A summary of prescriptions dispensed in the community by community pharmacists, appliance contractors and dispensing doctors in England. The bulletin highlights recent changes and the main trends between 2005 and 2015.

The specific source for these statistics is the Prescription Cost Analysis (PCA) data. The Health and Social Care Information Centre (HSCIC) publishes the Prescription Cost Analysis National Statistic, based on PCA figures for the most recent calendar year, annually, in April.

This report compares expenditure between primary and secondary care in total and for medicines positively appraised by NICE. All costs given in this report are net ingredient cost (NIC). This is the basic price of a drug excluding VAT (the price listed in the national Drug Tariff or in standard price lists). Cost changes have not been adjusted for inflation. It does not take account of discounts, dispensing costs, fees or prescription charges income. Please note, the key fact figure for the rise in the cost of medicines in hospitals since 2010/11 has been corrected to 81.8 per cent as stated in the accompanying report.

This file contains information about Social Security determinations of eligibility for Extra Help with Medicare Prescription Drug Plan Costs. Specific data elements are counts of Extra Help decisions made, counts of applicants found eligible for the Extra Help, and a percentage of Extra Help decisions found eligible by state.

The number of prescriptions dispensed in the U.S. has increased between 2009 and 2022. In 2009 the number of prescriptions dispensed was near **** billion, while in 2022 the number of prescriptions dispensed was around *** billion. The increase in the number of prescriptions dispensed has a multifactorial origin that includes health care sources, health insurance, and prescription drug benefits. However, the increase in prescription drug usage comes with a price tag as the price of drugs in the U.S. is also on the rise. Medication usage The total number of retail prescriptions filed annually in the United States is expected to also rise significantly by the year 2025. Medication usage varies depending on the population, for example, some data shows that prescription usage increases with age. Likewise, gender has an influence on prescription drug use. Females have a higher rate of prescription drug usage. Prescription drug costs The U.S. has some of the highest per capita drug spending in the world. That is largely because the prices of drugs in the U.S. are based solely on what the market can bear, rather than what the actual costs of production are. Personal health care expenditures in the U.S. have more than doubled since 2000. Estimates suggest that the cost of drugs will continue to increase. Estimated U.S. prescribed drug expenditures amounted to *** billion U.S. dollars by the end of 2021.

This issue has now been fixed and the correct data for Prescription Cost Analysis (PCA) - Financial Year 2022/23 is now available from this page

In the financial year 2023/24, the net ingredient cost of hormone replacement therapy (HRT) items prescribed in England was around 223 million British pounds. The net ingredient cost of HRT prescription items dispensed has more than tripled since 2017/18.

This statistic shows the average prices of non-prescription pharmaceuticals in Germany in 2015. The average price of phytopharmaceuticals amounted to ***** euros. In comparison, homoepathic remedies were sold for ***** euros on average.

This release contains data for the quarter October to December 2015, by Clinical Commissioning Group (CCG). Data for April 2013 to September 2015 is already available by CCG. Data for April 2008 to March 2013 is available by Primary Care Trust (PCT), see link below. The data is being made available via a tool called iView which will allow users to manipulate and extract data. In line with the Making Public Data Public initiative, the data for October to December 2015 is also being made available as a comma separated variable (csv) file. The structure of the file is: Quarter Area Team CCG BNF Chapter BNF Section Items Actual Cost Net Ingredient Cost Note that the column headings may differ from the iView files as this is an extract from ePACT (service provided by NHS Prescription Services). The data in the .csv file and in iView data now contain Area Team. HSCIC has also made another change, grouping the Cost Centres that were included in the data into a single category. For further detail, please see the link Methodological Change Note in the Related Links section below.

Overview

Opioids continue to remain a huge problem in many parts of the country. The Centers for Disease Control and Prevention recently analyzed opioid prescribing patterns in U.S. counties. Researchers found that while the amount of opioids prescribed dropped 18.2 percent nationally from 2010 to 2015, there was enormous variance in prescribing patterns by county.

The amount of opioids prescribed nationally peaked at the equivalent of 782 milligrams of morphine annually per capita in 2010 and fell to 640 in 2015. That’s an improvement, but it’s still three times higher than it was in 1999, Dr. Anne Schuchat, the CDC's principal deputy director, told the AP earlier this month.

Nearly half of all counties saw a significant decrease in prescription amounts from 2010 to 2015, but another 22.6 percent saw an increase of at least 10 percent during that time. The CDC analysis found that in 2015, the highest-prescribing counties had per-capita prescription amounts that were six times that of the lowest-prescribing counties.

The CDC analysis also found certain demographic and health characteristics were linked to -- but did not fully account for -- higher prescribing amounts. Counties with high prescribing often had these factors in common:

• Larger percentages of non-Hispanic white residents
• A higher prevalence of diabetes and arthritis
• Higher rates of unemployment
• Higher rates of Medicaid enrollment
• Were micropolitan areas (defined as counties with fewer than 50,000 people but with an urban cluster of at least 10,000 people)

The results of the CDC's look at possible contributing factors can be found here: https://www.cdc.gov/mmwr/volumes/66/wr/mm6626a4.htm?s_cid=mm6626a4_w#T2_down

The CDC produced a county-level map of the per-capita data here: https://www.cdc.gov/mmwr/volumes/66/wr/mm6626a4.htm?s_cid=mm6626a4_w#F2_down

The CDC also looked at other factors -- including the rate of prescriptions written (which dropped 13.1 percent nationally from 2012-2015); the number of high-dosage prescriptions written (which dropped 41.4 percent from 2010-2015); and the average daily milligrams of morphine equivalent per prescription, which dropped from 58.0 in 2010 to 48.1 in 2015. The one factor that rose was days' supply per prescription, which went up from 15.5 days' worth of medication in 2010 to 17.7 days in 2015.

CDC researchers point out that despite prescription amounts for legal drugs declining in many places, opiate-related deaths have continued to rise. Opioid overdoses -- from both legal and illegal drugs -- kill 91 people each day in the U.S. In 2015, roughly 15,000 people died from prescription opiate-related overdoses, according to CDC data.

The CDC researchers described their findings in detail in this report: http://bit.ly/2vH3AUW The report and data analysis will be used as a baseline to determine whether the CDC's 2016 guidelines for opioid prescribing (https://www.cdc.gov/mmwr/volumes/65/rr/rr6501e1.htm#B1_down) have been effective, a CDC spokeswoman said.

About This Data

This data was obtained by the CDC from QuintilesIMS Transactional Data Warehouse, which provides estimates of the number of opioid prescriptions dispensed in the United States based on a sample of approximately 59,000 pharmacies, representing 88 percent of prescriptions in the United States.

Prescriptions can vary widely by drug, dosage, and days' supply. Instead of merely counting the number of prescriptions written or pills dispensed, the CDC normalized the data to arrive at a single unit of measurement of opioids per capita for each county. The prescription amounts are measured in "Morphine Milligram Equivalents," or MMEs.

MMEs are a medically accepted method of measuring all the opioids a patient might be ingesting, so as to prevent overdoses and reduce the risk of addiction. In 2016, the CDC published guidelines recommending that clinicians use caution when increasing dosages past 50 MME a day, and to avoid reaching 90 MME a day except in the most extreme cases. General information about opioid dosing can be found here: https://www.cdc.gov/drugoverdose/pdf/calculating_total_daily_dose-a.pdf

The CDC placed each county into quartiles based on 2015 per-capita prescribing levels. In measuring change from 2010 to 2015, the CDC considered whether prescribing amounts had risen more than 10 percent ("Increased"), dropped more than 10 percent ("Decreased"), or stayed "Stable" (no change, or changes of less than 10 percent in either direction). These flags are included in the dataset.

The counties in the highest-prescribing quartile had an average of 1,319 MME per capita, while the counties in the lowest quartile had an average of 203 MME per capita.

Using This Data

According to the CDC's analysis, the national average daily MME per prescription in 2015 was 48.1. You can divide your county's annual per-capita MME by this number to find out the number of days' prescriptions per person in your county.

For Example: Surry County, N.C. has an annual 2015 per-capita MME of 2431.6. Divide that by 48.1 and you'll get 50.5.

This can be phrased as: "The prescription amounts in 2015 were the equivalent of a 50-day supply of opioids for every person in Surry County."

The CDC did similar calculations in a 2015 report, but instead of using the average daily MME prescription determined by this data, used as a basic guideline a 'typical' prescription of 5 mg of hydrocodone (5 MME) every 4 hours, for a total of 30 MME/day. Using this example, enough opioids were prescribed in Surry County, NC in 2015 to medicate every person in the county around the clock for 81 days.

You can also rank the counties in your state. To do this, click on "Rank Prescription Amounts in your state" under the 'Queries' tab in the upper right-hand bar on this page. Type the name of your state over the "STATE_NAME" placeholder text in the query. The resulting table will show you the counties in your state, ordered by 2015 MMEs. You can export this table. Keep in mind that the prescription data reflects where prescriptions were dispensed, not where recipients live.

Look for counties where prescription amounts have increased more than 10 percent since 2010. To do this, click on "Increasing prescription amounts" under the 'Queries' tab in the upper right-hand bar on this page. Type the name of your state over the "STATE_NAME". The resulting table will show you all the counties in your state that have seen prescription amounts increase by at least 10 percent, ordered by 2015 MMEs. You can export this table. Keep in mind that the prescription data reflects where prescriptions were dispensed, not where recipients live.

Data should be attributed to the CDC, based on raw prescription data obtained from QuintilesIMS, a pharmaceutical analytics company. Please give The Associated Press a contributing line on any story or graphic produced from this data distribution.

Caveats

• The county-level data reflects where an opioid is dispensed. Some of these prescriptions may have been obtained by people outside the county.

• Some counties did not have data robust enough for CDC to analyze. Of the 3,143 counties in the U.S., 180 counties did not have 2015 per-capita MME data that could be used. Still more counties did not have 2010 data. In all, the CDC was able to calculate a per-capita MME for both years in 2,734 counties.

• The data do not take into account illegal use of opiate drugs such as heroin.

• The data do not reflect drugs dispensed directly by a medical provider.

• Cold and cough products containing opioids and buprenorphine products indicated for conditions other than pain were excluded.

• The data does not include any details on the appropriateness of the prescriptions, or whether the opioids were dispensed for chronic, acute or end-of-life pain.

• The MME is calculated on an annual basis per capita. The CDC used American Community Survey data for population. Population estimates include all people in a county, including children.

AP Coverage of the Opioid Epidemic

The Associated Press has an ongoing series, Overcoming Opioids, running through this year, chronicling efforts to climb out of the worst drug epidemic in U.S. history. For earlier parts of this series, see: https://apnews.com/tag/OvercomingOpioids

If you have any questions about this data or its use, leave a comment in the discussion forum here or email Data Journalist Meghan Hoyer at mhoyer@ap.org

This data set contains prescription drugs, the prescribing physician, total pills and total costs of these prescriptions charged to Medicaid in Utah for years 2015, 2016, 2017.

The Part D Prescriber PUF is based on information from CMS’s Chronic Conditions Data Warehouse, which contains Prescription Drug Event records submitted by Medicare Advantage Prescription Drug (MAPD) plans and by stand-alone Prescription Drug Plans (PDP).

Warning: Large file size (over 1GB). Each monthly data set is large (over 4 million rows), but can be viewed in standard software such as Microsoft WordPad (save by right-clicking on the file name and selecting 'Save Target As', or equivalent on Mac OSX). It is then possible to select the required rows of data and copy and paste the information into another software application, such as a spreadsheet. Alternatively add-ons to existing software, such as the Microsoft PowerPivot add-on for Excel, to handle larger data sets, can be used. The Microsoft PowerPivot add-on for Excel is available using the link in the 'Related Links' section below. Once PowerPivot has been installed, to load the large files, please follow the instructions below. Note that it August take at least 20 to 30 minutes to load one monthly file. 1. Start Excel as normal 2. Click on the PowerPivot tab 3. Click on the PowerPivot Window icon (top left) 4. In the PowerPivot Window, click on the "From Other Sources" icon 5. In the Table Import Wizard e.g. scroll to the bottom and select Text File 6. Browse to the file you want to open and choose the file extension you require e.g. CSV Once the data has been imported you can view it in a spreadsheet. What does the data cover? General practice prescribing data is a list of all medicines, dressings and appliances that are prescribed and dispensed each month. A record will only be produced when this has occurred and there is no record for a zero total. For each practice in England, the following information is presented at presentation level for each medicine, dressing and appliance, (by presentation name): - the total number of items prescribed and dispensed - the total net ingredient cost - the total actual cost - the total quantity The data covers NHS prescriptions written in England and dispensed in the community in the UK. Prescriptions written in England but dispensed outside England are included. The data includes prescriptions written by GPs and other non-medical prescribers (such as nurses and pharmacists) who are attached to GP practices. GP practices are identified only by their national code, so an additional data file - linked to the first by the practice code - provides further detail in relation to the practice. Presentations are identified only by their BNF code, so an additional data file - linked to the first by the BNF code - provides the chemical name for that presentation.

This data set contains prescription drugs, the prescribing physician, total pills and total costs of these prescriptions charged to Medicaid in Utah for years 2015, 2016, 2017.

The Part D Prescriber PUF is based on information from CMS’s Chronic Conditions Data Warehouse, which contains Prescription Drug Event records submitted by Medicare Advantage Prescription Drug (MAPD) plans and by stand-alone Prescription Drug Plans (PDP).

This report compares expenditure between primary and secondary care in total and for medicines positively appraised by NICE.

This dataset is the NICE-appraised medicine breakdown. Datasets for national-level time series and area team level are available in the related links section. Prior to 2015 these datasets were combined in Excel workbooks, available on the national level page.

This statistic shows the costs of hospitalizations from medication non-adherence in the U.S. over a *** year period as of 2015, for select conditions. It was found that over *** year *** avoidable hospitalizations for diabetes resulting from poor medication habits resulted in over *** million U.S. dollars in avoidable costs.

The Centers for Medicare & Medicaid Services (CMS) has prepared a public data set, the Medicare Part D Opioid Prescriber Summary File, which presents information on the individual opioid prescribing rates of health providers that participate in Medicare Part D program. This file is a prescriber-level data set that provides data on the number and percentage of prescription claims (includes new prescriptions and refills) for opioid drugs, and contains information on each provider’s name, specialty, state, and ZIP code. This summary file was derived from the 2015 Part D Prescriber Summary Table (Documentation available at: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Downloads/Prescriber_Methods.pdf

The Medicare Part D opioid prescribing mapping tool is an interactive tool that shows geographic comparisons, at the state, county, and ZIP code levels, of de-identified Medicare Part D opioid prescription claims prescriptions written and then submitted to be filled within the United States. The mapping tool presents Medicare Part D opioid prescribing rates for 2015 as well as the change in opioid prescribing rates from 2013 to 2015. New for this release is additional information on extended-release opioid prescribing rates as well as spatial analyses to identify hot spots or clusters.

This statistic shows the ** U.S. cities with the highest rates of opioid abuse among those with an opioid prescription from 2011 to 2015. During this time, the city of Wilmington in North Caroline had the highest rate of opioid abuse, at over **** percent.

IntroductionCitalopram and escitalopram are among the most used medications and are key treatments for many psychiatric disorders. Previous findings suggest citalopram and escitalopram prescription rates are changing because of the patent for citalopram ending as opposed to evidence of a clear therapeutic advantage—so-called “evergreening”. This retrospective study focuses on characterizing the chronologic and geographic variation in the use of citalopram and escitalopram from 2015 to 2020 among US Medicaid and Medicare patients. We hypothesized that prescription rates of citalopram will decrease with a concurrent increase in escitalopram, consistent with “evergreening”.MethodsCitalopram and escitalopram prescription rates and costs per state were obtained from the Medicaid State Drug Utilization Database and Medicare Provider Utilization and Payment Data. States’ annual prescription rates outside a 95% confidence interval were considered significantly different from the average.ResultsOverall, a decreasing trend for citalopram and an increasing trend for escitalopram prescription rates were noted in both Medicare and Medicaid patients. The differences between generic and brand were noted for both drugs, with generic forms being less expensive than the brand-name version.DiscussionDespite limited evidence suggesting that citalopram and escitalopram have any meaningful differences in therapeutic or adverse effects, there exists a noticeable decline in the use of citalopram that cooccurred with an increase in escitalopram prescribing, consistent with our hypothesis. Moreover, among these general pharmacoepidemiologic trends exists significant geographic variability. There was disproportionate spending (relative to their use) on the brand versions of these medicines relative to their generic forms.