Drug Sales Data Research Articles

Seasonal And Trend Analysis of Drug Sales using Time Series Analysis

The SARIMA model is to analyse and forecast seasonal drug sales data, enhancing accuracy and understanding the complex of seasonal and trend patterns in fields like pharmaceutical sales, unlike the ARIMA model that only handles non-seasonal data. The model aims to improve predictions and provide deeper insights into the intricate seasonal and trend patterns in drug sales data. This study analyses drug sales behaviour across different seasons using drug store data from 1992 to 2010, grouping data by seasonal periods. Time series analysis is a method used to analyse data points over time to identify trends, patterns, and seasonal variations, providing valuable insights into drug sales. The study emphasizes the significance of drug sales data in real-time monitoring of best values of p,d,q activity

Using Fuzzy Tsukamoto Method In Forecasting The Amount Medication Requrementsat In The Hospital

The pharmaceutical installation as one of the hospital service locations is an inseparable part of the hospital health service system which is oriented towards patient service, including pharmaceutical services needed by patients such as consumable medical equipment that is affordable for all levels of society and the provision of quality medicines. The problem that arises is the uncertain number of patients and the medicines needed by each patient are different and often the supply of medicines that are currently needed by the community is empty, while medicines that are less needed are in abundant stock. Mistakes in ordering medicines can cause shortages or excesses of medicine stock. They tend to only use estimates of the amount of remaining stock without any special methods being used. Even hospital pharmacies tend to buy too many medicines because of uncertain demand and fear of shortages. A method that can help in predicting the amount needed for medicines is by applying the fuzzy Tsukamoto method. The prediction process begins with testing drug data in 2022 to predict the amount needed for medicines in 2023 before finally the drug data for 2023 is used to predict the amount needed in 2024.The prediction process will use drug sales data in the form of the amount of inventory, the amount needed and remaining stock to build a prediction model that projects the amount of drug need in the year 2023. This approach will involve analyzing historical data and applying the Tsukamoto method to produce predictions of the amount needed for all drugs in the following year.

Overview of Sales and Investment of Generık Drugs "X" Pharmacy, Kampung Satu District, Tarakan City Using ABC Analysis in 2022

Pharmacies as health facilities and businesses, require good management to avoid shortages or excess stock of medicines. This research aims to optimize generic drug inventory management at pharmacy "X" using the ABC method. Using a quantitative and retrospective descriptive approach, generic drug sales data for the period January- December 2022 were analyzed. As a result, Group A consisted of 23 drug items with a total sales transaction of 5,877 (69.89%), Group B consisted of 28 drug items with a total sales transaction of 1,654 (19.67%), and Group C consisted of 71 items with the number of sales transactions was 878 (10.44%). In terms of investment value, Group A consists of 31 medicinal items with an investment value of Rp. 39,058,600 (69.63% of the total), Group B consists of 33 medicinal items with an investment value of Rp. 11,290,000 (20.13% of the total), and Group C consists of 58 medicinal items with an investment value of Rp. 5,743,300 (10.24% of the total). The ABC method can optimize generic drug inventory management at pharmacy “X”, enable prioritization based on sales frequency and investment value, and increase efficiency.

Overview of Sales and Investment in Drugs Branded by Pharmacy "X" in Kampung Satu District, Tarakan City Using ABC Analysis for the 2022 Period

Pharmacies as facilities that are authorized to manage the inventory of pharmaceutical supplies need to anticipate inventory problems, both over stock and out stock. Pareto ABC analysis is one solution to overcome planning and procurement problems which tend to be ineffective and inefficient. This research aims to determine the sales and investment value of branded drugs at one of the private pharmacies in Tarakan City. This research is a retrospective study using the Pareto ABC analysis method on branded drug sales data in 2022. Based on sales data, 761 types of branded drugs were analyzed. The data is then grouped based on sales value. As for the results obtained, group A consisted of 189 types of drugs, or the equivalent of 24.84% of the total types of drugs, with a sales value of 17,986 items and an investment value of IDR. 209,828,250. Meanwhile, group B consists of 205 types of drugs, or the equivalent of 26.94% of the total types of drugs, with a sales value of 5,155 items and an investment value of IDR. 60,177,500. Meanwhile, for group C there are 367 types of drugs, or 48.23% of all types of drugs, with a sales value of 2,573 items and a total investment value of IDR. 30,107,100. This indicates that inventory monitoring and management can be adjusted to the characteristics and investment size of each drug category.

Prescriptions of antidepressants and anxiolytics in France 2012–2022 and changes with the COVID-19 pandemic: interrupted time series analysis

BackgroundDepression and anxiety have increased in prevalence since the start of the COVID-19 pandemic.ObjectiveTo evaluate the consumption of antidepressants and anxiolytics from 2012 to 2022 and the pandemic’s potential impact...

Association between air pollution levels and drug sales for asthma and allergy in 63 million people in metropolitan France

Introduction Air pollution is known to have an impact on respiratory health. However, the assessment of this relationship is far from complete and is rarely extended to the country level. We used drug sales data, both Over-The-Counter (OTC) and prescription drugs, to assess exhaustively the impact of air pollution on asthma and allergy at the national level in France. Methods The WHO Anatomical Therapeutic Chemical (ATC) classification system was used to describe the distribution of sales of drugs of class R03 (Drugs for obstructive airways diseases, overall for asthma) and R06 (Antihistamines for systemic use). We performed a Quasi-Poisson regression model with a generalized additive model (GAM) to estimate the relationship (Relative Risks and 95% Confidence Interval) between drug sales and air pollutants, that is Particulate Matter with a diameter less than 2.5 micrometers (PM2.5) and less than 10 micrometers (PM10) and Nitrogen dioxide (NO2), as assessed using the high-resolution CHIMERE dispersion model. We designed unadjusted and adjusted single-pollutant models as well as two-pollutant models. Results PM2.5, PM10, and NO2 were significantly and positively associated with sales of R03 and R06 class drugs, after adjustment for potential confounders. Results were confirmed in the two-pollutant model for PM10 and NO2 but not for PM2.5. Conclusions Our study confirms the presence of an association between major air pollutants and the sales of drugs against asthma and allergies. Further studies on larger databases and over several years are necessary to confirm and better understand these results.

Prediksi Penggunaan Obat Peserta Jaminan Kesehatan Nasional Menggunakan Algoritma Naïve Bayes Classifier

Currently, most of the patients seeking treatment at the hospital use the National Health Insurance (JKN) organized by the Healthcare and Social Security Agency (BPJS Kesehatan). In some hospitals, the figure is above 80%. Considering the very high number of BPJS Kesehatan participant seeking treatment at the hospital, a good data management method is needed, especially regarding the management of drug. Drug supply needs to be analyzed from time to time so that it can help predict future needs. An adequate supply of drugs and as needed is one of the things that affect service to patients. The availability of sufficient stock is expected to accelerate service to patients so that they do not have to wait long. Patients who are served quickly are expected to be satisfied. The impact of this patient satisfaction will increase the number of patient visits to the hospital. To support this, it is necessary to create a system that can estimate drug needs. The system can predict drug demand by using drug sales data to JKN participant patients for five years. Drug data used as research samples and then processed using an algorithm is the Naive Bayes Classifier. The Naive Bayes Classifier method is a method used to predict future opportunities using the basis of previous experience. A distinctive feature of this method is that it uses a very strong assumption of the independence of each event. While software testing uses the User Acceptance Test (UAT) model. Based on testing using this method, the system can be well received by users with a score of 78.64% (good).

Using a Pharmacy-Based Surveillance System to Improve Standards for TB Care in Kerala, India.

Eleven anti-TB drugs were included in the Government of India's Schedule H1 drug regulations in 2014. The National Strategic Plan for TB Elimination in India 2017-2025 recognized the opportunity to strengthen the TB surveillance system and improve the quality of TB care by implementing the Schedule H1 regulation. However, there were no documented systematic large-scale efforts to use Schedule H1 regulation to support TB surveillance or improve the quality of care. We aimed to document the process of implementation of the Schedule H1 regulation to enhance the quality of TB care and strengthen the TB surveillance system in Kerala, India. We conducted 33 in-depth interviews of the drugs control department enforcement officers, chemist shop owners, private-sector doctors, leaders of professional medical associations, and program managers and key staff of the TB Elimination Program in Kerala. Major themes identified were the process of implementation of Schedule H1 and how the National TB Elimination Program used the information. Findings from the qualitative interviews were corroborated with the quantitative information from the annual program performance reports and anti-TB drug sales data. The TB Elimination Program of Kerala used the information from the Schedule H1 drug register to identify the missing TB cases and strengthen TB notification, identify providers for engagement and extend support to them for ensuring standards of TB care, and provide feedback to providers regarding prescription practices. Stakeholders felt that implementation of Schedule H1 surveillance has helped to improve TB patient notifications from the private sector, build better public-private partnerships, and improve the quality of TB diagnosis and treatment in Kerala. Pharmacy-based drug sales data collected either through regulatory or non-regulatory methods have immense potential to support TB elimination programs.

Happy Pills? Mental Health Effects of the Dramatic Increase of Antidepressant Use

Despite the growing skepticism regarding the efficacy of antidepressants, global consumption has increased at an unprecedented rate with unknown implications for society. We estimate the causal effect of this increase on mental health outcomes using an instrumental variable strategy that exploits pharmaceutical company local market power and the availability of detailed drug sales data from Switzerland between 2002 and 2014. Our main instrument, a modified version of the popular shift-share instrument, relies on the national growth in antidepressant sales for pharmaceutical companies (the shift) – mainly due to product innovation – and assigns it locally using regional non-antidepressant market shares. Our estimates show that an increase in antidepressant sales causes a sharp increase of hospital admissions related to depression symptoms. An alternative instrument, which exploits prescribing practice spillovers from neighboring countries, leads to very similar point estimates providing further evidence about the validity of our results.

The More We Die, The More We Sell? A Simple Test of the Home-Market Effect.

The home-market effect, first hypothesized by Linder (1961) and later formalized by Krugman (1980), is the idea that countries with larger demand for some products at home tend to have larger sales of the same products abroad. In this article, we develop a simple test of the home-market effect using detailed drug sales data from the global pharmaceutical industry. The core of our empirical strategy is the observation that a country's exogenous demographic composition can be used as a predictor of the diseases that its inhabitants are most likely to die from and, in turn, the drugs they are most likely to demand. We find that the correlation between predicted home demand and sales abroad is positive and greater than the correlation between predicted home demand and purchases from abroad. In short, countries tend to be net sellers of the drugs they demand the most, as predicted by Linder (1961) and Krugman (1980).

Did we see it Coming? An Evaluation of the Swedish Early Awareness and Alert System.

BackgroundEarly awareness and alert systems have been established in many countries but evidence on their ability to accurately prioritize new medicines (for early assessment) is limited.ObjectiveThe purpose of this study was to assess whether the Swedish Early Awareness and Alert System identified and prioritized (i.e., produced early assessment reports for) new medicines that would go on to have substantial economic impact.MethodsWe adapted a study design commonly used in the assessment of diagnostic test accuracy. The prioritization made by the Swedish Early Awareness and Alert System prior to marketing authorization comprised the index test and the national drug sales data in the second year post-authorization served as the reference standard. All initial marketing authorization applications for medicinal products processed by the European Medicines Agency between 2010 and 2015 (study population) were classified using the index test and the reference standard.ResultsTwo hundred and fifty-three new medicinal products processed by the European Medicines Agency comprised the study population. Of these, 71 were prioritized by the Swedish Early Awareness and Alert System and 21 were classified as having substantial economic impact. The sensitivity and positive predictive value were 76.2% and 22.5%, respectively. Subgroup analyses showed that the accuracy of prioritization, in terms of sensitivity, was 100% for antineoplastic/immunomodulating agents.ConclusionsThe Swedish Early Awareness and Alert System identified all new medicines that would go on to have substantial economic impact and prioritized most of these medicines. Our findings provide reassurance to decision makers who rely on the outputs of the Swedish Early Awareness and Alert System to keep informed about new medicines. Moreover, this study also provides valuable insights to stakeholders willing to establish or evaluate their own early awareness and alert activities and systems.Electronic supplementary materialThe online version of this article (10.1007/s40258-018-0434-2) contains supplementary material, which is available to authorized users.

PHP198 - EVALUATION OF THE SWEDISH EARLY AWARENESS AND ALERT SYSTEM

Early awareness and alert activities (also known as horizon scanning) have been established in many countries but evidence on their ability to accurately prioritize new medicines is limited. The purpose of this study is to assess the accuracy of prioritization made by the Swedish Early Awareness and Alert (EAA) System between 2010 and 2015. We adapted a study design commonly used in the assessment of diagnostic test accuracy. The prioritization made by the Swedish EAA System prior to marketing authorization comprised the index test and national drug sales data in the second year post-authorization served as the reference standard. All new medicinal products processed by the European Medicines Agency (EMA) between 2010 and 2015 (study population) were classified using the index test and the reference standard. Outcome statistics were reported as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Two hundred and fifty-three new medicinal products processed by EMA comprised the study population. Of these, 71 were prioritized by the Swedish EAA System and 21 were classified as having a substantial economic impact. The sensitivity, specificity, PPV, and NPV were 85.7%, 76.3%, 24.7%, and 98.3%, respectively. Subgroup analyses showed that the accuracy of prioritization, in terms of sensitivity, was highest for antineoplastic/immunomodulating agents (100%). The Swedish EAA System accurately prioritized almost all new medicines that would have a substantial economic impact. Our findings provide reassurance to decision makers who rely on the outputs of the Swedish EAA System to keep informed about new medicines. Moreover, this study also provides valuable insights to stakeholders willing to establish and/or evaluate their own EAA activities.

The number of privately treated tuberculosis cases in India: an estimation from drug sales data

SummaryBackgroundUnderstanding the amount of tuberculosis managed by the private sector in India is crucial to understanding the true burden of the disease in the country, and thus globally. In the absence of quality surveillance data on privately treated patients, commercial drug sales data offer an empirical foundation for disease burden estimation.MethodsWe used a large, nationally representative commercial dataset on sales of 189 anti-tuberculosis products available in India to calculate the amount of anti-tuberculosis treatment in the private sector in 2013–14. We corrected estimates using validation studies that audited prescriptions against tuberculosis diagnosis, and estimated uncertainty using Monte Carlo simulation. To address implications for numbers of patients with tuberculosis, we explored varying assumptions for average duration of tuberculosis treatment and accuracy of private diagnosis.FindingsThere were 17·793 million patient-months (95% credible interval 16·709 million to 19·841 million) of anti-tuberculosis treatment in the private sector in 2014, twice as many as the public sector. If 40–60% of private-sector tuberculosis diagnoses are correct, and if private-sector tuberculosis treatment lasts on average 2–6 months, this implies that 1·19–5·34 million tuberculosis cases were treated in the private sector in 2014 alone. The midpoint of these ranges yields an estimate of 2·2 million cases, two to three times higher than currently assumed.InterpretationIndia's private sector is treating an enormous number of patients for tuberculosis, appreciably higher than has been previously recognised. Accordingly, there is a re-doubled need to address this burden and to strengthen surveillance. Tuberculosis burden estimates in India and worldwide require revision.FundingBill & Melinda Gates Foundation.

Acute Pancreatitis and Use of Pancreatitis-Associated Drugs: A 10-Year Population-Based Cohort Study.

To assess the use of acute pancreatitis (AP)-associated drugs in patients with AP, the relation between sales of these drugs and the incidence of AP, and the potential impact on AP severity and recurrence. All patients with incident AP between 2003 and 2012, in a well-defined area, were retrospectively identified. Data regarding AP etiology, severity, and recurrence and use of AP-associated drugs were extracted from medical records. Drugs were classified according to an evidence-based classification system. Annual drug sales data were obtained from the Swedish drug administration service. Overall, 1457 cases of incident AP were identified. Acute pancreatitis-associated drug users increased from 32% in 2003 to 51% in 2012, reflecting increasing user rates in the general population. The incidence of AP increased during the study period but was not related to AP-associated drug user rates (P > 0.05). Recurrent AP occurred in 23% but was unrelated to AP-associated drug use (P > 0.05). In logistic regression analysis, after adjustment for comorbidity, AP-associated drug use was not related to AP severity (P > 0.05). Use of AP-associated drugs is increasingly frequent in patients with AP. However, it does not have any major impact on the observed epidemiological changes in occurrence, severity, or recurrence of AP.

A syndromic surveillance system for clinical and non-clinical health data

The data used in health care and public health monitoring and surveillance are likely to be nonstationary and highly auto-correlated. These data may come from clinical and nonclinical sources, the latter being increasingly used in an attempt to supplement traditional surveillance. We propose an alternative approach applicable to multiple sets of health data that combines a method of preconditioning (ARMA filtering), a forecasting method (the method of analogues) and a monitoring method (the change-point). This procedure is applied to Influenza-Like Illness (ILI) surveillance in France using both clinical data (time series of weekly ILI incidence at the national level and in 22 regions) and nonclinical data (over-the-counter and prescribed weekly medication sales data at the national level). The outcomes show that multivariate monitoring provides an improved ability to recognize warning signals and drug sales data provide an indirect but earlier indication of influenza outbreaks. The proposed method has the advantage of being both practical and relatively simple to implement and could be easily adapted to develop adequate surveillance tools for specific diseases.

Dispensed volumes of anti‐asthmatic drugs related to the prevalence of asthma and COPD in Sweden

To explore the possibility of using dispensed volumes asthma/COPD drugs as a proxy for the combined prevalence of asthma plus COPD. The proportions of the Swedish population with inhalation drugs for asthma/COPD 2004 were obtained using three different databases. A pharmacy record database gave the volumes of dispensed drugs (defined daily doses, DDDs of R03A + R03B drugs) for each patient, 20 years and older. The X-plain database of Apoteket AB gave drug sales data for Sweden and Swedish population data were obtained from Swedish statistics. The sales volumes of asthma/COPD drugs were much higher for older than for younger people. The volumes increased from 18 DDD/TID for the 20-29 year group up to 124 DDD/TID for patients 70-79 years, or about seven times. The average volumes per patient in the different age groups corresponded to one DDD/day in only three of the age groups (50-79 years). In the youngest group the average drug volume per patient corresponded to one DDD every second day, which may indicate undermedication. The percentages of the Swedish population with asthma/COPD drugs increased from 4.0% for 20-29 years old to 14.5% for 80+ years old, or 3.6 times. When head-to-head comparisons could be made between reported prevalence data of asthma and COPD and our data the two sets of data were in a reasonable agreement. The prevalence of drug treatment, i.e. the proportion of the population with dispensed asthma/COPD drugs, could function as a proxy for the disease prevalence of asthma plus COPD.

Correlation of drug utilisation and morbidity at the municipality level. High use of antibiotics associated with low use of antihypertensives

The aim of the study was to explore the relationship between Swedish drug sales data per municipality and morbidity per municipality. The morbidity was expressed as "sickness numbers" which are assumed to function as proxy for morbidity. Sickness numbers per municipality were correlated to volumes of drug sales per municipality in 2003. In addition, the sales volumes of antibiotics were correlated to the sales volumes of other drugs. We found significant positive correlations between municipality sickness numbers and sales volumes for most drug groups, except for antibiotics where the correlations were negative in all age groups. When the volumes of antibiotics were related to the volumes of other drug groups it was observed that municipalities with volumes of antibiotics above the national average had reduced volumes of cardiovascular drugs, especially of diuretics, beta blockers and calcium antagonists, all primarily used as antihypertensives. For ACE inhibitors and statins, no such relationship was found. The findings might suggest a link between infection and hypertension, but a cause-effect relation is not established.

Air Pollution and Respiratory Drug Sales in the City of Le Havre, France, 1993–1996

The aim of this study is to evaluate ambulatory respiratory drug sales data as health indicators for the short-term effects of ambient air pollution in the city of Le Havre. Daily respiratory drug sales data were crossed with daily ambient air concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), and black smoke (BS) using an autoregressive Poisson regression model adjusting for time trends, seasonal variations, influenza epidemics, and weather. Relative risks (RR) were expressed for an increase of two standard deviations above the mean of each pollutant. Respiratory drug sales were associated with most pollutants studied with lags varying from 1 to 9 days. For daily mean concentrations of BS, RR=1.037 (95% confidence interval (CI) 1.009–1.066) for lag 1 and RR=1.052 (95% CI 1.023–1.081) for lag 8. For daily mean concentrations of NO2, RR=1.033 (95% CI 1.001–1.066) for lag 1 and RR=1.046 (95% CI 1.014–1.079) for lag 8. RR observed with a daily 1 h maximum of SO2 were RR=1.027 (95% CI 1.004–1.051) for lag 3 and RR=1.032 (95% CI 1.009–1.056) for lag 9. Our study concludes that ambulatory respiratory drug sales data could be useful for epidemiological surveillance of air pollutant health effects.

Antidepressant drug use in Italy since the introduction of SSRIs: national trends, regional differences and impact on suicide rates.

Little is known about the use of antidepressant drugs in Italy since the introduction of selective serotonin reuptake inhibitors (SSRIs). To fill this gap, we examined antidepressant drug sales data from 1988 to 1996 for the whole country, and for the years 1995 and 1996 on the regional level. National suicide trends from 1988 to 1994 were also examined to assess whether the increasing use of SSRI antidepressants was associated with changes in suicide rates. From 1988 to 1996 an increase of antidepressant sales of 53% was recorded. This increase reflected increasing use of SSRIs, which in 1996 accounted for more than 30% of total antidepressants sold. The analysis of regional differences demonstrated heterogeneity between north, center, and south. In the south prescriptions of antidepressants and use of SSRIs were lower than in the rest of the country. In the 7-year period over which SSRI use increased, male suicide rates increased from 9.8 to 10.2 per 100,000 inhabitants, and female suicide rates declined from 3.9 to 3.2 per 100,000. These data suggest that SSRIs gave a new impetus to antidepressant sales. However, possible public health benefits related to the shift from old to new antidepressants have yet to be demonstrated.

Descriptive epidemiology of Parkinson's disease through proxy measures.

In preparation for analytic study we undertook to describe areas of relative excess and deficit of Parkinson's Disease (PD) in Nebraska and tested two methodologic tools for inexpensive assessment of descriptive epidemiology of PD. In lieu of large-scale population screening and diagnosis, we obtained sales information of anti-PD drugs in the state in 1988-1990 as well as listings of all people dying from 1984 to 1993 who had Parkinson's Disease mentioned anywhere on their death certificate. The anti-PD drug sales data are intended as a proxy for prevalence, while the death certificate data are intended as a proxy for incidence. Sales divided by population over age 54 indicates where anti-PD drug sales differ from expected. We found high correlation of drug sales rates with several farming exposures. Age-adjusted death rates, however, showed a low degree of association with sales or farming variables. This may be attributable to differences in death certificate completion or in underlying incidence versus prevalence. These techniques provide a useful tool for delineating possible differences in incidence and prevalence. While not as accurate as full community survey with expert diagnosis, they are not as expensive, and can be followed by local cluster investigations and individual-level etiologic studies to test hypotheses resulting from the initial study.