Identifying patient-level health and social care costs for older adults discharged from acute medical units in England

Franklin, Matthew; Berdunov, Vladislav; Edmans, Judi; Conroy, Simon; Gladman, John; Tanajewski, Lukasz; Gkountouras, Georgios; Elliott, Rachel A.

doi:10.1093/ageing/afu073

Cited by 17 publications

(26 citation statements)

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“…Previous studies evaluating health care costs in the elderly evaluate younger elderly populations [5, 8, 14, 15, 18, 33] and few present data disaggregated by age category. In the senior elderly, age-related impairments (e.g., cognitive impairment, falls, fractures) become increasingly important, alongside multiple comorbidities [e.g., cancer, stroke, diabetes mellitus (DM)], but few studies have analyzed coded data for both impairments and morbidities [10].…”

Section: Introductionmentioning

confidence: 99%

Determinants of health care costs in the senior elderly: age, comorbidity, impairment, or proximity to death?

2017

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Ageing is assumed to be accompanied by greater health care expenditures but the association is also viewed as a ‘red herring’. This study aimed to evaluate whether age is associated with health care costs in the senior elderly, using electronic health records for 98,220 participants aged 80 years and over registered with the UK Clinical Practice Research Datalink and linked Hospital Episode Statistics (2010–2014). Annual costs of health care utilization were estimated from a two-part model; multiple fractional polynomial models were employed to evaluate the non-linear association of age with predicted health care costs while also controlling for comorbidities, impairments, and death proximity. Annual health care costs increased from 80 years (£2972 in men, £2603 in women) to 97 (men; £4721) or 98 years (women; £3963), before declining. Costs were significantly elevated in the last year of life but this effect declined with age, from £10,027 in younger octogenarians to £7021 in centenarians. This decline was steeper in participants with comorbidities or impairments; £14,500 for 80–84-year-olds and £6752 for centenarians with 7+ impairments. At other times, comorbidity and impairments, not age, were main drivers of costs. We conclude that comorbidities, impairments, and proximity to death are key mediators of age-related increases in health care costs. While the costs of comorbidity among survivors are not generally associated with age, additional costs in the last year of life decline with age.Electronic supplementary materialThe online version of this article (doi:10.1007/s10198-017-0926-2) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Determinants of health care costs in the senior elderly: age, comorbidity, impairment, or proximity to death?

2017

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“…Raw data extraction can be problematic, particularly in primary care [6]. For large studies, such as PLEASANT (with 108 practices with 12 months of data and 8190 patients), using CPRD may be considered an efficient approach, particularly given that CPRD data are relatively readily available, it is possible to plan study time horizons with expected extraction times, and a data dictionary is available to assess data availability against the needs of the study.…”

Section: Discussionmentioning

confidence: 99%

“…The logistical benefits of using large databases may be desirable for researchers, funding bodies looking for studies that use efficient designs and the National Health Services (NHSs) within the United Kingdom (UK) in general; such study designs have been suggested as an approach to enable patients to be entered into RCTs more quickly than traditional study designs [4]. The accurate measurement of patient-level resource-use information for the purpose of economic evaluation has historically been challenging when relying on self-reported methods [5] or raw data extracted from healthcare services [6–8]. These large observational databases provide a great deal of patient-level resource-use information, which includes data about doctors’ visits in clinic or at home, inpatient or outpatient care and prescribed drugs at the practice level—the type of data available depends on the database.…”

Section: Introductionmentioning

confidence: 99%

Economic Evaluations Alongside Efficient Study Designs Using Large Observational Datasets: the PLEASANT Trial Case Study

et al. 2017

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BackgroundLarge observational datasets such as Clinical Practice Research Datalink (CPRD) provide opportunities to conduct clinical studies and economic evaluations with efficient designs.ObjectivesOur objectives were to report the economic evaluation methodology for a cluster randomised controlled trial (RCT) of a UK NHS-delivered public health intervention for children with asthma that was evaluated using CPRD and describe the impact of this methodology on results.MethodsCPRD identified eligible patients using predefined asthma diagnostic codes and captured 1-year pre- and post-intervention healthcare contacts (August 2012 to July 2014). Quality-adjusted life-years (QALYs) 4 months post-intervention were estimated by assigning utility values to exacerbation-related contacts; a systematic review identified these utility values because preference-based outcome measures were not collected. Bootstrapped costs were evaluated 12 months post-intervention, both with 1-year regression-based baseline adjustment (BA) and without BA (observed).ResultsOf 12,179 patients recruited, 8190 (intervention 3641; control 4549) were evaluated in the primary analysis, which included patients who received the protocol-defined intervention and for whom CPRD data were available. The intervention’s per-patient incremental QALY loss was 0.00017 (bias-corrected and accelerated 95% confidence intervals [BCa 95% CI] –0.00051 to 0.00018) and cost savings were £14.74 (observed; BCa 95% CI –75.86 to 45.19) or £36.07 (BA; BCa 95% CI –77.11 to 9.67), respectively. The probability of cost savings was much higher when accounting for BA versus observed costs due to baseline cost differences between trial arms (96.3 vs. 67.3%, respectively).ConclusionEconomic evaluations using data from a large observational database without any primary data collection is feasible, informative and potentially efficient.Clinical Trials Registration Number: ISRCTN03000938.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-016-0484-y) contains supplementary material, which is available to authorized users.

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“…Such data could be used for service evaluations, albeit with a variety of time, monetary, technical and information governance restrictions [68]. As such, routine data are recommended but often difficult to utilise [28,68,75].…”

Section: Routine Data For Estimating Resource Use Costs and Non-monementioning

confidence: 99%

Conducting Value for Money Analyses for Non-randomised Interventional Studies Including Service Evaluations: An Educational Review with Recommendations

2020

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This article provides an educational review covering the consideration of conducting 'value for money' analyses as part of non-randomised study designs including service evaluations. These evaluations represent a vehicle for producing evidence such as value for money of a care intervention or service delivery model. Decision makers including charities and local and national governing bodies often rely on evidence from non-randomised data and service evaluations to inform their resource allocation decision-making. However, as randomised data obtained from randomised controlled trials are considered the 'gold standard' for assessing causation, the use of this alternative vehicle for producing an evidence base requires careful consideration. We refer to value for money analyses, but reflect on methods associated with economic evaluations as a form of analysis used to inform resource allocation decision-making alongside a finite budget. Not all forms of value for money analysis are considered a full economic evaluation with implications for the information provided to decision makers. The type of value for money analysis to be conducted requires considerations such as the outcome(s) of interest, study design, statistical methods to control for confounding and bias, and how to quantify and describe uncertainty and opportunity costs to decision makers in any resulting value for money estimates. Service evaluations as vehicles for producing evidence present different challenges to analysts than what is commonly associated with research, randomised controlled trials and health technology appraisals, requiring specific study design and analytic considerations. This educational review describes and discusses these considerations, as overlooking them could affect the information provided to decision makers who may make an 'ill-informed' decision based on 'poor' or 'inaccurate' information with long-term implications. We make direct comparisons between randomised controlled trials relative to non-randomised data as vehicles for assessing causation; given 'gold standard' randomised controlled trials have limitations. Although we use UK-based decision makers as examples, we reflect on the needs of decision makers internationally for evidence-based decision-making specific to resource allocation. We make recommendations based on the experiences of the authors in the UK, reflecting on the wide variety of methods available, used as documented in the empirical literature. These methods may not have been fully considered relevant to non-randomised study designs and/or service evaluations, but could improve and aid the analysis conducted to inform the relevant value for money decision problem.

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Identifying patient-level health and social care costs for older adults discharged from acute medical units in England

Abstract: this study highlights the costs accrued by older people discharged from acute medical units (AMUs): they are mainly (76%) in secondary care and half of all costs were incurred by a minority of participants (10%).

Cited by 17 publications

References 5 publications

Determinants of health care costs in the senior elderly: age, comorbidity, impairment, or proximity to death?

Determinants of health care costs in the senior elderly: age, comorbidity, impairment, or proximity to death?

Economic Evaluations Alongside Efficient Study Designs Using Large Observational Datasets: the PLEASANT Trial Case Study

Conducting Value for Money Analyses for Non-randomised Interventional Studies Including Service Evaluations: An Educational Review with Recommendations

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