This data aligns with WWC Certification requirements, and serves as the basis for our data warehouse and open data roadmap. It's a continual work in progress across all departments.Louisville Metro Technology Services builds data and technology platforms to ready our government for our community’s digital future.Data Dictionary:

Field Name

Description

Dataset Name

The official title of the dataset as listed in the inventory.

Brief Description of Data

A short summary explaining the contents and purpose of the dataset.

Data Source

The origin or system from which the data is collected or generated.

Home Department

The primary department responsible for the dataset.

Home Department Division

The specific division within the department that manages the dataset.

Data Steward (Business) Name

The name of person responsible for the dataset’s accuracy and relevance.

Data Custodian (Technical) Name)

The technical contact responsible for maintaining and managing the dataset infrastructure.

Data Classification

The sensitivity level of the data (e.g., Public, Internal, Confidential)

Data Format

The file format(s) in which the dataset is available (e.g., CSV, JSON, Shapefile).

Frequency of Data Change

How often the dataset is updated (e.g., Daily, Weekly, Monthly, Annually).

Time Spam

The overall time period the dataset covers.

Start Date

The beginning date of the data collection period.

End Date

The end date of the data collection period

Geographic Coverage

The geographic area that the dataset pertains to (e.g., Louisville Metro).

Geographic Granularity

The level of geographic detail (e.g., parcel, neighborhood, ZIP code).

Link to Existing Publication

A URL linking to the dataset’s public-facing page or open data portal entry.

Please note: This dataset has been superceded by dataset: Environment Agency and Natural England Public Facing Area Names v2. This is the archived version 1 of the authoritative controlled list which specifies the shared area names of the Environment Agency and Natural England. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved.

The Department of Health Care Services (DHCS) Long-Term Services and Supports (LTSS) Data Dashboard is an initiative of the Home and Community Based Services Spending Plan. The initiative's primary goal is to create a public-facing LTSS data dashboard to track demographic, utilization, quality, and cost data related to LTSS services. This dashboard will link statewide long-term care and home and community-based services (HCBS) data with the goal of increased transparency to make it possible for regulators, policymakers, and the public to be informed while the state continues to expand, enhance, and improve the quality of LTSS in all home, community, and congregate settings.

The first iteration of the LTSS Dashboard was released in December 2022 as an Open Data Portal file with 40 measures pertaining to LTSS beneficiaries, which includes ten different demographics, plan-related dimensions, and dual stratification. The December 2023 Data Release includes 16 new measures on the Medi-Cal LTSS Dashboard and Open Data Portal (Select “View Underlying Data”); and additional measures and dimensions, including dual stratification, will be added to the Open Data Portal in 2024.

Note: The LTSS Dashboard measures are based on certified eligible beneficiaries who were enrolled in Medi-Cal for one or more months during the reporting interval. Most of the DHCS LTSS dashboard measures report the annual number of certified eligible Medi-Cal beneficiaries who have used LTSS services within a year. Other departments may report on these programs differently. For example, the Department of Social Services (CDSS) reports monthly IHSS recipient/consumer counts. The California Department of Aging (CDA) reports monthly CBAS Medi-Cal participants. DHCS’ annual utilization / enrollment counts of IHSS and CBAS beneficiaries are larger than CDSS/CDA's monthly counts because of data source differences and new enrollment or program attrition over time. Monthly snap-shot measures (average monthly utilization) for IHSS and CBAS have been added to the LTSS Dashboard to align with CDSS and CDA monthly reporting.

Refer to the LTSS-Dashboard (ca.gov) program page for: 1) a Fact Sheet with highlights from the initial data release including changes over time in use of Home and Community-Based Services as well as select demographic information; 2) the Measure Specifications document – that describes business rules and inclusion/exclusion criteria related to age groups, plan types, aid code, geographic, or other important program/waiver-specific eligibility criteria; and 3) User guide – that shows how to navigate the Open Data Portal data file with specific examples.

Background: The analysis of clinical free text from patient records for research has potential to contribute to the medical evidence base but access to clinical free text is frequently denied by data custodians who perceive that the privacy risks of data-sharing are too high. Engagement activities with patients and regulators, where views on the sharing of clinical free text data for research have been discussed, have identified that stakeholders would like to understand the potential clinical benefits that could be achieved if access to free text for clinical research were improved. We aimed to systematically review all UK research studies which used clinical free text and report direct or potential benefits to patients, synthesizing possible benefits into an easy to communicate taxonomy for public engagement and policy discussions.Methods: We conducted a systematic search for articles which reported primary research using clinical free text, drawn from UK health record databases, which reported a benefit or potential benefit for patients, actionable in a clinical environment or health service, and not solely methods development or data quality improvement. We screened eligible papers and thematically analyzed information about clinical benefits reported in the paper to create a taxonomy of benefits.Results: We identified 43 papers and derived five themes of benefits: health-care quality or services improvement, observational risk factor-outcome research, drug prescribing safety, case-finding for clinical trials, and development of clinical decision support. Five papers compared study quality with and without free text and found an improvement of accuracy when free text was included in analytical models.Conclusions: Findings will help stakeholders weigh the potential benefits of free text research against perceived risks to patient privacy. The taxonomy can be used to aid public and policy discussions, and identified studies could form a public-facing repository which will help the health-care text analysis research community better communicate the impact of their work.

This dataset supports the SWAMP Data Dashboard, a public-facing tool developed by the Surface Water Ambient Monitoring Program (SWAMP) to provide accessible, user-friendly access to water quality monitoring data across California. The dashboard and its associated datasets are designed to help the public, researchers, and decision-makers explore and download monitoring data collected from California’s surface waters.

This dataset includes five distinct resources:

• SWAMP Stations – Geospatial and descriptive information about SWAMP monitoring sites.
• Water Quality Results – Field and lab analysis results for chemical and physical parameters measured in water samples.
• Toxicity Summary Results – Summarized results from aquatic toxicity tests. Summary records are entries in the database that summarize the results from multiple replicate toxicity tests of the same sample water.
• Habitat Results – Data on physical habitat conditions typically collected alongside biological monitoring to provide context for interpreting water quality conditions. Includes scores for the California Stream Condition Index (CSCI) and Algal Stream Condition Index (ASCI).
• Tissue Summary Results – Annual summary statistics of contaminant concentrations in aquatic organism tissue samples. The data are derived from raw individual and composite tissue sample results.

These data are collected by SWAMP and its partners to support water quality assessments, identify trends, and inform water resource management. The SWAMP Data Dashboard provides interactive visualizations and filtering tools to explore this data by region, parameter, and more.

The SWAMP dataset is sourced from the California Environmental Data Exchange Network (CEDEN), which serves as the central repository for water quality data collected by various monitoring programs throughout the state. As such, there is some overlap between this dataset and the broader CEDEN datasets also published on the California Open Data Portal (see Related Resources). This SWAMP dataset represents a curated subset of CEDEN data, specifically tailored for use in the SWAMP Data Dashboard.

Access the SWAMP Data Dashboard: https://gispublic.waterboards.ca.gov/swamp-data/

*This dataset is provisional and subject to revision. It should not be used for regulatory purposes.

The Metropolitan Transportation Plan/Sustainable Communities Strategy (MTP/SCS) for the Sacramento region pro-actively links land use, air quality, and transportation needs. The MTP/SCS supports the Sacramento Region Blueprint, which implements smart growth principles, including housing choice, compact development, mixed-use development, natural resource conservation, use of existing assets, quality design , and transportation choice. It also provides increased transportation options while reducing congestion, shortening commute times, and improving air quality. The MTP/SCS is key to the quality of life and economic health of our region. This dataset is intended for regional planning purposes only and may not reflect the granularity or accuracy required for local-level decision-making. Users should exercise caution when applying this data to site-specific analyses, zoning, or community-scale interventions.These shapes may change based on further review from project sponsers.Last Update August 2019

This dataset has now been Retired as it has been replaced by "Administrative Boundaries - Environment Agency and Natural England Public Face Areas". This is for Approval for Access product AfA015 Environment Agency Administrative Boundaries set at 1:10,000 scale. These consist of 2 discrete data layers showing: Water Management Areas and Public Face Areas. Water management and Public Face boundaries are attributed with the name and address for each head office. This dataset is for Environment Agency Public Face Areas. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

This data aligns with WWC Certification requirements, and serves as the basis for our data warehouse and open data roadmap. It's a continual work in progress across all departments.Louisville Metro Technology Services builds data and technology platforms to ready our government for our community’s digital future.Data Dictionary:Field NameDescriptionDataset NameThe official title of the dataset as listed in the inventory.Brief Description of DataA short summary explaining the contents and purpose of the dataset.Data SourceThe origin or system from which the data is collected or generated.Home DepartmentThe primary department responsible for the dataset.Home Department DivisionThe specific division within the department that manages the dataset.Data Steward (Business) NameThe name of person responsible for the dataset’s accuracy and relevance.Data Custodian (Technical) Name)The technical contact responsible for maintaining and managing the dataset infrastructure.Data ClassificationThe sensitivity level of the data (e.g., Public, Internal, Confidential)Data FormatThe file format(s) in which the dataset is available (e.g., CSV, JSON, Shapefile).Frequency of Data ChangeHow often the dataset is updated (e.g., Daily, Weekly, Monthly, Annually).Time SpamThe overall time period the dataset covers.Start DateThe beginning date of the data collection period.End DateThe end date of the data collection periodGeographic CoverageThe geographic area that the dataset pertains to (e.g., Louisville Metro).Geographic GranularityThe level of geographic detail (e.g., parcel, neighborhood, ZIP code).Link to Existing PublicationA URL linking to the dataset’s public-facing page or open data portal entry.

Dataset containing information related to non-NYPD Subjects involved in Force Incidents. The Threat, Resistance, or Injury (TRI) Report is the primary means by which the NYPD records use of force incidents. All reportable instances of force – whether used by a member of the Department, or against the member – are recorded on a TRI Report. Data provided here are a result of the information captured on TRI Reports. Each record corresponds to a non-NYPD subject involved in a force incident. The data can be used to explore the various categories of force incidents and when and in which precinct they occurred. For any given incident, there may be one or more members of service involved. Since NYPD policy requires two-person patrols, most incidents will have at least two members. The data is used to populate the public facing Force Dashboard. (https://app.powerbigov.us/view?r=eyJrIjoiOGNhMjVhYTctMjk3Ny00MTZjLTliNDAtY2M2ZTQ5YWI3N2ViIiwidCI6IjJiOWY1N2ViLTc4ZDEtNDZmYi1iZTgzLWEyYWZkZDdjNjA0MyJ9).

This is the controlled list of 14 operational area names, codes and descriptions of the Environment Agency and Natural England. This is the standard list for re-use across the Environment Agency and Natural England. The previous version contained only Environment Agency Area Names, but was updated to merge with Natural England public facing Areas. Attribution statement: © Environment Agency copyright and/or database right 2017. All rights reserved.

Cross-Agency Data Sharing for Public Safety Market Outlook

As per our latest research, the global market size for Cross-Agency Data Sharing for Public Safety in 2024 stands at USD 4.9 billion, reflecting a robust ecosystem driven by technological advancements and increasing inter-agency collaboration. The market is registering a strong CAGR of 13.2% and is forecasted to reach USD 14.6 billion by 2033. This growth is primarily fueled by the escalating demand for real-time data exchange and analytics among public safety entities, which is enhancing operational efficiency and incident response times globally.

One of the primary growth factors for the Cross-Agency Data Sharing for Public Safety market is the increasing complexity and frequency of threats facing urban and rural communities alike. Modern threats such as cyberattacks, terrorism, natural disasters, and pandemics require a coordinated response that transcends traditional agency boundaries. The need for seamless, secure, and real-time data sharing has never been more critical. Agencies are investing in advanced data integration platforms and analytics tools to ensure that relevant information can be shared instantly and securely across jurisdictions. This trend is further supported by governmental mandates and policy frameworks that prioritize inter-agency collaboration, resulting in the widespread adoption of cross-agency data sharing solutions.

Another significant driver is the rapid advancement of digital technologies, including artificial intelligence, machine learning, and cloud computing. These technologies are enabling agencies to analyze vast volumes of structured and unstructured data, derive actionable intelligence, and make informed decisions in real time. The integration of IoT devices, body-worn cameras, and sensor networks is generating a continuous stream of data, which, when effectively shared, can significantly improve situational awareness and emergency response. Additionally, the proliferation of mobile devices and the increasing mobility of first responders are necessitating the deployment of interoperable platforms that can facilitate data sharing in the field, further propelling market growth.

A third crucial growth factor is the rising public expectation for transparency, accountability, and efficiency in public safety operations. Communities are demanding faster response times and more effective incident management, which can only be achieved through enhanced collaboration and data sharing among various agencies. Furthermore, the availability of federal and state funding for the modernization of public safety infrastructure is encouraging agencies to invest in interoperable data sharing solutions. The growing emphasis on community policing, crime prevention, and disaster preparedness is also driving the adoption of these platforms, as agencies seek to leverage shared data to proactively address emerging threats and improve public trust.

From a regional perspective, North America continues to dominate the Cross-Agency Data Sharing for Public Safety market due to its well-established public safety infrastructure, strong regulatory frameworks, and significant investments in advanced technologies. However, Asia Pacific is emerging as a high-growth region, driven by rapid urbanization, increasing security threats, and government initiatives aimed at enhancing public safety. Europe is also witnessing substantial growth, supported by cross-border security collaborations and the adoption of EU-wide data sharing standards. Meanwhile, Latin America and the Middle East & Africa are gradually adopting these solutions, primarily in major urban centers, as they work to address rising crime rates and disaster management challenges.

Component Analysis

The Component segment of the Cross-Agency Data Sharing for Public Safety market is categorized into Software, Hardware, and Services. Software solutions form the

Google Trends data have been used to investigate various themes on online information seeking. It was unclear if the population from different parts of the world shared the same amount of attention to different mask types during the COVID-19 pandemic. This study aimed to reveal which types of masks were frequently searched by the public in different countries, and evaluated if public attention to masks could be related to mandatory policy, stringency of the policy, and transmission rate of COVID-19. By referring to an open dataset hosted at the online database Our World in Data, the 10 countries with the highest total number of COVID-19 cases as of 9th of February 2022 were identified. For each of these countries, the weekly new cases per million population, reproduction rate (of COVID-19), stringency index, and face covering policy score were computed from the raw daily data. Google Trends were queried to extract the relative search volume (RSV) for different types of masks from each of these countries. Results found that Google searches for N95 masks were predominant in India, whereas surgical masks were predominant in Russia, FFP2 masks were predominant in Spain, and cloth masks were predominant in both France and United Kingdom. The United States, Brazil, Germany, and Turkey had two predominant types of mask. The online searching behavior for masks markedly varied across countries. For most of the surveyed countries, the online searching for masks peaked during the first wave of COVID-19 pandemic before the government implemented mandatory mask wearing. The search for masks positively correlated with the government response stringency index but not with the COVID-19 reproduction rate or the new cases per million.

This dataset contains the underlying data for the public-facing 'Mental Health Related EMS and ED Events' dashboard. 7/25/2025: We have recently updated the data for more recent years on our public data hub. As a result, you may notice changes in the data for previous years. This change is due to a correction in our data processing methods, which has led to more accurate counts for past years.

Attribute-Based Access Control for Government Data Market Outlook

According to our latest research, the Global Attribute-Based Access Control (ABAC) for Government Data market size was valued at $1.2 billion in 2024 and is projected to reach $4.8 billion by 2033, expanding at a robust CAGR of 16.7% during 2024–2033. The primary growth driver for this market is the increasing necessity for dynamic and context-aware security frameworks in government agencies, which are dealing with ever-increasing volumes of sensitive data and facing sophisticated cyber threats. As governments worldwide transition to digital-first operations, the adoption of ABAC solutions is becoming critical for ensuring data privacy, compliance, and secure information sharing across departments and jurisdictions.

Regional Outlook

North America holds the largest share of the Attribute-Based Access Control for Government Data market, accounting for nearly 38% of the global revenue in 2024. The region’s dominance is attributed to its mature cybersecurity infrastructure, widespread adoption of cloud technologies, and stringent data protection regulations such as FedRAMP and FISMA. The presence of major technology vendors and a proactive approach to public sector digitalization have further accelerated ABAC deployment across federal, state, and local agencies. Additionally, ongoing investments in safeguarding critical infrastructure and national security data have led to higher demand for advanced access control solutions, ensuring North America remains at the forefront of this market segment throughout the forecast period.

The Asia Pacific region is anticipated to be the fastest-growing market, with a projected CAGR of 20.3% between 2024 and 2033. Rapid digital transformation initiatives, expanding government digital services, and increasing cybersecurity awareness are key drivers fueling this growth. Countries such as China, India, Japan, and South Korea are investing heavily in public sector IT modernization, leading to significant opportunities for ABAC solution providers. Government mandates for data localization and privacy, coupled with the rising frequency of cyber incidents targeting public data repositories, are compelling agencies to adopt more granular and dynamic access control frameworks. The influx of international technology vendors and robust venture capital activity are further catalyzing market expansion in the region.

Emerging economies in Latin America and the Middle East & Africa are witnessing a gradual uptake of ABAC solutions, primarily driven by increasing digitization of government services and evolving regulatory landscapes. However, adoption is tempered by challenges such as limited IT budgets, lack of skilled cybersecurity professionals, and fragmented policy frameworks. Despite these hurdles, localized demand for secure citizen data management, e-government initiatives, and cross-border data sharing is expected to spur incremental growth. Strategic collaborations with global technology partners and investments in capacity building are likely to help these regions overcome implementation barriers and accelerate ABAC adoption over the coming years.

Report Scope

NOTE: THIS LAYER HAS BEEN DEPRECATED (last updated 5/31/2022). This was formerly a weekly update. Summary The number of cases interviewed who had a completed answer to the question asking if they had physically gone to work in the last 14 days during their covidLINK interviews. Description MD COVID-19 - Contact Tracing Cases Reported Employment layer reflects the number of cases interviewed who had a completed answer to the question asking if they had physically gone to work in the last 14 days during their covidLINK interviews. Respondents may indicate more than one category of employment if they have multiple jobs. For a variety of reasons, some individuals choose not to answer particular questions during the course of their interview. Information about how to prevent and reduce COVID-19 transmission in businesses and workplaces — including for both employers and employees — is available from the Centers for Disease Control and Prevention. Note the following regarding select employment categories: Childcare/Education: Includes teachers, babysitters, school administrators, etc. Commercial Construction and Manufacturing: Includes poultry/meat processors, electricians, carpenters, HVAC workers, welders, contractors, painters Healthcare: Includes home healthcare and administrative positions in a healthcare setting Restaurant/Food Service: Includes cooks, waitstaff, food delivery personnel, alcohol delivery services, etc. Retail, Essential Worker: Includes grocery and pharmacy workers Retail, Other: Includes all retail establishments that do not sell food or medicine Transportation: Includes positions related to transport of people or goods Other, Non-Public-Facing: Includes workers that do not have direct interactions with the public, including warehouse workers, some office workers, some car mechanics, etc. Other, Public-Facing: Includes workers who have direct interactions with the public such as, but not limited to, administrative/front desk workers, home repair workers, lawncare workers, security guards, etc. Unknown: Indicates that the interviewer was unable to ascertain the employment category based on the information provided. Answers to interview questions do not provide strong evidence of cause and effect. Due to the nature of COVID-19 and the wide range of scenarios in which a person can become infected, most of the time it will not be possible to pinpoint exactly how and when a case became infected. Though a person may report employment at a particular location, that does not necessarily imply that transmission happened at that location. The covidLINK interview questionnaire is updated as necessary to capture relevant information related to case exposure and potential onward transmission. These revisions should be taken into consideration when evaluating trends in case responses over time. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

A. SUMMARY This dataset provides aggregated counts of victims and suspects involved in crimes that fall under San Francisco’s mandated crime reporting categories, as recorded by the San Francisco Police Department (SFPD). The data is sourced from Crime Data Warehouse (CDW), which has been in operation since January 1, 2013.

Because CDW was implemented on that date, data prior to 2013 is incomplete or unavailable. To protect the privacy and safety of vulnerable individuals, the dataset is aggregated and does not contain any personally identifiable information or individual case records. Crime categories are organized using:

• San Francisco’s 96A.5 “Quarterly Crime Victim Data Reporting”, legislated for victim demographic reporting (Definitions of crime types can be found in Chapter 96A.1)

• FBI Uniform Crime Reporting (UCR) system (Definitions can be found on the SFPD website.)

This dataset also powers the public crime dashboards on the SFPD website, where users can explore summary statistics.

B. HOW THE DATASET IS CREATED Data is added to open data once a quarter after extraction, transformation, and aggregation.

Disclaimer: The San Francisco Police Department does not guarantee the accuracy, completeness, timeliness or correct sequencing of the information as the data is subject to change as modifications and updates are completed.

C. UPDATE PROCESS Information is updated on a quarterly basis.

D. HOW TO USE THIS DATASET This dataset provides aggregated counts of individuals involved in reported crimes, categorized by key demographics and crime-related attributes. It is used to power public-facing dashboards on the San Francisco Police Department (SFPD) website, where summary statistics and visualizations allow users to explore crime and victimization trends across the city. While the SFPD public dashboard provides many useful summaries and visualizations, not all data details are displayed there. For deeper or custom analysis, the full dataset can be downloaded for personal use.

The LAFD updated the public facing metrics to include Operational Response Time in April 2017. This dataset is an archive of the metrics beginning January 2016.

The Stream Condition Index (SCI) is a grade card scoring District waterbodies. Learn about your local streams using the 'Stream Explorer' or dive deeper into the data DOEE collects to study these streams by using the 'Data Explorer'. The Stream Condition Index is public-facing and intended for a general audience. Please contact streamconditionindex@dc.gov if you have any questions related to the context of the Index or the data used to produce it.

Abstract

Brazil has made significant progress in human development over the last decade, thanks to a series of policy innovations, and equity of access has increased considerably. In health, consolidation of government health financing, the organization of the sector into a country-wide system (Unified Health System, or SUS) and the greater emphasis on primary care have been critical for these improvements.

Increasing the efficiency and effectiveness in the use of health resources to contain rising costs is perhaps the greatest challenge facing the Brazilian health system.

Brazil’s federal structure and the decentralized nature of the SUS make the financial flows difficult to track and monitor. Despite continuous upgrading, existing information systems do not permit accurate identification of how resources are allocated within the context of SUS, nor how expenditures are executed and services provided at the health unit level. Information is lacking regarding how much SUS as a whole (including the federal, state and municipal governments) spends on hospital and primary care. The levels of efficiency in health service provision are not systematically documented.

This study assesses how the processes of allocation, transfer and utilization of resources are conducted at the different levels of the system. The study provides valuable information regarding the reality of the executing units of the system and how these relate to the central levels. It also seeks to identify problems related to financial flows, analyze how resources are used at the local level, and estimate their impact on the efficiency and quality of health services in general. In this respect, the study provides a basis for improving the entire cycle of public resource management processes (i.e., planning, budgeting, budget execution, input management, and health service production) in the health sector.

The survey was based on a sample of six states, 17 municipalities in those six states, and 49 hospitals and 20 outpatient units in the sampled municipalities. While the sample is not statistically representative of SUS as a whole because of its small size, an effort was made to capture a variety of situations found in the Brazilian federation so that the findings would exemplify typical conditions found in SUS.

Geographic coverage

States of Amazonas, Ceará, Mato Grosso, Rio de Janeiro, Rio Grande do Sul and São Paulo.

Analysis unit

• Ministry of Health;
• State Health Secretariats;
• Municipal Health Secretariats;
• health units (hospitals and outpatient clinics).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample selected for the study was designed in order to highlight the regional variations between the health units and at the same time to keep logistical costs to a minimum. For these reasons, a non-randomized sampling in three stages was chosen: first, the sample covered states, second, the municipalities located in those states, and third, health units located within the municipalities. This sampling structure was chosen in order to permit tracking of the resource flows within a particular state and the cross-referencing of information at the three levels of the research.

Initially, the sample took into account six states with their respective state health secretariats, 18 municipalities and 76 health units (52 hospitals and 24 outpatient clinics). As a result of data collection being abandoned in one particular municipality as well as in a number of health units, and given the difficulty of accessing certain information, the final sample encompassed 17 municipalities (Municipal Health Secretariats), 49 hospitals (public and philanthropic), and 20 outpatient clinics (state and municipal).

Although the resulting sample reflects the very different circumstances existing within Unified Health System (SUS), it is too small for each stratum of units and consequently does not allow statistical extrapolation of the results.

In the sampling exercise, states were selected to represent each of the six Brazilian major regions (for the southeast region two states were included given the population density and a high concentration of health establishments). One of the main criteria for selection was to reflect the diversity in size and different characteristics of the states, municipalities and health units.

Municipalities were selected on the basis of size. State capitals were included, plus one middle-sized municipality per state (roughly 200,000 inhabitants) and at least one small-sized municipality (of approximately 50,000 inhabitants). The resulting sample of municipalities could be considered reasonably representative of the diverse nature of SUS.

The hospitals selected were required to meet the following requirements: to attend mainly to SUS users, to have a minimum of 50 beds, to possess reasonable information systems and to be broadly representative of SUS as such. Various hospitals were included in the sample that had been included in other recent studies which made it possible to cross-reference and compare information. The proposed distribution focused on public hospitals since the main thrust of the study concerned budget relationships and transfers of resources. This sample was stratified by size (medium-sized/big and small hospitals) and sphere, in order to try and obtain a sufficient number of units of each type to produce representative results. Efforts were also made to include hospitals with different characteristics such as those that undertake teaching and research and public hospitals administered under different kinds of management arrangements.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires were applied in the course of interviews with state health secretaries or someone designated by them (normally a professional charged with a specific area with access to the necessary information); municipal health secretaries (or designates); directors of hospitals; and directors of outpatient departments/clinics. Moreover, concurrent side interviews were undertaken with staff from a number of different technical and administrative divisions with the aim of clarifying and amplifying the research findings. Finally, together with the application of the questionnaire, reports and other supporting documents were requested relating to budgets, plans, management reports, etc.

The internal structure of the questionnaires was common to all types of units researched (SES and SMS, hospitals and outpatient clinics), although obviously the content of each section is specific to each type of unit.

The basic format of the questionnaire was organized around planning and budget allocation and implementation processes and the main inputs used in health service delivery (i.e., materials and medical drugs, human resources and equipment/installations). The component sections of the questionnaire were the following: • Section A - Information from the secretariats or health units. This section gives the identity details of the units researched, the name of the person responsible for the unit and details about the profile and type of unit (in the case of hospitals and outpatient clinics, the number of beds and services on offer are included). • Section B - Budgetary planning and processes. This section examines the budget and planning process at its different stages, the degree of autonomy in the preparation and implementation stages of the budget, the delays in releasing and applying funds, the differences between the values requested, approved and executed, including the use of the ‘up-front’ payment/petty cash system. • Section C - Purchases, materials and drugs management. This section deals with information regarding the purchasing and storage systems, including pharmacy. Surveys were done basically to elucidate the physical condition of stocks, delays in bidding processes and the impact of these elements on service delivery. • Section D - Equipment and installations. This section examined the equipment estate, covering inter alia the frequency rate of breakdowns/breakages in addition to examining the physical conditions of installations. • Section E - Human resources. Information was sought in the section regarding the staff, its distribution, qualifications, absenteeism and any failure to comply with working hours. • Section F - Hospital and outpatient clinic expenditure. In this section data was sought on the expenditure by type and receipts by source, together with an analysis of the service providers and the impact of receipts from SUS on overall expenditure. • Section G - Hospital and outpatient clinic productivity. Data was collected regarding the productivity of the units and, wherever possible, performance and quality indicators were calculated.

Supplementary documentation requested included: • Municipal/State Health Agenda (2002-2003); • Municipal/State Health Plan (2002-2003); • Current Multi-Year Plan (referring to health); • Budget Guidelines Law (2002-2003); • Municipal/State Health Budget (2002-2003); • Documentary evidence of present budget execution (2002 and first half of 2003); • Municipal/State Balance Sheets, Annex 2, 6 (Health section),10 and 11, for 2002; • Management Reports (2002). • Personnel Allocation Chart • Organization chart of Institution

“Platform focus” gives a brief description of the primary usage of the platform. “Text data available” lists the types of text content that the platform contains, as determined by manual inspection of platforms. “Drug discussion not restricted” column has a checkmark if the platform does not restrict drug-related discussion, and an X if the platform has some form of restrictions, as determined by inspection of platform terms of use (provided in Appendix G in S1 Text). “Has API” column has a checkmark if the platform has an API available (whether the API is freely available or requires authorization before access), and an X if not. “Has research portal” column has a checkmark if the platform has a non-API platform for acquisition of platform data or to receive more information about collaboration with the platform, and an X if not. API and research portal designations were determined by inspection of platform data availability (provided in Appendix H in S1 Text). “Previously researched for opioid pharmacovigilance” column reflects the relative amount of prior research related to opioid surveillance, with a checkmark indicating some prior use in the literature, two checkmarks indicating high prior use in the literature, and an X indicating no prior use in the literature (see “Prior Use in Research Literature” section of Results). “Geolocation available” column has a checkmark if explicit geolocation data is provided for any platform content (this does not indicate explicit geolocation available for all content), and an X if not, as determined by inspection of public-facing platform data specifications (see “Evaluating data accessibility for academic research purposes” section of Results). “Example of geolocation inference strategy” column provides one possible method for inferring geolocation of platform content, based on inference strategies previously employed in the broader literature (see “Evaluating data accessibility for academic research purposes section” of Results).