Mendelian randomization analysis of the causal impact of body mass index and waist-hip ratio on rates of hospital admission

We analyze how measures of adiposity – body mass index (BMI) and waist hip ratio (WHR) – causally influence rates of hospital admission. Conventional analyses of this relationship are susceptible to omitted variable bias from variables that jointly influence both hospital admission and adipose status. We implement a novel quasi-Poisson instrumental variable model in a Mendelian randomization framework, identifying causal effects from random perturbations to germline genetic variation. We estimate the individual and joint effects of BMI, WHR, and WHR adjusted for BMI. We also implement multivariable instrumental variable methods in which the causal effect of one exposure is estimated conditionally on the causal effect of another exposure. Data on 310,471 participants and over 550,000 inpatient admissions in the UK Biobank were used to perform one-sample and two-sample Mendelian randomization analyses. The results supported a causal role of adiposity on hospital admissions, with consistency across all estimates and sensitivity analyses. Point estimates were generally larger than estimates from comparable observational specifications. We observed an attenuation of the BMI effect when adjusting for WHR in the multivariable Mendelian randomization analyses, suggesting that an adverse fat distribution, rather than a higher BMI itself, may drive the relationship between adiposity and risk of hospital admission

Long term cost effectiveness of interventions for obesity:A Mendelian randomisation study

Background The prevalence of obesity has increased in the United Kingdom, and reliably measuring the impact on quality of life and the total healthcare cost from obesity is key to informing the cost-effectiveness of interventions that target obesity, and determining healthcare funding. Current methods for estimating cost-effectiveness of interventions for obesity may be subject to confounding and reverse causation. The aim of this study is to apply a new approach using mendelian randomisation for estimating the cost-effectiveness of interventions that target body mass index (BMI), which may be less affected by confounding and reverse causation than previous approaches. Methods and findings We estimated health-related quality-adjusted life years (QALYs) and both primary and secondary healthcare costs for 310,913 men and women of white British ancestry aged between 39 and 72 years in UK Biobank between recruitment (2006 to 2010) and 31 March 2017. We then estimated the causal effect of differences in BMI on QALYs and total healthcare costs using mendelian randomisation. For this, we used instrumental variable regression with a polygenic risk score (PRS) for BMI, derived using a genome-wide association study (GWAS) of BMI, with age, sex, recruitment centre, and 40 genetic principal components as covariables to estimate the effect of a unit increase in BMI on QALYs and total healthcare costs. Finally, we used simulations to estimate the likely effect on BMI of policy relevant interventions for BMI, then used the mendelian randomisation estimates to estimate the cost-effectiveness of these interventions. A unit increase in BMI decreased QALYs by 0.65% of a QALY (95% confidence interval [CI]: 0.49% to 0.81%) per year and increased annual total healthcare costs by £42.23 (95% CI: £32.95 to £51.51) per person. When considering only health conditions usually considered in previous cost-effectiveness modelling studies (cancer, cardiovascular disease, cerebrovascular disease, and type 2 diabetes), we estimated that a unit increase in BMI decreased QALYs by only 0.16% of a QALY (95% CI: 0.10% to 0.22%) per year. We estimated that both laparoscopic bariatric surgery among individuals with BMI greater than 35 kg/m2, and restricting volume promotions for high fat, salt, and sugar products, would increase QALYs and decrease total healthcare costs, with net monetary benefits (at £20,000 per QALY) of £13,936 (95% CI: £8,112 to £20,658) per person over 20 years, and £546 million (95% CI: £435 million to £671 million) in total per year, respectively. The main limitations of this approach are that mendelian randomisation relies on assumptions that cannot be proven, including the absence of directional pleiotropy, and that genotypes are independent of confounders. Conclusions Mendelian randomisation can be used to estimate the impact of interventions on quality of life and healthcare costs. We observed that the effect of increasing BMI on health-related quality of life is much larger when accounting for 240 chronic health conditions, compared with only a limited selection. This means that previous cost-effectiveness studies have likely underestimated the effect of BMI on quality of life and, therefore, the potential cost-effectiveness of interventions to reduce BMI

Developing decision support tools incorporating personalised predictions of likely visual benefit versus harm for cataract surgery:research programme

Background Surgery for established cataract is highly cost-effective and uncontroversial, yet uncertainty remains for individuals about when to proceed and when to delay surgery during the earlier stages of cataract. Objective We aimed to improve decision-making for cataract surgery through the development of evidence-based clinical tools that provide general information and personalised risk/benefit information. Design We used a mixed methodology consisting of four work packages. Work package 1 involved the development and psychometric validation of a brief, patient self-reported measure of visual difficulty from cataract and its relief from surgery, named Cataract Patient-Reported Outcome Measure, five items (Cat-PROM5). Work package 2 involved the review and refinement of risk models for adverse surgical events (posterior capsule rupture and visual acuity loss related to cataract surgery). Work package 3 involved the development of prediction models for the Cat-PROM5-based self-reported outcomes from a cohort study of 1500 patients; assessment of the validity of preference-based health economic indices for cataract surgery and the calibration of these to Cat-PROM5; assessment of patients’ and health-care professionals’ views on risk–benefit presentation formats, the perceived usefulness of Cat-PROM5, the value of personalised risk–benefit information, high-value information items and shared decision-making; development of cataract decision aid frequently asked questions, incorporation of personalised estimates of risks and benefits; and development of a cataract decision quality measure to assess the quality of decision-making. Work package 4 involved a mixed-methods feasibility study for a fully powered randomised controlled trial of the use of the cataract decision aid and a qualitative study of discordant or mismatching perceptions of outcome between patients and health-care professionals. Setting Four English NHS recruitment centres were involved: Bristol (lead centre), Brighton, Gloucestershire and Torbay. Multicentre NHS cataract surgery data were obtained from the National Ophthalmology Database. Participants Work package 1 – participants (n = 822) were from all four centres. Work package 2 – electronic medical record data were taken from the National Ophthalmology Database (final set > 1M operations). Work package 3 – cohort study participants were from Bristol (n = 1200) and Gloucestershire (n = 300); qualitative and development work was undertaken with patients and health-care professionals from all four centres. Work package 4 – Bristol, Brighton and Torbay participated in the recruitment of patients (n = 42) for the feasibility trial and recruitment of health-care professionals for the qualitative elements. Interventions For the feasibility trial, the intervention was the use of the cataract decision aid, incorporating frequently asked questions and personalised estimations of both adverse outcomes and self-reported benefit. Main outcome measures There was a range of quantitative and qualitative outcome measures: questionnaire psychometric performance metrics, risk indicators of adverse surgical events and visual outcome, predictors of self-reported outcome following cataract surgery, patient and health-care practitioner views, health economic calibration measures and randomised controlled trial feasibility measures. Data sources The data sources were patient self-reported questionnaire responses, study clinical data collection forms, recorded interviews with patients and health-care professionals, and anonymised National Ophthalmology Database data. Results Work package 1 – Cat-PROM5 was developed and validated with excellent to good psychometric properties (Rasch reliability 0.9, intraclass correlation repeatability 0.9, unidimensionality with residual eigenvalues ≤ 1.5) and excellent responsiveness to surgical intervention (Cohen delta –1.45). Work package 2 – earlier risk models for posterior capsule rupture and visual acuity loss were broadly affirmed (C-statistic for posterior capsule rupture 0.64; visual acuity loss 0.71). Work package 3 – the Cat-PROM5-based self-reported outcome regression models were derived based on 1181 participants with complete data (R2 ≈ 30% for each). Of the four preference-based health economic indices assessed, two demonstrated reasonable performance. Cat-PROM5 was successfully calibrated to health economic indices; adjusted limited dependent variable mixture models offered good to excellent fit (root-mean-square error 0.10–0.16). The personalised quantitative risk information was generally perceived as beneficial. A cataract decision aid and cataract decision quality measure were successfully developed based on the views of patients and health-care professionals. Work package 4 – data completeness was good for the feasibility study primary and secondary variables both before and after intervention/surgery (data completeness range 100–88%). Considering ability to recruit, the sample size required, instrumentation and availability of necessary health economic data, a fully powered randomised controlled trial (patients, n = 800, effect size 0.2 standard deviations, power 80%; p = 0.05) of the cataract decision aid would be feasible following psychometric refinement of the primary outcome (the cataract decision quality measure). The cataract decision aid was generally well-received by patients and health-care professionals, with cautions raised regarding perceived time and workload barriers. Discordant outcomes mostly related to patient dissatisfaction, with no clinical problem found. Limitations The National Ophthalmology Database data are expected to include some errors (mitigated by large multicentre data aggregations). The feasibility randomised controlled trial primary outcome (the cataract decision quality measure) displayed psychometric imperfections requiring refinement. The clinical occurrence of discordant outcomes is uncommon and the study team experienced difficulty identifying patients in this situation. Future work Future work could include regular review of the risk models for adverse outcomes to ensure currency, and the technical precision of complex-numbers analysis of refractive outcome to invite opportunities to improve post-operative spectacle-free vision. In addition, a fully powered randomised controlled trial of the cataract decision aid would be feasible, following psychometric refinement of the primary outcome (the cataract decision quality measure); this would clarify its potential role in routine service delivery. Conclusions In this research programme, evidence-based clinical tools have been successfully developed to improve pre-operative decision-making in cataract surgery. These include a psychometrically robust, patient-reported outcome measure (Cat-PROM5); prediction models for patient self-reported outcomes using Cat-PROM5; prediction models for clinically adverse surgical events and adverse visual acuity outcomes; and a cataract decision aid with relevant general information and personalised risk/benefit predictions. In addition, the successful mapping of Cat-PROM5 to existing health economic indices was achieved and the performances of indices were assessed in patients undergoing cataract surgery. A future full-powered randomised controlled trial of the cataract decision aid would be feasible (patients, n = 800, effect size 0.2 standard deviations, power 80%; p = 0.05). Trial registration This trial is registered as ISRCTN11309852. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 10, No. 9. See the NIHR Journals Library website for further project information

Can primary care research be conducted more efficiently using routinely reported practice-level data: A cluster randomised controlled trial conducted in England?

Objectives Conducting randomised controlled trials (RCTs) in primary care is challenging; recruiting patients during time-limited or remote consultations can increase selection bias and physical access to patients' notes is costly and time-consuming. We investigated barriers and facilitators to running a more efficient design. Design An RCT aiming to reduce antibiotic prescribing among children presenting with acute cough and a respiratory tract infection (RTI) with a clinician-focused intervention, embedded at the practice level. By using aggregate level, routinely collected data for the coprimary outcomes, we removed the need to recruit individual participants. Setting Primary care. Participants Baseline data from general practitioner practices and interviews with individuals from Clinical Research Networks (CRNs) in England who helped recruit practices and Clinical Commission Groups (CCGs) who collected outcome data. Intervention The intervention included: (1) explicit elicitation of parental concerns, (2) a prognostic algorithm to identify children at low risk of hospitalisation and (3) provision of a printout for carers including safety-netting advice. Coprimary outcomes For 0-9 years old - (1) Dispensing data for amoxicillin and macrolide antibiotics and (2) hospital admission rate for RTI. Results We recruited 294 of the intended 310 practices (95%) representing 336 496 registered 0-9 years old (5% of all 0-9 years old children). Included practices were slightly larger, had slightly lower baseline prescribing rates and were located in more deprived areas reflecting the national distribution. Engagement with CCGs and their understanding of their role in this research was variable. Engagement with CRNs and installation of the intervention was straight-forward although the impact of updates to practice IT systems and lack of familiarity required extended support in some practices. Data on the coprimary outcomes were almost 100%. Conclusions The infrastructure for trials at the practice level using routinely collected data for primary outcomes is viable in England and should be promoted for primary care research where appropriate

Estimating Marginal Healthcare Costs Using Genetic Variants as Instrumental Variables: Mendelian Randomization in Economic Evaluation

Accurate measurement of the marginal healthcare costs associated with different diseases and health conditions is important, especially for increasingly prevalent conditions such as obesity. However, existing observational study designs cannot identify the causal impact of disease on healthcare costs. This paper explores the possibilities for causal inference offered by Mendelian Randomization, a form of instrumental variable analysis that uses genetic variation as a proxy for modifiable risk exposures, to estimate the effect of health conditions on cost. Well-conducted genome-wide association studies provide robust evidence of the associations of genetic variants with health conditions or disease risk factors. The subsequent causal effects of these health conditions on cost can be estimated by using genetic variants as instruments for the health conditions. This is because the approximately random allocation of genotypes at conception means that many genetic variants are orthogonal to observable and unobservable confounders. Datasets with linked genotypic and resource use information obtained from electronic medical records or from routinely collected administrative data are now becoming available, and will facilitate this form of analysis. We describe some of the methodological issues that arise in this type of analysis, which we illustrate by considering how Mendelian Randomization could be used to estimate the causal impact of obesity, a complex trait, on healthcare costs. We describe some of the data sources that could be used for this type of analysis. We conclude by considering the challenges and opportunities offered by Mendelian Randomization for economic evaluation

Mendelian Randomization analysis of the causal effect of cigarette smoking on hospital costs

INTRODUCTIONKnowledge of the impact of smoking on healthcare costs is important for establishing the external effects of smoking and for evaluating policies intended to modify this behavior. Conventional analysis of this association is difficult because of omitted variable bias, reverse causality, and measurement error.METHODSWe approached these challenges using a Mendelian Randomization study design; genetic variants associated with smoking behaviors were used in instrumental variables models with inpatient hospital costs (calculated from electronic health records) as the outcome. We undertook genome wide association studies to identify genetic variants associated with smoking initiation and a composite smoking index (reflecting cumulative health impacts of smoking) on up to 300,045 individuals (mean age: 57 years at baseline, range 39 to 72 years) in the UK Biobank. We followed individuals up for a mean of six years.RESULTSGenetic liability to initiate smoking (ever versus never smoking) was estimated to increase mean per-patient annual inpatient hospital costs by £477 (95% confidence interval (CI): £187 to £766). A one-unit change in genetic liability to the composite smoking index (range: 0-4.0) increased inpatient hospital costs by £204 (95% CI: £105 to £303) per unit increase in this index. There was some evidence that the composite smoking index causal models violated the instrumental variable assumptions, and all Mendelian Randomization models were estimated with considerable uncertainty. Models conditioning on risk tolerance were not robust to weak instrument bias.CONCLUSIONSOur findings have implications for the potential cost-effectiveness of smoking interventions.IMPLICATIONSWe report the first Mendelian Randomization analysis of the causal effect of smoking on healthcare costs. Using two distinct smoking phenotypes, we identified substantial impacts of smoking on inpatient hospital costs, although the causal models were associated with considerable uncertainty. These results could be used alongside other evidence on the impact of smoking to evaluate the cost-effectiveness of anti-smoking interventions and to understand the scale of externalities associated with this behaviour