Allocation Of Health Care Resources Research Articles

Eliciting Societal Preferences for Non-health Outcomes: A Person Trade-Off Study in the Context of Genomics

Economic evaluations of health technologies traditionally aim to maximize population health outcomes measured by using quality-adjusted life-years (QALYs). Non-health outcomes, however, may have high social value, and their exclusion has the potential to bias decisions regarding allocation of health care resources. This research positions Australian participants as societal decision-makers to explore their willingness to trade-off health gains in adults for non-health benefits in families with a child affected by a rare disease. To estimate the social value of the different health care interventions, a person trade-off (PTO) method was used. PTOs present participants with groups of beneficiaries that vary in terms of the number of individuals who will benefit, the individuals' characteristics, their expected benefits, or a combination, and ask which group should be prioritized. Each trade-off presented health gains from the treatment of moderate physical and mental health conditions described by the 3-level version of the EuroQol 5-Dimension (EQ-5D-3L) health states. The health gains in these groups were traded-off against non-health gains in families accessing diagnostic genomic testing, and equivalence values were calculated, using median and ratio of means methods, based on the ratio of the group sizes at the point of equivalence. Participants were recruited through Prolific and were stratified according to age, sex, and education.The impact of participant characteristics on equivalence values was assessed using Kruskal-Wallis H tests and ordinary least-squares log-linear regressions. Participants (N=434) positioned as societal decision-makers were generally willing to trade-off adult health gains with the familial non-health benefits of genomic testing, showing a preference for valuing both types of outcomes within public health policy. The aggregation of preferences generated 2 weightings for genomic testing against each health treatment, an unadjusted value and a reweighted value to match target demographic characteristics. Converted into QALY value per test, it was found that participants valued the non-health benefits of genomic testing between 0.730 and 0.756 QALY. A minority of participants always prioritized diagnostic genomic testing over the physical (6.0%) or mental (4.6%) health treatments, with a larger minority always prioritizing the physical (15.4%) or mental (14.8%) health treatments. The findings indicate that participants perceived the non-health parental benefits in children experiencing rare disease to have comparable value to health gains in adults experiencing the moderate physical or mental health conditions described using EQ-5D-3L. These findings suggest that the benefits of genomic tests would be underestimated if only health benefits are included in economic evaluations.

Incidence, prevalence and mortality of chronic liver diseases in Sweden between 2005 and 2019

BackgroundUpdated data on the incidence, prevalence, and regional differences of chronic liver disease are missing from many countries. In this study, we aimed to describe time trends, incidence, prevalence, and mortality of a wide range of chronic liver diseases in Sweden.MethodsIn this register-based, nationwide observational study, patients with a register-based diagnosis of chronic liver disease, during 2005–2019, were retrieved from the Swedish National Board of Health and Welfare. Annual age-standardized incidence and mortality rates, and prevalence per 100,000 inhabitants was calculated and stratified on age, sex, and geographical region.ResultsThe incidence of alcohol-related cirrhosis increased by 47% (2.6% annually), reaching an incidence rate of 13.1/100,000 inhabitants. The incidence rate of non-alcoholic fatty liver disease and unspecified liver cirrhosis increased by 217% and 87% (8.0 and 4.3% annually), respectively, reaching an incidence rate of 15.2 and 18.7/100,000 inhabitants, and a prevalence of 24.7 and 44.8/100,000 inhabitants. Furthermore, incidence rates of chronic hepatitis C declined steeply, but liver malignancies have become more common. The most common causes of liver-related mortality were alcohol-related liver disease and unspecified liver disease.ConclusionThe incidence rates of diagnosed non-alcoholic fatty liver disease, alcohol-related cirrhosis, unspecified liver cirrhosis, and liver malignancies have increased during the last 15 years. Worryingly, mortality in several liver diseases increased, likely reflecting increasing incidences of cirrhosis in spite of a decreasing rate of hepatitis C. Significant disparities exist across sex and geographical regions, which need to be considered when allocating healthcare resources.

Assessing Geographic Variation in Rates of Cervical Cancer and Recurrent or Metastatic Cervical Cancer Among Medicaid Enrollees.

The objective of this study was to evaluate trends in prevalence of cervical cancer (CC) and rates of recurrent or metastatic cervical cancer (r/mCC) treatment initiation at the state and metropolitan statistical area (MSA) levels among Medicaid enrolled females from 2016 to 2019. Retrospective analyses of nationwide Medicaid claims data were used to identify adult CC and r/mCC patients from 2016 to 2019. CC prevalence was estimated as the proportion of females diagnosed with CC out of all adult female Medicaid beneficiaries, and r/mCC by the proportion of CC patients who initiated a systemic treatment not associated with surgery or radiation to the number of enrollees with CC diagnosis in each state or MSA. Overall and annual rates were calculated for each state and MSA from 2016 to 2019. The analytic cohort included 70,865 adult female Medicaid beneficiaries with CC from 2016 to 2019, among whom 3375 were identified as r/mCC patients. Nationwide annual prevalence of CC remained relatively stable from 2016 to 2019, while r/mCC decreased slightly over the study period. Several MSAs experienced increasing rates of r/mCC from 2016 to 2019, including Mayaguez, PR, Aguadeilla-Isabela, PR, and Green Bay, WI. Claims data demonstrate areas in the United States with disproportionately high or increasing CC or r/mCC burden, indicating a potential gap in preventative care for females and an unmet need for education and health care resource allocation. Future research should evaluate associations between community-level factors and r/mCC burden.

Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19.

As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatment and the appropriate allocation of limited healthcare resources remain unresolved issues. In this study we constructed a deep-learning (DL) model to predict the need for oxygen supplementation using clinical information and chest CT images of patients with COVID-19. We retrospectively enrolled 738 patients with COVID-19 for whom clinical information (patient background, clinical symptoms, and blood test findings) was available and chest CT imaging was performed. The initial data set was divided into 591 training and 147 evaluation data. We developed a DL model that predicted oxygen supplementation by integrating clinical information and CT images. The model was validated at two other facilities (n = 191 and n = 230). In addition, the importance of clinical information for prediction was assessed. The proposed DL model showed an area under the curve (AUC) of 89.9% for predicting oxygen supplementation. Validation from the two other facilities showed an AUC > 80%. With respect to interpretation of the model, the contribution of dyspnea and the lactate dehydrogenase level was higher in the model. The DL model integrating clinical information and chest CT images had high predictive accuracy. DL-based prediction of disease severity might be helpful in the clinical management of patients with COVID-19.

Using Age-Specific Values for Pediatric HRQoL in Cost-Effectiveness Analysis: Is There a Problem to Be Solved? If So, How?

Value sets for the EQ-5D-Y-3L published to date appear to have distinctive characteristics compared with value sets for corresponding adult instruments: in many cases, the value for the worst health state is higher and there are fewer values < 0. The aim of this paper is to consider how and why values for child and adult health differ; and what the implications of that are for the use of EQ-5D-Y-3L values in economic evaluations to inform healthcare resource allocation decisions. We posit four potential explanations for the differences in values: (a) The wording of severity labels may mean the worst problems on the EQ-5D-Y-3L are descriptively less severe than those on the EQ-5D-5L; (b) Adults may genuinely consider that children are less badly affected than adults by descriptively similar health issues. That is, for any given health problem, adult respondents in valuation studies consider children's overall health-related quality of life (HRQoL) on average to be higher than that for adults; (c) Values are being sought by eliciting adults' stated preferences for HRQoL in another person, rather than in themselves (regardless of whether the 'other person' concerned is a child); and (d) The need to elicit preferences for child HRQoL that are anchored at dead = 0 invokes special considerations regarding children's survival. Existing evidence does not rule out the possibility that (c) and (d) exert an upward bias in values. We consider the implications of that for the interpretation and use of values for pediatric HRQoL. Alternative methods for valuing children's HRQoL in a manner that is not 'age specific' are possible and may help to avoid issues of non-comparability. Use of these methods would place the onus on health technology assessment bodies to reflect any special considerations regarding child quality-adjusted life-year gains.

Developing Prediction Models for COVID-19 Outcomes: A Valuable Tool for Resource-Limited Hospitals.

Coronavirus disease is a global pandemic with millions of confirmed cases and hundreds of thousands of deaths worldwide that continues to create a significant burden on the healthcare systems. The aim of this study was to determine the patient clinical and paraclinical profiles that associate with COVID-19 unfavourable outcome and generate a prediction model that could separate between high-risk and low-risk groups. The present study is a multivariate observational retrospective study. A total of 483 patients, residents of the municipality of Timișoara, the biggest city in the Western Region of Romania, were included in the study group that was further divided into 3 sub-groups in accordance with the disease severity form. Increased age (cOR=1.09, 95% CI: 1.06-1.11, p<0.001), cardiovascular diseases (cOR=3.37, 95% CI: 1.96-6.08, p<0.001), renal disease (cOR=4.26, 95% CI: 2.13-8.52, p<0.001), and neurological disorder (cOR=5.46, 95% CI: 2.71-11.01, p<0.001) were all independently significantly correlated with an unfavourable outcome in the study group. The severe form increases the risk of an unfavourable outcome 19.59 times (95% CI: 11.57-34.10, p<0.001), while older age remains an independent risk factor even when disease severity is included in the statistical model. An unfavourable outcome was positively associated with increased values for the following paraclinical parameters: white blood count (WBC; cOR=1.10, 95% CI: 1.05-1.15, p<0.001), absolute neutrophil count (ANC; cOR=1.15, 95% CI: 1.09-1.21, p<0.001) and C-reactive protein (CRP; cOR=1.007, 95% CI: 1.004-1.009, p<0.001). The best prediction model including age, ANC and CRP achieved a receiver operating characteristic (ROC) curve with the area under the curve (AUC) = 0.845 (95% CI: 0.813-0.877, p<0.001); cut-off value = 0.12; sensitivity = 72.3%; specificity = 83.9%. This model and risk profiling may contribute to a more precise allocation of limited healthcare resources in a clinical setup and can guide the development of strategies for disease management.

Impact of prediabetes with a high risk of diabetes stratified by glycated hemoglobin level on the severity of coronavirus disease 2019 during admission.

Prediabetes with a glycated hemoglobin (HbA1c) level of 5.7 - 6.4% is associated with a poor prognosis of coronavirus disease 2019 (COVID-19), but whether the degree of glycemic control is associated with COVID-19 severity is unknown. The aim of this study was to evaluate the association between the degree of glycemic control and COVID-19 severity in patients with prediabetes. We reviewed 254 patients with COVID-19 admitted to our hospital between April 2020 and September 2021. Based on their HbA1c level, patients were classified into low (HbA1c level < 5.7%), moderate (HbA1c level, 5.7 - 5.9%), and high risk of diabetes (HbA1c level, 6.0 - 6.4%). The association between risk of diabetes and the worst COVID-19 symptom in terms of severity during admission was evaluated using binary logistic regression analysis. Seventy-one and 88 patients had moderate and high risks of diabetes, respectively. Sixty-three and seven patients presented severe (requiring non-invasive oxygen therapy) or critical (intensive care unit admission or artificial respiratory management) COVID-19. The multivariate logistic regression analysis showed that a high risk of diabetes was correlated with severe COVID-19 (P = 0.01) after adjusting for baseline characteristics, whereas a moderate risk of diabetes was not (P = 0.17). Prediabetes with a high risk of diabetes is associated with the worst COVID-19 symptom in terms of severity during admission. Our findings could aid in more efficient allocation of healthcare resources to a narrower population of prediabetic patients. The online version contains supplementary material available at 10.1007/s13340-023-00643-z.

Prevalence of Chronic Obstructive Pulmonary Disease in England from 2000 to 2019.

There is considerable variation in reported chronic obstructive pulmonary disease (COPD) prevalence internationally, partly due to differing definitions in use. Accurate estimates of disease prevalence are important for allocation of health-care resources, yet UK estimates of COPD prevalence have not been updated for a decade. We calculated yearly COPD prevalence in England between 2000 and 2019 using different definitions of COPD. We used routinely collected primary care electronic healthcare record (EHR) data from the Clinical Practice Research Datalink (CPRD) Aurum database linked with secondary care data from the Hospital Episode Statistics (HES) Admitted Patient Care (APC) database. Mid-year point prevalence was calculated yearly from 2000 to 2019 in English adults aged ≥40 years using 5 definitions: (i) validated COPD, (ii) Quality and Outcomes Framework (QOF) COPD, (iii) COPD symptoms, inhaler prescription, and no asthma diagnosis, (iv) hospitalisation with COPD as any diagnosis, (v) hospitalisation with COPD as primary or secondary diagnosis. Prevalence was further stratified by gender, age group, and region. A total of 12,745,793 people were included over the 20-year period. Annual cohort sizes ranged from 4,373,538 in 2000 to 6,159,496 in 2019. Estimates of COPD prevalence increased every year from 2000 and the difference in estimated prevalence between the validated and QOF definitions has grown over time. In 2019, a COPD prevalence of 4.9% was found using validated events in either primary or secondary care (definition 1 or definition 5). Additionally, including potentially undiagnosed cases (definition 3) in the COPD definition produced an increased prevalence of 6.7%. Common definitions of COPD (eg, QOF codes), may underestimate the true prevalence. The extent of this underestimate has increased over time and could lead to under-allocation of resources where need is estimated based on these definitions. Standardisation of COPD coding in routine EHRs and metrics such as spirometry is key to accurate disease monitoring.

Prevalence of HIV in Kazakhstan 2010-2020 and Its Forecasting for the Next 10 Years.

HIV is a growing public health burden that threatens thousands of people in Kazakhstan. Countries around the world, including Kazakhstan, are facing significant problems in predicting HIV infection prevalence. It is crucial to understand the epidemiological trends of infectious diseases and to monitor the prevalence of HIV in a long-term perspective. Thus, in this study, we aimed to forecast the prevalence of HIV in Kazakhstan for 10 years from 2020 to 2030 by using mathematical modeling and time series analysis. We use statistical Autoregressive Integrated Moving Average (ARIMA) models and a nonlinear epidemic Susceptible-Infected (SI) model to forecast the HIV infection prevalence rate in Kazakhstan. We estimated the parameters of the models using open data on the prevalence of HIV infection among women and men (aged 15-49 years) in Kazakhstan provided by the Kazakhstan Bureau of National Statistics. We also predict the effect of pre-exposure prophylaxis (PrEP) control measures on the prevalence rate. The ARIMA (1,2,0) model suggests that the prevalence of HIV infection in Kazakhstan will increase from 0.29 in 2021 to 0.47 by 2030. On the other hand, the SI model suggests that this parameter will increase to 0.60 by 2030 based on the same data. Both models were statistically significant by Akaike Information Criterion corrected (AICc) score and by the goodness of fit. HIV prevention under the PrEP strategy on the SI model showed a significant effect on the reduction of the HIV prevalence rate. This study revealed that ARIMA (1,2,0) predicts a linear increasing trend, while SI forecasts a nonlinear increase with a higher prevalence of HIV. Therefore, it is recommended for healthcare providers and policymakers use this model to calculate the cost required for the regional allocation of healthcare resources. Moreover, this model can be used for planning effective healthcare treatments.

Trends in the incidence and prevalence of dysphagia requiring medical attention among adults in South Korea, 2006-2016: A nationwide population study.

The prevalence of dysphagia is increasing, resulting in socioeconomic burden, but previous reports have only been based on a limited populations. Therefore, we aimed to investigate the nationwide incidence and prevalence of dysphagia requiring medical attention to provide adequate information for healthcare planning and resource allocation. In this nationwide retrospective cohort study, the data of adults aged ≥20 years recorded from 2006 to 2016 were sourced from the Korean National Health Insurance Service database. Medical claim codes based on ICD-10-CM were used to define dysphagia and possible causes. The annual incidence and prevalence of dysphagia were calculated. Cox regression was used to estimate dysphagia risk in people with possible dysphagia etiology. Survival analysis was performed to estimate the mortality and hazard ratio of dysphagia. The crude annual incidence of dysphagia increased continuously from 7.14 in 2006 to 15.64 in 2016. The crude annual prevalence of dysphagia in 2006 was 0.09% and increased annually to 0.25% in 2016. Stroke (odds ratio [OR]: 7.86, 95% confidence interval [CI]: 5.76-6.68), neurodegenerative disease (OR: 6.20, 95% CI: 5.76-6.68), cancer (OR: 5.59, 95% CI: 5.17-6.06), and chronic obstructive pulmonary disease (OR: 2.94, 95% CI: 2.71-3.18) were associated with a high risk of dysphagia. The mortality in the dysphagia group was 3.12 times higher than that in the non-dysphagia group (hazard ratio: 3.12, 95% CI: 3.03-3.23). The incidence and prevalence of dysphagia requiring medical attention are increasing annually. The increasing trend was conspicuous in the geriatric population. The presence of stroke, neurodegenerative disease, cancer, and chronic obstructive pulmonary disease is associated with a high risk of dysphagia. Therefore, adequate screening, diagnosis, and management of dysphagia in the older population must be emphasized in geriatric healthcare.

Patterns in Patient Encounters and Emergency Department Capacity in California, 2011-2021

The health care system has undergone major changes in the past decade, and emergency department (ED) crowding has worsened over time; however, the most recent patterns in ED capacity and use in California have yet to be studied. To analyze patterns in ED capacity and utilization in California hospitals from 2011 to 2021. This retrospective cohort study used data from the California Department of Health Care Access and Information and the US Census Bureau to analyze ED facility characteristics from more than 400 general acute care hospitals with more than 320 EDs in California as well as patients who presented to those EDs between January 1, 2011, and December 31, 2021. Linear patterns (measured by percentage change) in total annual ED capacity (volume of hospital beds, EDs, ED treatment stations, and trauma centers) and ED use (ED visits by disposition and acuity) were assessed as primary outcomes. Patterns in ambulance diversion hours and the number of patients who left the ED without being seen were also examined as secondary outcomes. Visit acuity was categorized into 5 levels by increasing severity (minor, low to moderate, moderate, severe without threat, and severe with threat) based on California Department of Health Care Access and Information descriptions corresponding to Current Procedural Terminology codes. In the prepandemic period (2011-2019), the total population of California increased from 37 638 369 to 39 512 223 (5.0%; 95% CI, 4.1%-5.8%), then decreased to 39 237 836 in 2021 (0.7%; 95% CI, -3.9% to 2.5%). Over the entire study period (2011-2021), the total California population increased by 4.2% (95% CI, 3.3%-5.2%). From 2011 to 2019, the annual number of ED visits increased from 12 054 885 to 14 876 653 (23.4%; 95% CI, 20.0%-26.8%) before decreasing to 12 944 692 in 2021 (-13.0%; 95% CI, -33.1% to 7.1%); from 2011 to 2021, total ED visits increased by 7.4% (95% CI, 5.6%-9.1%). From 2011 to 2021, the total number of EDs decreased from 339 to 326 (-3.8%; 95% CI, -4.4% to -3.2%) and the total number of hospital beds decreased from 75 940 to 74 052 (-2.5%; 95% CI, -3.3% to -1.6%), while the number of ED treatment stations in these fewer EDs increased from 7159 to 8667 (21.1%; 95% CI, 19.7%-22.4%). The number of visits rated as severe with threat also increased, from 2 011 637 in 2011 to 3 375 539 in 2021 (67.8%; 95% CI, 59.7%-75.9%), while visits rated as minor decreased from 913 712 to 336 071 (-63.2%; 95% CI, -75.2% to -51.2%) over the same period. In this cohort study, multiple measures of ED capacity did not proportionally increase with the increasing demand for services; however, the COVID-19 pandemic appears to have substantially affected some of these patterns. These findings may be helpful to policy makers and health care stakeholders when planning resource allocation of limited health care resources.

Rationing in healthcare-a scoping review.

Healthcare rationing has been the subject of numerous debates and concerns in the field of health economics in recent years. It is a concept which refers to the allocation of scarce healthcare resources and involves the use of different approaches to the delivery of health services and patient care. Regardless of the approach used, healthcare rationing fundamentally involves withholding potentially beneficial programs and/or treatments from certain people. As the demands placed on health services continue to rise and with that significant increases to the cost, healthcare rationing has become increasingly popular and is deemed necessary for the delivery of affordable, patient-care services. However, public discourse on this issue has largely been centered on ethical considerations with less focus on economic rationality. Establishing the economic rationality of healthcare rationing is essential in healthcare decision-making and consideration of its adoption by healthcare authorities and organizations. This scoping review of seven articles demonstrates that the economic rationality of healthcare rationing is the scarcity of healthcare resources amidst increased demand and costs. Therefore, supply, demand, and benefits are at the core of healthcare rationing practices and influence decisions on its suitability. Given the increased costs of care and resource scarcity, healthcare rationing is a suitable practice towards ensuring healthcare resources are allocated to people in a rational, equitable, and cost-effective manner. The rising costs and demands for care place significant pressure on healthcare authorities to identify suitable strategies for the allocation of healthcare resources. Healthcare rationing as a priority-setting strategy would support healthcare authorities identify mechanisms to allocate scarce resources in a cost-effective manner. When used in the context of a priority-setting approach, healthcare rationing helps healthcare organizations and practitioners to ensure that patient populations achieve maximum benefits at reasonable costs. It represents a fair allocation of healthcare resources to all populations, especially in low-income settings.

986-P: Near-Infrared Device for Screening of Diabetes—ANODE02 Study

Background: Currently, screening for diabetes mellitus (DM) can be complex, inefficient and often resource-intensive with invasive sampling. Affordable screening tools with high specificity will be essential for smart allocation of limited healthcare resources for those in need of further diagnostics. Materials and methods: We explored the accuracy and safety of a novel compact miniaturized near-infrared (NIR) full-spectrum spectroscopy device intended for DM screening. Assessments of up to 30 NIR spectra per each middle fingernail and hand, in a cohort of people living with (n=50) or without (n=50) DM focused on detection of dichotomised DM status (&amp;lt; HbA1c 48 mmol/mol or 6.5% ≥) and specificity (%) based chemometrics outcomes, in assessment of glycated nail keratin versus an HbA1c point-of-care assay as control. Results: Demographics of a community primary care centre-based cohort (n=100) are presented in Table. Overall, no adverse NIR-device-related events were being reported for any of the over 6000 spectral measurements. Based on the best chemometrics model, the NIR-device demonstrated a specificity of 98.5% (95%CI 95.5 - 100.0) and 88.9% (80.5 - 97.3) at 48 and 42 mmol/mol cut-off levels, respectively, in detection of those without DM. Conclusions: Our preliminary results are indicative of high specificity and safe implementation of non-invasive NIR technology with potential for early detection and management of increasing DM risk. Disclosure F.Cos: Advisory Panel; AstraZeneca, Boehringer Ingelheim International GmbH, Abbott Diagnostics, Lilly, Novo Nordisk, Consultant; Glyconics Ltd, Menarini Group, Speaker's Bureau; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly. D.Lazaro-pacheco: Consultant; Glyconics Ltd. J.E.Salter: None. P.M.Paldanius: Consultant; Glyconics Ltd, Speaker's Bureau; Novartis. Funding Glyconics

Different regulatory framework for medical devices and drugs in the European Union: Impact on clinical research and health technology assessments.

Understanding whether the regulatory framework in the European Union creates different standards for medical devices and drugs, assessing whether there is evidence on the impact of those standards on clinical and HTA research, and reflecting on findings in order to propose legislative changes for promoting a more efficient allocation of resources in the healthcare systems. Reviewing and comparing the legal framework for the approval of medical devices and drugs in the EU, with a particular focus on the changes brought by Regulation (EU) 2017/745. Investigating the available information on manufacturer sponsored clinical studies and HTA-supported recommendations for medical devices and drugs. The review of the legislation identified different standards for approval of devices and drugs on their quality, safety, and performance/efficacy dimensions and fewer manufacturer sponsored clinical studies and HTA-supported recommendations for medical devices versus drugs. Policy changes could be implemented in order to promote an integrated evidence-based assessment system for a better allocation of resources in healthcare, namely: a consensual classification of medical devices from an HTA perspective, which could be used as a guide for generating outcomes in clinical investigation, and the adoption of conditional coverage practices including mandatory post-approval evidence development for performing periodic technology assessments.

Risk stratification models for predicting preventable hospitalization in commercially insured late middle-aged adults with depression

BackgroundA significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods.MethodsThis retrospective cohort study involved 71,682 commercially insured older adults aged 55–64 years diagnosed with depression. National health insurance claims were used to capture demographics, health care utilization, and health status during the base year. Health status was captured using 70 chronic health conditions, and 46 mental health conditions. The outcomes were 1- and 2-year preventable hospitalization. For each of our two outcomes, we evaluated seven modelling approaches: four prediction models utilized logistic regression with different combinations of predictors to evaluate the relative contribution of each group of variables, and three prediction models utilized machine learning approaches - logistic regression with LASSO penalty, random forests (RF), and gradient boosting machine (GBM).ResultsOur predictive model for 1-year hospitalization achieved an AUC of 0.803, with a sensitivity of 72% and a specificity of 76% under the optimum threshold of 0.463, and our predictive model for 2-year hospitalization achieved an AUC of 0.793, with a sensitivity of 76% and a specificity of 71% under the optimum threshold of 0.452. For predicting both 1-year and 2-year risk of preventable hospitalization, our best performing models utilized the machine learning approach of logistic regression with LASSO penalty which outperformed more black-box machine learning models like RF and GBM.ConclusionsOur study demonstrates the feasibility of identifying depressed middle-aged adults at higher risk of future hospitalization due to burden of chronic illnesses using basic demographic information and diagnosis codes recorded in health insurance claims. Identifying this population may assist health care planners in developing effective screening strategies and management approaches and in efficient allocation of public healthcare resources as this population transitions to publicly funded healthcare programs, e.g., Medicare in the US.

The impact of the COVID-19 pandemic on nasopharyngeal carcinoma patients – a national cancer centre experience

Purpose or objectiveThe COVID-19 pandemic has resulted in significant healthcare implications, with care for cancer patients compromised due to resource diversion towards battling the pandemic. We aim to investigate the impact of the peak wave of the pandemic in 2020 on the delivery of cancer care in Singapore, specifically via our nasopharyngeal carcinoma (NPC) treatment data. This study applies real world numbers to the impact of COVID-19 on cancer care delivery in Singapore. The choice of nasopharyngeal cancer allows a good direct estimate of common treatment measures such as time to biopsy, time to staging scans, time to treatment commencement, due to its clear protocol and algorithms for staging and treatment; thus serving as an excellent surrogate for the effectiveness and timeliness of the different aspects of cancer care delivery.Materials and methodsIn this retrospective study, we included all patients with newly diagnosed NPC from 1st January to 31st May from 2017 to 2020 at our centre. This time period was chosen as it coincided with the period in 2020 during the COVID-19 pandemic where there was the most strain on healthcare resources and the most restrictions on population movement within Singapore, which may impact on healthcare seeking behaviour. Narrowing down the time period to the first 5 months of the 4 respective years also allowed us to reduce the effect of annual seasonal variation in patient numbers seen as a result of holidays and festive periods such as the Lunar New Year and scheduled school holidays. Electronic medical records (EMR) were accessed. Only newly diagnosed NPC cases were included in our analysis. Patients with second synchronous primary malignancies or NPC disease recurrence were excluded. Data analysis was carried out using a combination of SPSS and Microsoft Excel.ResultsSignificantly, there was a reduction of 37–46.3% in newly diagnosed NPC cases during the peak of the COVID-19 pandemic from January to end May 2020 compared to the preceding three years. Despite the reduction in numbers of newly diagnosed NPC, there was no statistically significant differences in delay from biopsy to the first radiation oncology visit and from biopsy to the first day of treatment in 2020 compared to the preceding years. All the patients treated in our centre also received the standard NPC treatment for their disease stage as per international guidelines.ConclusionWe recommend a heightened awareness of the dangers of delaying cancer presentation and care in healthcare policies and resource allocation and at the same time, encourage patient’s confidence in their ability to seek care. With the resurgence of new COVID-19 variants and case numbers worldwide and in Singapore, this study focuses upon the need to be aware of the exigencies of other clinical groups in resource utilization. It would be instructive to compare this study with future long term follow up to investigate the trajectory of our cancer care delivery, as well as survival outcomes.

On resource allocation in health care: The case of concierge medicine

Resource allocation generally involves a tension between efficiency and equity, particularly in health care. The growth in exclusive physician arrangements using non-linear prices is leading to consumer segmentation with theoretically ambiguous welfare implications. We study concierge medicine, in which physicians only provide care to patients paying a retainer fee. We find limited evidence of selection based on health and stronger evidence of selection based on income. Using a matching strategy that leverages the staggered adoption of concierge medicine, we find large spending increases and no average mortality effects for patients impacted by the switch to concierge medicine.

The impacts of diagnosis-intervention packet payment on the providers' behavior of inpatient care-evidence from a national pilot city in China.

In 2020, the Chinese government developed and implemented an innovative case-based payment method under the regional global budget called the diagnosis-intervention packet (DIP) payment to pay for inpatient care. This study aims to assess the changes to inpatient care provision in hospitals after the DIP payment reform was implemented. This study used inpatient medical costs per case, the proportion of the out-of-pocket (OOP) expenditure in inpatient medical costs, and the average length of stay (LOS) of inpatient care as outcome variables, and conducted an interrupted time series analysis to evaluate changes after the DIP payment reform. January 2021 was taken as the intervention point when a national pilot city of the DIP payment reform in the Shandong province began using the DIP payment to pay for inpatient care of secondary and tertiary hospitals. The data used in this study were obtained from the aggregated monthly claim data of inpatient care of secondary and tertiary hospitals. Compared to the pre-intervention trend, the inpatient medical costs per case, the proportion of the OOP expenditure in inpatient medical costs both in tertiary and secondary hospitals significantly decreased after the intervention. After the intervention, the reduction in the inpatient medical costs per case, the proportion of the OOP expenditure in inpatient medical costs in tertiary hospital were both higher than those in secondary hospital (p < 0.001). The average LOS of inpatient care in secondary hospital significantly increased after the intervention, and it immediately increase 0.44 day after intervention (p = 0.211). Moreover, the change of average LOS of inpatient care in secondary hospital after intervention was opposite to that in tertiary hospital, it had no statistical difference (p = 0.269). In the short term, the DIP payment reform could not only effectively regulate provider behavior of inpatient care in hospitals, but also improves the rational allocation of the regional healthcare resources. However, the long-term effects of the DIP payment reform need to be investigated in the future.

EE217 Efficiency-Equity Trade-Offs in Cost-Effectiveness Analyses: An Umbrella Review of Previous Systematic Literature Reviews

Cost-effectiveness analyses (CEA) are important in healthcare decision-making and resource allocation for assessing costs and benefits of new treatments in terms of clinical efficiency. Expanding value elements, such as health equity, for new technologies in traditional CEAs has attracted interest. This study synthesized evidence on how equity concepts were considered in modified CEAs by conducting an umbrella review of systematic literature reviews (SLR).

Assessment of healthcare accessibility in Guangdong-Hong Kong-Macao Greater Bay Area

Hospitals play a crucial role in promoting public health and well-being, but their distribution and accessibility can significantly affect healthcare outcomes. The Guangdong-Hong Kong-Macau Greater Bay Area (GBA) boasts an abundance of healthcare resources; however, access to these facilities can be challenging due to population clustering and inadequate resource allocation. Unfortunately, quantifying residents' access to healthcare is complicated by the lack of a centralized database and complex transportation systems. To address these limitations, we developed a comprehensive healthcare facility dataset comprising 54,902 sites, utilizing internet maps' precise location services. Furthermore, we developed a novel framework for real-time driving-based path planning that integrates facility geographic coordinates and population density data. Our analysis revealed that 6.7% (4.8 million people) could not reach a healthcare facility within 30 min. Even after accounting for rural clinic facilities, 2.5% (1.8 million people) remained unable to access healthcare, mainly in peripheral cities. Finally, we simulated a scenario in which 12 new hospitals were added to demonstrate the effectiveness of reasonable hospital placement in improving healthcare accessibility. By investigating GBA's experience, we can derive best practices, pinpoint areas for improvement, and inform policy development aimed at enhancing healthcare accessibility for all residents. Our findings can serve as a blueprint for other provinces in China and provide insights into healthcare accessibility and resource allocation strategies for other nations with similar hierarchical healthcare systems.