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The introduction of universal health insurance coverage aims to provide equal accessibility and affordability of health care, but whether such a policy eliminates health inequalities has not been conclusively determined. This research aims to examine the healthcare outcomes of oral cancer and determine whether the universal coverage system in Taiwan has reduced health inequality. Linking the databases of the National Cancer Registry with the National Mortality Registry in Taiwan, we stratified patients with oral squamous cell carcinoma by gender and income to estimate the incidence rate, cumulative incidence rate aged from 20 to 79 (CIR20-79), life expectancy, and expected years of life lost (EYLL). The difficulties with asymmetries and short follow-up periods were resolved through applying survival analysis extrapolation methods. While all people showed a general improvement in life expectancy after the introduction of the NHI, the estimated change in EYLL's of the high-, middle-, and low-income female patients were found to have +0.3, -0.5 and -7 years of EYLL, respectively, indicating a reduction in health inequality. Improvements for the male patients were unremarkable. There was no drop in the CIR20-79 of oral cancer in disadvantaged groups as in those with higher incomes. Universal coverage alone may not reduce health inequality across different income groups for oral cancer unless effective preventive measures are implemented for economically disadvantaged regions.

BackgroundThe proportion of the elderly aged 65 years old or above will reach 16% in 2050 worldwide. Early investment in effective prevention would generally reduce the morbidity, complication, functional disability, and mortality of most chronic illnesses and save resources in both healthcare and social services. This research aims to investigate how the optimal allocation of medical resources between prevention and treatment adds value to the population’s health as well as examine the interaction between ageing, health, and economic performance.MethodsThis research undertakes ageing-health analyses by developing an economic growth model. Based on the Organization for Economic Co-Operation and Development (OECD) countries’ experiences over the period from 2000 to 2017, this research further examines the hypothesis that an ageing society could increase demand for preventive and curative healthcare.ResultsTheoretical analysis found that the prevention share for maximizing growth is the same as that for minimizing ill health and maximizing welfare; this share increases with treatment share and ageing ratios. Estimation results from OECD countries’ experiences indicate that when treatment share increases by 1%, the prevention demand increases by 0.036%. A one-percent increase in the ageing ratio yields a change in prevention share of 0.0368%. The optimal share of prevention health expenditure to GDP would be 1.175% when the prevalence rate of ill health isat 6.13%; a higher or lower share of prevention would be accompanied with a higher prevalence of ill health. For example, a zero and 1.358% preventive health expenditure would be associated with an 18.01% prevalence of ill health, while the current share of prevention of 0.237% is associated with a 10.26% prevalence of ill health.ConclusionThis study shows that appropriate prevention is associated with decreases in the prevalence rates of ill health, which in turn attains sustainable growth in productivity. Too much prevention, however, could lead to higher detection of new chronic diseases with mild severity, which would result in longer illness duration, and higher prevalence rates of ill health. With suitable allocation of medical resources, the economic growth rate will help to cancel out increases in healthcare spending for the elderly and for expenses needed for the improvement of the population’s health as a whole.

This research examines whether the Coronavirus disease 2019 (COVID-19) did harm to the population’s health through comparing the changes in the life expectancy of Canadians with those of Australians over the period from March 2019 to February 2021 by using a difference-in-differences (DID) estimation method. We found that the pandemic did cause differences in life expectancies between Canada and Australia, probably because of different initial control policies for COVID-19. This study uses the indicator of disability-adjusted life years (DALYs) to measure the societal health burden, which was corroborated by estimating temporal productivity loss (TPL) and permanent productivity loss (PPL) based on the human capital approach (HCA) using data from Health Canada. The societal health burden in Canada amounted to 6.493 DALYs per 1000 male persons and 5.316 DALYs per 1000 female persons. The economy’s permanent productivity loss was around USD 5.3 billion, while the temporary productivity loss was around USD 3 billion from February 2020 to April 2022. The sum of the above two losses amounted to 0.477% of the GDP in 2019. Swift and decisive decisions at the very early stage of a pandemic can nip contagions in the bud before numbers get out of hand and would be less damaging to people’s health and the economy, as seen in Australia, in contrast to what happened in Canada. We thus recommend that such policies plus telecommunication systems in healthcare services be implemented early on to cope with the future outbreak of any emerging infectious diseases such as COVID-19.

Background Advanced medical technologies can prolong life of stroke survivors. Dynamic change of health outcomes provides essential information to manage stroke. Mathematical models, to extrapolate health status over a lifetime from cross-sectional data, can be used to investigate long term health outcomes among stroke survivors. This study aimed to estimate the health outcomes of ischemic stroke (IS) and intracerebral hemorrhage (ICH) at each survival time point. Methods The cohort of 5391 patients with IS and ICH stroke, registered at Ramathibodi Hospital from 2005 to 2013, were followed up regarding survival status until 2016 with the National Mortality Registry. Survival functions were extrapolated over 50 years to age- and sex-matched referents simulated from the national data of the Thailand National Health Statistic Office. From July to December 2016, the EuroQoL 5-dimension questionnaire was used to measure quality of life (QoL) among 300 consecutive, cross-sectional subsamples. The survival functions were then adjusted by the utility values of QoL for the stroke cohort to estimate quality adjusted life expectancy (QALE). Results The average health utility values were lower in the initial months, then slowly increased to stable levels. However, male stroke survivors presented higher health utility than females. Throughout lifetime estimation, patients with IS stroke exhibit better health outcomes than those with ICH [10.27 vs. 7.74 quality-adjusted life years (QALYs)]. Patients with ICH presented a significantly decreased QoL than patients with IS (16.15 and 8.41 QALYs). Conclusion Preventing stroke can save people from reduced years and QoL, which can be quantified by loss-of-QALE in QALY units to compare health benefits from prevention, clinical diagnosis and direct treatment.

This study cross-validated the factor structure of the Self-Stigma Scale-Short (SSS-S) in a cohort of patients with mental illness in southern Taiwan. The measurement invariance of the SSS-S factor structure across mental illness and gender was also examined. The sample consisted of 161 patients with schizophrenia (51.6% males; mean age ± SD = 40.53 ± 10.38 years) and 189 patients with other mental illnesses (34.9% males; mean age = 46.52 ± 11.29 years). The internal reliability (total score: α = 0.948) and concurrent validity (r = 0.335 to 0.457 with Depression and Somatic Symptoms Scale; r = -0.447 to -0.556 with WHOQOL-BREF) of the SSS-S were both satisfactory, and the results verified that the factor structure in our Taiwan sample (RMSEA = 0.0796, CFA = 0.992) was the same as that of the Hong Kong population. In addition, the results supported the measurement invariance of the SSS-S across mental illness (ΔRMSEAs = -0.0082 to -0.0037, ΔCFAs = 0.000) and gender (ΔRMSEAs = -0.0054 to -0.0008, ΔCFAs = -0.001 to 0.000). Future studies can use the SSS-S to compare self-stigma between genders and between patients with different kinds of mental illnesses.

Infection with COVID-19 could result in lockdown, quarantine of contacts, absenteeism from work, and temporary productivity loss. This research aims to calculate (1) how the pandemic affects on-the-job probability and earnings for the working population, and (2) how much productivity loss is associated with self or a family member sick with COVID-19. Based on data collected from the U.S Research and Development Survey (RANDS), this research projects the relationship between on-the-job possibility and age of the index group and calculates the employment possibilities of the index group relative to the healthy group, namely the employment ratio. The weekly loss of productivity, presented by earnings, associated with COVID-19 for groups aged 18–44 years and 45–64 years was calculated, since the 18- to 64-year-old population is an economy’s active workforce. Analytical results indicate that the older the age group, the lower the on-the-job possibility, and the higher the weekly productivity loss due to self or a family member being sick from COVID-19. For the group aged 45–64 years, the employment ratio of the index group relative to the healthy group dropped from 0.863 to 0.39, corresponding to a weekly productivity loss of 136–590 US dollars. The overall impact would be about a 9% loss in GDP. Infected or quarantined people would be confined to working in relatively isolated offices or places to allow for social distancing. Proactive health promotion in the workplace plus reactive work through telecommunication systems would reduce such losses. Such preparedness needs to be implemented early for more vulnerable workers who are of middle or old age and/or those comorbid with diabetes.

In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (<35 weeks) were 92.6%, 92.9%, and 93.1% in DT, DNN, and LR respectively. Adjusted odds ratios examined with LR for low WBCs [≤ 3.2 (x10 3 / μL )], fever (≥38°C), low platelet counts [< 100 (x10 3 / μL )], and elderly (≥ 65 years) were 5.17 [95% confidence interval (CI): 3.96–6.76], 3.17 [95%CI: 2.74–3.66], 3.10 [95%CI: 2.44–3.94], and 1.77 [95%CI: 1.50–2.10], respectively. Our prediction models can readily be used in resource-poor countries where viral/serologic tests are inconvenient and can also be applied for real-time syndromic surveillance to monitor trends of dengue cases and even be integrated with mosquito/environment surveillance for early warning and immediate prevention/control measures. In other words, a local community hospital/clinic with an instrument of complete blood counts (including platelets) can provide a sentinel screening during outbreaks. In conclusion, the machine learning approach can facilitate medical and public health efforts to minimize the health threat of dengue epidemics. However, laboratory confirmation remains the primary goal of surveillance and outbreak investigation.

Low-dose computed tomography screening can be used to diagnose lung cancer at a younger age compared to no screening. Real-world studies observing mortality after lung cancer diagnosis are subject to lead-time bias. This study developed a method using a nationwide cancer registry and stage shift from trial for the adjustment of lead-time bias. 78,897 Taiwanese nationwide lung cancer patients aged 55–82 were matched with 788,820 referents randomly selected from the general population at a ratio of 1:10 by age, sex, calendar year, and comorbidities, to estimate the pathology- and stage-specific life expectancy (LE). Loss-of-LE is the difference between the LE of cancer patients and that of referents. By multiplying LE and loss-of-LE by the pathology and stage shift in the National Lung Screening Trial (NLST), we compared the effectiveness of cancer screening measured by LE gained and loss-of-LE saved. The mean LEs of stage IA and IV adenocarcinoma were 14.5 and 1.9 years, respectively, indicating a LE gain of 12.6 years. However, the mean loss-of-LEs of stage IA and IV adenocarcinoma were 3.7 and 15.1 years, respectively, with a saving of only 11.4 years, implying an adjustment of different distributions of age, sex, and calendar year of diagnosis from stage shift and a reduction in lead-time bias. Applying such estimations on the results of 10,000 participants with the same pathology and stage shift in the NLST, the benefit of screening using LE gained would be 410.3 (95% prediction interval: 328.4 to 503.3) years. It became 297.1 (95% prediction interval: 187.8 to 396.4) years when using loss-of-LE saved, indicating the former approach would overestimate the effectiveness by 38%. Our approach of multiplying loss-of-LE by pathology and stage shift to estimate loss-of-LE saved could adjust for different distributions of age, sex, and calendar year at early diagnosis and reduce lead-time bias.

Health services provided through the telecommunications system aim to improve the population’s health and well-being. This research aims to explore what digital, economic, and health factors are associated with the provision of telehealth services, especially in ageing communities. Applying Organization for Economic Cooperation and Development (OECD) countries’ experiences, this research tries to construct a logistic regression model between adopting a telehealth system or not, a binary outcome variable, and a group of potentially explanatory variables. Estimation results showed that there were thresholds for telehealth provision: The demand for telehealth service usually began when the provision of telecommunication accessibility reached 50%, the proportion of elders exceeded 10%, or the proportion of health spending occupied more than 3–5% of the gross domestic product (GDP); the slope of each variable seemed to correspond with an increase in demand for such a provision. A growing number of individuals in OECD countries are now readily served by telehealth systems under the COVID-19 pandemic. These findings could be regarded as a model for other countries for implementing the necessary infrastructure early on when any of these parameters reaches its threshold. Moreover, telehealth applied in developing countries could be elevated for wider populations to access basic health services and for the remote delivery of health care. A rational decision could be made to appropriately use additional resources in telehealth provision. With accessible e-health services, the population’s health could be improved, which in turn would possibly increase productivity and social welfare.

A sample of heroin users (n = 250) in methadone maintenance treatment (MMT) was used in this cross-sectional study to clarify the mechanisms of the effects of stigma on quality of life (QoL) through psychological distress and social functioning. All the participants had their self-stigma, psychological distress, social functioning, and QoL measured. Psychological distress and social functioning were proposed to be mediators between self-stigma and QoL. Several linear models using structural equation modeling were conducted to examine the mediated effects. The negative effects of self-stigma on QoL were significantly mediated by psychological distress, as self-stigma directly and significantly influenced psychological distress, but not social functioning. This study demonstrated a linear model describing the effects of self-stigma on QoL for opioid-dependent individuals; psychological distress was also an important mediator between self-stigma and their QoL. Clinicians were able to notice the importance of reducing self-stigma for opioid-dependent individuals according to the following results: higher levels of self-stigma were associated with high psychological distress, decreased social functioning, and impaired QoL. Our mediation findings suggest that treating psychological distress is better than treating social functioning if we want to eliminate the effects of self-stigma on QoL for heroin users.