An introduction to the methods of decision-analytic modelling used in economic evaluations for Dermatologists

Burden of chronic skin disease from an Asian perspective: Assessment of health state utilities and quality of life in a Singapore cohort

Background

Understanding health state utilities of skin diseases is essential for health economic evaluations in an era of rising health care costs.

Objective

To create a catalog of utility values of chronic skin diseases among Asians.

Methods

This is a cross-sectional study of adults attending a dermatology outpatient clinic from February 2019 to March 2023 with one of the following skin diseases: (1) eczema, (2) psoriasis, (3) acne vulgaris, (4) chronic urticaria, (5) pigmentary disorders, (6) hair loss, (7) viral warts, (8) fungal infections, and (9) keloids. Demographic and socioeconomic information was collected. Health status and utilities (Dermatology Life Quality Index, Skindex-16, EuroQol-5 Dimension, visual analog scale, time-trade-off, and willingness-to-pay) were measured.

Results

A total of 183 patients with a median age of 35.5 (21-77 years) years were included. Majority (76.9%) were Chinese, followed by Malays (11.5%). The time-trade-off utilities were lower than EuroQol-5 Dimension utilities across all disease severity and most skin diseases. Patients were willing to pay $740 USD or more for a hypothetical drug to cure conditions such as psoriasis, acne, hair loss, and keloids.

Limitations

Limited sample size and ethnic representation.

Conclusions

This study provides a catalog of utilities in skin diseases and highlights the strengths and challenges of different measures.

Synthesized economic evidence on the cost-effectiveness of screening familial hypercholesterolemia

BACKGROUND

Familial hypercholesterolemia (FH) is a prevalent genetic disorder with global implications for severe cardiovascular diseases. Motivated by the growing recognition of the need for early diagnosis and treatment of FH to mitigate its severe consequences, alongside the gaps in understanding the economic implications and equity impacts of FH screening, this study aims to synthesize the economic evidence on the cost-effectiveness of FH screening and to analyze the impact of FH screening on health inequality.

METHODS

We conducted a systematic review on the economic evaluations of FH screening and extracted information from the included studies using a pre-determined form for evidence synthesis. We synthesized the cost-effectiveness components involving the calculation of synthesized incremental cost-effectiveness ratios (ICERs) and net health benefit (NHB) of different FH screening strategies. Additionally, we applied an aggregate distributional cost-effectiveness analysis (DCEA) to assess the impact of FH screening on health inequality.

RESULTS

Among the 19 studies included, over half utilized Markov models, and 84% concluded that FH screening was potentially cost-effective. Based on the synthesized evidence, cascade screening was likely to be cost-effective, with an ICER of $49,630 per quality-adjusted life year (QALY). The ICER for universal screening was $20,860 per QALY as per evidence synthesis. The aggregate DCEA for six eligible studies presented that the incremental equally distributed equivalent health (EDEH) exceeded the NHB. The difference between EDEH and NHB across the six studies were 325, 137, 556, 36, 50, and 31 QALYs, respectively, with an average positive difference of 189 QALYs.

CONCLUSIONS

Our research offered valuable insights into the economic evaluations of FH screening strategies, highlighting significant heterogeneity in methods and outcomes across different contexts. Most studies indicated that FH screening is cost-effective and contributes to improving overall population health while potentially reducing health inequality. These findings offer implications that policies should promote the implementation of FH screening programs, particularly among younger population. Optimizing screening strategies based on economic evidence can help identify the most effective measures for improving health outcomes and maximizing cost-effectiveness.

Evaluation of Internet-based pharmaceutical care effect on young and middle-aged patients with hypertension by the principal component analysis and the Markov cohort during COVID-19 pandemic

BACKGROUND

Pharmaceutical care has the potential to improve hypertension control rates in young and middle-aged patients. Due the COVID-19 epidemic, standard intervention methods may not be applicable. We propose establishing an internet-based pharmaceutical care (IPC) route to improve blood pressure control in young and middle-aged patients with hypertension. An evaluation method based on Principal Component Analysis (PCA) and Orthogonal Partial Least-Discriminant Analysis (OPLS-DA) was established to evaluate the effect of the IPC method.

METHODS

1) Internet-based Pharmaceutical care (IPC) was provided by pharmacists mainly using Wechat software for one year after enrollment; 2) PCA and OPLS-DA were applied to analyze questionnaire reliability and data variability; 3) Markov cohort was used to evaluate the IPC effect.

RESULTS

Ninety-seven young and middle-aged patients were enrolled. 96 patients received the IPC. 1) The blood pressure control rate increased to 71.88% after IPC in 96 patients. 2) After conducting PCA and OPLS-DA analysis, 10 questions in the questionnaire were significantly improved after the IPC. 3) Markov cohort results showed that patient survival after 28 cycles was 18.62 years and the quality-adjusted life year (QALY) was extended by 5.40 years. The cumulative cost-effectiveness ratio was ¥87.10 per QALY.

CONCLUSIONS

The IPC method could significantly improve the blood pressure control rate of patients. The questionnaire analysis method based on PCA and OPLS-DA is an effective method to evaluate the effect of the IPC method. The Markov cohort showed that the IPC had an effect on blood pressure control rate changes. Patients had a strong willingness to pay for IPC.

Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models

Background

We aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS.

Methods

This is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history. Resampling technology was used to preprocess the data imbalance problems, and then MetS risk prediction models were constructed based on logistic regression (LR) and decision tree (DT). In addition, nomograms and tree diagrams of DT were used to explain and visualize the model.

Results

Of the 25,542 participants included in the study, 3,267 (12.8%) were diagnosed with MetS, and 22,275 (87.2%) were diagnosed with non-MetS. Both the LR and DT models based on the random undersampling dataset had good AUROC values (0.846 and 0.913, respectively). The accuracy, sensitivity, specificity, and AUROC values of the DT model were higher than those of the LR model. Based on a random undersampling dataset, the LR model showed that exercises such as walking (OR=0.769) and running (OR= 0.736) were protective factors against MetS. Age 60 ~ 74 years (OR=1.388), previous diabetes (OR=8.902), previous hypertension (OR=2.830), fatty liver (OR=3.306), smoking (OR=1.541), high systolic blood pressure (OR=1.044), and high diastolic blood pressure (OR=1.072) were risk factors for MetS; the DT model had 7 depth layers and 18 leaves, with BMI as the root node of the DT being the most important factor affecting MetS, and the other variables in descending order of importance: SBP, previous diabetes, previous hypertension, DBP, fatty liver, smoking, and exercise.

Conclusions

Both DT and LR MetS risk prediction models have good prediction performance and their respective characteristics. Combining these two methods to construct an interpretable risk prediction model of MetS can provide methodological references for the prevention and control of MetS.