Wholesale Data Research Articles

Trends in statin utilization and ischemic heart disease mortality in Lithuania and Sweden, 2000-2020.

To compare statin utilization and ischemic heart disease (IHD) mortality trends in Lithuania and Sweden and to assess correlations between the total utilization of statins and IHD mortality. An ecological study assessing time trends in statin utilization (DDDs per 1000 inhabitants per day; DDD/TID) and IHD mortality in Lithuania and Sweden between 2000 and 2020. Statin utilization data in Lithuania were wholesale trade data, and Swedish data were drugs dispensed at pharmacies. IHD mortality data were extracted from national databases as rates per 100 000 inhabitants. Associations between statin utilization and IHD mortality in Lithuania and Sweden were examined using Spearman's rank and Pearson's correlation coefficients, respectively. Statin utilization increased from 16.8 to 135.8 DDD/TID in Sweden and from 0.2 to 61.8 DDD/TID in Lithuania between 2000 and 2020. Medium intensity was the most common statin dosage in Lithuania, while Sweden used more high intensity than moderate-intensity statins from 2017. IHD mortality in Lithuania remained high between 2000 and 2020 (from 359.1 to 508.8 deaths per 100 000 population), while it decreased markedly in Sweden (from 226.87 to 88.7 deaths per 100 000 population). IHD mortality and statin utilization were inversely correlated in Sweden (r = -0.993, P < 0.001), while a positive correlation was found in Lithuania (rs = 0.871, P < 0.001). Despite the growing statin utilization in both countries, Lithuania recorded a slight increase in IHD mortality rates unlike the situation in Sweden. This indicates room for improvement in the management of modifiable cardiovascular risk factors in Lithuania including how statins are prescribed and used in clinical practice.

Price linkages in major EU virgin olive oil markets

This study examines the price relationships between the three major EU olive oil markets; Spain, Italy and Greece. The empirical analysis utilises a series of linear and non-linear econometric techniques to explore long and short run relations examining market integration as well as the pattern of price transmission. The study utilises monthly wholesale data for virgin olive oil for the three countries, covering the period January 2000 to April 2022. Results from the Diks and Panchenko nonlinear causality test suggest Spain to be the central market and stable long-run relations are revealed between the examined price pairs through the non-linear Momentum Threshold Cointegration model, with the strongest relation being identified between Italy and Greece. Regarding the pattern of price transmission, it is found to be asymmetric for the pairs Spain-Greece and Spain-Italy, whereas for price pair Italy-Greece symmetry is confirmed, and the Law of One Price holds in its strong version. This suggests that while the markets are integrated, the EU olive oil market is characterised by inefficiencies indicating the need for further reforms.

Optimization research on automatic pricing and replenishment decision method for vegetable commodities

This paper addresses the issues of replenishment and pricing of vegetable commodities in fresh food supermarkets. By analyzing sales data and loss rate data, mathematical models are established to solve the following problems. Spearman correlation analysis and cluster analysis methods are employed. Firstly, data preprocessing is conducted by excluding returned vegetable products from a certain supermarkets distribution, and then integrating sales transaction details and wholesale price-related data. Secondly, monthly data is used to succinctly present the sales distribution patterns of various categories and individual items of vegetables through line charts. Finally, the models of Spearman correlation analysis and cluster analysis are respectively applied to illustrate the relationship between the sales volume of 6 major categories of vegetables and 251 individual items. Linear regression models, time series analysis, and XG-Boost regression analysis models are used. This paper requires replenishment plans to be made by category, analyzing the relationship between the total sales volume and cost-plus pricing of various vegetable categories. Firstly, a linear regression model is applied to fit historical sales volume and cost-plus pricing, resulting in a linear functional relationship, indicating that only the sales volume of tomatoes is negatively correlated with cost-plus pricing. Based on historical sales data, the daily replenishment total for each vegetable category within the next week is predicted. Predictive models and time series analysis are employed. Meanwhile, for pricing strategies, the XGBoost algorithm is utilized to provide reasonable pricing strategies. Based on the optimal solution, replenishment plans and pricing strategies for the next week are formulated to ensure the maximization of sales revenue objectives.

Predicting Environmental Risks of Pharmaceuticals.

Environmental Risk Assessment (ERA) of pharmaceuticals relies on available measured environmental concentrations, but often such data are sparse. Predicted Environmental Concentrations (PECs), calculated from sales weights, are an attractive alternative, but often cover only prescription sales. We aimed to rank, by environmental risk in Norway, around 200 Active Pharmaceutical Ingredients (APIs) over 2016 to 2019, based on sales PECs. To assess the added value of wholesale and veterinary data we compared exposure and risk predictions with and without these additional sources. Finally, we aimed to characterise the persistence, mobility, and bioaccumulation of these APIs. We compared our PECs to available Norwegian measurements, then, using public Predicted No Effect Concentrations, we calculated Risk Quotients (RQs), and appended experimental and predicted persistence and bioaccumulation. Our approach overestimated environmental concentrations compared to measurements for 18 of 20 APIs with comparable predictions and measurements. 17 APIs had mean RQs > 1, indicating potential risk, while the mean RQ was 2.05 and median 0.001, driven by sex hormones, antibiotics, the antineoplastic abiraterone, and common painkillers. Some high-risk APIs were also potentially persistent or bioaccumulative (e.g. levonorgestrel (RQ = 220) and ciprofloxacin (RQ = 56), potentially persistent), raising the possibility of impacts beyond their RQs. Exposure and risk were also calculated with and without over-the-counter sales, showing prescriptions explained 70% of PEC magnitude. Likewise, human sales, compared to veterinary, explained 85%. Sales-predicted environmental concentrations provide an efficient option for ERA, designed to overestimate compared to analytical techniques, and potentially held back by limited data availability and an inability to quantify uncertainty, but nevertheless, an ideal initial approach for identification and ranking of risks.

Analysis of the Impact of Supply Chain Concentration of Chinese Trading Companies on Digital Transformation Based on Multiple Linear Regression Model

In order to explore the supply chain concentration influence on digital transformation level among enterprises, the analysis in this paper takes a sample about the Chinese wholesale and retail industry enterprise data from 2010 to 2020. Python is used to obtain the data of enterprises about digital transformation, and studies the impact of supply chain concentration on the digital transformation of trading enterprises. Empirical results show that the higher the concentration of the supply chain, the lower the investment of enterprises in digital transformation. In addition, further exploring the impact of the concentration of upstream and downstream partners in the supply chain on digital transformation, it is concluded that the concentration of downstream customers has a more significant negative correlation. At the conclusion of this paper, this paper makes suggestions on how to effectively achieve digital transformation for enterprises with different degrees of supply chain concentration.

THE EFFECT OF RECESSİONS IN THE WORLD ECONOMY ON STOCK EXCHANGES

Recession is defined as recession in the literal sense. It represents a significant economy-wide decline that lasts more than a few months. In other words, because the growth rate in the countrys economy is lower than the population growth rate, it is the per capita income. All macroeconomic indicators; Industrial production, employment, real income, wholesale and retail trade data are also included in this measurement. Recession periods are the periods in which there are decreases in the stock market in general and there are recessions in share prices. But investors who know the history of the stock market know that this can often be an opportunity. Companies that make good use of these periods will also gain greater profits after the recession. Recession periods may end after two quarters or may last longer. An economys exit from recession at the end of two quarters is called a V-type exit, and a longer-term exit is called a U-type exit. If the economy has contracted again after exiting a recession, then it is called a W-type recession or a double-dip recession. While the US economy recovered after the 2020 Covid period recession, it entered the recession period in 2022, where the Russia-Ukraine war was effective. However, the important point here is that with the end of the W-type recessions, the growth path in the economy is long-term and continues for at least 4-5 years. In this study, the effect of recessions on stock markets will be investigated. KEYWORDS: Economıc Recession, Stock Exchange, Econom Ic System.

Impact of Antibiotic Consumption on Antimicrobial Resistance to Invasive Hospital Pathogens.

The aim of our investigation is to correlate the wholesale data on antibiotic consumption expressed in daily doses per 1000 inhabitants per day (DID) with the resistance rate of invasive pathogen bacteria from 2017 to 2021. The data on antimicrobial resistance were collected from an analysis of the primary isolates of hospitalized patients. According to the CAESAR manual, the selected pathogens isolated from blood culture and cerebrospinal fluids were tested. The consumption of antibiotics for systematic use showed a statistically significant increasing trend (β = 0.982, p = 0.003) from 21.3 DID in 2017 to 34.5 DID in 2021. The ratio of the utilization of broad-spectrum to narrow-spectrum antibiotics increased by 16% (β = 0.530, p = 0.358). The most consumed antibiotic in 2021 was azithromycin (15% of total consumption), followed by levofloxacin (13%) and cefixime (12%). A statistically positive significant correlation was discovered between the percentage of resistant isolates of K. pneumoniae and consumption of meropenem (r = 0.950; p = 0.013), ertapenem (r = 0.929; p = 0.022), ceftriaxone (r = 0.924; p = 0.025) and levofloxacin (r = 0.983; p = 0.003). Additionally, the percentage of resistant isolates of E. coli and consumption of ertapenem showed significant correlation (r = 0.955; p = 0.011). Significant correlation with consumption of the antibiotics widely used at the community level, such as levofloxacin, and resistance isolated in hospitals indicates that hospital stewardship is unlikely to be effective without a reduction in antibiotic misuse at the community level.

A nationwide study on consumption of opioid analgesics in Iran from 2000 to 2018.

Opioid analgesics play a unique role in pain management. National opioid consumption studies could provide indirect evidence of pain management in a country. National, regional, and global opioid consumption have been studied in other countries so far; however, conducting a focused study to illuminate the consumption of opioid analgesics over the past decades in Iran seemed necessary. The main objective of this study was to determine the consumption of opioid analgesics and explore the trend of their use during 19years in Iran. Iran pharmaceutical wholesale data were used to extract the annual consumption figures of the opioid analgesics in group N02A of the World Health Organization (WHO) Anatomical Therapeutic Chemical classification and were available in Iran (morphine, fentanyl, pethidine, and oxycodone as strong opioids and, tramadol and pentazocine as weak opioids), from 2000 to 2018. Using Defined Daily Dose (DDD) by WHO and Oral Morphine Equivalent (OMEQ), the amount of annual consumption was determined in DDD/1000 inhabitants/Day (DID) and OMEQ (mg)/1000 inhabitants/Day (OID). Total opioid analgesic utilization based on DID and OID increased 31.12-fold (from 0.0196 to 0.61) and 21.06-fold (from 1.97 to 41.5mg) over 19years, respectively with a significant sharp increase from 2003 to 2006 (ß = 1.78 (DID), P value < 0.001). Medications that constituted 70% of annual opioid analgesics utilization were morphine in 2000, compared to tramadol in 2018. The annual weak and strong opioids share were 86.7% and 13.2% in 2018, respectively. Despite considerable growth in the consumption of opioid analgesics in Iran over nearly two decades, the consumption amount remained low, which might signal sub-optimal pain management. On the other hand, misuse and abuse seem to be the main reason behind significant increases in the consumption of opioid analgesics with less controlled distribution.

Pharmaceutical pollution: Prediction of environmental concentrations from national wholesales data.

The regulation and monitoring of pharmaceutical pollution in Europe lag behind that of more prominent groups. However, the repurposing of sales data to predict surface water environmental concentrations is a promising supplement to more commonly used market-based risk assessment and measurement approaches. The Norwegian Institute of Public Health (NIPH) has since the 1980s compiled the Drug Wholesale Statistics database - covering all sales of both human and veterinary pharmaceuticals to retailers, pharmacies, and healthcare providers. To date, most similar works have focused either on a small subset of Active Pharmaceutical Ingredients (APIs) or used only prescription data, often more readily available than wholesale data, but necessarily more limited. By using the NIPH's product wholesale records, with additional information on API concentrations per product from, we have been able to calculate sales weights per year for almost 900 human and veterinary APIs for the period 2016-2019. In this paper, we present our methodology for converting the provided NIPH data from a public health to an ecotoxicological resource. From our derived dataset, we have used an equation to calculate Predicted Environmental Concentration per API for inland surface waters, a key component of environmental risk assessment. We further describe our filtering to remove ecotoxicological-exempt and data deficient APIs. Lastly, we provide a limited comparison between our dataset and similar publicly available datasets for a subset of APIs, as a validation of our approach and a demonstration of the added value of wholesale data. This dataset will provide the best coverage yet of pharmaceutical sales weights for an entire nation. Moreover, our developed routines for processing 2016-2019 data can be expanded to older Norwegian wholesales data (1974-present). Consequently, our work with this dataset can contribute to narrowing the gap between desk-based predictions of exposure from consumption, and empirical but expensive environmental measurement.

Application of SARIMA Model on Forecasting Wholesale Prices of Food Commodities in Tanzania: A Case of Maize, Rice and Beans

This research used a time series model called the Seasonal Autoregressive Integrated Moving Average (SARIMA) technique to model and forecast wholesale prices of Tanzania`s key food crops, notably maize, rice, and beans. The SARIMA model was selected due to its ability of fitting data with seasonality. Monthly wholesale prices data of the three crops between February 2004 to October 2021 in Tanzania were retrieved from the website of the Bank of Tanzania (BoT), resulting in 213 observations on each crop. The data from February 2004 to October, 2020 were used to fit a SARIMA model and data of November 2020 to October 2021 were used to validate the model. The results show that SARIMA (0,1,2) (1,0,1)12, SARIMA (0,1,0) (1,1,1)12 and SARIMA (0,1,0) (0,1,1)12 are the most suitable models for forecasting wholesale prices of maize, rice and beans respectively. The model’s accuracy was tested using Mean Absolute Percent Error (MAPE), and the results were found satisfactory. The results reveal that maize, rice, and beans will all have higher peak prices in February 2022, with TZS 54,083/=, TZS 167,258/=, and TZS 180,117.68/= per 100kg, respectively. Therefore, SARIMA (0,1,2)(1,0,1)12, SARIMA (0,1,0)(1,1,1)12 and SARIMA (0,1,0) (0,1,1)12 models could serve as a useful tool for modelling and forecasting monthly wholesale prices of maize, rice and beans respectively in Tanzania.

The Price and Market Share Evolution of the Original Biologics and Their Biosimilars in Finland.

BackgroundBiological drugs are generally expensive and produce a continuously growing share of drug costs. Yet they are essential in the treatment of many chronic diseases. Biosimilars, clinically equivalent to biological originator products, are expected to restrain drug costs in the biological market.ObjectiveThis study aimed to examine the impact of the biosimilar market entry on the prices of the reference products in outpatient care in Finland, investigate the impact of biosimilar market entries on price competition among biological medicinal products, and examine how the prices and market shares of outpatient biosimilars have developed in Finland during 2009–2020.MethodsThis retrospective register study applied data from IQVIA covering national community pharmacy wholesale data between 1 January, 2009, and 31 August, 2020, for somatropin, epoetin, filgrastim, follitropin, insulin glargine, insulin lispro, etanercept, pegfilgrastim, adalimumab, teriparatide, and enoxaparin biosimilars and their reference products, in addition to two relevant insulin products. We determined the monthly wholesale amounts in defined daily doses and wholesale weighted average prices (excluding value-added tax) per defined daily dose for each product. We analyzed the evolution of the price and market shares. We performed a linear segmented regression analysis to examine the impact of the market entry of biosimilars on the prices of reference products.ResultsThe prices of the reference products mainly decreased after the biosimilar entered the market. If the reference product price was not reduced, it was no longer reimbursable after evaluation under the Health Insurance Act, leading to marginal market shares. The changes in the prices of biosimilars were not as remarkable as the changes in the prices of reference products after the biosimilar market entry. For most active substances, biosimilar prices were stable or decreased. The utilization of biosimilars varied widely between different active substances at the end of the observation period.ConclusionsChanges in pricing policy and the public reimbursement scheme related to the market entry of biosimilars were the main reasons for the decrease in the prices of reference products. Therefore, biosimilars did not generate genuine price competition between biological products. In many of the drug groups examined, the market shares of biosimilars have growth potential in the future.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40259-022-00540-y.

Pharmaceutical pollution: Prediction of environmental concentrations from national wholesales data

The regulation and monitoring of pharmaceutical pollution in Europe lag behind that of more prominent groups. However, the repurposing of sales data to predict surface water environmental concentrations is a promising supplement to more commonly used market-based risk assessment and measurement approaches. The Norwegian Institute of Public Health (NIPH) has since the 1980s compiled the Drug Wholesale Statistics database - covering all sales of both human and veterinary pharmaceuticals to retailers, pharmacies, and healthcare providers. To date, most similar works have focused either on a small subset of Active Pharmaceutical Ingredients (APIs) or used only prescription data, often more readily available than wholesale data, but necessarily more limited. By using the NIPH’s product wholesale records, with additional information on API concentrations per product from, we have been able to calculate sales weights per year for almost 900 human and veterinary APIs for the period 2016–2019. In this paper, we present our methodology for converting the provided NIPH data from a public health to an ecotoxicological resource. From our derived dataset, we have used an equation to calculate Predicted Environmental Concentration per API for inland surface waters, a key component of environmental risk assessment. We further describe our filtering to remove ecotoxicological-exempt and data deficient APIs. Lastly, we provide a limited comparison between our dataset and similar publicly available datasets for a subset of APIs, as a validation of our approach and a demonstration of the added value of wholesale data. This dataset will provide the best coverage yet of pharmaceutical sales weights for an entire nation. Moreover, our developed routines for processing 2016–2019 data can be expanded to older Norwegian wholesales data (1974–present). Consequently, our work with this dataset can contribute to narrowing the gap between desk-based predictions of exposure from consumption, and empirical but expensive environmental measurement.

Trend of Antihypertensive Medicine Use in the Baltic States between 2008 and 2018: A Retrospective Cross-National Comparison

High blood pressure is a major risk factor contributing to death and disability rates in the Baltic states. The aim of this study was to compare the utilization of antihypertensive medicines in Estonia, Latvia and Lithuania from 2008 to 2018. In this retrospective cross-national comparison, nationally representative wholesale data from the IQVIA National Retail Audit were analyzed. The utilization of inhibitors of the renin–angiotensin system, beta blockers, calcium channel blockers, diuretics and centrally acting antihypertensives by Defined Daily Doses per 1000 inhabitants and day (DDD/TID) was used to calculate utilization. Time series analysis was used to analyze trends. The utilization increased annually by 10.88, 8.04 and 6.42 DDD/TID in Estonia, Latvia and Lithuania, respectively, from 2008. The utilization of antihypertensive drugs in 2018 was 372, 267 and 379.5 DDD/TID, respectively. Inhibitors of the renin–angiotensin system were the most commonly used class in 2008 and 2018. From 2008, the utilization of beta blockers and fixed-dose combinations including renin–angiotensin system inhibitors increased substantially, while that of calcium channel blockers decreased. Country-specific utilization trends were noted; e.g., the utilization of centrally acting antihypertensives was 30.9 DDD/TID in Lithuania compared to 3.01 DDD/TID in Estonia and 16.17 DDD/TID in Latvia. The use of antihypertensive medicines increased over the study period, but the trends for the different drug classes differed between countries.

Global trends in symptomatic medication use against dementia in 66 countries/regions from 2008 to 2018

The aim was to determine trends and patterns of symptomatic medication used against dementia in 66 countries and regions. This was a cross-sectional study that used the wholesale data from the IQVIA Multinational Integrated Data Analysis System database. Sale data for symptomatic medication against dementia from 66 countries and regions from 2008 to 2018 were analysed and stratified by income level (low/middle-income countries [LMICs], n=27; high-income countries [HICs], n=37; regions, n=2). The medication use volume was estimated by defined daily dose (DDD) per 1000 inhabitants per day (World Health Organization DDD harmonized the size, strength and form of each pack and reflects average dosing). Changes in medication use over time were quantified as percentage changes in compound annual growth rates (CAGRs). Total symptomatic medication against dementia sales increased from 0.85 to 1.33 DDD per 1000 inhabitants per day between 2008 and 2018 (LMICs 0.094-0.396; HICs 3.88-5.04), which is an increase of CAGR of 4.53% per year. The increase was mainly driven by the LMICs (CAGR=15.42%) in comparison to the HICs (CAGR=2.65%). The overall medication use from 2008 to 2018 increased for all four agents: memantine (CAGR=8.51%), rivastigmine (CAGR=6.91%), donepezil (CAGR=2.72%) and galantamine (CAGR=0.695%). In 2018, the most commonly used medication globally was donepezil, contributing to 49.8% of total use volume, followed by memantine (32.7%), rivastigmine (11.24%) and galantamine (6.36%). There was an increasing trend in the use of symptomatic medications against dementia globally, but the use remained low in LMICs. Interventions may be needed to support the medication use in some countries.

An overview of the current status of follow-on biologicals in Iran

Background The advent of follow-on biologicals in Iran and biosimilars worldwide have provided various treatment options for several severe and chronic diseases. The goal of the present study was to provide an overview of their current status in Iran. Methods A comprehensive search of clinical trial registry sites and other databases that publish scholarly articles, such as PubMed and Google scholar, enabled the current follow-on biologicals landscape in Iran to be mapped. In addition, the annual national wholesale data of pharmaceutical products published by the Iranian pharmaceutical regulatory were analysed. The share of biotechnological therapeutics in terms of the whole medicines market, was evaluated, along with the share of follow-on biologicals and the potential and actualized cost-saving associated with using them. Data were collected and analysed over the 2013–2018 time period. Results At the time of writing, 21 follow-on biologicals were available in Iran and these are related to 17 different originator molecules. In 2018, approximately 13.5% of medicines spending in Iran was devoted to biotechnological therapeutics. Follow-on biologicals comprised approximately 47.2% of the biotechnological therapeutics’ total market value, up from 35.2% in 2013. The use of follow-on biologicals in Iran was associated with more than US$300 million cost-saving in 2018. A number of follow-on biological candidates, mostly monoclonal antibodies, are under development and will be subject to head-to-head clinical trials against originator products prior to regulatory approval and marketing. Conclusion Despite a significant rise in the use of follow-on biologicals in Iran, the proportional use of biotechnological therapeutics compared to the total medicines market has remained constant in recent years. Iranian healthcare authorities can improve patients’ access to life-saving biological medicines through promoting the use of follow-on biologicals instead of costly originators after making sure of the quality, efficacy and safety of the follow-on biologicals. The significant cost saving associated with using follow-on biologicals can also be utilized for other biotechnological medicines that are not currently in Iran’s drug list.

Food products identified as source of a foodborne disease outbreak by a fast and robust likelihood estimation

Identifying a specific product causing a foodborne disease outbreak can be difficult, especially when dealing with a large amounts of suspicious food items and weak epidemiological evidence. A previously described likelihood model (Norström et al. 2015), improved within the OHEJP NOVA project, helps to prioritize food products that should be sampled for laboratory analysis. It is the aim of our study to integrate this approach into state of the art tracing software FoodChain-Lab (FCL; https://foodrisklabs.bfr.bund.de/foodchain-lab) developed at BfR to facilitate outbreak investigations. The model improved by Kausrud et al. in R (Ihaka and Gentleman 1996) uses wholesale data, the distribution of disease cases and census data to sort food items by their estimated likelihood to be the source of an outbreak. We developed a fast and secure intuitive software module using the Web Assembly technology (Haas et al. 2017) allowing professionals to embed the module easily into other applications. We integrated the module into the FCL web application for tracing (FCL Web; https://fcl-portal.bfr.berlin) to provide an intuitive and user-friendly solution. This solution combines a simple data input with extended data wrangling to make the calculation of the NOVA model as easy as possible. Since the model can be executed directly inside the web browser and therefore does not rely on any server environment, the possibility of data leakage can be highly reduced. The implementation of the advanced likelihood model into FCL Web increase the availability of this model and provides investigators easy, fast and reliable usage to improve outbreak investigation workflows.

Wavelet based long memory model for modelling wheat price in India

Agricultural time-series data concerning production, prices, export and import of several agricultural commodities is published by Indian government along with other private agricultural sectors every year. The analysis of these factors is necessary to formulate and apply several policies regarding food acquisition and its distribution, quality and quantity of import and export products, pricing structure, MSP of agricultural commodities etc. Box - Jenkins's Autoregressive integrated moving average (ARIMA) model is broadly utilized in the field of time-series. In the field of time-series analysis, it is assumed by most of the researchers that the data points of different time lags do not depend on each other, i.e. absence of long memory process. But in agriculture, market price data exhibits that the observation are dependent on distant past. This is the possible indication of long memory process or long range dependency in the mean model. Autoregressive fractionally integrated autoregressive moving average (ARFIMA) model is generally used to portray the characteristic features of the long memory time series models as well as for the forecasting purposes. In this study wavelet decomposition is used for increasing the forecasting accuracy of the ARFIMA model. Daily wholesale data of wheat of Rewari market of Haryana for the period of January, 2010 to November, 2017 is used for the demonstration of our approach.

Nilotinib and Imatinib Utilization in Iran over 14 years

Background: Tyrosine Kinase Inhibitors (TKIs) are drugs of choice for Chronic Myeloid Leukemia (CML) treatment. CML healthcare costs greatly exceed of other haematological malignancies treatment mostly due to TKIs. There are several generic and brand preparations of imatinib and nilotinib, the only available TKIs, in Iran with different prices and varied insurance coverage. We have studied TKIs utilization and also investigate the effect of different insurance coverage on TKIs utilization in Iran. Methods: This was drug utilization study about Imatinib and Nilotinib over 14 years. It was conducted in two phases; data extraction from pharmaceutical wholesale data (2003-2017) for utilization trend assessment and registered data of prescriptions from Sizdah-Aban Pharmacy (2011-2014) for utilization trend and insurance coverage assessment such as; prescriptions frequency, number of TKIs, insurance companies and their cost coverage in each prescription. Results: Imatinib consumption increased significantly from 2003 to 2013. This trend stopped afterward. Nilotinib consumption had ascending trend. The trend line of years 2014 to 2017 was steeper and statistically significant (β=0.0014, p-value=0.02). The amount of nilotinib cost coverage by insurance companies increased significantly from 2011 to 2014 (p-value=0.04). The coverage of imatinib costs by insurance companies changed slightly during the study period that was not statistically significant. Frequency of prescriptions with full cost coverage doubled for nilotinib, while did not change remarkably for imatinib, from 2011 to 2014. Mean (SD) of imatinib and nilotinib counts per prescription was significantly higher in prescriptions for which 100% of the cost was covered. Conclusion: We found increasing trend in nilotinib utilization and observed some effects from nilotinib cost coverage by insurance on its consumption. This study made a clear picture for policy makers to monitor imatinib and nilotinib use appropriateness and design the proper cost-effective studies to make evidence-based decisions.

Where does the Fair Trade money go? How much consumers pay extra for Fair Trade coffee and how this value is split along the value chain

Abstract Fair Trade certification aims at transferring wealth from the consumer to the farmer; however, coffee passes through many hands before reaching final consumers. Bringing together retail, wholesale, and stock market data, this study estimates how much more consumers are paying for Fair Trade-certified coffee in US supermarkets and finds estimates around $1.50 per lb. The study then assesses how this price premium is split between the different stages of the value chain: most of the premium goes to the roaster’s profit margin, while the retailer surprisingly makes smaller absolute profits on Fair Trade-certified coffee, compared to conventional coffee. In the period studied in this study, the coffee farmer receives about a sixth of the price premium paid by the consumer.

Estimating global trends in total and childhood antibiotic consumption, 2011-2015

IntroductionUnderstanding patterns of antibiotic consumption is essential to ensure access to appropriate antibiotics when needed and to minimise overuse, which can lead to antibiotic resistance. We aimed to describe changes...