Exogenous variable driven deep learning models for improved price forecasting of TOP crops in India.

Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction. Deep learning (DL) models, with advanced feature extraction and predictive abilities, offer a promising solution. However, their application to agricultural price data ignored the exogenous factors. Hence, the study explored advanced versions of the well-known univariate models, NBEATSX and TransformerX. The research employed price data for essential crops like Tomato, Onion, and Potato (TOP) from major Indian markets and complemented it with corresponding weather data (precipitation and temperature). To provide a comprehensive analysis, the study also evaluated traditional statistical methods (ARIMAX and MLR) and a suite of ML algorithms (ANN, SVR, RFR, and XGBoost). The performance of these models was rigorously evaluated using error metrics like RMSE, MAE, sMAPE, MASE and QL. The findings were significant indicating DL models, particularly when augmented with exogenous variables, consistently outshone other methods with NBEATSX and TransformerX showing an average RMSE of 110.33 and 135.33, MAE of 60.08 and 74.92, sMAPE of 22.14 and 24.00, MASE of 1.02 and 1.32 and QL of 30.04 and 34.07, respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in agricultural crop price forecasting. This study not only bridged a crucial research gap but also highlighted the robust potential of DL models in enhancing the accuracy of agricultural commodity price predictions in India.

The 30-Billion-Dollar Distribution Markups and Taxes of Pharmaceuticals in Latin American Countries: Impact, Options, and Trade-Offs.

OBJECTIVES: The World Health Organization provides 10 specific guidelines for managing the prices of pharmaceutical products. Many of those are widely known and used such as reference pricing, value-based pricing, price transparency, and tendering. Less attention and knowledge is concentrated in markup regulation across the pharmaceutical supply chain and distribution and in tax exemptions or reductions. This article quantifies the impact of these price components in the Latin American (LatAm) region and places the findings in the context of economic theory and international policy experiences. METHODS: 2020 retail pharmaceutical sales data from 8 major LatAm markets covered in the IQVIA database were decomposed into ex-factory, distributor markups, and taxes using price build up information and the Price Decipher Methodology developed by the Novartis Global Pricing Governance and Negotiation team. The findings were reviewed by an international panel representing academia, health policy, health economics, patient, and industry. RESULTS: The ex-factory market value of the analyzed markets was $49 billion. Distribution markups added $20 billion and taxes a further $10.5 billion. This represented a 63% increase over ex-factory prices, considered high if compared with 24% for an international benchmark of 35 ex-LatAm countries. Reducing markups for these LatAm countries to 24% would represent up to $19 billion in savings for payers and patients. CONCLUSIONS: There is potential for significant cost reductions associated with tax and distribution markup refinements in the LatAm retail pharmaceutical market. National policies should be informed by additional context-specific research for effective implementation.

Electric vehicle usage, pollution damages, and the electricity price elasticity of driving.

I study battery electric vehicle (BEV) usage and ownership characteristics with fundamental implications for the electrification of passenger transportation. Using data covering the entire BEV population in New York, I quantify BEV mileage and electricity consumption and highlight the important role of vehicle utilization in contributing to real-world pollution damages and their spatial variation. I then study the factors influencing how much BEVs are driven with a focus on estimating the electricity price elasticity of BEV mileage. Understanding how drivers respond to these changes in operating costs may help align the social and private costs of BEV driving and illustrates how electric utilities may affect transportation outcomes in the future. I find a 10% increase in residential electricity prices reduces mileage by 1%, but responsiveness falls as public charging stations-where prices are often decoupled from electricity costs-become available.

How much does overnutrition weigh? The environmental and social impacts of Metabolic Food Waste in Italy.

Excess Food Energy Intake (EFEI), namely Metabolic Food Waste (MFW) corresponds to excess calorie intake related to overconsumption of food and is responsible for overweight (OW) and obesity (OB) conditions. Identifying its causes and impacts could be important, so that it can be prevented and reduced, generating health, environmental and societal benefits. Therefore, this research quantifies MFW among OW and OB adult populations (18-75 years) in Italy and its environmental and social implications. Life cycle assessment (LCA) through the Simapro 9.5 software was used and then, the results were monetized according to the Environmental Price Handbook to understand the real environmental cost. Finally, Social LCA (S-LCA) was considered following the Product Social Impact Assessment (PSILCA) guidelines to understand the potential social risks behind the food that ends up on our plates. The results highlight the amount of MFW in Italy is 2696 billion kcal/year corresponding to 1.59 Mtons over-consumed food/year, while the impacts are mainly related to global warming (8.78 Mtons CO2 eq/year, or 2.29 % of the total Italian CO2 emissions), terrestrial ecotoxicity (843,451 tons 1.4-DCB/year), freshwater ecotoxicity (222,483 tons 1.4 DCB eq/year), and land consumption (8 million m2a eq/year), mostly due to the meat, fats and oils and sweets overconsumption. Impacts monetization also shows that MFW could induce an environmental price of € 1340/per capita/year, and finally, the S-LCA reveals how overconsumption of food has the potential to affect gender discrimination, water depletion, trade union, and social discrimination due to the high proportion of labor migrants in the agricultural sector.

Sales revenues for new therapeutic agents approved by the US Food and Drug Administration from 1995 to 2014: a retrospective study.

OBJECTIVES: To analyze worldwide sales of new therapeutic agents and to estimate the time it takes for product sales to exceed industry-wide average drug development costs. METHODS: Data obtained from company reports were analyzed to track worldwide sales of new medicines approved by the US Food and Drug Administration from 1995 to 2014. All sales figures were reported in 2019 US dollars. Kaplan-Meier curves were used to evaluate the time it took for discounted product sales to exceed the average costs associated with developing one new drug (accounting for the costs of failed trials), using published estimates of these costs. RESULTS: Based on data for 361 of 558 new therapeutic agents approved over the study period (median follow-up: 13.2 years), mean sales revenue per product was $15.2 billion through the end of 2019; the median was $6.7 billion. These products jointly generated global sales of $5.5 trillion since approval. Revenues were highly skewed, with the 25 best selling products (7%, 25/361) accounting for 38% of this amount ($2.1 trillion/$5.5 trillion). About 47% of products had discounted sales that exceeded the estimated industry-wide average costs of development within 5 years of approval, and 75% within 10 years. After attributing potential production, marketing, and other costs, these numbers dropped to 21% of products within 5 years of approval, and 46% within 10 years. CONCLUSIONS: Sales of new medicines approved from 1995 to 2014 were highly skewed, but a majority of products had net discounted sales that exceeded the industry-wide average costs of development within 10 years of approval. An understanding of how sales revenues accrue in the years after initial approval, alongside data on business costs, can inform discussions about how to incentivize private investment in innovation while ensuring affordable prices for patients and the health-care system.

Inequitable Distribution of Global Economic Benefits from Pneumococcal Conjugate Vaccination.

Many low- and middle-income countries have been slow to introduce the pneumococcal conjugate vaccine (PCV) into their routine childhood immunization schedules despite a high burden of disease. We estimated the global economic surplus of PCV, defined as the sum of the net value to 194 countries (i.e., monetized health benefits minus net costs) and to vaccine manufacturers (i.e., profits). We further explored the distribution of global economic surplus across country income groups and manufacturers and the effect of different pricing strategies based on cross-subsidization, pooled procurement, and various tiered pricing mechanisms. We found that current PCV pricing policies disproportionately benefit high-income countries and manufacturers. Based on the 2021 birth cohort, high-income countries and manufacturers combined received 76.5% of the net economic benefits generated by the vaccine. Over the two decades of PCV availability, low- and middle-income countries have not received the full economic benefits of PCV. Cross-subsidization of the vaccine price for low- and middle-income countries and pooled procurement policies that would relate the vaccine price to the value of economic benefits generated for each country could reduce these inequalities. This analysis offers important considerations that may improve the equitable introduction and use of new and under-utilized vaccines.

Modeling impact of inflation reduction act price negotiations on new drug pipeline considering differential contributions of large and small biopharmaceutical companies.

BACKGROUND/AIMS: Provisions of the Inflation Reduction Act mandating drug price negotiation by the Centers for Medicare & Medicaid Services have been criticized as a threat to pharmaceutical innovation. This study models potential impacts of the Inflation Reduction Act on drug approvals based on the differential contributions of large pharmaceutical companies and smaller biotechnology firms to clinical trials and the availability of capital. METHODS: This study examined research and development expense, revenue, and new investment (sale of common and preferred stock) by public biopharmaceutical companies and sponsorship of phased clinical trials in ClinicalTrials.gov. Financial data were incorporated in a model that estimates the number of drugs in each phase and approvals from reported phase-specific costs and transition rates, proportional sponsorship of trials by companies of different size, projected reductions in research and development spending based on company size, and three scenarios by which large companies may allocate reductions in research and development spending among clinical phases: (1) research and development proportionally reduced across phases; (2) research and development disproportionally reduced in phases 2-3; and (3) research and development disproportionately reduced in phases 1-2. RESULTS: Financial data were examined for 1378 public biopharmaceutical companies (2000-2018). Research and development expense was associated with revenue for 79 large companies with market capitalization ≥$7 billion with a 10% reduction in revenue reducing research and development expense by 8.4%. For 1299 smaller companies with market capitalization <$7 billion, research and development was associated with new investment, but not revenue. Smaller companies sponsored 55.2% of phase 1, 55.6% of phase 2, and 49.8% of phase 3 trials in ClinicalTrials.gov 2013-2018. In a model of clinical development that apportions clinical trials between large and smaller companies and determines the number of trials based on research and development resources, 400 drugs entering development produced 47.3 approvals (11.83% rate). A 10% reduction in revenue, reflecting the upper boundary of observed changes 2000-2018, with (1) proportional reduction across phases 1-3 produced 45.1 approvals (4.61% reduction); (2) disproportional reduction of phases 2-3 produced 42.8 approvals (9.55% reduction); and (3) disproportional reduction of phases 1-2 produced 46.9 approvals (0.95% reduction). CONCLUSION: This work suggests that the drug price negotiation provisions of the Inflation Reduction Act could have little or no impact on the number of drug approvals. While large pharmaceutical companies may reduce research and development spending, continued research and development by smaller companies and strategic allocation of research and development resources by large companies may mitigate any negative effects of the Inflation Reduction Act.

Rethinking counterfeit medical supply chains: A critical review of the current literature.

The medical device and pharmaceutical industries include a range of drugs, machines, instruments, and apparatuses used to prevent, diagnose, treat disease and illness, or aid in rehabilitation for patients, and are expected to grow substantially in the coming years. However, they are often targets of criminal organizations who manufacture and profit from fraudulent products, infiltrating the market with counterfeit medical supply chains. In this paper, we discuss and analyze the extent and nature of this problem and make suggestions for mitigation and prevention of this worldwide challenge. Ultimately, we argue that a holistic approach is essential to addressing this problem, including the creation and dissemination of reliable and good quality data, developing healthcare systems to be more robust, establishing/enhancing intra- and international cooperation around this issue, and employing effective technological solutions, such as digital tracing.

Evaluating the effects of supervised consumption sites on housing prices in Montreal, Canada using interrupted time series and hedonic price models.

Background: In 2017, three brick and mortar supervised consumption sites (SCS) opened in Montreal, Canada. Opponents argued the sites would attract people who use drugs and reduce local real estate prices. Methods: We used interrupted time series and hedonic price models to evaluate the effects of Montreal's SCS on local real estate prices. We linked the Quebec Professional Association of Real Estate Brokers' housing sales data provided by Centris Inc. with census tract data and gentrification scores. Homes sold within 200 m of the SCS locations between 1 January 2014 and 31 December 2021 were included. We adjusted for internal (e.g., number of bed/bathrooms, unit size) and external attributes (e.g., neighbourhood demographics), and included a spatio-temporal lag to account for correlation between sales. For sensitivity analysis we used site-specific dummy variables to better account for unmeasured neighbourhood differences, and repeated analyses using 500 m and 1000 m radii. Results: We observed a price shock after the opening of the first two SCS in June 2017 (level effect: -10.5%, 95% CI: -19.1%, -1.1%) but prices rose faster month-to-month (trend effect: 1.1%, 95% CI: 0.7%, 1.6%) after implementation. Following the implementation of the third site in November 2017 there was no immediate impact (level effect: 2.4%, 95% CI: -10.4%, 17.0%) but once more prices roses faster (0.9%, 95% CI: 0.4%, 1.5%) thereafter. When we replaced neighbourhood attributes with a site-specific dummy variable, we observed the same pattern. Sales' prices dropped (level effect: -9.6%, 95% CI: -15.0%, -3.8%) but rose faster month-to-month (trend effect: 0.9%, 95% CI: 0.6%, 1.2%) following June 2017's SCS implementations, with no level effect (4.9%, 95% CI: -7.3%, 18.6%) and a positive trend (0.9%, 95% CI: 0.5%, 1.3%) after November 2017's SCS opening. In most 500 m and 1000 m radii models, there were no immediate shocks following SCS opening, however, positive trend effects persisted in all models. Conclusion: Our models suggest homes sold near SCS may experience a price shock immediately post-implementation, with evidence of market recovery in the months that follow.

Domestic and international impacts of rice export restrictions: The recent case of indian non-basmati rice.

This study examines the impact of India's export restrictions on domestic retail rice prices using a dynamic panel GARCH model. The findings suggest that export restrictions are not a sufficient condition to lower domestic prices. Export restrictions are associated with lower retail price volatility in the East Zone. Moreover, the international price transmission to a sample of Asian and African economies shows that all countries are vulnerable, but the degree and kinds of vulnerability differ. Rice exporters appear to be the most susceptible as domestic prices increase in these countries. Rice importers are also vulnerable because of price increases, but the increases are less than in countries where the private sector decides on import quantities.

Food prices and the wages of the poor: A cost-effective addition to high-frequency food security monitoring.

The affordability of nutritious food for "all people, at all times" is a critically important dimension of food security. Yet surprisingly, timely high-frequency indicators of food affordability are rarely collected in any systematic fashion despite price volatility emerging as major source of food insecurity in the 21st Century. The 2008 global food crisis prompted international agencies to invest heavily in monitoring domestic food prices in low and middle income countries (LMICs). However, food price monitoring is not sufficient for measuring changes in diet affordability; for that, one must also measure changes either in income or in an income proxy. We propose using the wages of unskilled workers as a cheap and sufficiently accurate income proxy, especially for the urban and rural non-farm poor. We first outline alternative measures of "food wage" indices, defined as wages deflated either by consumer food price indices or novel healthy diet cost indices. We then discuss the conceptual strengths and limitations of food wages. Finally, we examine patterns and trends in different types of real food wage series during well-known food price crises in Ethiopia (2008, 2011 and 2022), Sri Lanka (2022) and Myanmar (2022). In all these instances, food wages declined by 20-30%, often in the space of a few months. In Myanmar, the decline in real wages during 2022 closely matches declines in household disposable income. We strongly advocate tracking the wages of the poor as a timely, accurate and cost-effective means of monitoring food affordability for important segments of the world's poor.

Why does the cost of employer-sponsored coverage keep rising?

Over the past 25 years, the gap between the increase in health insurance costs and workers' wages has significantly expanded. This trend has led to significant concerns about healthcare affordability, with surveys revealing conflicting opinions regarding whether hospitals or health insurance companies bear the blame for escalating costs. To better understand these dynamics, we examined consumer price indices for health insurance, hospital services, and professional services from 2006 to 2023 using Bureau of Labor Statistics data. Our analysis shows that the hospital price index rose steadily between 2006 and 2023, faster than insurance premiums or professional services. To examine whether differences in underlying costs are driving higher hospital price increases, we evaluated the profit margins of hospitals and health insurance companies using the National Academy for State Health Policy''s Hospital Cost Tool and National Association of Insurance Commissioners Industry Reports. Our findings reveal that hospitals (for-profit and nonprofit) have consistently maintained higher profit margins than insurance companies. As health insurance costs continue to weigh heavily on working Americans, our analysis suggests that high hospital prices drive insurance premiums.

Effect of exchange rate uncertainty, energy prices and sectoral spending on agriculture value added, household consumption, and domestic investment.

The agricultural value chain is underpinned by the interdependence of agricultural value added, household consumption and domestic investment. Understanding the complex interactions between these microeconomic outcomes and the uncertainties in the macroeconomic variables of exchange rates, energy prices and sectoral spending remains under-researched. Therefore, this study examines the impact of exchange rate, energy prices and sectoral spending on agricultural value added, household consumption and domestic investment in Nigeria from 1981 to 2020. Using Kernel regularized least squares (KRLS), the results show that the average pointwise marginal effects of exchange rate and agricultural spending are positive, while the average pointwise marginal effect of energy price is significantly negative for the agricultural value-added model. The results also show that the exchange rate, energy prices and agricultural expenditure all have a positive effect on household consumption. Regarding domestic investment, the effect of the exchange rate is positive and statistically insignificant, while the effects of energy prices and agricultural expenditure are negative and statistically significant. The study recommends the need to strengthen the social safety nets currently in place in Nigeria to support households that are vulnerable to exchange rate fluctuations. In addition, incentives should be given to households and farmers to help use renewable energy sources such as solar or wind power for agricultural activities. Also, investment in value chains and agribusiness initiatives should be encouraged rather than just in crop production.

An assessment on the new impetus of green energy development and its impact on climate change: a non-linear perspective.

The purpose of this article is to investigate the new driving forces behind China's green energy and further assess the impact of green energy on climate change. The existing literature has used linear methods to investigate green energy, ignoring the non-linear relationships between economic variables. The nonparametric models can accurately simulate nonlinear relationships between economic variables. This paper constructs a nonparametric additive model and uses it to explore green energy. The empirical results show that the impact of green finance on green energy is more prominent in the later stage (a U-shaped impact). Fiscal decentralization also exerts a positive U-shaped impact, meaning that expanding local fiscal autonomy has contributed to green energy growth in the later stage. Similarly, the impact of oil prices and foreign direct investment demonstrates a positive U-shaped pattern. However, the nonlinear impact of environmental pressure displays an inverted U-shaped pattern. Furthermore, this article explores the impact of green energy on climate change and its impact mechanisms. The results exhibit green energy generates a positive U-shaped impact on climate change, meaning that the role of green energy in mitigating climate change gradually becomes prominent over time. Mechanism analysis exhibits that industrial structure and energy structure both produce a nonlinear influence on climate change.

Decomposition of medical imaging spending growth between 2010 and 2021 in the US employer-insured population.

Medical imaging, identified as a potential driver of unsustainable US health care spending growth, was subject to policies to reduce prices and use in low-value settings. Meanwhile, the Affordable Care Act increased access to preventive services-many involving imaging-for employer-sponsored insurance (ESI) beneficiaries. We used a large insurance claims database to examine imaging spending trends in the ESI population between 2010 and 2021-a period of considerable policy and benefits changes. Nominal spending on imaging increased 35.9% between 2010 and 2021, but as a share of total health care spending fell from 10.5% to 8.9%. The 22.5% growth of nominal imaging prices was below inflation, 24.3%, as measured by the Consumer Price Index. Other key contributors to imaging spending growth were increased use (7.4 percentage points [pp]), shifts toward advanced modalities (4.0 pp), and demographic changes (3.5 pp). Shifts in care settings and provider network participation resulted in 2.5-pp and 0.3-pp imaging spending decreases, respectively. In sum, imaging spending decreased as a share of all health care spending and relative to inflation, as intended by concurrent cost-containment policies.

Relationship Between Product Features and the Prices of e-Cigarette Devices Sold in Web-Based Vape Shops: Comparison Study Using a Linear Regression Model.

BACKGROUND: Open-system electronic cigarette (EC) product features, such as battery capacity, maximum output wattage, and so forth, are major components that drive product costs and may influence use patterns. Moreover, continued innovation and monitoring of product features and prices will provide critical information for designing appropriate taxation policies and product regulations. OBJECTIVE: This study will examine how product features are associated with the prices of devices sold in web-based vape shops. METHODS: We draw samples from 5 popular, US-based, web-based vape shops from April to August 2022 to examine starter kits, device-only products, and e-liquid container-only products. We implemented a linear regression model with a store-fixed effect to examine the association between device attributes and prices. RESULTS: EC starter kits or devices vary significantly by type, with mod prices being much higher than pod and vape pen prices. The prices of mod starter kits were even lower than those of mod devices, suggesting that mod starter kits are discounted in web-based vape shops. The price of mod kits, mod device-only products, and pod kits increased as the battery capacity and output wattage increased. For vape pens, the price was positively associated with the volume size of the e-liquid container. On the other hand, the price of pod kits was positively associated with the number of containers. CONCLUSIONS: A unit-based specific tax, therefore, will impose a higher tax burden on lower-priced devices such as vape pens or pod systems and a lower tax burden on mod devices. A volume- or capacity-based specific tax on devices will impose a higher tax burden on vape pens with a larger container size. Meanwhile, ad valorem taxes pegged to wholesale or retail prices would apply evenly across device types, meaning those with advanced features such as higher battery capacities and output wattage would face higher rates. Therefore, policy makers could manipulate tax rates by device type to discourage the use of certain device products.

List prices and clinical value of anticancer drugs in China, Japan, and South Korea: a retrospective comparative study.

Background: High prices of anticancer drugs have raised concerns due to their financial impact on patients and healthcare systems. This study aimed to assess the initial and latest list prices and clinical value of reimbursed anticancer drugs in China, Japan, and South Korea. Methods: We identified anticancer drugs newly approved by the National Medical Products Administration of China from January 2012 to June 2022, and by the Pharmaceuticals and Medical Devices Agency of Japan and the Ministry of Food and Drug Safety of South Korea up until June 2022. We compared initial and latest treatment prices between countries and assessed clinical value using patients' survival, quality of life (QoL), and European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS). We calculated Spearman rank correlation coefficients of treatment prices with clinical value for individual countries and employed regression analyses to investigate whether the relationship between prices and clinical value was modified by the country setting. Findings: Our cohort included 91 anticancer drug indications, with 60 listed for reimbursement in China, 91 in Japan, and 87 in South Korea. Median treatment prices were highest in Japan, followed by South Korea, and lowest in China, both for initial prices (US$64082 vs. US$45529 vs. US$19144, p < 0.0001) and latest prices (US$50859 vs. US$31611 vs. US$18666, p < 0.0001). Over time, China (ß = -0.047, p < 0.0001) and South Korea (ß = -0.049, p < 0.0001) witnessed more significant price reductions compared to Japan (ß = -0.013, p = 0.011). The correlations between both initial and latest treatment prices and clinical value (QoL and ESMO-MCBS) were more significant and stronger in China and South Korea than in Japan, although Japan exhibited slightly stronger correlations in terms of survival compared to China and South Korea. The relationship between clinical value and treatment prices may not be modified by the country setting. Interpretation: In comparison, South Korea's list prices and their correlations with clinical value appear reasonable. Policymakers in Japan could enhance efficiency by controlling prices and aligning them with clinical value, while China would need to take substantial steps to expand anticancer drug coverage. Funding: National Natural Science Foundation of China (72374149 and 72074163), and China Center for South Asian Studies, Sichuan University.

Crude Oil Prices Forecast Based on Mixed-Frequency Deep Learning Approach and Intelligent Optimization Algorithm.

Precisely forecasting the price of crude oil is challenging due to its fundamental properties of nonlinearity, volatility, and stochasticity. This paper introduces a novel hybrid model, namely, the KV-MFSCBA-G model, within the decomposition-integration paradigm. It combines the mixed-frequency convolutional neural network-bidirectional long short-term memory network-attention mechanism (MFCBA) and generalized autoregressive conditional heteroskedasticity (GARCH) models. The MFCBA and GARCH models are employed to respectively forecast the low-frequency and high-frequency components decomposed through variational mode decomposition optimized by Kullback-Leibler divergence (KL-VMD). The classification of these components is performed using the fuzzy entropy (FE) algorithm. Therefore, this model can fully exploit the advantages of deep learning networks in fitting nonlinearities and traditional econometric models in capturing volatilities. Furthermore, the intelligent optimization algorithm and the low-frequency economic variable are introduced to improve forecasting performance. Specifically, the sparrow search algorithm (SSA) is employed to determine the optimal parameter combination of the MFCBA model, which is incorporated with monthly global economic conditions (GECON) data. The empirical findings of West Texas Intermediate (WTI) and Brent crude oil indicate that the proposed approach outperforms other models in evaluation indicators and statistical tests and has good robustness. This model can assist investors and market regulators in making decisions.

Description of the African Cigarette Prices Project Data.

The African Cigarette Price Project is a project that collects tobacco prices from African countries. Amongst other things, the data enable users to estimate price differences across brands, urban/rural divides, types of packaging, retail types, and trends in price over time. A total of 215 354 individual prices were collected during the first twelve rounds of the project (collected biannually from 2016 to 2022). Data collection continues to date. Data have been collected from 19 African countries, with most data from South Africa, Zimbabwe, Lesotho, Namibia and Botswana. Other countries include Ethiopia, Malawi, Tanzania, Chad, Eswatini, Mozambique, Nigeria, Zambia, Ghana, Madagascar, Kenya, Mauritius, Uganda and Cameroon. The project employs a novel data collection approach, by contracting local and international University of Cape Town (UCT) students as fieldworkers to collect price data while at home over the long university vacation. The data were collected at the retail level; the lowest level of geographic detail available in the public use dataset is the suburb. While the price data are not nationally representative, the data collection method is simple and affordable and provides an indication of the range of prices and the brands available in the respective countries. While cigarette prices make up the bulk of the data, other common tobacco products included are hookah tobacco, snuff, pipe tobacco, cigars, e-cigarettes, hand-rolled tobacco, and others. The collection of these other tobacco products started in round 4 (2017).

Timeliness of Health Technology Assessments and Price Negotiations for Oncology Drugs in Canada.

Purpose: To evaluate whether time targets for Canadian Agency for Drugs and Technologies in Health (CADTH) reimbursement reviews and pan-Canadian Pharmaceutical Alliance (pCPA) price negotiations are being achieved for oncology drugs. Materials and Methods: Recommendations, dates of submission and publication, and indications for oncology medicines issued between January 2014 and December 2023 were recorded from CADTH's reimbursement reports webpage. The date any negotiation began and the date it was completed (successfully or not), or when a decision was made not to pursue negotiation was extracted from the pCPA's webpage. The duration of each CADTH review and pCPA negotiation was calculated, together with time between CADTH's recommendation and start of the pCPA negotiation or a decision not to negotiate. Percentages of reviews completed within CADTH's target and of times taken by the pCPA to decide whether to negotiate and by its price negotiations completed within the relevant targets were calculated. Results: CADTH achieved its 270-days target in 88.2% to 100% of reviews issued between 2015 and 2019 but only in 65.9% to 73.1% of reviews issued in the last three years of the decade. CADTH's "typical timeline" of 180 days was achieved in under 40% of reviews issued in 2015 and not attained in any review in 2021, 2022 or 2023. The pCPA's target of 60 days for deciding whether to negotiate was achieved for all recommendations issued in 2014 but dropped below 40% for the last seven years of the decade; its target of 130 days for negotiations was achieved for over 85% of the recommendations in 2014 but decreased to only 14.3% in 2016 and then gradually increased to 61.5% in 2023. Conclusion: CADTH's "typical timeline" and the pCPA's targets were not met sufficiently to be meaningful. Their processes take too long for cancer drugs.

Canadian patients and providers are often frustrated and concerned about the timeliness of the country's health technology assessment (HTA) and price negotiation processes, especially for cancer drugs. HTAs are carried out to evaluate the benefit of a medicine in comparison with its cost to see whether the drug is of sufficient value to add it to the benefit lists of government drug plans. HTAs are performed by the Canadian Agency for Drugs and Technologies in Health (CADTH) for all of Canada, except the province of Quebec, and price negotiations with drug developers are carried out by the pan-Canadian Pharmaceutical Alliance (pCPA) on behalf of all government drug plans. We used data from the websites of CADTH and the pCPA on HTA reviews of cancer drugs issued between January 2014 and December 2023 and price negotiations for these drugs to assess whether CADTH and the pCPA complied with their stated target times for completing their processes. We found that CADTH's reviews and the pCPA's price negotiations failed to meet their targets for cancer drugs in the past 10 years and that the timeliness of their performance has, in most cases, deteriorated. HTA and price negotiation processes for cancer drugs take too long in Canada.