Impact of an innovative bundled payment to TB health care providers in China: an economic simulation analysis.

BACKGROUND: Tuberculosis is the second most deadly infectious disease after COVID-19 and the 13th leading cause of death worldwide. Among the 30 countries with a high burden of TB, China ranks third in the estimated number of TB cases. China is in the top four of 75 countries with a deficit in funding for TB strategic plans. To reduce costs and improve the effectiveness of TB treatment in China, the NHSA developed an innovative BP method. This study aimed to simulate the effects of this payment approach on different stakeholders, reduce the economic burden on TB patients, improve the quality of medical services, facilitate policy optimization, and offer a model for health care payment reforms that can be referenced by other regions throughout the world. METHODS: We developed a simulation model based on a decision tree analysis to project the expected effects of the payment method on the potential financial impacts on different stakeholders. Our analysis mainly focused on comparing changes in health care costs before and after receiving BPs for TB patients with Medicare in the pilot areas. The data that were used for the analysis included the TB service claim records for 2019-2021 from the health insurance agency, TB prevalence data from the local Centre for Disease Control, and health care facilities' revenue and expenditure data from the Statistic Yearbook. A Monte Carlo randomized simulation model was used to estimate the results. RESULTS: After adopting the innovative BP method, for each TB patient per year, the total annual expenditure was estimated to decrease from $2,523.28 to $2,088.89, which is a reduction of $434.39 (17.22%). The TB patient out-of-pocket expenditure was expected to decrease from $1,249.02 to $1,034.00, which is a reduction of $215.02 (17.22%). The health care provider's revenue decreased from $2,523.28 to $2,308.26, but the health care provider/institution's revenue-expenditure ratio increased from -6.09% to 9.50%. CONCLUSIONS: This study highlights the potential of BPs to improve medical outcomes and control the costs associated with TB treatment. It demonstrates its feasibility and advantages in enhancing the coordination and sustainability of medical services, thus offering valuable insights for global health care payment reform.

Physician Group Practices Accrued Large Bonuses Under Medicare's Bundled Payment Model, 2018-20.

The Bundled Payments for Care Improvement Advanced Model (BPCI-A), a voluntary Alternative Payment Model for Medicare, incentivizes hospitals and physician group practices to reduce spending for patient care episodes below preset target prices. The experience of physician groups in BPCI-A is not well understood. We found that physician groups earned $421 million in incentive payments during BPCI-A's first four performance periods (2018-20). Target prices were positively associated with bonuses, with a mean reconciliation payment of $139 per episode in the lowest decile of target prices and $2,775 in the highest decile. In the first year of the COVID-19 pandemic, mean bonuses increased from $815 per episode to $2,736 per episode. These findings suggest that further policy changes, such as improving target price accuracy and refining participation rules, will be important as the Centers for Medicare and Medicaid Services continues to expand BPCI-A and develop other bundled payment models.

Did private election administration funding advantage Democrats in 2020?

Private donors contributed more than $350 million to local election officials to support the administration of the 2020 election. Supporters argue these grants were neutral and necessary to maintain normal election operations during the pandemic, while critics worry these grants mostly went to Democratic strongholds and tilted election outcomes. How much did these grants shape the 2020 presidential election? To answer this question, we collect administrative data on private election administration grants and election outcomes. We then use advances in synthetic control methods to compare presidential election results and turnout in counties that received grants to counties with similar election results and turnout before 2020. While Democratic counties were more likely to apply for a grant, we find that the grants did not have a noticeable effect on the presidential election. Our estimates of the average effect on Democratic vote share range from 0.03 to 0.36 percentage points. Our estimates of the average effect of receiving a grant on turnout range from 0.03 to 0.14 percentage points. Across specifications, our 95% CIs typically include negative effects and all fail to include effects on Democratic vote share larger than 0.58 percentage points and effects on turnout larger than 0.40 percentage points. We characterize the magnitude of our effects by asking how large they are compared to the margin by which Biden won the 2020 election. In simple bench-marking exercises, we find that the effects of the grants were likely too small to have changed the outcome of the 2020 presidential election.

Evaluating social protection mitigation effects on HIV/AIDS and Tuberculosis through a mathematical modelling study.

The global economic downturn due to the COVID-19 pandemic, war in Ukraine, and worldwide inflation surge may have a profound impact on poverty-related infectious diseases, especially in low-and middle-income countries (LMICs). In this work, we developed mathematical models for HIV/AIDS and Tuberculosis (TB) in Brazil, one of the largest and most unequal LMICs, incorporating poverty rates and temporal dynamics to evaluate and forecast the impact of the increase in poverty due to the economic crisis, and estimate the mitigation effects of alternative poverty-reduction policies on the incidence and mortality from AIDS and TB up to 2030. Three main intervention scenarios were simulated-an economic crisis followed by the implementation of social protection policies with none, moderate, or strong coverage-evaluating the incidence and mortality from AIDS and TB. Without social protection policies to mitigate the impact of the economic crisis, the burden of HIV/AIDS and TB would be significantly larger over the next decade, being responsible in 2030 for an incidence 13% (95% CI 4-31%) and mortality 21% (95% CI 12-34%) higher for HIV/AIDS, and an incidence 16% (95% CI 10-25%) and mortality 22% (95% CI 15-31%) higher for TB, if compared with a scenario of moderate social protection. These differences would be significantly larger if compared with a scenario of strong social protection, resulting in more than 230,000 cases and 34,000 deaths from AIDS and TB averted over the next decade in Brazil. Using a comprehensive approach, that integrated economic forecasting with mathematical and epidemiological models, we were able to show the importance of implementing robust social protection policies to avert a significant increase in incidence and mortality from AIDS and TB during the current global economic downturn.

Socioeconomic impact and sufficiency of government financial support during COVID-19 pandemic: A retrospective study.

This study examines the socioeconomic impact of the COVID-19 pandemic and the sufficiency of government support. Based on an online survey with 920 respondents, the cross-tabulation and binary logistic regression results show: firstly, in terms of loss of income, male respondents are more likely to have a loss of income as compared to female counterparts, and secondly, among different categories of employment status, the self-employed respondents are the most vulnerable group, given that more than 20 percent of them experienced loss of income due to the COVID-19 pandemic. Moreover, respondents working in small-and-medium enterprises (SMEs) and the informal sector are more likely to face loss of income as compared to respondents working in other sectors of employment. Likewise, respondents without tertiary education level are more likely to have a loss of income as compared to respondents with university certification. The baseline results highlight the insufficiency of government financial support programs based on the perspective of Malaysians from different demographic backgrounds. As a policy implication, the findings could guide the State in formulating the right policies for target groups who need more assistance than others in the community.

Food price trends during the COVID-19 pandemic in Brazil.

The present study aims to analyze the trends in food price in Brazil with emphasis on the period of the Covid-19 pandemic (from March 2020 to March 2022). Data from the Brazilian Household Budget Survey and the National System of Consumer Price Indexes were used as input to create a novel data set containing monthly prices (R$/Kg) for the foods and beverages most consumed in the country between January 2018 and March 2022. All food items were divided according to the Nova food classification system. We estimated the mean price of each food group for each year of study and the entire period. The monthly price of each group was plotted to analyze changes from January 2018 to March 2022. Fractional polynomial models were used to synthesize price changes up to 2025. Results of the present study showed that in Brazil unprocessed or minimally processed foods and processed culinary ingredients were more affordable than processed and ultra-processed foods. However, trend analyses suggested the reversal of the pricing pattern. The anticipated changes in the prices of minimally processed food relative to ultra-processed food, initially forecasted for Brazil, seem to reflect the impact of the Covid-19 pandemic on the global economy. These results are concerning as the increase in the price of healthy foods aggravates food and nutrition insecurity in Brazil. Additionally, this trend encourages the replacement of traditional meals for the consumption of unhealthy foods, increasing a health risk to the population.

Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation.

BACKGROUND: Respiratory pathogens inflict a substantial burden on public health and the economy. Although the severity of symptoms caused by these pathogens can vary from asymptomatic to fatal, the factors that determine symptom severity are not fully understood. Correlations in symptoms between infector-infectee pairs, for which evidence is accumulating, can generate large-scale clusters of severe infections that could be devastating to those most at risk, whilst also conceivably leading to chains of mild or asymptomatic infections that generate widespread immunity with minimal cost to public health. Although this effect could be harnessed to amplify the impact of interventions that reduce symptom severity, the mechanistic representation of symptom propagation within mathematical and health economic modelling of respiratory diseases is understudied. METHODS AND FINDINGS: We propose a novel framework for incorporating different levels of symptom propagation into models of infectious disease transmission via a single parameter, α. Varying α tunes the model from having no symptom propagation (α = 0, as typically assumed) to one where symptoms always propagate (α = 1). For parameters corresponding to three respiratory pathogens-seasonal influenza, pandemic influenza and SARS-CoV-2-we explored how symptom propagation impacted the relative epidemiological and health-economic performance of three interventions, conceptualised as vaccines with different actions: symptom-attenuating (labelled SA), infection-blocking (IB) and infection-blocking admitting only mild breakthrough infections (IB_MB). In the absence of interventions, with fixed underlying epidemiological parameters, stronger symptom propagation increased the proportion of cases that were severe. For SA and IB_MB, interventions were more effective at reducing prevalence (all infections and severe cases) for higher strengths of symptom propagation. For IB, symptom propagation had no impact on effectiveness, and for seasonal influenza this intervention type was more effective than SA at reducing severe infections for all strengths of symptom propagation. For pandemic influenza and SARS-CoV-2, at low intervention uptake, SA was more effective than IB for all levels of symptom propagation; for high uptake, SA only became more effective under strong symptom propagation. Health economic assessments found that, for SA-type interventions, the amount one could spend on control whilst maintaining a cost-effective intervention (termed threshold unit intervention cost) was very sensitive to the strength of symptom propagation. CONCLUSIONS: Overall, the preferred intervention type depended on the combination of the strength of symptom propagation and uptake. Given the importance of determining robust public health responses, we highlight the need to gather further data on symptom propagation, with our modelling framework acting as a template for future analysis.

Utilisation, out-of-pocket payments and access before and after COVID-19: Thailand's Universal Health Coverage Scheme.

The goal of Universal Health Coverage (UHC) is that everyone needing healthcare can access quality services without financial hardship. Recent research covering countries with UHC systems documents the emergence, and acceleration following the COVID-19 pandemic of unapproved informal payment systems by providers that collect under-the-table payments from patients. In 2001, Thailand extended its '30 Baht' government-financed coverage to all uninsured people with little or no cost sharing. In this paper, we update the literature on the performance of Thailand's Universal Health Coverage Scheme (UCS) with data covering 2019 (pre-COVID-19) through 2021. We find that access to care for Thailand's UCS-covered population (53 million) is similar to access provided to populations covered by the other major public health insurance schemes covering government and private sector workers, and that, unlike reports from other UHC countries, no evidence that informal side payments have emerged, even in the face of COVID-19 related pressures. However, we do find that nearly one out of eight Thailand's UCS-covered patients seek care outside the UCS delivery system where they will incur out-of-pocket payments. This finding predates the COVID-19 pandemic and suggests the need for further research into the performance of the UHC-sponsored delivery system.

Cost-effectiveness of mask mandates on subways to prevent SARS-CoV-2 transmission in the United States.

BACKGROUND: Community-based mask wearing has been shown to reduce the transmission of SARS-CoV-2. However, few studies have conducted an economic evaluation of mask mandates, specifically in public transportation settings. This study evaluated the cost-effectiveness of implementing mask mandates for subway passengers in the United States by evaluating its potential to reduce COVID-19 transmission during subway travel. MATERIALS AND METHODS: We assessed the health impacts and costs of subway mask mandates compared to mask recommendations based on the number of infections that would occur during subway travel in the U.S. Using a combined box and Wells-Riley infection model, we estimated monthly infections, hospitalizations, and deaths averted under a mask mandate scenario as compared to a mask recommendation scenario. The analysis included costs of implementing mask mandates and COVID-19 treatment from a limited societal perspective. The cost-effectiveness (net cost per averted death) of mandates was estimated for three different periods based on dominant SARS-CoV-2 variants: Alpha, Beta, and Gamma (November 2020 to February 2021); Delta (July to October 2021); and early Omicron (January to March 2022). RESULTS: Compared with mask recommendations only, mask mandates were cost-effective across all periods, with costs per averted death less than a threshold of $11.4 million (ranging from cost-saving to $3 million per averted death). Additionally, mask mandates were more cost-effective during the early Omicron period than the other two periods and were cost saving in January 2022. Our findings showed that mandates remained cost-effective when accounting for uncertainties in input parameters (e.g., even if mandates only resulted in small increases in mask usage by subway ridership). CONCLUSIONS: The findings highlight the economic value of mask mandates on subways, particularly during high virus transmissibility periods, during the COVID-19 pandemic. This study may inform stakeholders on mask mandate decisions during future outbreaks of novel viral respiratory diseases.

Long-term health conditions and UK labour market outcomes during the COVID-19 pandemic.

BACKGROUND: Long-term health conditions can affect labour market outcomes. COVID-19 may have increased labour market inequalities, e.g. due to restricted opportunities for clinically vulnerable people. Evaluating COVID-19's impact could help target support. AIM: To quantify the effect of several long-term conditions on UK labour market outcomes during the COVID-19 pandemic and compare them to pre-pandemic outcomes. METHODS: The Understanding Society COVID-19 survey collected responses from around 20,000 UK residents in nine waves from April 2020-September 2021. Participants employed in January/February 2020 with a variety of long-term conditions were matched with people without the condition but with similar baseline characteristics. Models estimated probability of employment, hours worked and earnings. We compared these results with results from a two-year pre-pandemic period. We also modelled probability of furlough and home-working frequency during COVID-19. RESULTS: Most conditions (asthma, arthritis, emotional/nervous/psychiatric problems, vascular/pulmonary/liver conditions, epilepsy) were associated with reduced employment probability and/or hours worked during COVID-19, but not pre-pandemic. Furlough was more likely for people with pulmonary conditions. People with arthritis and cancer were slower to return to in-person working. Few effects were seen for earnings. CONCLUSION: COVID-19 had a disproportionate impact on people with long-term conditions' labour market outcomes.

Investing in health preparedness, response and resilience: a genomics costing tool focused on next generation sequencing.

The world has seen unprecedented gains in the global genomic surveillance capacities for pathogens with pandemic and epidemic potential within the last 4 years. To strengthen and sustain the gains made, WHO is working with countries and partners to implement the Global Genomic Surveillance Strategy for Pathogens with Pandemic and Epidemic Potential 2022-2032. A key technical product developed through these multi-agency collaborative efforts is a genomics costing tool (GCT), as sought by many countries. This tool was developed by five institutions - Association of Public Health Laboratories, FIND, The Global Fund to Fight AIDS, Tuberculosis and Malaria, UK Health Security Agency, and the World Health Organization. These institutions developed the GCT to support financial planning and budgeting for SARS-CoV-2 next-generation sequencing activities, including bioinformatic analysis. The tool costs infrastructure, consumables and reagents, human resources, facility and quality management. It is being used by countries to (1) obtain costs of routine sequencing and bioinformatics activities, (2) optimize available resources, and (3) build an investment case for the scale-up or establishment of sequencing and bioinformatics activities. The tool has been validated and is available in English and Russian at https://www.who.int/publications/i/item/9789240090866. This paper aims to highlight the rationale for developing the tool, describe the process of the collaborative effort in developing the tool, and describe the utility of the tool to countries.

A time-varying analysis between economic uncertainty and tourism development in Singapore.

Tourism development (TO) is seen as a viable solution to address economic policy uncertainty (EPU) risks. However, previous studies have largely ignored the relationship between short, medium, and long term by decomposing TO and EPU index at different time-frequency scales, especially in Singapore. In this study, the Wavelet tools analysis and a rolling window algorithm are employed to re-visit the causal relationship between EPU, industrial production index (IPI), government revenue (GR), and tourism development (TO) in Singapore from January 2003 to February 2022. The findings revealed the heterogeneous effects of EPU on TO at different time horizons in terms of importance and magnitude over time. A rise in EPU results in a decline in TO at the low frequencies, indicating that EPU has a detrimental effect on TO over the short term. Conversely, in the long term, an increase in TO results in a decrease in EPU. Furthermore, the outcome also indicated that there is a uni-directional causality running from TO to EPU, GR and IPI. Expressly, we confirm that the negative co-movement is more pronounced in the aftermath of the COVID-19 crisis, particularly for EPU, and GR at low-medium frequencies throughout the research period. The findings provide tourism policymakers with insight to develop strategic plans for tourism development that consider the effects of economic policy uncertainty. By understanding how uncertainty impacts tourism, governments can tailor development strategies to mitigate risks and capitalize on opportunities.

Evaluating private hospital performance before and during COVID-19 in China.

The coronavirus disease 2019 (COVID-19) pandemic had a tremendous impact on the global medical system. The development of private hospitals is an important measure to deepen the reform of China's medical and health system, and an important driving force to improve the effective supply of medical services. This study aims to compare the performance of China's private hospitals before and during COVID-19 and determine the factors that affect hospital profitability between the 2 periods. Data are collected from 10 private listed hospitals from 2017 to 2022, and ratio analysis is used to measure hospital performance in 5 aspects, namely profitability, liquidity, leverage, activity (efficiency), and cost coverage. Multiple regression analysis is used to determine the influencing factors of hospital profitability. The results show a negative impact of COVID-19 on private hospital performance. Specifically, regardless of region, hospital profitability, liquidity, and cost coverage were reduced due to COVID-19, while hospital leverage was increased. COVID-19 had also an impact on hospital efficiency. In addition, before COVID-19, current ratio and cost coverage ratio were the determinants of hospital profitability, while only cost coverage ratio affected hospital profitability during the COVID-19 outbreak. We provide evidence that COVID-19 had an impact on China private hospitals, and the findings will aid private hospitals in improving their performance in the post-COVID-19 era.

Lessons Learned from Model-based Economic Evaluations of COVID-19 Drug Treatments Under Pandemic Circumstances: Results from a Systematic Review.

BACKGROUND: Following clinical research of potential coronavirus disease 2019 (COVID-19) treatments, numerous decision-analytic models have been developed. Due to pandemic circumstances, clinical evidence was limited and modelling choices were made under great uncertainty. This study aimed to analyse key methodological characteristics of model-based economic evaluations of COVID-19 drug treatments, and specifically focused on modelling choices which pertain to disease severity levels during hospitalisation, model structure, sources of effectiveness and quality of life and long-term sequelae. METHODS: We conducted a systematic literature review and searched key databases (including MEDLINE, EMBASE, Web of Science, Scopus) for original articles on model-based full economic evaluations of COVID-19 drug treatments. Studies focussing on vaccines, diagnostic techniques and non-pharmaceutical interventions were excluded. The search was last rerun on 22 July 2023. Results were narratively synthesised in tabular form. Several aspects were categorised into rubrics to enable comparison across studies. RESULTS: Of the 1047 records identified, 27 were included, and 23 studies (85.2%) differentiated patients by disease severity in the hospitalisation phase. Patients were differentiated by type of respiratory support, level of care management, a combination of both or symptoms. A Markov model was applied in 16 studies (59.3%), whether or not preceded by a decision tree or an epidemiological model. Most cost-utility analyses lacked the incorporation of COVID-19-specific health utility values. Of ten studies with a lifetime horizon, seven adjusted general population estimates to account for long-term sequelae (i.e. mortality, quality of life and costs), lasting for 1 year, 5 years, or a patient's lifetime. The most often reported parameter influencing the outcome of the analysis was related to treatment effectiveness. CONCLUSION: The results illustrate the variety in modelling approaches of COVID-19 drug treatments and address the need for a more standardized approach in model-based economic evaluations of infectious diseases such as COVID-19. TRIAL REGISTRY: Protocol registered in PROSPERO under CRD42023407646.

A systematic review on the relationship between socioeconomic conditions and emotional disorder symptoms during Covid-19: unearthing the potential role of economic concerns and financial strain.

BACKGROUND: Covid-19 has disrupted the lives of many and resulted in high prevalence rates of mental disorders. Despite a vast amount of research into the social determinants of mental health during Covid-19, little is known about whether the results are consistent with the social gradient in mental health. Here we report a systematic review of studies that investigated how socioeconomic condition (SEC)-a multifaceted construct that measures a person's socioeconomic standing in society, using indicators such as education and income, predicts emotional health (depression and anxiety) risk during the pandemic. Furthermore, we examined which classes of SEC indicators would best predict symptoms of emotional disorders. METHODS: Following PRISMA guidelines, we conducted search over six databases, including Scopus, PubMed, etc., between November 4, 2021 and November 11, 2021 for studies that investigated how SEC indicators predict emotional health risks during Covid-19, after obtaining approval from PROSPERO (ID: CRD42021288508). Using Covidence as the platform, 362 articles (324 cross-sectional/repeated cross-sectional and 38 longitudinal) were included in this review according to the eligibility criteria. We categorized SEC indicators into 'actual versus perceived' and 'static versus fluid' classes to explore their differential effects on emotional health. RESULTS: Out of the 1479 SEC indicators used in these 362 studies, our results showed that 43.68% of the SEC indicators showed 'expected' results (i.e., higher SEC predicting better emotional health outcomes); 51.86% reported non-significant results and 4.46% reported the reverse. Economic concerns (67.16% expected results) and financial strains (64.16%) emerged as the best predictors while education (26.85%) and living conditions (30.14%) were the worst. CONCLUSIONS: This review summarizes how different SEC indicators influenced emotional health risks across 98 countries, with a total of 5,677,007 participants, ranging from high to low-income countries. Our findings showed that not all SEC indicators were strongly predictive of emotional health risks. In fact, over half of the SEC indicators studied showed a null effect. We found that perceived and fluid SEC indicators, particularly economic concerns and financial strain could best predict depressive and anxiety symptoms. These findings have implications for policymakers to further understand how different SEC classes affect mental health during a pandemic in order to tackle associated social issues effectively.