Depicting deterministic variables within directed acyclic graphs (DAGs): An aid for identifying and interpreting causal effects involving derived variables and compositional data.

Deterministic variables are variables that are functionally determined by one or more parent variables. They commonly arise when a variable has been functionally created from one or more parent variables, as with derived variables, and in compositional data, where the 'whole' variable is determined from its 'parts'. This article introduces how deterministic variables may be depicted within directed acyclic graphs (DAGs) to help with identifying and interpreting causal effects involving derived variables and/or compositional data. We propose a two-step approach in which all variables are initially considered, and a choice is made whether to focus on the deterministic variable or its determining parents. Depicting deterministic variables within DAGs brings several benefits. It is easier to identify and avoid misinterpreting tautological associations, i.e., self-fulfilling associations between deterministic variables and their parents, or between sibling variables with shared parents. In compositional data, it is easier to understand the consequences of conditioning on the 'whole' variable, and correctly identify total and relative causal effects. For derived variables, it encourages greater consideration of the target estimand and greater scrutiny of the consistency and exchangeability assumptions. DAGs with deterministic variables are a useful aid for planning and interpreting analyses involving derived variables and/or compositional data.

Choice of Outcome in COVID-19 Studies and Implications for Policy: Mortality and Fatality.

In this brief communication, we discuss the confusion of mortality with fatality in the interpretation of evidence in the coronavirus disease 2019 (COVID-19) pandemic, and how this confusion affects the translation of science into policy and practice. We discuss how this confusion has influenced COVID-19 policy in France, Sweden, and the United Kingdom and discuss the implications for decision-making about COVID-19 vaccine distribution. We also discuss how this confusion is an example of a more general statistical fallacy we term the "Missing Link Fallacy."

Causal and Associational Language in Observational Health Research: A Systematic Evaluation.

We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations. Reviewers rated the causal implication of exposure/outcome linking language as none (no causal implication) in 13.8%, weak in 34.2%, moderate in 33.2%, and strong in 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was "associate" (45.7%). Reviewers' ratings of linking word roots were highly heterogeneous; over half of reviewers rated "association" as having at least some causal implication. This research undercuts the assumption that avoiding "causal" words leads to clarity of interpretation in medical research.

The Association Between Childhood Leukemia and Population Mixing: An Artifact of Focusing on Clusters?

BACKGROUND: Studies investigating the population-mixing hypothesis in childhood leukemia principally use two analytical approaches: (1) nonrandom selection of areas according to specific characteristics, followed by comparisons of their incidence of childhood leukemia with that expected based on the national average; and (2) regression analyses of region-wide data to identify characteristics associated with the incidence of childhood leukemia. These approaches have generated contradictory results. We compare these approaches using observed and simulated data. METHODS: We generated 10,000 simulated regions using the correlation structure and distributions from a United Kingdom dataset. We simulated cases using a Poisson distribution with the incidence rate set to the national average assuming the null hypothesis that only population size drives the number of cases. Selection of areas within each simulated region was based on characteristics considered responsible for elevated infection rates (population density and inward migration) and/or elevated leukemia rates. We calculated effect estimates for 10,000 simulations and compared results to corresponding observed data analyses. RESULTS: When the selection of areas for analysis is based on apparent clusters of childhood leukemia, biased assessments occur; the estimated 5-year incidence of childhood leukemia ranged between zero and eight per 10,000 children in contrast to the simulated two cases per 10,000 children, similar to the observed data. Performing analyses on region-wide data avoids these biases. CONCLUSIONS: Studies using nonrandom selection to investigate the association between childhood leukemia and population mixing are likely to have generated biased findings. Future studies can avoid such bias using a region-wide analytical strategy. See video abstract at, http://links.lww.com/EDE/B431.

Risk estimates of recurrent congenital anomalies in the UK: a population-based register study.

BACKGROUND: Recurrence risks for familial congenital anomalies in successive pregnancies are known, but this information for major structural anomalies is lacking. We estimated the absolute and relative risks of recurrent congenital anomaly in the second pregnancy for women with a history of a congenital anomaly in the first pregnancy, for all major anomaly groups and subtypes. METHODS: Population-based register data on 18,605 singleton pregnancies affected by major congenital anomaly occurring in 872,493 singleton stillbirths, live births and terminations of pregnancy for fetal anomaly were obtained from the Northern Congenital Abnormality Survey, North of England, UK, for 1985-2010. Absolute risks (ARs) and relative risks (RRs) for recurrent congenital anomaly (overall, from a similar group, from a dissimilar group) in the second pregnancy were estimated by history of congenital anomaly (overall, by group, by subtype) in the first pregnancy. RESULTS: The estimated prevalences of congenital anomaly in first and second pregnancies were 275 (95% CI 270-281) and 163 (95% CI 159-168) per 10,000 respectively. For women whose first pregnancy was affected by congenital anomaly, the AR of recurrent congenital anomaly in the second pregnancy was 408 (95% CI 365-456) per 10,000, 2.5 (95% CI 2.3-2.8, P < 0.0001) times higher than for those with unaffected first pregnancies. For similar anomalies, the recurrence risk was considerably elevated (RR = 23.8, 95% CI 19.6-27.9, P < 0.0001), while for dissimilar anomalies the increase was more modest (RR = 1.4, 95% CI 1.2-1.6, P = 0.001), although the ARs for both were 2%. CONCLUSIONS: Absolute recurrence risks varied between 1 in 20 and 1 in 30 for most major anomaly groups. At pre-conception and antenatal counselling, women whose first pregnancy was affected by a congenital anomaly and who are planning a further pregnancy may find it reassuring that, despite high relative risks, the absolute recurrence risk is relatively low.

Langerhans cell histiocytosis in children with congenital anomalies: a population-based record linkage study.

BACKGROUND: The etiology of Langerhans cell histiocytosis (LCH), a rare cancer-like disorder of the immune system, is largely unknown although a genetic component has been suggested based on familial cases, and reports of chromosome instability and genetic mutation. Associations between various cancers and congenital anomalies have been reported and although congenital anomalies have been noted in children with LCH only one study to date has reported their frequency. An association between congenital anomalies and LCH may suggest a common etiological pathway, in particular, a genetic pathway. METHODS: Data from two coterminous registries in the same geographic region were used. All cases of LCH on the Northern Region Young Persons Malignant Disease Register diagnosed between 1985 and 2010 were cross-matched with live-born cases of congenital anomaly registered by the Northern Congenital Abnormality Survey. RESULTS: A total of 819,890 children and young people were born during 1985 to 2008. Of these, 13,799 (1.7%) had a congenital anomaly and 39 (0.005%) were diagnosed with LCH. Three LCH cases were identified among those with congenital anomalies, all three of whom had congenital heart disease. The relative risk of LCH for those with a congenital anomaly, compared with those without, was 4.87 (95% confidence interval, 1.50-15.81; p = 0.03). CONCLUSION: LCH was associated with congenital anomaly in a small but statistically significant number of patients, raising the possibility of a common genetic pathway in some cases.

Variation in health care for children and young people with cerebral palsies: a retrospective multicentre audit study.

AIM: To explore the provision and variations in care for children and young people with cerebral palsies (CP) registered with the population-based North of England Collaborative Cerebral Palsy Survey (NECCPS). METHOD: This is a retrospective multicentre record audit of 389 children with CP (220 males, 148 females, 21 no data; median age at time of audit 12y 3mo), born between 1995 and 2002. Data were collected on cranial magnetic resonance imaging (MRI), hip and spine surveillance and management, and pain presence and management. Variations over time and between the districts in the north of England (Northumberland, North and West Cumbria, North and South Tyneside, Newcastle-upon-Tyne, Gateshead, Sunderland, Durham, Darlington, Bishop Auckland, Hartlepool, Stockton-on-Tees, Middlesbrough, Redcar, and Cleveland), and by socio-economic status (SES) (estimated from the Index of Multiple Deprivation [IMD] 2004) were estimated by generalized estimating equations. RESULTS: There was significant variation between districts in access to MRI (p<0.001), orthopaedic surgeons (p=0.005), recording state of spine (p<0.001), and discussions about pain (p<0.001). Fifty-seven per cent (95% CI 52-62) had evidence of a reported MRI brain scan, the proportion of which increased over time (p<0.001). Sixty-seven per cent (95% CI 62-71) had a discussion about pain recorded. Of those in pain, 87% (95% CI 80-93) had a pain management plan. The proportion with documented discussion about pain increased with increasing SES (p=0.04). INTERPRETATION: The provision of care for children with CP in the north of England varies between districts. Internationally agreed, evidence-based standards are urgently needed to ensure more equitable health care and improved outcomes for all.

Pre-existing diabetes, maternal glycated haemoglobin, and the risks of fetal and infant death: a population-based study.

AIMS/HYPOTHESIS: Pre-existing diabetes is associated with an increased risk of stillbirth, but few studies have excluded the effect of congenital anomalies. This study used data from a long-standing population-based survey of women with pre-existing diabetes to investigate the risks of fetal and infant death and quantify the contribution of glycaemic control. METHODS: All normally formed singleton offspring of women with pre-existing diabetes (1,206 with type 1 diabetes and 342 with type 2 diabetes) in the North of England during 1996-2008 were identified from the Northern Diabetes in Pregnancy Survey. RRs of fetal death (≥20 weeks of gestation) and infant death were estimated by comparison with population data from the Northern Perinatal Morbidity and Mortality Survey. Predictors of fetal and infant death in women with pre-existing diabetes were examined by logistic regression. RESULTS: The prevalence of fetal death in women with diabetes was over four times greater than in those without (RR 4.56 [95% CI 3.42, 6.07], p < 0.0001), and for infant death it was nearly doubled (RR 1.86 [95% CI 1.00, 3.46], p = 0.046). There was no difference in the prevalence of fetal death (p = 0.51) or infant death (p = 0.70) between women with type 1 diabetes and women with type 2 diabetes. There was no evidence that the RR of fetal and infant death had changed over time (p = 0.95). Increasing periconception HbA1c concentration above 49 mmol/mol (6.6%) (adjusted odds ratio [aOR] 1.02 [95% CI 1.00, 1.04], p = 0.01), prepregnancy retinopathy (aOR 2.05 [95% CI 1.04, 4.05], p = 0.04) and lack of prepregnancy folic acid consumption (aOR 2.52 [95% CI 1.12, 5.65], p = 0.03) were all independently associated with increased odds of fetal and infant death. CONCLUSIONS/INTERPRETATION: Pre-existing diabetes is associated with a substantially increased risk of fetal and infant death in normally formed offspring, the effect of which is largely moderated by glycaemic control.

Childhood infectious disease and premature death from cancer: a prospective cohort study.

Studies of the association between early life infections and cancer have produced inconsistent findings, possibly due to limited adjustment for confounding and retrospective designs. This study utilised data from the Newcastle Thousand Families Study, a prospective cohort of 1,142 individuals born in Newcastle-upon-Tyne in 1947, to assess the impact of various childhood infectious diseases on cancer mortality during ages 15-60 years. Detailed information was collected prospectively on a number of early life factors. Deaths from cancer during ages 15-60 years were analysed in relation to childhood infections, adjusting for potential early-life confounders, using Cox proportional-hazards regression. In a subsample who returned questionnaires at aged 49-51 years, additional adjustment was made for adult factors to predict death from cancer during ages 50-60 years. Childhood history of measles and influenza, were both independently associated with lower cancer mortality during ages 15-60 years (adjusted hazard ratios = 0.39, 95% CI 0.17-0.88 and 0.49, 95% CI 0.24-0.98 respectively). In contrast, childhood pertussis was associated with higher cancer mortality during ages 15-60 years (adjusted hazard ratio = 4.88, 95% CI 2.29-10.38). In the subsample with additional adjustment for adult variables, measles and pertussis remained significantly associated with cancer mortality during ages 50-60 years. In this pre-vaccination cohort, childhood infection with measles and influenza were associated with a reduced risk of death from cancer in adulthood, while pertussis was associated with an increased risk. While these results suggest some disease-specific associations between early-life infections and cancer, further studies are required to confirm the specific associations identified.

Lifecourse predictors of mammographic density: the Newcastle Thousand Families cohort Study.

Dense mammographic patterns are a strong predictor of breast cancer risk. Factors at differing stages of life have been linked to breast cancer risk, although rarely studied simultaneously. We aimed to investigate whether birth weight and factors later in life were associated with mammographic density in the Newcastle Thousand Families Study. The Study originally consisted of all 1142 babies born in May and June 1947 to mothers resident in Newcastle upon Tyne in Northern England. Detailed information was collected prospectively during childhood, including birth weight and socio-economic circumstances. At age 49-51 years, 574 study members completed a 'Health and Lifestyle' questionnaire. Of the 307 surviving women who returned these questionnaires, 199 returned a further questionnaire asking for details of routine mammographic screening, their reproductive and contraceptive history. Mammographic patterns were coded into Wolfe categories. This was analysed, by ordinal logistic regression, in relation to a range of variables at different stages of life. Increased standardised birth weight (odds ratio, OR 1.32 (95% CI 1.02-1.71) P = 0.03) was a significant independent predictor of higher density. Increasing body mass index (BMI) was predictive of lower density (OR 0.86 per Kg/m(2) (95% CI 0.81-0.92) P < 0.001), as was having reached menopause (OR, compared to pre- and peri-menopausal, 0.41 (95% CI 0.23-0.73) P = 0.002). Interactions were seen between menopausal status and both BMI and age at menarche (P = 0.06) on density, although for neither did the direction of association change. After adjustment for factors acting throughout life, we identified a significant association between standardised birth weight and density in adulthood, consistent with previous research suggesting that heavier babies have an increased risk of breast cancer in later life. We also confirmed associations between both BMI and menopausal status.

Analyses of 'change scores' do not estimate causal effects in observational data.

BACKGROUND: In longitudinal data, it is common to create 'change scores' by subtracting measurements taken at baseline from those taken at follow-up, and then to analyse the resulting 'change' as the outcome variable. In observational data, this approach can produce misleading causal-effect estimates. The present article uses directed acyclic graphs (DAGs) and simple simulations to provide an accessible explanation for why change scores do not estimate causal effects in observational data. METHODS: Data were simulated to match three general scenarios in which the outcome variable at baseline was a (i) 'competing exposure' (i.e. a cause of the outcome that is neither caused by nor causes the exposure), (ii) confounder or (iii) mediator for the total causal effect of the exposure variable at baseline on the outcome variable at follow-up. Regression coefficients were compared between change-score analyses and the appropriate estimator(s) for the total and/or direct causal effect(s). RESULTS: Change-score analyses do not provide meaningful causal-effect estimates unless the baseline outcome variable is a 'competing exposure' for the effect of the exposure on the outcome at follow-up. Where the baseline outcome is a confounder or mediator, change-score analyses evaluate obscure estimands, which may diverge substantially in magnitude and direction from the total and direct causal effects. CONCLUSION: Future observational studies that seek causal-effect estimates should avoid analysing change scores and adopt alternative analytical strategies.

Adjustment for energy intake in nutritional research: a causal inference perspective.

BACKGROUND: Four models are commonly used to adjust for energy intake when estimating the causal effect of a dietary component on an outcome: 1) the "standard model" adjusts for total energy intake, 2) the "energy partition model" adjusts for remaining energy intake, 3) the "nutrient density model" rescales the exposure as a proportion of total energy, and 4) the "residual model" indirectly adjusts for total energy by using a residual. It remains underappreciated that each approach evaluates a different estimand and only partially accounts for confounding by common dietary causes. OBJECTIVES: We aimed to clarify the implied causal estimand and interpretation of each model and evaluate their performance in reducing dietary confounding. METHODS: Semiparametric directed acyclic graphs and Monte Carlo simulations were used to identify the estimands and interpretations implied by each model and explore their performance in the absence or presence of dietary confounding. RESULTS: The "standard model" and the mathematically identical "residual model" estimate the average relative causal effect (i.e., a "substitution" effect) but provide biased estimates even in the absence of confounding. The "energy partition model" estimates the total causal effect but only provides unbiased estimates in the absence of confounding or when all other nutrients have equal effects on the outcome. The "nutrient density model" has an obscure interpretation but attempts to estimate the average relative causal effect rescaled as a proportion of total energy. Accurate estimates of both the total and average relative causal effects may instead be derived by simultaneously adjusting for all dietary components, an approach we term the "all-components model." CONCLUSIONS: Lack of awareness of the estimand differences and accuracy of the 4 modeling approaches may explain some of the apparent heterogeneity among existing nutritional studies. This raises serious questions regarding the validity of meta-analyses where different estimands have been inappropriately pooled.

Avoiding overadjustment bias in social epidemiology through appropriate covariate selection: a primer.

Obtaining accurate estimates of the causal effects of socioeconomic position (SEP) on health is important for public health interventions. To do this, researchers must identify and adjust for all potential confounding variables, while avoiding inappropriate adjustment for mediator variables on a causal pathway between the exposure and outcome. Unfortunately, 'overadjustment bias' remains a common and under-recognized problem in social epidemiology. This paper offers an introduction on selecting appropriate variables for adjustment when examining effects of SEP on health, with a focus on overadjustment bias. We discuss the challenges of estimating different causal effects including overadjustment bias, provide guidance on overcoming them, and consider specific issues including the timing of variables across the life-course, mutual adjustment for socioeconomic indicators, and conducting systematic reviews. We recommend three key steps to select the most appropriate variables for adjustment. First, researchers should be clear about their research question and causal effect of interest. Second, using expert knowledge and theory, researchers should draw causal diagrams representing their assumptions about the interrelationships between their variables of interest. Third, based on their causal diagram(s) and causal effect(s) of interest, researchers should select the most appropriate set of variables, which maximizes adjustment for confounding while minimizing adjustment for mediators.

Estimating the effects of lockdown timing on COVID-19 cases and deaths in England: A counterfactual modelling study.

BACKGROUND: During the first wave of the COVID-19 pandemic, the United Kingdom experienced one of the highest per-capita death tolls worldwide. It is debated whether this may partly be explained by the relatively late initiation of voluntary social distancing and mandatory lockdown measures. In this study, we used simulations to estimate the number of cases and deaths that would have occurred in England by 1 June 2020 if these interventions had been implemented one or two weeks earlier, and the impact on the required duration of lockdown. METHODS: Using official reported data on the number of Pillar 1 lab-confirmed cases of COVID-19 and associated deaths occurring in England from 3 March to 1 June, we modelled: the natural (i.e. observed) growth of cases, and the counterfactual (i.e. hypothetical) growth of cases that would have occurred had measures been implemented one or two weeks earlier. Under each counterfactual condition, we estimated the expected number of deaths and the time required to reach the incidence observed under natural growth on 1 June. RESULTS: Introducing measures one week earlier would have reduced by 74% the number of confirmed COVID-19 cases in England by 1 June, resulting in approximately 21,000 fewer hospital deaths and 34,000 fewer total deaths; the required time spent in full lockdown could also have been halved, from 69 to 35 days. Acting two weeks earlier would have reduced cases by 93%, resulting in between 26,000 and 43,000 fewer deaths. CONCLUSIONS: Our modelling supports the claim that the relatively late introduction of social distancing and lockdown measures likely increased the scale, severity, and duration of the first wave of COVID-19 in England. Our results highlight the importance of acting swiftly to minimise the spread of an infectious disease when case numbers are increasing exponentially.

20-year survival of children born with congenital anomalies: a population-based study.

BACKGROUND: Congenital anomalies are a leading cause of perinatal and infant mortality. Advances in care have improved the prognosis for some congenital anomaly groups and subtypes, but there remains a paucity of knowledge about survival for many others, especially beyond the first year of life. We estimated survival up to 20 years of age for a range of congenital anomaly groups and subtypes. METHODS: Information about children with at least one congenital anomaly, delivered between 1985 and 2003, was obtained from the UK Northern Congenital Abnormality Survey (NorCAS). Anomalies were categorised by group (the system affected), subtype (the individual disorder), and syndrome according to European Surveillance of Congenital Anomalies (EUROCAT) guidelines. Local hospital and national mortality records were used to identify the survival status of liveborn children. Survival up to 20 years of age was estimated by use of Kaplan-Meier methods. Cox proportional hazards regression was used to examine factors that affected survival. FINDINGS: 13,758 cases of congenital anomaly were notified to NorCAS between 1985 and 2003. Survival status was available for 10 850 (99.0%) of 10 964 livebirths. 20-year survival was 85.5% (95% CI 84.8-86.3) in individuals born with at least one congenital anomaly, 89.5% (88.4-90.6) for cardiovascular system anomalies, 79.1% (76.7-81.3) for chromosomal anomalies, 93.2% (91.6-94.5) for urinary system anomalies, 83.2% (79.8-86.0) for digestive system anomalies, 97.6% (95.9-98.6) for orofacial clefts, and 66.2% (61.5-70.5) for nervous system anomalies. Survival varied between subtypes within the same congenital anomaly group. The proportion of terminations for fetal anomaly increased throughout the study period (from 12.4%, 9.8-15.5, in 1985 to 18.3%, 15.6-21.2, in 2003; p<0.0001) and, together with year of birth, was an independent predictor of survival (adjusted hazard ratio [HR] for proportion of terminations 0.95, 95% CI 0.91-0.99, p=0.023; adjusted HR for year of birth 0.94, 0.92-0.96, p<0.0001). INTERPRETATION: Estimates of survival for congenital anomaly groups and subtypes will be valuable for families and health professionals when a congenital anomaly is detected, and will assist in planning for the future care needs of affected individuals. FUNDING: BDF Newlife.

Sex differences in the prevalence of congenital anomalies: a population-based study.

BACKGROUND: Limited data is available concerning the sex distribution of various congenital anomaly subtypes. This study investigated sex differences in the prevalence of congenital anomalies, overall and by subtype, using high quality population-based data from the North of England. METHODS: Information on congenital anomalies occurring among singleton pregnancies during 1985-2003 were extracted from the Northern Congenital Abnormality Survey (NorCAS). Anomalies were categorized by groups, subtypes, and syndromes according to the European Surveillance of Congenital Anomalies guidelines. Relative risks (RRs) comparing the prevalences in males to that in females were calculated for a range of congenital anomaly subtypes. RESULTS: A total of 12,795 eligible cases of congenital anomaly were identified during the study period, including 7019 (54.9%) males and 5776 (45.1%) females. Overall, male fetuses were significantly more prevalent in pregnancies affected by a congenital anomaly than female fetuses (RR, male vs. female = 1.15; 95% confidence interval [CI], 1.11-1.19), but there was significant heterogeneity between subtypes (p < 0.001). Forty-four of 110 (40%) unique subtypes were at least 40% more prevalent in males than females, with affected subtypes occurring across all major anomaly groups. Thirteen of 110 (12%) unique subtypes were at least 40% more prevalent in females than males, but the female-biased RR of a neural tube defect was less pronounced than previously reported (RR = 0.84; 95% CI, 0.73-0.95). CONCLUSION: This study adds to the growing evidence of sex-specific differences in the prevalence of a wide range of congenital anomaly subtypes.

Agreement between pedometer and accelerometer in measuring physical activity in overweight and obese pregnant women.

BACKGROUND: Inexpensive, reliable objective methods are needed to measure physical activity (PA) in large scale trials. This study compared the number of pedometer step counts with accelerometer data in pregnant women in free-living conditions to assess agreement between these measures. METHODS: Pregnant women (n = 58) with body mass index ≥25 kg/m(2) at median 13 weeks' gestation wore a GT1M Actigraph accelerometer and a Yamax Digi-Walker CW-701 pedometer for four consecutive days. The Spearman rank correlation coefficients were determined between pedometer step counts and various accelerometer measures of PA. Total agreement between accelerometer and pedometer step counts was evaluated by determining the 95% limits of agreement estimated using a regression-based method. Agreement between the monitors in categorising participants as active or inactive was assessed by determining Kappa. RESULTS: Pedometer step counts correlated moderately (r = 0.36 to 0.54) with most accelerometer measures of PA. Overall step counts recorded by the pedometer and the accelerometer were not significantly different (medians 5961 vs. 5687 steps/day, p = 0.37). However, the 95% limits of agreement ranged from -2690 to 2656 steps/day for the mean step count value (6026 steps/day) and changed substantially over the range of values. Agreement between the monitors in categorising participants to active and inactive varied from moderate to good depending on the criteria adopted. CONCLUSIONS: Despite statistically significant correlations and similar median step counts, the overall agreement between pedometer and accelerometer step counts was poor and varied with activity level. Pedometer and accelerometer steps cannot be used interchangeably in overweight and obese pregnant women.