors for long COVID. RESULTS: Out of 89 patients interviewed, 55 (62%) had long COVID for 3 months, 46 (52%) for 6 months and 37 of the 75 patients admitted to the hospital with acute COVID-19 had long COVID for 9 months (49%). The most common long COVID symptoms were fatigue and breathlessness. CONCLUSION: Nearly two-thirds of patients at 3 months and a half at 9 months had long COVID. COVID-19 pneumonia was the strongest predictor of long COVID in Caucasians at 3 months.

Salivary cortisol, cortisone and serum cortisol concentrations are related to age and body mass index in healthy children and young people.

BACKGROUND: Saliva is an ideal medium in which to measure cortisol in children. However, there are very few data reporting salivary cortisol or cortisone concentrations in healthy children since the introduction of liquid chromatography-mass spectrometry (LC-MS/MS) to routine laboratory practice. DESIGN: Early morning serum cortisol, salivary cortisol and cortisone were measured on fasting samples, and salivary hormones were measured in samples collected every 2 hours during waking hours, and 30 minutes after waking the following morning. PARTICIPANTS: 43 healthy paediatric volunteers (19 female), median age 11.5 years, range 6.2-18.7, participated. RESULTS: Early morning serum cortisol (265 nmol/L, 156-516) correlated strongly with salivary cortisol (4.7 nmol/L, 1.1-14.6) and cortisone (28.8 nmol/L, 11.7-56.6), P < .0001 for both. Serum cortisol, salivary cortisol and salivary cortisone correlated directly with age (P < .0001, P = .002 and P = .015, respectively), and salivary cortisone/cortisol ratio correlated indirectly with age (P = .007). Between 08.00 and 21.00, area under the curve for salivary cortisol (mean ± 1 SD) was 41.8 ± 19.1 and for cortisone 213.0 ± 61.2. Salivary cortisol was undetectable in 25/130 (19%) of samples collected after 13.00, while cortisone was always detectable. DISCUSSION: Salivary cortisol and cortisone concentrations are strongly related to serum cortisol concentrations; however, cortisone may be a preferable measure as cortisol is often undetectable. Age may be an important factor in the interpretation of early morning cortisol measurements made in serum and saliva.

Instrumental variable estimation in semi-parametric additive hazards models.

Instrumental variable methods allow unbiased estimation in the presence of unmeasured confounders when an appropriate instrumental variable is available. Two-stage least-squares and residual inclusion methods have recently been adapted to additive hazard models for censored survival data. The semi-parametric additive hazard model which can include time-independent and time-dependent covariate effects is particularly suited for the two-stage residual inclusion method, since it allows direct estimation of time-independent covariate effects without restricting the effect of the residual on the hazard. In this article, we prove asymptotic normality of two-stage residual inclusion estimators of regression coefficients in a semi-parametric additive hazard model with time-independent and time-dependent covariate effects. We consider the cases of continuous and binary exposure. Estimation of the conditional survival function given observed covariates is discussed and a resampling scheme is proposed to obtain simultaneous confidence bands. The new methods are compared to existing ones in a simulation study and are applied to a real data set. The proposed methods perform favorably especially in cases with exposure-dependent censoring.

Quantifying the association between progression-free survival and overall survival in oncology trials using Kendall's τ.

This paper considers methods for estimating the association between progression-free and overall survival in oncology trials. Copula-based, nonparametric, and illness-death model-based methods are reviewed. In addition, the approach based on an underlying illness-death model is generalized to allow general parametric models. The performance of these methods, in terms of bias and efficiency, is investigated through simulation and also illustrated using data from a clinical trial of treatments for colon cancer. The simulations suggest that the illness-death model-based method provides good estimates of Kendall's τ across several scenarios. In some situations, copula-based methods perform well but their performance is sensitive to the choice of copula. The Clayton copula is most appropriate in scenarios, which might realistically reflect an oncology trial, but the use of copula models in practice is questionable.

Creation of the WHO Indicators of Infant and Young Child Development (IYCD): metadata synthesis across 10 countries.

BACKGROUND: Renewed global commitment to the improvement of early child development outcomes, as evidenced by the focus of the United Nations Sustainable Development Goal 4, highlights an increased need for reliable and valid measures to evaluate preventive and interventional efforts designed to affect change. Our objective was to create a new tool, applicable across multicultures, to measure development from 0 to 3 years through metadata synthesis. METHODS: Fourteen cross-sectional data sets were contributed on 21 083 children from 10 low/middle-income countries (LMIC), assessed using seven different tools (caregiver reported or directly assessed). Item groups, measuring similar developmental skills, were identified by item mapping across tools. Logistic regression curves displayed developmental trajectories for item groups across countries and age. Following expert consensus to identify well-performing items across developmental domains, a second mapping exercise was conducted to fill any gaps across the age range. The first version of the tool was constructed. Item response analysis validated our approach by putting all data sets onto a common scale. RESULTS: 789 individual items were identified across tools in the first mapping and 129 item groups selected for analysis. 70 item groups were then selected through consensus, based on statistical performance and perceived importance, with a further 50 items identified at second mapping. A tool comprising 120 items (23 fine motor, 23 gross motor, 20 receptive language, 24 expressive language, 30 socioemotional) was created. The linked data sets on a common scale showed a curvilinear trajectory of child development, highlighting the validity of our approach through excellent coverage by age and consistency of measurement across contributed tools, a novel finding in itself. CONCLUSIONS: We have created the first version of a prototype tool for measuring children in the early years, developed using novel easy to apply methodology; now it needs to be feasibility tested and piloted across several LMICs.

Performance of different clinical trial designs to evaluate treatments during an epidemic.

In the 2013-2016 west Africa outbreak of Ebola Virus Disease (EVD), most of the planned clinical trials failed to reach a conclusion within the time frame of the epidemic. The performance of clinical trial designs for the evaluation of one or more experimental treatments in the specific context of an ongoing epidemic with changing case fatality rates (CFR) and unpredictable case numbers is unclear. We conduct a comprehensive evaluation of commonly used two- and multi-arm clinical trial designs based on real data, which was recorded during the 2013-16 EVD epidemic in west Africa. The primary endpoint is death within 14 days of hospitalization. The impact of the recruitment start times relative to the time course of the epidemic on the operating characteristics of the clinical trials is analysed. Designs with frequent interim analyses with the possibility of early stopping are shown to outperform designs with only a single analysis not only in terms of average time to conclusion and average sample size, but also in terms of the probability of reaching any conclusion at all. Historic control designs almost always result in substantially inflated false positive rates, when the case fatality rate changes over time. Response-adaptive randomization may be a compromise between the goal of scientific validity and the ethical goal of minimizing the number of patients allocated to ineffective treatments.

PET-PANC: multicentre prospective diagnostic accuracy and health economic analysis study of the impact of combined modality 18fluorine-2-fluoro-2-deoxy-d-glucose positron emission tomography with computed tomography scanning in the diagnosis and management of pancreatic cancer.

BACKGROUND: Pancreatic cancer diagnosis and staging can be difficult in 10-20% of patients. Positron emission tomography (PET)/computed tomography (CT) adds precise anatomical localisation to functional data. The use of PET/CT may add further value to the diagnosis and staging of pancreatic cancer. OBJECTIVE: To determine the incremental diagnostic accuracy and impact of PET/CT in addition to standard diagnostic work-up in patients with suspected pancreatic cancer. DESIGN: A multicentre prospective diagnostic accuracy and clinical value study of PET/CT in suspected pancreatic malignancy. PARTICIPANTS: Patients with suspected pancreatic malignancy. INTERVENTIONS: All patients to undergo PET/CT following standard diagnostic work-up. MAIN OUTCOME MEASURES: The primary outcome was the incremental diagnostic value of PET/CT in addition to standard diagnostic work-up with multidetector computed tomography (MDCT). Secondary outcomes were (1) changes in patients' diagnosis, staging and management as a result of PET/CT; (2) changes in the costs and effectiveness of patient management as a result of PET/CT; (3) the incremental diagnostic value of PET/CT in chronic pancreatitis; (4) the identification of groups of patients who would benefit most from PET/CT; and (5) the incremental diagnostic value of PET/CT in other pancreatic tumours. RESULTS: Between 2011 and 2013, 589 patients with suspected pancreatic cancer underwent MDCT and PET/CT, with 550 patients having complete data and in-range PET/CT. Sensitivity and specificity for the diagnosis of pancreatic cancer were 88.5% and 70.6%, respectively, for MDCT and 92.7% and 75.8%, respectively, for PET/CT. The maximum standardised uptake value (SUVmax.) for a pancreatic cancer diagnosis was 7.5. PET/CT demonstrated a significant improvement in relative sensitivity (p = 0.01) and specificity (p = 0.023) compared with MDCT. Incremental likelihood ratios demonstrated that PET/CT significantly improved diagnostic accuracy in all scenarios (p < 0.0002). PET/CT correctly changed the staging of pancreatic cancer in 56 patients (p = 0.001). PET/CT influenced management in 250 (45%) patients. PET/CT stopped resection in 58 (20%) patients who were due to have surgery. The benefit of PET/CT was limited in patients with chronic pancreatitis or other pancreatic tumours. PET/CT was associated with a gain in quality-adjusted life-years of 0.0157 (95% confidence interval -0.0101 to 0.0430). In the base-case model PET/CT was seen to dominate MDCT alone and is thus highly likely to be cost-effective for the UK NHS. PET/CT was seen to be most cost-effective for the subgroup of patients with suspected pancreatic cancer who were thought to be resectable. CONCLUSION: PET/CT provided a significant incremental diagnostic benefit in the diagnosis of pancreatic cancer and significantly influenced the staging and management of patients. PET/CT had limited utility in chronic pancreatitis and other pancreatic tumours. PET/CT is likely to be cost-effective at current reimbursement rates for PET/CT to the UK NHS. This was not a randomised controlled trial and therefore we do not have any information from patients who would have undergone MDCT only for comparison. In addition, there were issues in estimating costs for PET/CT. Future work should evaluate the role of PET/CT in intraductal papillary mucinous neoplasm and prognosis and response to therapy in patients with pancreatic cancer. STUDY REGISTRATION: Current Controlled Trials ISRCTN73852054 and UKCRN 8166. FUNDING: The National Institute for Health Research Health Technology Assessment programme.

The role of the home environment in neurocognitive development of children living in extreme poverty and with frequent illnesses: a cross-sectional study.

Background: The home environment is reported to contribute significantly to children's developing cognitive skills. However, it is not yet evident whether this role prevails in the context of extreme poverty and frequent ill-health. We therefore investigated the role of the home environment in Ugandan children taking into account the frequent infections and extreme poverty in which they lived. Methods: Cognitive abilities of 163 5-year-old children were assessed. Home environments of these children, their health status and family socioeconomic status (SES) were assessed respectively using the EC-HOME, anthropometry and illnesses, and traditional SES measures. Structural equation analyses compared five models on the influence of the home environment, SES, and child health on the cognitive scores. Results: The model in which the home environment mediates the combined influence of SES and child health on cognitive performance showed a particularly good fit to the data compared with the four alternative models, i.e. those in which the HOME, SES and health independently influence cognitive performance. Conclusions: Home environments providing cognitive stimulation can enable children to overcome effects of major adverse life experiences on cognitive development.

Sample size re-estimation in paired comparative diagnostic accuracy studies with a binary response.

BACKGROUND: The sample size required to power a study to a nominal level in a paired comparative diagnostic accuracy study, i.e. studies in which the diagnostic accuracy of two testing procedures is compared relative to a gold standard, depends on the conditional dependence between the two tests - the lower the dependence the greater the sample size required. A priori, we usually do not know the dependence between the two tests and thus cannot determine the exact sample size required. One option is to use the implied sample size for the maximal negative dependence, giving the largest possible sample size. However, this is potentially wasteful of resources and unnecessarily burdensome on study participants as the study is likely to be overpowered. A more accurate estimate of the sample size can be determined at a planned interim analysis point where the sample size is re-estimated. METHODS: This paper discusses a sample size estimation and re-estimation method based on the maximum likelihood estimates, under an implied multinomial model, of the observed values of conditional dependence between the two tests and, if required, prevalence, at a planned interim. The method is illustrated by comparing the accuracy of two procedures for the detection of pancreatic cancer, one procedure using the standard battery of tests, and the other using the standard battery with the addition of a PET/CT scan all relative to the gold standard of a cell biopsy. Simulation of the proposed method illustrates its robustness under various conditions. RESULTS: The results show that the type I error rate of the overall experiment is stable using our suggested method and that the type II error rate is close to or above nominal. Furthermore, the instances in which the type II error rate is above nominal are in the situations where the lowest sample size is required, meaning a lower impact on the actual number of participants recruited. CONCLUSION: We recommend multinomial model maximum likelihood estimation of the conditional dependence between paired diagnostic accuracy tests at an interim to reduce the number of participants required to power the study to at least the nominal level. TRIAL REGISTRATION: ISRCTN ISRCTN73852054 . Registered 9th of January 2015. Retrospectively registered.

Estimation in multi-arm two-stage trials with treatment selection and time-to-event endpoint.

We consider estimation of treatment effects in two-stage adaptive multi-arm trials with a common control. The best treatment is selected at interim, and the primary endpoint is modeled via a Cox proportional hazards model. The maximum partial-likelihood estimator of the log hazard ratio of the selected treatment will overestimate the true treatment effect in this case. Several methods for reducing the selection bias have been proposed for normal endpoints, including an iterative method based on the estimated conditional selection biases and a shrinkage approach based on empirical Bayes theory. We adapt these methods to time-to-event data and compare the bias and mean squared error of all methods in an extensive simulation study and apply the proposed methods to reconstructed data from the FOCUS trial. We find that all methods tend to overcorrect the bias, and only the shrinkage methods can reduce the mean squared error. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Estimation of time-shift models with application to survival calibration in health technology assessment.

The incremental life expectancy, defined as the difference in mean survival times between two treatment groups, is a crucial quantity of interest in cost-effectiveness analyses. Usually, this quantity is very difficult to estimate from censored survival data with a limited follow-up period. The paper develops estimation procedures for a time-shift survival model that, provided model assumptions are met, gives a reliable estimate of incremental life expectancy without extrapolation beyond the study period. Methods for inference are developed both for individual patient data and when only published Kaplan-Meier curves are available. Through simulation, the estimators are shown to be close to unbiased and constructed confidence intervals are shown to have close to nominal coverage for small to moderate sample sizes. Copyright © 2016 John Wiley & Sons, Ltd.

Item response theory and structural equation modelling for ordinal data: Describing the relationship between KIDSCREEN and Life-H.

Both item response theory and structural equation models are useful in the analysis of ordered categorical responses from health assessment questionnaires. We highlight the advantages and disadvantages of the item response theory and structural equation modelling approaches to modelling ordinal data, from within a community health setting. Using data from the SPARCLE project focussing on children with cerebral palsy, this paper investigates the relationship between two ordinal rating scales, the KIDSCREEN, which measures quality-of-life, and Life-H, which measures participation. Practical issues relating to fitting models, such as non-positive definite observed or fitted correlation matrices, and approaches to assessing model fit are discussed. item response theory models allow properties such as the conditional independence of particular domains of a measurement instrument to be assessed. When, as with the SPARCLE data, the latent traits are multidimensional, structural equation models generally provide a much more convenient modelling framework.

Transition probability estimates for non-Markov multi-state models.

Non-parametric estimation of the transition probabilities in multi-state models is considered for non-Markov processes. Firstly, a generalization of the estimator of Pepe et al., (1991) (Statistics in Medicine) is given for a class of progressive multi-state models based on the difference between Kaplan-Meier estimators. Secondly, a general estimator for progressive or non-progressive models is proposed based upon constructed univariate survival or competing risks processes which retain the Markov property. The properties of the estimators and their associated standard errors are investigated through simulation. The estimators are demonstrated on datasets relating to survival and recurrence in patients with colon cancer and prothrombin levels in liver cirrhosis patients.

Development of a high-throughput pyrosequencing assay for monitoring temporal evolution and resistance associated variant emergence in the Hepatitis C virus protease coding-region.

A new generation of drugs targeting the non-structural (NS) proteins of the Hepatitis C virus (HCV) will substantially increase treatment success rates, reducing global infections. Amongst the NS proteins, the NS3 protease represents an important drug target, responsible for liberation of mature NS proteins from the nascent HCV polyprotein and suppression of host innate immunity. Despite this, the evolutionary stability of the genomic locus encoding the NS3 protease is poorly characterized in chronic HCV infection. To address this shortfall, we developed a high-throughput amplicon pyrosequencing protocol and utilised it to monitor NS3 protease coding-sequence evolution for over a decade in two patients. Although patient-specific evolutionary trends were apparent, the protease amino acid population consensus remained stable with a massive excess of synonymous mutations observed, confirming this locus is under strong purifying selection during chronic infection within individual patients. No evidence for continuous immune escape was detected. Additionally, both patients failed protease inhibitor (PI) therapy and protease sequence diversity pre- and post-therapy were also assessed. No baseline resistance associated variants (RAVs) contributed to treatment failure. Significant reductions in viral diversity were observed post-PI therapy, indicating a population bottleneck occurred. The genetic vestiges of this bottleneck were still detectable 18months after therapy discontinuation. Although significant enrichment of the Q80L mutation was observed in one patient, genetic and phenotypic data reveal no detectable RAV persistence post-therapy failure. Together this investigation provides a sensitive and reproducible high-throughput framework to interrogate viral sequence diversity at high-resolution, with potential applications for routine monitoring of treatment regimens. This study also reveals novel insights into the evolutionary processes that shape NS3 sequence divergence in both chronic HCV infection and post PI-therapy failure.

Measurement of flood peak effects as a result of soil and land management, with focus on experimental issues and scale.

As a result of several serious flood events which have occurred since 2000, flooding across Europe is now receiving considerable public and media attention. The impact of land use on hydrology and flood response is significantly under-researched, and the links between land use change and flooding are still unclear. This study considers runoff data available from studies of arable in-field land use management options, applied with the aim of reducing diffuse pollution from arable land, in order to investigate whether these treatments also have potential to reduce downstream flooding. Intensive monitoring of 17 hillslope treatment areas produced a record of flood peak data covering different mitigation treatments for runoff which occurred in the winter of 2007-2008. We investigated event total runoff responses to rainfall, peak runoff, and timing of the runoff peaks from replicates of different treatments, in order to assess whether there is a significant difference in flood peak response between different mitigation options which could be used to mitigate downstream flood risk. A mixed-modelling approach was adopted in order to determine whether differences observed in runoff response were significant. The results of this study suggest that changes in land use management using arable in-field mitigation treatments can affect local-scale runoff generation, with differences observed in the size, duration and timing of flood peaks as a result of different management practices, but the study was unable to allow significant treatment effects to be determined. We suggest that further field studies of the effects of changes in land use and land use management need to upscale towards farm and catchment scale experiments which consider high quality before-and-after data over longer temporal timescales. This type of data collection is essential in order to allow appropriate land use management decisions to be made.

Two-year outcomes for infants with low cord pH at birth.

OBJECTIVE: To determine the long-term outcome of infants born with cord pH ≤ 7.0 and no clinical evidence of asphyxia at birth. SETTING: Tertiary Referral Centre. A prospective matched cohort study was conducted. 51 term infants were recruited following singleton birth with venous cord pH ≤ 7.0. For each recruited baby a healthy baby with normal cord pH ≥ 7.20 was recruited matched for gestation, gender and mode of delivery. Ages and Stages Questionnaires (ASQ)(TM) and Health Screening Questionnaires (HSQ) were sent out at 24 months of age. Two independent assessors, blinded to the case assignment, reviewed intrapartum and neonatal events to look for clinical evidence of birth asphyxia among the cases. RESULT: From 102 infants recruited, 62 questionnaires (24 cases, 38 controls) were returned. 20 matched pairs with no clinical evidence of birth asphyxia were available for analysis. The groups were similar except in terms of birth weight; the pH ≥ 7.0 group had mean birth weight 584 g lower than controls (p = 0.005). The ASQ motor scores were lower in children born with low cord pH (p = 0.019); however, once adjusted for birth weight, the difference was not significant (p = 0.289). CONCLUSION: It is unlikely that abnormal cord pH in otherwise healthy neonates leads to a substantially increased risk of abnormal neurodevelopmental outcome or severe health problems by 2 years of age.