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 statistic depicts the total number of e-prescriptions in the United States, based on data of the country's largest e-prescribing operator Surescripts, from 2013 to 2024. In 2024, the total number of electronic prescriptions processed reached some *** billion.

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 dataset is comprised of data submitted to HCAI by prescription drug manufacturers for wholesale acquisition cost (WAC) increases that exceed the statutorily-mandated WAC increase threshold of an increase of more than 16% above the WAC of the drug product on December 31 of the calendar year three years prior to the current calendar year. This threshold applies to prescription drug products with a WAC greater than $40 for a course of therapy. Required WAC increase reports are to be submitted to HCAI within a month after the end of the quarter in which the WAC increase went into effect. Please see the statute and regulations for additional information regarding reporting thresholds and report due dates.

Key data elements in this dataset include the National Drug Code (NDC) maintained by the FDA, narrative descriptions of the reasons for the increase in WAC, and the five-year history of WAC increases for the NDC. A WAC Increase Report consists of 27 data elements that have been divided into two separate Excel data sets: Prescription Drug WAC Increase and Prescription Drug WAC Increase – 5 Year History. The datasets include manufacturer WAC Increase Reports received since January 1, 2019. The Prescription Drugs WAC Increase dataset consists of the information submitted by prescription drug manufacturers that pertains to the current WAC increase of a given report, including the amount of the current increase, the WAC after increase, and the effective date of the increase. The Prescription Drugs WAC Increase – 5 Year History dataset consists of the information submitted by prescription drug manufacturers for the data elements that comprise the 5-year history of WAC increases of a given report, including the amount of each increase and their effective dates.

There are 2 types of WAC Increase datasets below: Monthly and Annual. The Monthly datasets include the data in completed reports submitted by manufacturers for calendar year 2025, as of July 8, 2025. The Annual datasets include data in completed reports submitted by manufacturers for the specified year. The datasets may include reports that do not meet the specified minimum thresholds for reporting.

The Quick Guide explaining how to link the information in each data set to form complete reports is here: https://hcai.ca.gov/wp-content/uploads/2024/03/QuickGuide_LinkingTheDatasets.pdf

The program regulations are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/CTRx-Regulations-Text.pdf

The data format and file specifications are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/Format-and-File-Specifications-version-2.0-ada.pdf

DATA NOTES: Due to recent changes in Excel, it is not recommended that you save these files to .csv format. If you do, when importing back into Excel the leading zeros in the NDC number column will be dropped. If you need to save it into a different format other than .xlsx it must be .txt

DATA UPDATES: Annual datasets of reports from the preceding year are reviewed in the second half of the current year to identify if any revisions or additions have been made since the original release of the datasets. If revisions or additions have been found, an update of the datasets will be released. Datasets will be clearly marked with 'Updated' in their titles for convenient identification. Not all datasets may require an updated release. The review of previously released datasets will only be conducted once to determine if an updated release is necessary. Datasets with revisions or additions that may have been made after the one-time review can be requested. These requests should be sent via email to ctrx@hcai.ca.gov. Due to regulatory changes that went into effect April 1, 2024, reports submitted prior to April 1, 2024, will include the data field "Unit Sales Volume in US" and reports submitted on or after April 1, 2024, will instead include "Total Volume of Gross Sales in US Dollars".

This statistic depicts the total number of e-prescriptions for controlled substances (like opioids) in the United States, based on data of the country's largest e-prescribing operator Surescripts, from 2013 to 2024. In 2024, the total number of filed electronic prescriptions for controlled substances exceeded 310 million.

Snapshot img

E-Prescribing Market Size 2024-2028

The E-prescribing market size is forecast to increase by USD 1.98 billion at a CAGR of 16.81% between 2023 and 2028. The market is experiencing significant growth due to the increasing need for automation across various departments in hospitals and nursing homes. The use of Internet-enabled mobile devices in healthcare IT is a major trend, enabling physicians to prescribe medications remotely and reducing the risk of errors. However, privacy and data security concerns remain a challenge, as sensitive patient information must be protected. Cost savings are also a driving factor, as e-prescribing reduces the likelihood of prescription errors and associated deaths, resulting in significant cost savings for healthcare providers and health alliance plans. CVS Caremark Corporation and other industry players are capitalizing on this trend by offering comprehensive e-prescribing solutions to hospitals, nursing homes, and individual physicians. The market is expected to continue growing, as the benefits of e-prescribing become increasingly apparent.

Request Free Sample

In the realm of healthcare technology, e-prescribing has emerged as a transformative solution, revolutionizing the way prescriptions are written and managed. This digital approach offers numerous benefits, including prescription accuracy, improved patient safety, and reduced costs. Electronic connectivity between clinicians, pharmacies, and health plan formularies enables real-time access to patient eligibility, medication history, and health alliance plans. At the point of care, e-prescribing applications facilitate medication orders, reducing medication errors, adverse drug events, and even deaths. By integrating drug interactions, allergy alerts, and health plan formularies, e-prescribing minimizes the risk of errors and enhances patient safety. Hospitals, nursing homes, pharmacies, and CVS Caremark Corporation are among the key adopters of this technology.

Furthermore, the cost savings derived from reduced medication errors, improved patient outcomes, and streamlined workflows make e-prescribing an indispensable tool for physicians and health care providers managing medications for their patients.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premise
  Cloud-based


Geography

  North America

    US


  APAC

    China
    Japan


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Deployment Insights

The on-premise segment is estimated to witness significant growth during the forecast period. The market is experiencing notable growth due to the implementation of patient eligibility and medication history checks at the point of care. E-prescribing applications offer numerous benefits, including reduced medication errors and adverse drug events. The on-premises segment in the market is anticipated to expand at a considerable rate, owing to the enhanced security and protection provided by on-premise solutions. On-premise solutions require the acquisition of licenses or software copies, and the entire software solution is installed and managed on the company's premises. This proximity to on-premise electronic health record (EHR) systems enables seamless integration and data sharing. However, the high costs associated with managing and maintaining the data on a company's premises may hinder the adoption of on-premise solutions during the forecast period.

Get a glance at the market share of various segments Request Free Sample

The on-premise segment was valued at USD 849.40 million in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 45% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

In 2023, North America holds a substantial share of The market due to the rising prevalence of chronic conditions like cancer and cardiovascular diseases (CVDs). Similarly, heart diseases, including coronary artery disease, are common health issues in North America, as per the Centers for Disease Control and Prevention (CDC). The adoption of advanced technologies in e-prescribing, increasing healthcare expenditure, and the presence of numerous market players contribute to the market's growth in the region.

Furthermore, E-prescribing applications offer several benefits, such as improved patient eligibility verification, medication history access, and point-of-care prescription processing. Thes

This dataset provides detailed information on various drugs, including their common uses, reported side effects, and associated medical conditions. It also includes user ratings and reviews, brand names, and regulatory classifications, offering a valuable resource for understanding pharmaceutical products and their impact on health. The data covers drugs used for a range of conditions such as Acne, Cancer, and Heart Disease.

Columns

• drug_name: The common name of the drug.
• medical_condition: The medical condition for which the drug is prescribed or used.
• side_effects: A list of reported side effects associated with the drug.
• generic_name: The chemical name of the drug, distinct from its brand name.
• drug_classes: The classification of the drug based on its chemical structure, mechanism of action, or therapeutic use.
• brand_names: The commercial names under which the drug is marketed.
• activity: An indicator of recent site visitor interest relative to other medications in the list.
• rx_otc: Details whether the drug requires a prescription (Rx), is available over-the-counter (OTC), or can be either.
• pregnancy_category: A classification indicating the potential risk of the drug to a foetus during pregnancy (categories A, B, C, D, X, N).
  • A: Adequate and well-controlled studies show no risk to the foetus in the first trimester.
  • B: Animal studies show no foetal risk, but no adequate human studies exist.
  • C: Animal studies show adverse foetal effects; no adequate human studies, but potential benefits may warrant use.
  • D: Positive evidence of human foetal risk, but potential benefits may warrant use despite risks.
  • X: Studies show foetal abnormalities; risks clearly outweigh potential benefits in pregnant women.
  • N: FDA has not classified the drug.
• csa: Controlled Substances Act (CSA) Schedule, indicating the drug's potential for abuse and accepted medical use in the United States (M, U, N, 1, 2, 3, 4, 5).
  • M: Multiple possible schedules depending on dosage or strength.
  • U: Schedule is unknown.
  • N: Not subject to the Controlled Substances Act.
  • 1: High potential for abuse, no accepted medical use.
  • 2: High potential for abuse, accepted medical use with severe restrictions.
  • 3: Lower abuse potential than schedules 1 and 2, accepted medical use.
  • 4: Low abuse potential relative to schedule 3, accepted medical use.
  • 5: Low abuse potential relative to schedule 4, accepted medical use.
• alcohol: Indicates if the drug interacts with alcohol.
• rating: User-assigned effectiveness rating, typically on a scale of 1 to 10 (1 = not effective, 10 = most effective).

Distribution

The dataset is typically provided in a CSV file format. Specific details regarding the exact number of rows or records are not currently available in the provided information. A sample file will be updated separately to the platform.

Usage

This dataset is ideal for: * Pharmaceutical research: Analysing drug properties, side effects, and interactions. * Healthcare applications: Developing tools for patients and professionals to access drug information. * Public health studies: Investigating patterns of drug usage, side effect prevalence, and medication effectiveness. * Educational purposes: Supporting studies in pharmacology, medicine, and public health. * Data analytics: Building models for drug efficacy, safety monitoring, and market analysis.

Coverage

The data provides global coverage of drug information. It was listed as of 05/06/2025. The information on drugs, side effects, and medical conditions is relevant across various populations, with specific details such as pregnancy categories and controlled substance classifications reflecting regulatory and safety considerations.

License

CCO

Who Can Use It

• Researchers in pharmacology, epidemiology, and public health.
• Healthcare professionals seeking quick reference for drug details, side effects, and regulatory statuses.
• Data scientists and analysts developing health-related applications or conducting market research.
• Academics and students for educational and research projects.
• Patients and the general public interested in understanding medications they use.

Dataset Name Suggestions

• Global Drug Information Database
• Pharmaceutical Drugs and Side Effects Data
• Medication Details and Patient Reviews
• Drug Efficacy and Safety Dataset
• Healthcare Drug Reference

Attributes

Original Data Source: Drugs, Side Effects and Medical Condition

U.S. Opiate Prescriptions

Accidental death by fatal drug overdose is a rising trend in the United States. What can you do to help?

This dataset contains summaries of prescription records for 250 common opioid and non-opioid drugs written by 25,000 unique licensed medical professionals in 2014 in the United States for citizens covered under Class D Medicare as well as some metadata about the doctors themselves. This is a small subset of data that was sourced from cms.gov. The full dataset contains almost 24 million prescription instances in long format. I have cleaned and compiled this data here in a format with 1 row per prescriber and limited the approximately 1 million total unique prescribers down to 25,000 to keep it manageable. If you are interested in more data, you can get the script I used to assemble the dataset here and run it yourself. The main data is in prescriber-info.csv. There is also opioids.csv that contains the names of all opioid drugs included in the data and overdoses.csv that contains information on opioid related drug overdose fatalities.

The increase in overdose fatalities is a well-known problem, and the search for possible solutions is an ongoing effort. My primary interest in this dataset is detecting sources of significant quantities of opiate prescriptions. However, there is plenty of other studies to perform, and I am interested to see what other Kagglers will come up with, or if they can improve the model I have already built.

The data consists of the following characteristics for each prescriber

• NPI – unique National Provider Identifier number
• Gender - (M/F)
• State - U.S. State by abbreviation
• Credentials - set of initials indicative of medical degree
• Specialty - description of type of medicinal practice
• A long list of drugs with numeric values indicating the total number of prescriptions written for the year by that individual
• Opioid.Prescriber - a boolean label indicating whether or not that individual prescribed opiate drugs more than 10 times in the year

This statistic depicts the rate of electronic prescriptions in the United States from 2017 to 2021, based on data of the country's largest e-prescribing operator Surescripts. In 2021, 94 percent of all prescriptions filled were e-prescriptions.

The VA Drug Pricing database contains the current prices for pharmaceuticals purchased by the federal government. These listed prices are based on the Federal Supply Schedule (FSS). This database is mandated by Public Law 102-585, the Veterans Health Care Act of 1992, which sets the maximum amount that a drug may be bought for by the Veterans Health Administration (VHA). The source of this information is contained in printed contracts or data files supplied by the drug manufacturers, representing the pricing agreements between VHA and the manufacturers. Price data is input by the National Acquisition Center (NAC) into the database administered by the Pharmacy Benefits Management Strategic Health Care Group. Information from this database is published on the World Wide Web at the following site: http://www.pbm.va.gov. The users of this database include pharmaceutical manufacturers, drug wholesalers, Office of Inspector General (OIG) and those who purchase pharmaceuticals for the VHA and other government agencies.

The VA National Clozapine Registry tracks the health and demographics of patients who have been prescribed clozapine by the VA. Clozapine, or the brand name Clozaril, is a drug used to treat the most serious cases of schizophrenia. Unfortunately, clozapine may also affect portions of the blood, lowering the body's resistance to infection and sometimes creating life-threatening circumstances. Realizing the severity of the problem, the Food and Drug Administration (FDA) established guidelines for analysis of White Blood Cells and Neutrophils and set strict minimum limits. The FDA also mandated that any manufacturer of clozapine must maintain a Clozapine Registry. These registries are to track the location and the health of clozapine patients and to ensure 'weekly White Blood Cell testing prior to delivery of the next week's supply of medication'. To date, the clozapine manufacturer registries have been unable to develop sufficient controls to meet these requirements, especially the ability to prevent dispensing clozapine when blood results are abnormal. However, because of the unique structure of Veterans Health Information Systems and Technology Architecture, the Veterans Health Administration obtained permission from the FDA and clozapine manufacturers to use its in-place computer network to gather and evaluate weekly patient information, then export this data to manufacturer clozapine registries. The VA assigned functional administration of this effort to the National Clozapine Coordinating Center (NCCC) located in Dallas, Texas. Weekly data on each VA clozapine patient is processed at two locations. Facility Level --When a clozapine prescription is written, a computer program in each facility's internal computer system retrieves white blood cell count, neutrophil count, and clozapine dose and evaluates the information according to FDA guidelines. If an adverse blood condition is found, the computer may warn to trigger a physician reevaluation, or lock out entirely to prevent dispensing, depending on the severity. Weekly, this information, along with certain patient demographic information, is gathered locally and transmitted to Hines Office of Information & Technology Field Office for centralized storage. This data can only be accessed by the NCCC. Raw data is downloaded from the Hines OI Field Office database on a weekly basis. An ancillary computer program reformats the data and evaluates the information for inconsistencies and data gathering errors. The computer-corrected data is manually compared with hand-written facsimile information sent to the NCCC by each site. This manually corrected data is again reformatted for data storage in MS Access format at the NCCC. The corrected data is also reformatted into American Standard Code for Information Interchange fixed-length fields and transmitted via modem to the manufacturers' Clozapine Registry and, in turn, to the FDA.

The National Health and Nutrition Examination Survey (NHANES) provides data and have considerable potential to study the health and environmental exposure of the non-institutionalized US population. However, as NHANES data are plagued with multiple inconsistencies, processing these data is required before deriving new insights through large-scale analyses. Thus, we developed a set of curated and unified datasets by merging 614 separate files and harmonizing unrestricted data across NHANES III (1988-1994) and Continuous (1999-2018), totaling 135,310 participants and 5,078 variables. The variables conveydemographics (281 variables),dietary consumption (324 variables),physiological functions (1,040 variables),occupation (61 variables),questionnaires (1444 variables, e.g., physical activity, medical conditions, diabetes, reproductive health, blood pressure and cholesterol, early childhood),medications (29 variables),mortality information linked from the National Death Index (15 variables),survey weights (857 variables),environmental exposure biomarker measurements (598 variables), andchemical comments indicating which measurements are below or above the lower limit of detection (505 variables).csv Data Record: The curated NHANES datasets and the data dictionaries includes 23 .csv files and 1 excel file.The curated NHANES datasets involves 20 .csv formatted files, two for each module with one as the uncleaned version and the other as the cleaned version. The modules are labeled as the following: 1) mortality, 2) dietary, 3) demographics, 4) response, 5) medications, 6) questionnaire, 7) chemicals, 8) occupation, 9) weights, and 10) comments."dictionary_nhanes.csv" is a dictionary that lists the variable name, description, module, category, units, CAS Number, comment use, chemical family, chemical family shortened, number of measurements, and cycles available for all 5,078 variables in NHANES."dictionary_harmonized_categories.csv" contains the harmonized categories for the categorical variables.“dictionary_drug_codes.csv” contains the dictionary for descriptors on the drugs codes.“nhanes_inconsistencies_documentation.xlsx” is an excel file that contains the cleaning documentation, which records all the inconsistencies for all affected variables to help curate each of the NHANES modules.R Data Record: For researchers who want to conduct their analysis in the R programming language, only cleaned NHANES modules and the data dictionaries can be downloaded as a .zip file which include an .RData file and an .R file.“w - nhanes_1988_2018.RData” contains all the aforementioned datasets as R data objects. We make available all R scripts on customized functions that were written to curate the data.“m - nhanes_1988_2018.R” shows how we used the customized functions (i.e. our pipeline) to curate the original NHANES data.Example starter codes: The set of starter code to help users conduct exposome analysis consists of four R markdown files (.Rmd). We recommend going through the tutorials in order.“example_0 - merge_datasets_together.Rmd” demonstrates how to merge the curated NHANES datasets together.“example_1 - account_for_nhanes_design.Rmd” demonstrates how to conduct a linear regression model, a survey-weighted regression model, a Cox proportional hazard model, and a survey-weighted Cox proportional hazard model.“example_2 - calculate_summary_statistics.Rmd” demonstrates how to calculate summary statistics for one variable and multiple variables with and without accounting for the NHANES sampling design.“example_3 - run_multiple_regressions.Rmd” demonstrates how run multiple regression models with and without adjusting for the sampling design.

The Prescriber Details dataset provides information on prescribers such as the prescriber code, prescriber name, prescriber type and details of the employing practices. This dataset is published each month. The information NHS Prescription Services hold on practices is supplied to us by SICBLs, ICBs, commissioning support units, hospital trusts, local authorities and independent sector healthcare providers, and provider organisations. GP practices may consist of a number of different types of prescribers, including GPs, nurse prescribers, pharmacist prescribers, optometrist supplementary prescribers, physiotherapist supplementary prescribers and podiatrist supplementary prescribers. NHS Prescription services only captures the initial and surname of prescribers. Some of the 'practices' listed in the information are not traditional practices. They are other services which the Primary Care Organisation has set up as a practice, such as specialist units or out of hours services. These units are often indicated as having one prescriber linked to the practice however there may be more. The data includes prescriber details for Isle of Man, Jersey , Guernsey and Alderney. We do not keep records of prescriber details from Scotland, Wales and Northern Ireland. You can view all definitions for the fields included in the dataset in the Prescriber Details data dictionary (XLSX: 12KB) This data was previously published on the NHSBSA Information Services Portal. Changes have been made to the data since migration to the Open Data Portal and you can read about these changes in the Prescriber Details migration changes documentation (ODT: 217KB).

In 2004, sertraline was prescribed over 28 million times. In comparison, in 2022, the last year with available data, the number of prescriptions for sertraline was nearly 40 million. Sertraline is primarily used as an antidepressant of the selective serotonin reuptake inhibitor (SSRI) class. This statistic shows the total annual number of sertraline prescriptions in the U.S. from 2004 to 2022, in millions.

Abstract (en): The National Survey on Drug Use and Health (NSDUH) series (formerly titled National Household Survey on Drug Abuse) primarily measures the prevalence and correlates of drug use in the United States. The surveys are designed to provide quarterly, as well as annual, estimates. Information is provided on the use of illicit drugs, alcohol, and tobacco among members of United States households aged 12 and older. Questions included age at first use as well as lifetime, annual, and past-month usage for the following drug classes: marijuana, cocaine (and crack), hallucinogens, heroin, inhalants, alcohol, tobacco, and nonmedical use of prescription drugs, including pain relievers, tranquilizers, stimulants, and sedatives. The survey covered substance abuse treatment history and perceived need for treatment, and included questions from the Diagnostic and Statistical Manual (DSM) of Mental Disorders that allow diagnostic criteria to be applied. The survey included questions concerning treatment for both substance abuse and mental health related disorders. Respondents were also asked about personal and family income sources and amounts, health care access and coverage, illegal activities and arrest record, problems resulting from the use of drugs, and needle-sharing. Questions introduced in previous administrations were retained in the 2006 survey, including questions asked only of respondents aged 12 to 17. These "youth experiences" items covered a variety of topics, such as neighborhood environment, illegal activities, drug use by friends, social support, extracurricular activities, exposure to substance abuse prevention and education programs, and perceived adult attitudes toward drug use and activities such as school work. Several measures focused on prevention-related themes in this section. Also retained were questions on mental health and access to care, perceived risk of using drugs, perceived availability of drugs, driving and personal behavior, and cigar smoking. Questions on the tobacco brand used most often were introduced with the 1999 survey. Background information includes gender, race, age, ethnicity, marital status, educational level, job status, veteran status, and current household composition. Due to unequal selection probabilities at multiple stages of sample selection and various adjustments, such as those for nonresponse and poststratification, the 2006 NSDUH sample design is not self-weighting. Analysts are advised to use the final sample weight when attempting to use the 2006 NSDUH data to draw inferences about the target population or any subdomains of the target population. All estimates published in SAMHSA reports (such as the results from the 2006 NSDUH) are weighted using the final analysis weight for the full sample (ANALWT). For the public use file, the corresponding final sample weight is denoted as ANALWT_C, with the "C" denoting confidentiality protection. This sample weight represents the total number of target population persons each record on the file represents. Note that the sum of ANALWT_C, over all records on the data file, represents an estimate of the total number of people in the target population. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Response Rates: Strategies for ensuring high rates of participation resulted in a weighted screening response rate of 90 percent and a weighted interview response rate for the CAI of 74 percent. (Note that these response rates reflect the original sample, not the subsampled data file referenced in this document.) The civilian, noninstitutionalized population of the United States aged 12 and older, including residents of noninstitutional group quarters such as college dormitories, group homes, shelters, rooming houses, and civilians dwelling on military installations. A multistage area probability sample for each of the 50 states and the District of Columbia was used since 1999. The 2005 NSDUH is the first survey in a coordinated five-year sample design. Although there is no...

The US Department of Homeland Security, Homeland Infrastructure Foundations - Level Data (HIFLD) published spatial data set describing the locations of pharmacies and medical supplies in the United States. This feature class/shapefile contains pharmacies found in the US territories of Puerto Rico. A pharmacy is a facility whose primary function is to store, prepare and legally dispense prescription drugs under the professional supervision of a licensed pharmacist. It meets any licensing or certification standards set forth by the jurisdiction where it is located. The tabular data was gathered from the National Plan and Provider Enumeration System (NPPES) dataset. Pharmacies that were verified to service only animal populations were excluded from the dataset. The records within this dataset was compiled between 2010-03-30 through 2010-10-25.

The complete dataset for the United States and its territories can be obtained from the HIFLD website: https://hifld-dhs-gii.opendata.arcgis.com/datasets/19145a0e403a4af4b2e4b76a6f2ec0ee_0 The shape file metadata: https://www.arcgis.com/sharing/rest/content/items/19145a0e403a4af4b2e4b76a6f2ec0ee/info/metadata/metadata.xml?format=default&output=html

This is digital research data corresponding to a published manuscript, "Using NDVI For Variable Rate Cotton Irrigation Prescriptions", in Applied Engineering in Agriculture, 2022, Volume 38(5): 787-795. doi:10.13031/aea.15071.
Irrigation timing is crucial for achieving high cotton yields and lint quality. This irrigation timing is more challenging in the southeastern U.S. Coastal Plain region due to its spatial variable sandy soils with low water and nutrient holding capacities and rainfall variability during the growing season. To address these challenges, we conducted a 2-year (2017 and 2018) study evaluating two irrigation scheduling methods under a variable rate irrigation system. The two irrigation methods were: (1) a uniform irrigation management based on weekly crop water usage, and (2) spatial crop coefficients derived from normalized difference vegetation indices (NDVI). We compared cotton yields and water use efficiency using the two irrigation scheduling methods at two different planting densities. The two plant populations were 5 and 11.5 plants m-1 of row to provide different NDVI readings and water requirements.

YEARMonth, ICB Code, Sub_ICB, Practice code/private prescriber (if possible), BNF Code, Prescription Items, Quantity. If it’s a big dataset then please break it down monthly I would say 2021, 2022 and 2023 ytd would be ok If Pharmacy’s don’t return prescriptions to you I’m not sure if this data would be collated in another way for reporting? Anyway, best advice would be appreciated. Your request was received on 23 May 2023 and I am dealing with it under the terms of the Freedom of Information Act 2000. Response I am writing to advise you that following a search of our paper and electronic records, I have established that the information you requested is not held by the NHS Business Services Authority. Contractors are required to send us the private prescriptions for controlled drugs in schedule 2 and 3 only, as per the drug tariff: https://www.nhsbsa.nhs.uk/pharmacies-gp-practices-and-appliance-contractors/drug-tariff

The global seasonal allergy market enjoys a valuation of US$ 1.15 Billion in 2022, and it is further projected to expand at a CAGR of 7.8% over the forecasted years. According to a recent study by Future Market Insights, tablets/capsules by form leading the market with a share of about 37.1% in the year 2022, within the global market.

Data Points	Market Insights
Market Value 2022	US$ 1.15 Billion
Market Value 2033	US$ 2.58 Billion
CAGR 2023 to 2033	7.8%
Market Share of Top 5 Countries	62.1%
Key Market Players	Merck KGaA, Johnson & Johnson, Allergopharma, Sanofi SA, McNeil Consumer Healthcare, Genentech Inc., GlaxoSmithKline PLC, Leti Pharma, Alerpharma S.A, Allergan, Inc., Meda Pharmaceuticals, Inc, Novartis International AG, Bausch Health Companies Inc., Allergopharma, ALK Abello, Stallergenes, Greer, Allergy Therapeutics, Aimmune Therapeutics, Biomay AG, HAL Allergy Group, Bayer, AstraZeneca

Report Scope as per Seasonal Allergy Industry Analysis

Attribute	Details
Forecast Period	2018 to 2022
Historical Data Available for	2023 to 2033
Market Analysis	US$ Million for Value
Key Regions Covered	North America, Latin America, Europe, South Asia, East Asia, Oceania, and the Middle East & Africa
Key Countries Covered	The USA, Canada, Brazil, Mexico, Argentina, The United kingdom, Germany, Italy, Russia, Spain, France, Benelux, India, Thailand, Indonesia, Malaysia, Japan, China, South Korea, Australia, New Zealand, Türkiye, GCC Countries, North Africa and South Africa
Key Market Segments Covered	Form, Prescription Type, Route of Administration, Treatment, Distribution Channel
Key Companies Profiled	Merck KGaA Johnson & Johnson Allergopharma Sanofi SA McNeil Consumer Healthcare, Genentech Inc. GlaxoSmithKline PLC Leti Pharma, Alerpharma S.A Allergan, Inc. Meda Pharmaceuticals, Inc Novartis International AG Bausch Health Companies Inc. Allergopharma ALK Abello Stallergenes Greer Allergy Therapeutics Aimmune Therapeutics Biomay AG HAL Allergy Group Bayer AstraZeneca
Pricing	Available upon Request