Protocol for a longitudinal study examining the trajectory of COVID-19, post-COVID, multidimensional disadvantage and health-related quality of life in India: the IndiQol Project.

INTRODUCTION: The COVID-19 pandemic has raised concerns about the persistence of symptoms after infection, commonly referred to as 'post-COVID' or 'long-COVID'. While countries in high-resource countries have highlighted the increased risk of disadvantaged communities, there is limited understanding of how COVID-19 and post-COVID conditions affect marginalised populations in low-income and middle-income countries. We study the longitudinal patterns of COVID-19, post-COVID symptoms and their impact on the health-related quality of life through the IndiQol Project. METHODS AND ANALYSIS: The IndiQol Project conducts household surveys across India to collect data on the incidence of COVID-19 and multidimensional well-being using a longitudinal design. We select a representative sample across six states surveyed over four waves. A two-stage sampling design was used to randomly select primary sampling units in rural and urban areas of each State. Using power analysis, we select an initial sample of 3000 household and survey all adult household members in each wave. The survey data will be analysed using limited dependent variable models and matching techniques to provide insights into the impact of COVID-19 pandemic and post-COVID on health and well-being of individuals in India. ETHICS AND DISSEMINATION: Ethics approval for the IndiQol Project was obtained from the Macquarie University Human Research Ethics Committee in Sydney, Australia and Institutional Review Board of Morsel in India. The project results will be published in peer-reviewed journals. Data collected from the IndiQol project will be deposited with the EuroQol group and will be available to use by eligible researchers on approval of request.

Methods to Quantify the Importance of Parameters for Model Updating and Distributional Adaptation.

PURPOSE: Decision models are time-consuming to develop; therefore, adapting previously developed models for new purposes may be advantageous. We provide methods to prioritize efforts to 1) update parameter values in existing models and 2) adapt existing models for distributional cost-effectiveness analysis (DCEA). METHODS: Methods exist to assess the influence of different input parameters on the results of a decision models, including value of information (VOI) and 1-way sensitivity analysis (OWSA). We apply 1) VOI to prioritize searches for additional information to update parameter values and 2) OWSA to prioritize searches for parameters that may vary by socioeconomic characteristics. We highlight the assumptions required and propose metrics that quantify the extent to which parameters in a model have been updated or adapted. We provide R code to quickly carry out the analysis given inputs from a probabilistic sensitivity analysis (PSA) and demonstrate our methods using an oncology case study. RESULTS: In our case study, updating 2 of 21 probabilistic model parameters addressed 71.5% of the total VOI and updating 3 addressed approximately 100% of the uncertainty. Our proposed approach suggests that these are the 3 parameters that should be prioritized. For model adaptation for DCEA, 46.3% of the total OWSA variation came from a single parameter, while the top 10 input parameters were found to account for more than 95% of the total variation, suggesting efforts should be aimed toward these. CONCLUSIONS: These methods offer a systematic approach to guide research efforts in updating models with new data or adapting models to undertake DCEA. The case study demonstrated only very small gains from updating more than 3 parameters or adapting more than 10 parameters. HIGHLIGHTS: It can require considerable analyst time to search for evidence to update a model or to adapt a model to take account of equity concerns.In this article, we provide a quantitative method to prioritze parameters to 1) update existing models to reflect potential new evidence and 2) adapt existing models to estimate distributional outcomes.We define metrics that quantify the extent to which the parameters in a model have been updated or adapted.We provide R code that can quickly rank parameter importance and calculate quality metrics using only the results of a standard probabilistic sensitivity analysis.

Assessing the uptake of incentivised physical health checks for people with serious mental illness: a cohort study in primary care.

BACKGROUND: People with serious mental illness are more likely to experience physical illnesses. The onset of many of these illnesses can be prevented if detected early. Physical health screening for people with serious mental illness is incentivised in primary care in England through the Quality and Outcomes Framework (QOF). GPs are paid to conduct annual physical health checks on patients with serious mental illness, including checks of body mass index (BMI), cholesterol, and alcohol consumption. AIM: To assess the impact of removing and reintroducing QOF financial incentives on uptake of three physical health checks (BMI, cholesterol, and alcohol consumption) for patients with serious mental illness. DESIGN AND SETTING: Cohort study using UK primary care data from the Clinical Practice Research Datalink between April 2011 and March 2020. METHOD: A difference-in-difference analysis was employed to compare differences in the uptake of physical health checks before and after the intervention, accounting for relevant observed and unobserved confounders. RESULTS: An immediate change was found in uptake after physical health checks were removed from, and after they were added back to, the QOF list. For BMI, cholesterol, and alcohol checks, the overall impact of removal was a reduction in uptake of 14.3, 6.8, and 11.9 percentage points, respectively. The reintroduction of BMI screening in the QOF increased the uptake by 10.2 percentage points. CONCLUSION: This analysis supports the hypothesis that QOF incentives lead to better uptake of physical health checks.

The Health Impact of Waiting for Elective Procedures in the NHS in England: A Modeling Framework Applied to Coronary Artery Bypass Graft and Total Hip Replacement.

INTRODUCTION: The aim of this study is to demonstrate a practical framework that can be applied to estimate the health impact of changes in waiting times across a range of elective procedures in the National Health Service (NHS) in England. We apply this framework by modeling 2 procedures: coronary artery bypass graft (CABG) and total hip replacement (THR). METHODS: We built a Markov model capturing health pre- and postprocedure, including the possibility of exiting preprocedure to acute NHS care or self-funded private care. We estimate the change in quality-adjusted life-years (QALYs) over a lifetime horizon for 10 subgroups defined by sex and Index of Multiple Deprivation quintile groups and for 7 alternative scenarios. We include 18 wk as a baseline waiting time consistent with current NHS policy. The model was populated with data from routinely collected data sets where possible (Hospital Episode Statistics, Patient-Reported Outcome Measures, and Office for National Statistics Mortality records), supplemented by the academic literature. RESULTS: Compared with 18 wk, increasing the wait time to 36 wk resulted in a mean discounted QALY loss in the range of 0.034 to 0.043 for CABG and 0.193 to 0.291 for THR. The QALY impact of longer NHS waits was greater for those living in more deprived areas, partly as fewer patients switch to private care. DISCUSSION/CONCLUSION: The proposed framework was applied to 2 different procedures and patient populations. If applied to an expanded group of procedures, it could provide decision makers with information to inform prioritization of waiting lists. There are a number of limitations in routine data on waiting for elective procedures, primarily the lack of information on people still waiting. HIGHLIGHTS: We present a modeling framework that allows for an estimation of the health impact (measured in quality-adjusted life-years) of waiting for elective procedures in the NHS in England.We apply our model to waiting for coronary artery bypass graft (CABG) and total hip replacement (THR). Increasing the wait for THR results in a larger health loss than an equivalent increase in wait for CABG.This model could potentially be used to estimate the impact across an expanded group of procedures to inform prioritization of activities to reduce waiting times.

Examining the hospital costs of children born into relative deprivation in England.

OBJECTIVE: To examine the association between being born into relative deprivation and hospital costs during childhood. DESIGN: Retrospective cohort study. METHODS: We created a birth cohort using Hospital Episode Statistics for children born in NHS hospitals in 2003/2004. The Index of Multiple Deprivation (IMD) rank at birth was missing from 75% of the baby records, so we linked mother and baby records to obtain the IMD decile from the mother's record. We aggregated and costed each child's hospital inpatient admissions, and outpatient and emergency department (ED) attendances up to 15 years of age. We used 2019/2020 NHS tariffs to assign costs. We constructed an additional cohort, all children born in 2013/2014, to explore any changes over time, comparing the utilisation and costs up to 5 years of age. RESULTS: Our main cohort comprised 567 347 babies born in 2003/2004, of which we could include 91%. Up to the age of 15 years, children born into the most deprived areas used more hospital services than those born in the least deprived, reflected in higher costs of inpatient, outpatient and ED care. The highest costs and greatest differences are in the year following birth. Comparing this with the later cohort (up to age 5 years), the average cost per child increased across all deprivation deciles, but differences between the most and least deprived deciles appeared to narrow slightly. CONCLUSIONS: Healthcare utilisation and costs are consistently higher for children who are born into the most deprived areas compared with the least.

Patients' experience on pain outcomes after hip arthroplasty: insights from an information tool based on registry data.

BACKGROUND: Arthroplasty registries are rarely used to inform encounters between clinician and patient. This study is part of a larger one which aimed to develop an information tool allowing both to benefit from previous patients' experience after total hip arthroplasty (THA). This study focuses on generating the information tool specifically for pain outcomes. METHODS: Data from the Geneva Arthroplasty Registry (GAR) about patients receiving a primary elective THA between 1996 and 2019 was used. Selected outcomes were identified from patient and surgeon surveys: pain walking, climbing stairs, night pain, pain interference, and pain medication. Clusters of patients with homogeneous outcomes at 1, 5, and 10 years postoperatively were generated based on selected predictors evaluated preoperatively using conditional inference trees (CITs). RESULTS: Data from 6,836 THAs were analysed and 14 CITs generated with 17 predictors found significant (p < 0.05). Baseline WOMAC pain score, SF-12 self-rated health (SRH), number of comorbidities, SF-12 mental component score, and body mass index (BMI) were the most common predictors. Outcome levels varied markedly by clusters whilst predictors changed at different time points for the same outcome. For example, 79% of patients with good to excellent SRH and less than moderate preoperative night pain reported absence of night pain at 1 year after THA; in contrast, for those with fair/poor SHR this figure was 50%. Also, clusters of patients with homogeneous levels of night pain at 1 year were generated based on SRH, Charnley, WOMAC night and pain scores, whilst those at 10 years were based on BMI alone. CONCLUSIONS: The information tool generated under this study can provide prospective patients and clinicians with valuable and understandable information about the experiences of "patients like them" regarding their pain outcomes.

Mixed Methods EvAluation of the high-volume low-complexity Surgical hUb pRogrammE (MEASURE): a mixed methods study protocol.

INTRODUCTION: The waiting list for elective surgery in England recently reached over 7.8 million people and waiting time targets have been missed since 2010. The high-volume low complexity (HVLC) surgical hubs programme aims to tackle the backlog of patients awaiting elective surgery treatment in England. This study will evaluate the impact of HVLC surgical hubs on productivity, patient care and the workforce. METHODS AND ANALYSIS: This 4-year project consists of six interlinked work packages (WPs) and is informed by the Consolidated Framework for Implementation Research. WP1: Mapping current and future HVLC provision in England through document analysis, quantitative data sets (eg, Hospital Episodes Statistics) and interviews with national service leaders. WP2: Exploring the effects of HVLC hubs on key performance outcomes, primarily the volume of low-complexity patients treated, using quasi-experimental methods. WP3: Exploring the impact and implementation of HVLC hubs on patients, health professionals and the local NHS through approximately nine longitudinal, multimethod qualitative case studies. WP4: Assessing the productivity of HVLC surgical hubs using the Centre for Health Economics NHS productivity measure and Lord Carter's operational productivity measure. WP5: Conducting a mixed-methods appraisal will assess the influence of HVLC surgical hubs on the workforce using: qualitative data (WP3) and quantitative data (eg, National Health Service (NHS) England's workforce statistics and intelligence from WP2). WP6: Analysing the costs and consequences of HVLC surgical hubs will assess their achievements in relation to their resource use to establish value for money. A patient and public involvement group will contribute to the study design and materials. ETHICS AND DISSEMINATION: The study has been approved by the East Midlands-Nottingham Research Ethics Committee 23/EM/0231. Participants will provide informed consent for qualitative study components. Dissemination plans include multiple academic and non-academic outputs (eg, Peer-reviewed journals, conferences, social media) and a continuous, feedback-loop of findings to key stakeholders (eg, NHS England) to influence policy development. TRIAL REGISTRATION: Research registry: Researchregistry9364 (https://www.researchregistry.com/browse-the-registry%23home/registrationdetails/64cb6c795cbef8002a46f115/).

Measuring the overall performance of mental healthcare providers.

To date there have been no attempts to construct composite measures of healthcare provider performance which reflect preferences for health and non-health benefits, as well as costs. Health and non-health benefits matter to patients, healthcare providers and the general public. We develop a novel provider performance measurement framework that combines health gain, non-health benefit, and cost and illustrate it with an application to 54 English mental health providers. We apply estimates from a discrete choice experiment eliciting the UK general population's valuation of non-health benefits relative to health gains, to administrative and patient survey data for years 2013-2015 to calculate equivalent health benefit (eHB) for providers. We measure costs as forgone health and quantify the relative performance of providers in terms of equivalent net health benefit (eNHB): the value of the health and non-health benefits minus the forgone benefit equivalent of cost. We compare rankings of providers by eHB, eNHB, and by the rankings produced by the hospital sector regulator. We find that taking account of the non-health benefits in the eNHB measure makes a substantial difference to the evaluation of provider performance. Our study demonstrates that the provider performance evaluation space can be extended beyond measures of health gain and cost, and that this matters for comparison of providers.

Financial incentives and prescribing behavior in primary care.

Many healthcare systems prohibit primary care physicians from dispensing the drugs they prescribe due to concerns that this encourages excessive, ineffective or unnecessarily costly prescribing. Using data from the English National Health Service for 2011-2018, we estimate the impact of physician dispensing rights on prescribing behavior at the extensive margin (comparing practices that dispense and those that do not) and the intensive margin (comparing practices with different proportions of patients to whom they dispense). We control for practices selecting into dispensing based on observable (OLS, entropy balancing) and unobservable practice characteristics (2SLS). We find that physician dispensing increases drug costs per patient by 3.1%, due to more, and more expensive, drugs being prescribed. Reimbursement is partly based on a fixed fee per package dispensed and we find that dispensing practices prescribe smaller packages. As the proportion of the practice population for whom they can dispense increases, dispensing practices behave more like non-dispensing practices.