NIHRDataInsights/HRCSData huggingface dataset:
Title: Health systems strengthening through person-centred care: development of a feasible and acceptable theory-based workforce approach to improve quality.
Funder: Medical Research Council

Abstract: Background The Institute of Medicine describes high quality care as "safe, effective, patient-centred, efficient, timely and equitable." Person-centred care (PCC) is relevant to low and middle income country (LMIC) health systems to detect and manage socio-economic barriers to health system access and retention. Re-engineering LMIC health systems to become person-centred can improve outcomes and reduce clinic time. Within the WHO 6 Health System Buildings Blocks, Block #3 (Health Workforce) Block #5 (Information) and Block #6 (Service Delivery) could promote PCC for a stronger health system and improved quality. Methods Sequential mixed methods design. This 24-month study will develop a mechanism to achieve improved outcomes across 3 relevant WHO Health System Building blocks, and a valid measure of PCC. These key practical outputs will enable us to design and deliver a subsequent large scale implementation and evaluation study. We focus on conditions with high prevalence in LMIC, clinical uncertainty and prior evidence of high multidimensional needs: cancer, heart failure, and COPD. -Objectives i & ii- qualitative in-depth interviews with patients (n=50), family caregivers (n=30) and professionals (n=30). Semi-structured topic guide data will be analysed using framework analysis, both deductive (i.e. appraise existing PCC theories) and inductive (generate new theory). -Objective iii - design of a systems-oriented strategy. Drawing on the data from Objs i+ii, our prior work, and partner expertise we model the mechanism, intended process and outcomes. The strategy will take a multi-disciplinary approach with nurses at the centre of systems strengthening. -Objective iv - development and validation of PCC measure. Cognitive interviews with n=15 patients n=15 family caregivers, framework analysis and refinement of tool then validation in cross-sectional survey with n=80 patients in each of the three diagnostic groups.


Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: GENERIC HEALTH RELEVANCE
Title: Strengthening private-sector medicine systems to tackle the persistence of poor-quality medicines in Africa: a proof-of-concept study
Funder: Medical Research Council

Abstract: Private sector medical supply chains in sub-Saharan Africa are not centrally planned. Their structure emerges from individual behaviour by actors with limited resources and information, and may be complex and counter-intuitive. Agent-Based Modelling (ABM) allows us to represent this heterogeneous behaviour in a supply system model. Ethnographic data on actors' motivations and decision-making behaviours can be incorporated into ABMs, enabling prediction of the emerging supply system structure. Predictions can be validated using empirical observation to confirm model assumptions. The validated model can then legitimately explore likely outcomes of changes relevant to policy makers. We have already developed an ABM representing a simplified medicine supply chain using pilot work in Ghana and Tanzania (Ackland et al. 2019). Our goal for this project is to develop a geographically-accurate model using a systematic strategy for calibration and validation based on good statistically-derived estimates of retail outlet distribution, supply chain structure and transport connections. Using GIS data, we will overlay economic, geographic and socio-cultural information (including information on formal and informal regulatory mechanisms) onto each supply chain node to explore the system-level consequences. The ABM will incorporate theoretically and ethnographically informed algorithms for decision-making at each node. Independent data will be used for model calibration and validation. The model will be designed to scale so that (in a full proposal), with the supercomputing capability at Edinburgh University, we will be able to simulate millions of medicine transactions involving thousands of suppliers. A second technical goal will be to develop prototype 'user-friendly' versions of the ABMs to share with policy makers, enabling them to design and explore virtual interventions and associated supply chain outcomes and thus promoting more systematic and informed policy debate.


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Consumer Cost-Sharing in Primary Care: Unintended Health and Economic Outcomes
Funder: Medical Research Council

Abstract: The main objective of this proposal is to establish whether increased levels of patient cost-sharing reduce the use of primary care services, and increase undiagnosed chronic conditions, hospitalizations, health care costs, and mortality. To fulfill this objective, we need: (a) a dataset representative of the population, and that is large enough so that effects on hospitalizations, chronic condition diagnosis, and mortality can be precisely estimated, not only for the entire population, but also for subgroups of especial interest (e.g. poor SES individuals); (b) An empirical design that will allow the researchers to go beyond associations, and estimate the causal impact of patient cost-sharing on the relevant outcomes in the absence of an experiment. Regarding (a), the data for this proposal consists of health administrative data for the years 2011 to 2018, covering 97% of the Colombian population and containing patients records of all health care services provided in the Colombian Health System, including date and type of service used (outpatient, hospital, etc.), prescriptions, treatment costs, ICD-10 codes, sociodemographic characteristics of individuals (including income or wealth scores) and mortality. The person identifier is consistent across the seven years, providing a uniquely rich and detailed longitudinal administrative database. Regarding (b), We will exploit a unique feature of the Colombian Health System that makes it particularly suitable to the use of the Regression Discontinuity quasi-experimental method: the level of patient cost-sharing changes abruptly at determined thresholds of a continuous variable. The Regression Discontinuity method is highly regarded for its ability to estimate causal estimates (Lee and Lemieux 2010). The results of the study constitute the first step towards investigating the consequences of consumer cost-sharing to health systems in middle income countries, which will be pursued in a future proposal.


Research activity codes: 8.2 HEALTH AND WELFARE ECONOMICS
Health categories: GENERIC HEALTH RELEVANCE
Title: Addressing conflict of interest driving irrational prescribing of antibiotics in pluralistic health systems: an interventional study in Pakistan
Funder: Medical Research Council

Abstract: Health systems research and the design of interventions to improve quality of care has typically focused on the hardware of health systems, even though it is known that the software, including values and culture, is critical. Our research is situated at the intersection of two 'wicked problems' - governance of private providers and antimicrobial resistance - and addresses conflict of interest (COI) driving irrational prescription of antibiotics by private doctors in Pakistan. Being cognisant that regulatory bodies in many low and middle countries are unable to effectively enforce rules, we view soft governance approaches as being essential to the internalisation of voluntary ethical rules of behaviour. We start by applying innovative qualitative methods, involving vignettes and a card placing exercise, to facilitate rich data collection that will inform the design of an evidence-based soft governance intervention. Data from policy actor interviews will be used to conduct a stakeholder analysis examining actors' positions on tackling COI in private doctors, paying attention to vested interests that lead to governance conflicts. Interviews with private doctors will investigate how health systems dynamics - such as reliance on pharmaceutical companies to support professional development - shape their perceptions of ethically acceptable COI with respect to prescribing antibiotics. These interviews will also enable an analysis to inform how messages that sensitise doctors to professional ethics and COI should be framed. We will embed these messages into a continuing medical education seminar (our intervention), and conduct a randomised controlled study to compare behaviour of doctors who attend the intervention seminars with those who attend placebo seminars with no mention of COI. We will generate robust evidence on the impact of our intervention by using a novel objective assessment of doctors' behaviour vis-à-vis receiving benefits for prescribing antibiotics.


Research activity codes: 8.3 POLICY, ETHICS AND RESEARCH GOVERNANCE
Health categories: GENERIC HEALTH RELEVANCE
Title: The impact of federalisation on Nepal's health system: a longitudinal analysis
Funder: Medical Research Council

Abstract: The proposed project will track, in real time, the massive health system reform process that Nepal is undertaking as a result of the implementation of its new Constitution, and the shift to a Federal government system, with primary responsibility for health being devolved to seven new Provincial governments, and further to Municipalities and Rural Municipalities. Although the changes have the potential to bring greater localism and responsiveness in health service management and delivery, there are real concerns about the 'readiness' of the system for this change, and the capacity of Provincial and Municipal governments to deliver. Key elements of the new system are still to be determined, and national health policy has not yet been revised to reflect the new government structures. Working in eight communities selected to represent the variety of Nepal's ecological and social-economic regions (mountain; hill; terai; urban), the proposed project will utilize Participatory Policy Analysis backed by more traditional forms of qualitative and quantitative data to track the perceptions of health systems stakeholders at all levels as the reform process unfolds. Taking a holistic approach to the health system, the project will examine all of the WHO's health system building blocks as well as investigating the complex relationships between them. This is both a timely and highly innovative project, proposing work that has not yet been done in Nepal, and utilising methods that are at the cutting edge of the health systems research field. The project is also highly interdisciplinary, drawing on the variety of disciplinary backgrounds of the project team, and placing a strong emphasis on co-design and co-production of knowledge through continuing stakeholder engagement and a series of Participatory Policy Analysis (PPA) workshops across governance levels.


Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES;8.3 POLICY, ETHICS AND RESEARCH GOVERNANCE
Health categories: GENERIC HEALTH RELEVANCE
Title: Understanding and eliminating health sector corruption impeding UHC at district level in Nigeria and Malawi: institutions, individuals and incentives
Funder: Medical Research Council

Abstract: Weak accountability and corruption in public health systems (abuse of entrusted power for private gain) are widely acknowledged as impeding access to health care, yet empirical research on the drivers and potential solutions is limited. To design contextually appropriate interventions to tackle these, we will explore the most prevalent/harmful corruption practices at district and local level; individual and organisational characteristics that drive these, and critically, how formal structures (policies, enforcement) interplay with informal social, economic and political structures (local elites and kinship networks) to shape everyday practice within the health system. We will then identify pragmatic strategies ('openings') to address corruption within the broader organisational and political distribution of power. The district will be the unit of analysis. In Nigeria research will be in 3 urban and 3 rural Local Government Authorities in Enugu(S) and Kano(N) States, and in Malawi, in 2 districts in the South, 2 in the Central region. We ask: how do health systems structures and practices, and informal socio-political and economic structures incentivise corruption at district level and how can these be overcome? We will develop novel and ethically robust approaches and methods: content analysis of policies, regulations, media; institutional ethnography; interviews; a household survey with users (2 districts); crowdsourcing of corruption cases. Analysis will also draw on: political economy, analysing actors, their power and their informal networks; systems theory, especially complexity; and involves local and national co-production and policy engagement workshops to interpret and validate findings and formulate actions. Engaging at all levels of the health system we will promote anti-corruption action by building a community of practice, sharing knowledge and supporting researchers and implementers in LMICs-linked to new global accountability initiatives.


Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES;8.3 POLICY, ETHICS AND RESEARCH GOVERNANCE
Health categories: GENERIC HEALTH RELEVANCE
Title: Big data analysis of electronic hospital records: inpatient trajectories and pharmacological exposures associated with mortality in older adults.
Funder: Medical Research Council

Abstract: Increasing numbers of older adults require hospital admission. Some older adults age well but others develop frailty, characterised by reduced ability to withstand stressors, and many experience co-morbidity and polypharmacy. We aim to understand how older adults use emergency hospital services by characterising the different inpatient trajectories that describe the majority of admissions. We will also explore if prescribed medications are associated with inpatient mortality to identify potentially inappropriate medications (PIMs). Data from 80 000 inpatient episodes in older adults (>/=65 years old) admitted as an emergency to Addenbrooke's Hospital (Cambridge) over four years are available for large scale retrospective analysis after the introduction of an electronic patient record. Information describing all aspects of admission from patient characteristics to bedside observations, prescribed medications and laboratory tests are available. These data will have many repeated measurements resulting in a large, complex longitudinal dataset and physicians at Addenbrooke's have collaborated with the European Bioinformatics Institute (Wellcome Genome Campus, Hinxton, Cambridge) to combine clinical and technical expertise. After anonymization, machine learning (ML) will be used to define the different types of inpatient trajectory. For example, admission episodes may be characterised by their length of stay or operational factors, e.g., number of ward moves. Secondly, ML will model risk factors associated with inpatient mortality with the aim of identifying PIMs and evaluating interactions between PIMs and patient factors or the type of admission in this heterogenous group. Other outcomes such as new institutionalisation will be considered and we will use ML to simulate how the patient outcome might change if a hypothetical alternative treatment plan had been employed. The Northeast-Newcastle & North Tyneside research ethics service committee approved the study.


Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: GENERIC HEALTH RELEVANCE
Title: MICA: Clinical trial estimands: from definition to estimation
Funder: Medical Research Council

Abstract: In clinical trials a number of complications may arise which mean that a range of different types of treatment effect, or estimand, could be contemplated as the target of inference. For example, in trials where patients change treatments during follow-up (either as intended by the protocol or not), scientific interest may lie in estimation of the effects of a so called treatment regime which either was not adhered to in the trial or differs (e.g. for ethical reasons) from the trial's intended treatment regime. A second example is when the trial aims to assess effects on time to an event of interest but patients may experience a competing event first. A third example is where the trial aims to compare treatments' effects on some outcome but this outcome cannot be observed in a non-trivial proportion of patients because they have died (truncation by death). The recognition of the importance of clear specification of the estimand and appropriate choice of statistical method is evidenced by the commissioning of the ICH E9 addendum on clinical trial estimands. The aim of this research is to apply the concepts and statistical estimation methods developed in modern causal inference theory to the problem of estimand definition and estimation in randomised trials. For a range of important estimand problems, we will use counterfactual language to precisely specify the estimand mathematically. We will use causal inference theory to characterize under what conditions such estimands can be identified or estimated from the observed data. Lastly we will critically evaluate, analytically, through simulation, and application to exemplar trial datasets, the different statistical methods which have been developed to estimate such estimands in regards the plausibility of their assumptions and their statistical properties. Open source software in R will be developed implementing these methods where implementations currently do not exist, with accompanying tutorials.


Research activity codes: 6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION)
Health categories: GENERIC HEALTH RELEVANCE
Title: HCD: Novel approaches of multi-parameter evidence synthesis and decision modelling for efficient evaluation of diagnostic health technologies
Funder: Medical Research Council

Abstract: Recent advances in research have led to development of a multitude of diagnostic health technologies as well as methods for their assessment, generating heterogeneous data sources on accuracy of these technologies. We will develop statistical tools for synthesis of complex data on multiple diagnostic tests and their inclusion in a health technology assessment (HTA) decision framework. We will carry out methodological research in three work packages (WPs). WP1: We will develop multivariate meta-analytic methods for synthesis of evidence on test accuracy measures of multiple diagnostic tests and/or thresholds, employing a range of copulas to account for the correlations between sensitivities and specificities of multiple tests most effectively. We will develop further methods for evaluating the utility of dynamic test thresholds to maximise diagnostic ability of sequential tests. WP2: We will develop evidence synthesis methods combining data on effectiveness of biomarkers from heterogeneous study designs ("randomise-all", enrichment, single-arm, and biomarker-strategy trials), building on a range of methods developed for combining randomised trial evidence with real-world data at aggregate level (including meta-analytic methods with bias adjustment and hierarchical methods differentiating between study designs). WP1 and WP2: We will develop methods for inclusion of individual patient data from electronic health records to enable the development of more robust evidence for HTA of diagnostic tools, including prognostic biomarkers, and for evaluation of surrogate markers which are predictors of relative treatment effect. WP3: We will develop a comprehensive decision modelling framework taking into account all relevant evidence on diagnostic tests and related uncertainty to assess potential for improvement in decision making process when using the methods developed in WP1 and WP2.


Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: GENERIC HEALTH RELEVANCE
Title: HOD2: Instrumental Variable approaches for estimating heterogeneity of treatment effects to inform personalisation using electronic health records
Funder: Medical Research Council

Abstract: Personalisation medicine requires evidence on how the comparative effectiveness and harms of alternative treatments differs across individual patients, a concept known as Heterogeneity of Treatment Effect (HTE). Electronic Health Records (EHRs) have the potential to provide this information, but major methodological challenges must be addressed. The three major interrelated issues are that: the comparison groups may differ according to unmeasured prognostic characteristics (e.g. frailty), unmeasured confounders may also predict HTEs, and it is unknown which observed characteristics truly predict HTE. Instrumental Variable (IV) methods can address confounding due to unobserved as well as observed characteristics. However, when there is a HTE, standard IV approaches do not provide treatment effects that can be easily interpreted for treatment decisions. Local Instrumental Variable (LIV) methods have been developed that use continuous IV to provide treatment effect estimates of direct relevance for personalisation. However, current LIV methods assume that the analyst chooses those covariates which are true treatment effect modifiers. While data-adaptive approaches to covariate selection have been developed elsewhere, they have not been extended to the selection of covariate by treatment interaction terms for IV designs. We will develop data-adaptive IV methods that address concerns about whether the underlying parametric models are correctly specified, and selects those observed covariates which do actually modify the treatment effect. We will subject these IV methods to careful testing within simulation studies, grounded in two live NIHR-funded studies which aim to inform personalised treatment strategies. We will develop an analytical strategy for future studies. We will target translation activity at those designing, analysing and interpreting studies that aim to provide the requisite evidence to personalise clinical guidelines and treatment decisions.


Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: GENERIC HEALTH RELEVANCE