quality improvement research paper sample

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• Published: 04 January 2021

A practical guide to publishing a quality improvement paper

• Stephen A. Pearlman   ORCID: orcid.org/0000-0003-3027-8794 1 , 2 &
• Jonathan R. Swanson 3  

Journal of Perinatology volume  41 ,  pages 1454–1458 ( 2021 ) Cite this article

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Quality improvement (QI) is a relatively new and evolving field as it applies to healthcare. Hence, publishing a QI paper may present certain challenges as QI differs from standard types of scientific research. Some considerations in writing are inherent to all types of manuscripts submitted for publication, whereas others are unique to QI papers. This paper, the final in a series of eight papers related to QI in the neonatal setting, describes the best practices for writing and publishing QI manuscripts. Common pitfalls to avoid are also highlighted.

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• Stephen A. Pearlman

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Pearlman, S.A., Swanson, J.R. A practical guide to publishing a quality improvement paper. J Perinatol 41 , 1454–1458 (2021). https://doi.org/10.1038/s41372-020-00902-w

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Received : 17 September 2020

Revised : 05 November 2020

Accepted : 01 December 2020

Published : 04 January 2021

Issue Date : June 2021

DOI : https://doi.org/10.1038/s41372-020-00902-w

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Advancements in neonatology through quality improvement.

Journal of Perinatology (2022)

Do quality improvement projects require IRB approval?

• Kanekal S. Gautham
• Stephen Pearlman

Journal of Perinatology (2021)

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How to Write a Conference Abstract

• Finding Conferences
• Abstract Preparation
• How to Write a Scientific or Research Abstract
• How to Write a Case Report Abstract

What is a Quality Improvement Project Abstract?

Author information, writing a title, introduction.

• Writing Tips
• Reasons for Rejection

A quality improvement project abstract submission should share your ‘innovative quality improvement project or quality measures/analyses that you implemented in your own practice.’ This usually has a short word count.

• You should aim for completeness; Use full names and formal credentials; department and institution worked. The author information usually does NOT count against the total word count but be sure you check the instructions.
• There may be a limit on how many authors can be on the submission.
• The first author is the one who conceived the study and did most of the work; will be the person who presents. Sometimes you have to be a member of an association to submit an abstract, so check for those rules as well.
• Full disclosure on sponsors.
• Check how your abstract is being reviewed. Is it blind? You may see instructions like, To ensure blinded peer-review, no direct references to the author(s) or institution(s) of origin should be made anywhere in the title, body, tables or figures.

Your best strategy in writing a title: Write the abstract first.  Then pull out 6-10 key words or key phrases found in the abstract, and string them together into various titles. Brainstorm lots of keywords to help find the best mix.

• Ideally 10-12 words long
• Title should highlight the case​
• Avoid low-impact phrases like ‘effect of... ‘ or ‘influence of…’; Do not include jargon or unfamiliar acronyms
• 2-3 sentences long
• First, define the problem that your project investigated. What are you trying to solve?​
• ​You may also give background on why you created the project or background on the topic. Was the problem internal or external? ​
• ​Did you review supporting evidence? What does the literature say about this topic?​
• ​You probably have a research question, or perhaps a PICO question or EBP question, if you are in healthcare. This may also be in the form of a statement of purpose
• 5-8 sentences long
• This will be the longest part of the abstract. Describe your study design or intervention
• ​Describe the population involved with your project
• ​What target measures did you set to show improved performance? ​
• ​Describe the procedures in your project, basically the process, each step taken, and the tools/techniques/strategies used
• ​What analytic approach did you use to evaluate the impact of the intervention?​
• ​How was data collected, analyzed, and interpreted?
• 5-6 sentences long
• Summarize, analyze and interpret the data you collected
• Formulate conclusions and present data that indicated that your project made a difference
• Share any Limitations such as: Factors such as environmental barriers, personnel issues, sample size, that impact findings and conclusions
• 3-4 sentences
• Explain how the data relates to your original question
• Explain how the project solved a problem and could benefit others. Meaning, you’ll strategize on how you improved quality and give the significance of your findings.
• You may list recommendations, resources, personnel, delivery date and benchmarks.
• Remember to share the implication of your findings and how this project could benefit others in the same field.
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How to Write a Quality Improvement (QI) Report | An Ultimate Guide

Quality Improvement, or QI, is a big thing in the healthcare industry. Healthcare systems always have opportunities to optimize, test, develop, and streamline processes. QI is a continuous process and is done through a QI team.

 According to AAFP , quality improvement refers to the systematic and formal approach to analyzing practice performance using various quality assessment tools and using different models to improve performance in healthcare settings. Quality improvement is a proven and effective way to improve the care of patients, clients, and residents and practice for staff.

Quality improvement directly impacts patient safety, satisfaction, and outcomes. It ensures Safe, Timely, Effective, Efficient, Equitable, and Patient-Centered Care (STEEP).

As a nursing student, you will be assigned to write a quality improvement paper or report. If you are not conversant with what to include in your paper, this guide will take you through the step-by-step process of creating a good quality improvement project paper or report. You should not confuse original research with a quality improvement report.

Steps of Creating a Quality Improvement Project Report

Healthcare sciences, Medical, or nursing students write quality improvement reports or papers to document the problems in their practice areas and develop appropriate interventions, evaluation measures, timelines, and implementation plans to improve healthcare quality. It is a rich document that helps hospital managers to address challenges facing their health organizations by incorporating evidence-based strategies. Both undergraduate and graduate students can write quality improvement projects. When tasked with writing one, follow the steps below:

1. Request for permission

If you write a quality improvement report or paper based on a case study , skip this step. However, if you are addressing a real-life scenario in a healthcare setting, writing to the management requesting to conduct research for your quality improvement paper is vital.

In most cases, if you have identified a potential practice problem, you must write a proposal on how and why you intend to address the issue.

Suppose you are investigating a problem and need access to pertinent data such as hospital performance records, books of accounting, patient feedback forms, HCHAPS and patient survey results, administrative data, clinical data, SOPs, duty rosters, etc. In that case, you will need clearance with healthcare institutions' top management and leadership teams.

Write a letter to the management explaining your reasons for conducting the quality improvement research and the relevance of engaging their healthcare institution. In addition, you can ask your instructor, preceptor, or nurse educator on the way forward so that you do not land into trouble when you begin writing the report.

With the permission, you then need to move to the next step.

2. Determine and Prioritize a Practice Problem

The initial step of a quality improvement project is to map out the specific area that needs improvement. You can identify the area from personal experience of patient care, observation, a critical incident or adverse event, evidence review, patient feedback (complaints, compliments, and discussions), or an audit. Next, you can observe processes and review documentation.

When you have identified an area that needs improvement, the next step is to utilize specific tools to understand the underlying issues.

 By implementing evidence-based interventions, you can perform a root course analysis to identify the underlying cause and prevent a recurrence. When looking at the causes, consider the physical, human, and organizational causes.

The physical causes are material items that could fail in one way or the other. Human causes, on the other hand, refer to challenges, mistakes, or failures arising from the healthcare personnel, patients, or those caring for the patients.

Finally, organizational causes refer to the processes, systems, standard operating procedures, or policies that do not function as intended.

Begin by examining the patient population to identify the barriers to care, conditions, or groups of high-risk patients. Next, consider the at-risk patients or patients with chronic conditions and check the problems that might affect them and need QI initiatives.

You should also examine the practice operations. For instance, you can identify the management issues such as high attrition, burnout, low morale, poor patient outcomes, long wait times, poor communication, medical errors, etc.

To do a root cause analysis, you can utilize many tools, including the five whys, drill down, priority matrix, cause and effect diagram (fishbone diagram), driver diagram, Health Failure Modes and Effects Analysis (HFMEA), Sigma's DMAIC model, Failure Modes and Effects Analysis (FMEA) tool, Pareto charts/diagrams, Ishikawa diagram, process mapping, affinity diagrams, and check sheets.

These QI tools should help you identify and prioritize the specific quality improvement problem.

Related Article: Ideas for a capstone change project paper.

3. Develop an Objective

Having identified the problem and its underlying causes, it is important to define the project's scope to clarify what you intend to achieve. Your goals should be Specific, Measurable, Achievable, Realistic, and Time-bound (SMART).

You should then develop a multidisciplinary team to facilitate the project. First, consider the stakeholders, such as healthcare professionals, the management team, patients, patient representatives, and government representatives. 

Coming up with the goals and the project team helps shape the project ideas and is a positive indicator of the need for improvement. Next, consider engaging all the stakeholders and solving any issues leading to resistance.

4. Baseline Data Collection

To successfully evaluate the progress or effectiveness of a quality improvement intervention, it is imperative to measure the change. You should, therefore, take measurements to demonstrate the success or failure of the project.

Before implementing any changes, have a baseline measurement to track the project's progress. Your baseline should have at least 15 data points so that you can analyze the changes through time.

Consider all the healthcare quality measures , such as structural, process, balancing, and outcome measures, to identify the areas that need improvement.

Look at the patient medical records, patient surveys, patient comments, feedback from social media pages, standardized clinical data, and administrative data to prioritize the quality issues or problems in a healthcare setting.

Prioritize the problems based on their urgency to the specific organization and choose one that needs to be addressed immediately for your paper. You can also create a questionnaire to measure the baseline data.

5. Collect and Analyze Data about the problem

With the data collection results and comprehensive analysis of the baseline data, you need to develop interventions to address the issue.

Here is where you also choose the most appropriate QI model. Some of the quality improvement models include PDSA, Six SIGMA, Model for Improvement (MFI), ISBAR, Rapid Cycle Intervention (RCI), Experience-based Co-design (EBCD), FADE model, six sigma DMADV model (define, measure, analyze, design, verify), business process reengineering, total quality management (TQM), and the lean model.

It is important to note that planning and implementing the intervention (s) needs to be done through small-scale changes. Piloting the interventions on a smaller scale than a single extensive intervention addresses the challenges with resources. Effectively, most QI models entail aspects of the plan, do, study, act (PDSA) cycle.

So you need to plan the intervention, implement it and collect the data, analyze the collected data and compare them to predictions, reflect on the lessons learned, and plan the next cycle of change or go into full implementation. So be very meticulous when planning the intervention.

6. Develop an Action Plan

You must then develop a strategic map or plan to help you implement the change at the full scope. Include the timeline for implementing different aspects of the QI project, the responsible teams, evaluation measures, the process of monitoring and evaluation, and how to ensure that everything progresses well.

Detail how you will sustain the changes you have achieved so far. Therefore, after implementing the small changes and making the necessary adjustments, you must schedule the full implementation of the intervention. Consequently, it is crucial to anticipate the success factors and some of the challenges that might affect the performance of the QI project.

You should document how to incorporate the changes into standardized frameworks to sustain them. For example, common frameworks could include proformas, checklists, protocols, SOPs, hospital policies, and guidelines. You can also incorporate the project into the hospital QI database for sustainability.

7. Disseminate your QI project

After concluding your project, you need to do a QI project report or write-up to disseminate the findings. You can develop flyers, presentations, reports, or blog posts to share your results with your peers and the senior hospital management. Doing a QI report can also be handy as it will reach a wider audience once published online. You can also share your project during grand rounds or QI project symposia so that people learn new ways to address certain aspects of healthcare. Also, include it in your online portfolio or blog to boost your resume. If your interventions yielded significant results, consider writing up the project as a journal article or abstract presentation.

In the next section of this guide, we take you through the necessary parts of a QI project report.

Structure of a Quality Improvement Paper or Report

There is no prescribed format for writing up a QI project report. However, you should ensure that it is professionally written. This means writing it using formatting styles such as AMA, APA, or Harvard. In addition, you can follow the SQUIRE guidelines when developing the report. In the many years we have helped nursing and med students write QI project reports, we have followed the structure below, and all the projects have been successful.

Type the title of the paper. It should be around 50 words and indicate the area of improvement you are focusing on.

The abstract is the summary of your work, attracting your readers' attention. Ensure that you offer a brief background of the problem, the method for your quality improvement project, the QI models and tools used, the timelines, results, and the conclusion.

An abstract is about 200-300 words. It should be factual, succinct, and refined. If you are writing in APA or Harvard, do not indent the abstract.

Introduction

The introduction should describe the importance and relevance of the QI problem beyond your current station of practice (hospital, clinic, nursing home, or community health center).

You should also state the gap between what is currently known and done and what needs to be done or known to achieve the desired quality improvement outcomes. You should also provide the context of the project, which entails describing the healthcare setting and the relevance of the problem.

You should give a brief overview of the problem, the proposed intervention strategies, the steps, and the timeline for intervention.

Your introduction also includes the measures you used to prioritize the problem and the evaluation measures for the interventions.

In the introduction, you also introduce the quality improvement teams you worked with when implementing and assessing the effectiveness of the interventions.

Methodology

Under the methodology section, you should focus on the measurement, design, and strategy.

Measurement section deals with explaining the measures you selected to study the processes and outcomes of the intervention.

You should describe the rationale for choosing the measures and their definition and comment on their reliability and validity.

Describe how you planned to collect the data through the project and how frequently the data was collected. You should also outline how you planned to establish if the observed outcomes were due to the implemented interventions.

Under the design sub-topic, describe the intervention (s) you implemented to improve the quality of care in your healthcare setting. Describe any assumptions and rationale for developing the interventions. If you used QI tools, ensure to mention them.

Also, mention the QI models that guided the implementation of the interventions. Finally, introduce the project team and elaborate on how you engaged or consulted with the team members or the entire organization.

If there were any barriers, mention them, including how you overcame them. You should also report the report's timeline, detailing every step taken and when it was taken. Also, describe how you planned to make the intervention sustainable.

Finally, you need to explain the strategy for improvement, demonstrating how you implemented your improvement cycles. Then, focus on the interventions and improvement cycles that worked.

If there are any hurdles, mention them. Then, describe the progressive improvement cycles, lessons learned, how such learning influenced change, and if the change predictions were needed to influence the outcomes.

The results section should be a paragraph or a few paragraphs that summarize the essential findings from the implementation.

You should provide a summary of the results. If there are visuals such as tables or charts, explain what they mean. Describe any variation in your data to elaborate on whether or not the interventions worked. You should also describe the contextual elements that interacted with the interventions and how they might have influenced the results.

You should briefly compare the results to the baseline measurements you took before the QI project. Also, comment on how you assessed the data's completeness, validity, and accuracy.

You should also comment on whether there were unintended consequences such as unexpected delays, failures, problems, or costs associated with the interventions.

Share with your readers the ongoing findings after implementing the interventions. Is there a positive or negative change? Are your objectives being met? What areas need to be tweaked or changed? Are there any challenges?

Reflect on the implications of the results on the setting. If any lessons are learned, especially those that impact the result, include them in this section.

As well as reflect on the limitations to the implementation of the project. Describe any biases and confounding factors that could have affected the results and your efforts to adjust to the limitations.

Also, discuss the limitations of the chosen models or steps and how they could have affected the findings. Also, briefly mention and explain the potential future recommendations or actions to make it work well.

It could be that the time was limited or there was resistance from the workforce. Let the readers understand why things never went as they were predicted.

You can use subheadings to organize this section.

The conclusion reflects on the project's background, noting what is known on the topic and the new knowledge that your project brings forth.

This is never a chance to introduce any new concepts. If your project had goals, ensure that you state how they were met or if you adjusted the scope of the aims as you proceeded.

Explain if the measures were appropriate and if there were any balancing measures used in your project. You can describe the cost analysis and demonstrate the effectiveness of the intervention.

Report on the sustainability of the intervention based on the data. If the intervention can be generalized, give recommendations that can be used to make it a success in different settings. Also, mention any steps you would recommend for further study so that the limitations of the current QI project are overcome.

Organize your references alphabetically in APA, AMA, or Harvard format. Ensure that you only include the sources that are referenced within your report.

Include supplementary materials such as graphs, flow charts, diagrams, and relevant images in the report.

Also Read: How to write a change management report in nursing school.

Tips for writing a successful QI project Report

If you are at the writing stage, here are some considerations.

• Ensure that your QI project is written chronologically. A chronological approach will help you avoid confusion and give the correct narrative.
• When writing the report, you are allowed to write in the first person. This is because you are reflecting on a process whose implementation you steered.
• You should ensure that every detail borrowed from scholarly nursing journals and other sources is well cited.
• Reducing admission-to-chemotherapy delays
• Make your QI report readable using visuals such as images, tables, pictures, process maps, diagrams, and illustrations.
• If you are using an executive summary, ensure that it captures whatever your project is about and gives a snapshot of what to expect.
• Make your project easy to navigate using a table of content. You can also use the IMRAD scientific format to make it easy to navigate.
• Contextualize the report when writing about a project implemented in a local setting.
• Ensure that the measurement section captures every detail. For example, give the rationale for choosing provided measurements and how they influenced project outcomes. You can include flow charts, timeline diagrams, and tables for clarity.
• Identify the limits of generalizability of the outcomes of your project
• Describe the project's sustainability and some success factors should it be implemented in other settings or contexts.
• Ensure that you record the impacts of the project on systems and people.
• Detail the challenges faced and how you overcame them
• Clarify any reasons for the differences in observed and predicted outcomes
• Describe the factors that might have affected the internal validity, such as measurements, analysis, design, and bias factors.

Quality Improvement Areas and Topics to Consider

Consider these ideas and topic areas if you plan to undertake a quality improvement project.

• Emergency room overcrowding
• Anxiety and depression during cancer
• Reducing constipation or lymphedema among cancer patients
• Addressing hot flashes among cancer patience
• Reducing immune-mediated adverse events among cancer patients
• Reducing cancer-related fatigue among cancer patients
• Improving outcomes of Sleep-Wake Disturbance during Cancer Treatment 
• Improving team-based care for cancer patients using CDSS
• Improving self-management prevention strategies for diabetic patients
• Improving healthy lifestyle among obese patients
• Improving diagnosis of congenital cataracts by introducing NGS genetic testing
• Improving the outcome of self-management practices by diabetes patients who are elderly
• Addressing and responding to sepsis in accident and emergency departments
• Implementing text messaging to reduce smoking among elderly men
• Reducing medical-related adverse events
• Reducing post-operative infections
• Using a data-driven approach to shorten hospital stays
• Reducing near-death events in a cardiac facility
• Addressing the shortage of medicines in a healthcare facility
• Reducing hospital readmissions
• Reducing mortality rates of spinal cord injury for road traffic accident patients
• Reducing the number of urinary catheter infections
• Reducing blood contamination during and after transfusion
• Optimizing sepsis care
• Optimizing wound care in a healthcare setting
• Reducing patient falls at a nursing home facility
• Improving the documentation of electronic medical records
• Improving access to quality healthcare
• Reducing low uptake of health insurance
• Improving coordination among healthcare departments in a hospital
• Decreasing electronic medical error
• Reducing mortality rates of pre-term babies at a NICU facility
• Improving exclusive breastfeeding uptake among new mothers
• Improving medical adherence for elderly patients
• Reducing polypharmacy in elderly patients
• Improving hospital discharge
• Improving equitable access to kidney transplantation and dialysis through the referral process
• Preventing healthcare-associated infection-related deaths
• Improving communication during handoffs
• Improving the handoff process
• Reducing nurse fatigue and stress
• Improving inter-professional collaboration in healthcare settings
• Addressing high nursing workload to address patient safety
• Implementing "Quiet Please!" drug round tabards to reduce medication administration errors
• Reducing urinary catheter-related infections
• Reducing ventilator-associated pneumonia in healthcare settings
• Reducing pressure ulcers among burn patients
• Improving adherence to hand hygiene practices

Related Article:

• How to write a nurse student resume with no experience.
• Nursing research paper topics and ideas.
• Nursing theories and theorists to consider for your nursing paper.
• Interesting argumentative nursing topics.
• Steps and tips for nursing capstone project writing.
• Basics of Quality Improvement
• Continuous quality improvement in nursing
• Examples of quality improvement in healthcare
• Examples of quality improvement in hospitals
• How to Write Up Your Quality Improvement Initiatives for Publication
• Quality Improvement Models
• Quality improvement tools
• Root cause analysis Tools
• Ways to Approach the Quality Improvement Process
• What is quality improvement in healthcare?

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How can we reduce violence and aggression in psychiatric inpatient units? Arokia Antonysamy, consultant psychiatrist, Lancashire Care NHS Foundation Trust

Bmj qual improv report 2013;2:doi: 10.1136/bmjquality.u201366.w834.

Aggression and violence are common in psychiatric wards, especially in psychiatric intensive care units. The use of restraint and seclusion does not help to reduce levels of aggression and may actually exacerbate it in the unit. The use of PRN (as required) drugs and secluding patients without actively engaging them in any therapeutic activity does not deal with patients’ needs appropriately and makes them more lethargic and less able to express emotions, even normal ones. Patients are expected to reach a reasonable level of stability in their mental state before they can be escorted to the occupational therapy room or the gym. But how can we get them into that stable phase while in the psychiatric intensive care unit without making them drowsy from drugs?

Lack of structured activities in psychiatric units and the resulting boredom increase aggression in the ward and assaults increase during the evenings and at weekends. Patients in psychiatric intensive care units are more likely than those on acute wards to get abusive towards others. They usually have a diagnosis of schizophrenia or bipolar or personality disorder, and lack of access to outside space further contributes to their hostile behaviour. Patients with a mental illness have expressed the need for more face to face sessions with staff.

Baseline measurement

Our hospital trust was the third highest for reports of violence and aggression (Advancing Quality Alliance report 2011); it should be noted, however, that even the most trivial episode of aggression (such as verbal abuse or an aggressive gesture) was captured in the data. The rates of patients who had “absconded without leave” were also high. Our psychiatric intensive care unit accounted for one quarter of the trust’s rates for aggression in 2011. This also affected staff’s coping levels, with stress resulting in 247 reports of sickness in the unit within six months in 2011.

We were keen on using the skills and resources available in the ward to improve safety measures. All the staff on our ward took part in this project and were enthusiastic and motivated to change the status quo. Our objective was to implement an innovative initiative aimed at dealing with patient boredom, which was identified as a major contributor to violence and aggression. Blackpool Zoo was near to the unit and we considered providing our patients with the opportunity to visit and also to allow the occupational therapist to perform an assessment of the patients’ risk handling, social skills, behaviour in public, and handling stresses such as waiting in queues and working in teams.

The primary outcome measures included reduction in the incidents of violence and aggression. The secondary outcome measures included reduction in average length of stay, reduction in seclusion rates, direct discharges to community from psychiatric intensive care units for a selected group of patients who may not cope well on the acute wards, and reduction in staff sickness rates. Progress was discussed with staff at ward governance meetings held monthly.

When patients are identified as being ready to be tried on leave outside the hospital grounds, staff escort them on walking trips. When the trip is successful, the patients are booked for zoo visits with nurses and an occupational therapist. Initially, zoo trips were tried without the walking trips and this did not help all the patients because some felt too anxious and could not cope for long in a “real” environment. Weekly feedback was also provided about the leave outcomes for each patient.

At the end of three months we began to hear positive feedback from patients. Quotes included, “thank you for trusting us and taking us out,” “I thought I’ll be locked up here forever, but it is a totally different experience here and I will miss you all,” and “when I was out, I thought of absconding, but you all trusted me so much that I just couldn’t do it.” Carers also provided positive feedback and some wrote to the chief executive appreciating the commitment shown to this difficult patient group. The Care Quality Commission visited the unit and gave positive feedback, commenting that the patient care plan was one of the best they had ever seen.

The project led to a substantial reduction in aggression. Along with the zoo visit we also incorporated other initiatives such as a breakfast club, walking trips, and training at the gym. The average length of patient stay decreased from 90 to 30 days for an initial period and then remained stable at 55 days. The seclusion rates reduced significantly, as did the rates for staff sickness. Patients provided positive feedback about the zoo visit, and apart from one delayed return to the ward there were no reports of patients absconding without leave.

Limitations of the project:

1. This project included only the psychiatric intensive care unit and therefore the sample size is small.

2. Psychiatric intensive care units are better resourced than acute wards and therefore this initiative might not be successful if replicated in acute wards. Patients are, however, likely to be more settled in acute wards and may not need similar resources as psychiatric intensive care units for escorted leave.

Cost implications: this project was carried out at no extra cost, utilising the resources available in the psychiatric intensive care unit. The project led to efficiencies in bed occupancy, from about 90 to 55 days on average for each patient. A bed in the psychiatric intensive care unit costs £600 a day. A reduction of 30 days on average for each patient would constitute a saving of about £18 000 per patient.

We were able to achieve the aims of our project in delivering recovery focused care for our patients. The use of resources outside the hospital helped us to seek collaboration with partner organisations such as the local authority and third sector. Although tackling stigma was not one of our key intentions, our patients reported feeling less stigmatised. We are keen to expand our partnership with other agencies, seeking all opportunities we can to support our patients and help them achieve their full potential.

Read the full version here: http://qir.bmj.com/content/2/1/u201366.w834.full .

Improving junior doctor handover between jobs. Laura Hayes, foundation year 1 doctor, Croydon University Hospital

Bmj qual improv report 2014;3:doi: 10.1136/bmjquality.u201125.w713.

Patient safety is one of the most important issues in healthcare. In recent years there has been a lot of focus on “Black Wednesday;” the day that foundation doctors start their first job. Great efforts have been made to ensure that patient safety on this day has improved, with the newly qualified doctors now using some of their free time to shadow their outgoing counterparts between medical school and starting their first job.

However, because foundation doctors start a new job about every four months for two years, subsequent job changeovers were identified as a time of potential problems and risks to patient safety. It is not practical to shadow before every job because junior doctors are needed in their current post right up until changeover day, so a simple way to smooth this transition was needed.

A handover lunch seemed to be a feasible solution. The day before foundation doctors change jobs, an hour is dedicated for foundation year 1 doctors to sit together over lunch and make a formal handover of all relevant information about their forthcoming job and discuss current inpatients.

Results showed that 100% of those surveyed mentioned face to face handover as essential, 93.8% said that it was either helpful or extremely helpful to have a dedicated time for foundation year 1 doctors to hand over, and 12.5% said they would not have sought a face to face handover otherwise. Apart from being extremely simple and cheap, the handover was also popular with the foundation year 1 doctors. It enables effective working from day 1 and is a great team building activity.

Read the full version at http://qir.bmj.com/content/3/1/u201125.w713.full .

Developing a platform for learning from mistakes: changing the culture of patient safety amongst junior doctors. Sinead Millwood, foundation year 2 doctor, Bristol Royal Infirmary

Bmj qual improv report 2014;3:doi: 10.1136/bmjquality.u203658.w2114.

Junior doctors commonly make mistakes that might compromise patient safety. Despite the recent push by the National Health Service to encourage a “no blame” culture, mistakes are still viewed as shameful, embarrassing, and demoralising events.

A survey was designed by the author for all the 21 foundation year 1 doctors at Yeovil District Hospital to understand better the culture surrounding mistakes, and the types of mistake that were being made. Using the results of the survey and the support of senior staff, a “near misses” session has been introduced once a month for foundation year 1 doctors where mistakes that have been made are discussed, with a consultant present to facilitate the proceedings. The aims of these sessions are to promote a culture of no blame, feed back information to clinical governance, and share learning experiences.

Every foundation year 1 doctor (100%) had made a mistake that could potentially compromise patient safety. Overall, 63% discussed their mistakes with colleagues, 44% with senior staff, and only 13% with their educational supervisor. Barriers to discussing mistakes included shame, embarrassment, fear of judgment, and unapproachable senior professionals. Ninety four per cent thought a “near misses” session would be useful. After the third session, 100% of the foundation year 1 doctors agreed that the sessions were useful; 53% had changed their practice as a result of something they had learnt at the sessions.

After discussing errors as a group we have worked with the clinical governance department, enacting strategies to avoid repetition of mistakes. Feedback from the junior doctors has been overwhelmingly positive and we have found these sessions to be a simple, inexpensive, and popular solution to cultural change in our organisation.

Read the full version here: http://qir.bmj.com/content/3/1/u203658.w2114.full .

Originally published as: Student BMJ 2015;23:h4360

Competing interests: None declared.

Provenance and peer review: Not commissioned; not externally peer reviewed.

The Largest Roundup of Healthcare Quality Improvement Examples and Projects

By Kate Eby | June 17, 2019 (updated November 13, 2023)

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Healthcare organizations have performed tens of thousands of continuous quality improvement projects over the past two decades. Organized by topical area, this article provides dozens of useful examples of those projects as well as links that offer further details.

Included on this page, you'll find a roundup of examples, including CQI projects that improved overall healthcare , CQI projects that improve long-term disease management , projects that reduced medical errors, and many more.

What Is Quality Improvement in HEA?

Healthcare quality improvement is the process of continually assessing and enhancing the effectiveness, safety, and efficiency of healthcare services given to patients. Actions include analyzing data, identifying improvement areas, and installing evidence-based solutions to enhance the quality of patient care.

Examples of Quality Improvement Projects in Overall Patient Care

Blue Cross Blue Shield of New Mexico performed a quality improvement project to improve how it processed complaints from its members about quality-of-care issues at health facilities. Other projects include the following:

• The Safer Care Patient-Centered Checklist : This was an intervention to promote safe, high-quality practice and improved outcomes. This project was conducted by the University Hospital Southampton National Health Service Foundation Trust in the UK.
• Quality Improvement and Person-Centeredness : This was a test of the “Always Event” concept, a method in healthcare settings in which you identify care issues that are important to patients and families and encourage medical providers to focus on those issues. The test was conducted by the National Health Service in Lanarkshire, UK.
• The Improvement of Order Sets: This project was undertaken by Boston Medical Center, the Graduate Medical Education Program, to create a standard process to increase quality and utilization.
• The Implementation and Improvement of a Pediatric Rapid Assessment Clinic: Brock University in St. Catherines, Ontario, Canada implemented this project in a large community hospital system, namely St. Catherines Site.  
• The Improvement of the Quality and Impact of Interdisciplinary Rounds: Tulane Medical Center at Tulane University conducted this study.
• The Improvement of the Consistency and Efficiency of Medicare Annual Wellness Visits: The Tulane University School of Medicine conducted this study.

Examples of Quality Improvement Projects in Patient Screening and Other Diagnostic Procedures to Detect Disease

Stanford University Healthcare instituted a quality improvement project to improve the breast positions in mammograms in order to detect breast cancer . This project helped increase the accuracy of Stanford’s mammograms by 36 percent, meaning that breast cancer was detected earlier in many cases. Other projects include the following:

• A Resident-Led Quality Improvement Initiative: The Louis Stokes VA Medical Center in Cleveland, Ohio undertook this project to improve outpatient colorectal cancer screening rates.
• The Improvement of Metabolic and Diabetes Screenings: This was an effort to improve metabolic and diabetes screenings for patients in Missouri who received Medicaid and Medicare services and had a mental illness and at least three chronic health conditions. The initiative significantly increased the percentage of patients who had appropriate screening.

Examples of Quality Improvement Projects in the Long-Term Management of Diseases

Blood pressure control reduces the chance of serious complications associated with diabetes. The Huron Valley Physicians Association in Ann Arbor, Michigan implemented a quality improvement project to better monitor and control the blood pressure of patients with diabetes . Other projects include the following:

• The Cholesterol (LDL) Screening after an Acute Coronary Event : The Dean Health System in Madison, Wisco nsin implemented this project.
• The Improvement of Congestive Heart Failure Discharge Teaching Efficiency : Mountain States Health Alliance’s Indian Path Medical Center conducted this study.
• The Improvement of Quality of Care for Diabetes in an Outpatient Setting : Austin (Texas) Diagnostic Clinic conducted this study.
• The Reduction of Time to First Antibiotic Dose in Pneumonia Patients : El Camino Hospital in Mountain View, California implemented this project.
• The Optimization of Patient Outcomes and the Reduction of Cost through the Enhanced Management of Invasive Fungal Infection : The Barts Health National Health Service Trust in London implemented this project.
• The Improvement of the Cancer Patient Experience with a Rapid Access Multidisciplinary Palliative Assessment and Radiotherapy Treatment Clinic : University Hospital in Southampton, UK undertook this project.
• The Dementia “Golden Ticket,” an Emerging New Model of Care : Buxted Medical Centre in Uckfield, UK undertook this project.
• Home Monitoring to Support Patients during Chemotherapy : The Christie NHS Foundation Trust in London did this project.
• The Diabetes Passport to Health: The Development and Piloting of a Self-Management Tool for High-Risk Patients: Emory University in Atlanta conducted this study.
• The Increase of Appropriate Statin Use in the Primary Care Clinic: The Louis Stokes VA Medical Center in Cleveland, Ohio undertook this project.

Examples of Quality Improvement Projects in Managing Pregnancies and Improving Perinatal Outcomes

Identifying and managing pregnancy risks early in a woman’s pregnancy is vital for the health of the mother and unborn baby. The National Health Service in Scotland implemented a quality improvement program that engages women in the development of a system that will assess and manage those risks . Other projects include the following:

• Home Monitoring of High Blood Pressure in Pregnancy: An Innovative App to Monitor Women’s Health during Pregnancy : St George’s University Hospitals National Health Service Foundation Trust in London conducted this project.
• A Nurse-Driven Quality Improvement Program to Improve Perinatal Outcomes : In Bethesda, Maryland, a nursing team joined forces with a managed care organization to develop and implement a quality program that would improve perinatal outcomes for pregnant women enrolled in the managed care organization.

Examples of Quality Improvement Projects in Reducing Medical Errors, Medication Errors, and Adverse Drug Events

Communication errors are one of the leading causes of medical errors — this translates to poor patient outcomes, longer hospital stays, and increased costs. Duke Children’s Hospital in Durham, North Carolina. worked on a quality improvement project to improve communication among medical professionals during the providers’ daily rounds. Other projects include the following:

• The Addressing of Risks for Error in the Process of Administering Dialysis : This study involved a Utah hospital.
• The Targeting of Wrong Dose Medication Errors: This study involved a California hospital.
• The Addressing of Infusion Drug Errors: This study involved an 11-bed pediatric intensive care unit in a children’s hospital.
• The Systematic Analysis for Improvement in the Ordering and Administration of Potassium Chloride and Potassium Phosphate: This study involved three Canadian hospitals.
• The Addressing of Medication Order Errors with Pediatric Oncology Patients: This study involved a Maryland academic medical center.
• Error Detection Associated with Medication Administration: Baccalaureate nursing students at Pennsylvania University conducted this study.
• The Addressing of Medication Errors and Adverse Drug Events (ADEs): The increased pharmacist staffing on patient care units that review orders reduced errors by 45 percent.
• The Addressing of Errors Associated with Chemotherapy: A standardized procedure for changes in chemotherapy was established at a Netherlands hospital in order to prevent prior changes in chemotherapy that staffers made without the knowledge of nurses or other medical providers.
• The Addressing of Adverse Drug Events Associated with Patient-Controlled Analgesia: Quality improvement changes at a California hospital included using a standard dosage protocol, ensuring that patient-controlled pumps were programmed correctly, and monitoring patients who were using the pumps.
• A Transition of Care Pharmacy Program: This quality improvement program encourages pharmacists at Stanford Health Care to participate in daily rounds and reconcile medications on patient admission and discharge. Pharmacists also provide in-depth medication education to patients when they are discharged from the hospital.

Examples of Quality Improvement Projects in Reducing Hospital-Acquired Infections, Injuries, and Other Illnesses

Nationwide Children’s Hospital in Columbus, Ohio embarked on a quality improvement program to try to prevent all hospital-acquired harm to patients — a significant problem at all U.S. hospitals. This quality improvement structure was expanded to create more than 150 patient harm-related projects. Over a three-year period, the hospital reduced its preventable harm events by 55 percent and its risk-adjusted mortality rate by almost 40 percent. Other projects include the following:

• “Lose the Tube”: The Reduction of the Use of Catheters and Incidences of Catheter-Associated Urinary Tract Infections : Mount Sinai Hospital in New York City conducted this improvement program.
• The Reduction of Central Line Associated Bloodstream Infection in Unfunded Dialysis Patients: The University of Texas Southwestern Medical Center implemented this program.
• The Development of a Standardized Process for the Review of Key Indicators Associated with Surgical Site Infections: The University of Texas Southwestern Medical Center implemented this program.
• The Addressing of Medical Provider Noncompliance with Infection Control Measures: A Netherlands hospital undertook this project.
• The Reduction in the Rate of Colon and Vascular Surgical Site Infections: At a West Virginia medical center, surgical site infection rates decreased by 91 percent, with an estimated potential annual savings of more than $1 million.
• A Program to Improve Hand Washing and Proper Hand Hygiene: Increased education and other interventions significantly increased compliance.
• A Reduction in the Number of In-Facility Falls and Injuries due to Falls: At 100 acute and long-term United States VA facilities, researchers implemented a quality improvement program that led to 34 percent of those facilities reporting a reduction in the number of falls and 38.9 percent reporting a reduction in major injuries due to falls.

Improving the Treatment of Sepsis

Thibodaux Regional Medical Center in Louisiana embarked on a quality improvement program to reduce the number of patients who die from sepsis at its hospital . The quality improvement team implemented new protocols that followed best practices, improved the hospital’s analytics systems, and helped educate medical providers with data. After the team instituted the improvements, sepsis rates at the hospital declined to half the national average.

To learn more about the Thibodaux quality improvement program, check out “CQI in Healthcare: Principles, Process, and Tools.”

Examples of Quality Improvement Projects in the Continuity of Care for Patients

About one third of the residents of long-term care facilities transfer to a hospital every year — many not able to communicate their medical needs. Communication and transfer reports between caregivers at each facility is often poor, resulting in unnecessary tests, increased hospital stays, and other issues. The University of Toronto hospital embarked on a quality improvement program to improve those communications. Other projects include the following:

• The Improvement in the Continuity of Maternity Care : The Naval Hospital in Camp Pendleton, California implemented this program.
• The Provision of Complete Discharge Instructions to the Heart Failure Patient : Spectrum Health in Grand Rapids, Michigan undertook this project.
• Early Experience with the Implementation of the I-PASS Handoff Bundle: Boston Medical Center’s Graduate Medical Education Program conducted this study.
• The Improvement of Patient Understanding at Discharge: Medical Student Enhanced Patient Education: Boston University conducted this quality improvement program.
• The Improvement of Patient Handoffs in OR-ICU and OR-OR Settings: The University of Texas Southwestern Medical Center conducted this quality improvement program.

Reducing Unnecessary Antibiotic Use

Stanford Healthcare implemented a quality improvement program to reduce the use of unnecessary antibiotics. The use of such antibiotics often doesn’t help patients, exposes them to unnecessary risks, and fosters the development across the world of drug-resistant bacteria.

Since 2012, Stanford has reduced its use of certain antibiotics by 50 percent and has stopped using an expensive antibiotic medicine that was sometimes administered to treat respiratory virus infections in certain patients. Stanford determined that there were no benefits to patients using the medicine.

Examples of Quality Improvement Projects in Reducing the Use of Expensive Technology and Care When It Won’t Help the Patient

The University of California at San Diego hospital implemented a quality improvement program to reduce the number of heart patients in special telemetry beds (which allow for continuous heart monitoring) when the patients received no benefit from that constant monitoring. Other projects include the following:

• The Reduction of the Overuse of Cardiac Telemetry through the Implementation of Guideline-Specific Electronic Order Sets: Boston Medical Center implemented this program.
• The Reduction of Continuous Pulse Oximetry Use: Cincinnati Children’s Hospital initiated this campaign for patients with asthma and bronchiolitis.
• The Detection of the Overuse of Renal Ultrasound to Diagnose Obstructive Acute Kidney Injury: Researchers at the Icahn School of Medicine at New York City-based Mount Sinai Hospital implemented this program.

Examples of Quality Improvement Projects in Reducing Unnecessary Medical Procedures that Can Increase Patient Risks and Medical Costs

Restricting blood transfusions to only patients who unquestionably need them results in better outcomes for all patients. Rush University Hospital in Chicago implemented a quality improvement program to decrease the number of blood transfusions it performed every year. In the 13 months following the implementation of the change, blood transfusions were reduced by 36 percent. Other projects include the following:

• The Reduction of Unnecessary Lab Orders on the Inpatient General Internal Medicine Service: Boston University conducted this study.
• A Resident-Led QI Initiative to Reduce Serum Folate Testing in a Primary Care Clinic: The Louis Stokes VA Medical Center in Cleveland, Ohio implemented this initiative.
• The Reduction of Unnecessary Routine Post-Operative CBCs in the Pediatric Intensive Care Unit: The Children's Hospital of Philadelphia implemented this initiative.

Examples of Quality Improvement Projects in Reducing Health Inequities among Groups of Patients

About 30 percent of patients at Boston Medical Center had limited English proficiency, and the hospital had no system to ensure that families had in-person language interpreters when doctors were making their daily rounds to check on patients in the hospital’s pediatric ward. The center implemented a quality improvement program with the goal of having language interpreters available for at least 75 percent of daily rounds visits. Other projects include the following:

• The Use of Technology and an Evidence-Based, Outcome-Led Approach to Reducing Health Inequalities for People with Learning Disabilities : UK-based Home Farm Trust undertook this project.
• Screening for and Tracking Social Determinants of Health in a Federally Qualified Health Center: The Oregon Health & Science University undertook this project.

Examples of Quality Improvement Projects in Surgical Outcomes

A multidisciplinary team at Stanford Healthcare — including surgeons, nurses, anesthesiologists, physical therapists, and others — worked on quality improvement initiatives to improve patient care before and after surgery. The initiatives tried to better manage pain in patients, reduce opiate use, and get patients walking and moving more quickly after surgery. The overall goal was to reduce the stress of surgery and promote quicker surgical recoveries.

Other projects include the following:

• Post-Operative Venous Thromboembolism Prevention in Thoracic Surgery: Implementing the Caprini Risk Assessment Model: ​Boston University implemented this project.
• The Effect of a “SMaRT” Enhanced Recovery Pathway for Elective Laparoscopic Colorectal Surgery: The University of Texas Southwestern Medical Center conducted this study.

Examples of Quality Improvement Projects in Managing and Increasing Efficiencies for Patient Service: Appointments, Discharges, Follow-Up Care, and Emergency Department Service

Delays in discharging patients from a hospital at the appropriate time is frustrating to patients and costly for both patients and hospitals. Cincinnati Children’s Hospital Medical Center worked on a quality improvement project to improve the process and allow for discharges soon after medical providers determine that patients are ready to be discharged . After the hospital implemented the project, at least four fifths of patients were discharged within two hours of meeting the medical criteria for discharge — which also meant an estimated $5.9 million in yearly cost savings. Other projects include the following:

• The Formalization of Communication in Discharge Planning: Tulane University implemented this project.
• The Use of Electronic Data to Identify Bottlenecks in Secondary Care on Weekends and Improve Patient Flow : Royal Derby Hospital in the U.K. initiated this project.
• The Decrease of the Failed Appointment Rate for Flexible Sigmoidoscopy : Harbor-UCLA Medical Center implemented this project.
• The Surprising Complexity of Decreasing Patient Visit Times at a Student-Run Clinic: Emory University conducted this study.
• The Decrease in Patient Visit Durations at a Student-Run Free Clinic through a Clinic Flow Intervention: The University of Texas Southwestern Medical Center implemented this program.
• A Program to Decrease Total Patient Time Spent During a Routine Clinic Appointment: This program included interventions to properly direct non-English-speaking patients to a check-in desk. These interventions increased the efficiency with which these patients saw providers and finished appointments.
• The Reduction of Time in the Emergency Department for Minor Illness and Injuries: A Maryland Hospital implemented this program.

Examples of Quality Improvement Projects in Operational Efficiencies in a Medical Facility

A student-run, free health clinic at the University of South Florida worked on a quality improvement project to increase the efficiency of patient care. The clinic had a four-month wait time for routine appointments. The project’s aim was to increase efficiency and reduce the time it took for medical providers to see patients and complete patient visits. Other projects include the following:

• Lean Principles in the Anesthesiology Technician Workflow: Decreasing Waste to Improve Value-Added Time: Tulane University implemented this project.
• The Optimization of the Efficient Use of Intensive Care Unit Patient Rooms: Stanford Healthcare implemented this project.

To learn more about quality improvement projects in nursing care and get a specific guide on improved nursing care, check out “Quality Improvement in Nursing 101: Strategies, Examples and Tools.”

Hypothetical Example Projects from the American College of Physicians

The American College of Physicians offers some guidance on quality improvement projects , including a number of example projects that medical facilities throughout the U.S. could tackle.  Example projects include the following:

• Document that your providers have checked with patients as to whether they’ve had an influenza vaccination.
• Make a list of diabetic patients from 19 to 59 years old and determine whether or not they’ve had a Hepatitis B vaccination.
• Conduct a review of your facility’s healthcare workers to determine whether or not they’ve had immunizations for hepatitis B, measles, mumps and rubella, and varicella.
• Provide information to pregnant women about the Tdap vaccine, which contains vaccines for tetanus, diphtheria, and pertussis.
• Identify patients who have started the series of vaccinations against the human papillomavirus (HPV) and send those patients a reminder to get the follow-up dose.
• Use Medicare’s Hospital Compare data to track your organization’s rates of vaccinations for influenza and pneumococcal pneumonia.

To learn more about steps to implement a quality improvement program, features of an effective program, and how to measure results, check out “A Business Guide to Effective Quality Improvement in Healthcare.”

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Internal logic and driving path of enterprise green innovation performance improvement under TOE framework: based on linkage effect analysis of fsQCA and NCA

• Published: 17 May 2024

Cite this article

• Lin Li   ORCID: orcid.org/0000-0002-6292-0473 1 &
• Wenjing Che   ORCID: orcid.org/0009-0008-2727-0912 1  

Specialized and sophisticated enterprises, as a crucial component in the implementation of innovation strategies, serve as the cornerstone for promoting high-quality economic development. The enhancement of the status and green innovation performance of specialized and emerging enterprises is a pressing issue that necessitates immediate attention. Drawing upon the technology–organization–environment (TOE) framework, this study examines a sample of 123 specialized and sophisticated enterprises to investigate the underlying mechanisms and pathways through which they enhance their green innovation performance, employing necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis (fsQCA). The results indicate that: (1) The single antecedent factor is not a necessary condition for achieving high green innovation performance in NCA; (2) There are four types of configuration paths involving the coupling and interaction of internal and external factors that can lead to high green innovation performance in specialized and sophisticated enterprises SMEs: technology organization-driven, capital abundance-driven, multiple synergy-driven, and innovative technology-driven. These findings contribute to the research on green innovation performance and offer practical insights into how to improve it in specialized and sophisticated enterprises.

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Li, L., Che, W. Internal logic and driving path of enterprise green innovation performance improvement under TOE framework: based on linkage effect analysis of fsQCA and NCA. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04971-1

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Mentorship in Health Research Institutions in Africa: A Systematic Review of Approaches, Benefits, Successes, Gaps and Challenges

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In Africa, where the burden of diseases is disproportionately high, significant challenges arise from a shortage of skilled researchers, lack of research funding, and limited mentorship opportunities. The continent faces a substantial gap in research output largely attributed to the dearth of mentorship opportunities for early career researchers. We conducted this systematic review to explore existing mentorship approaches, identify challenges, gaps, successes, and benefits, and provide insights for strengthening mentorship programs in African health research institutions. We registered the review protocol on the International Prospective Register of Systematic Reviews [CRD42021285018] and searched six electronic databases – EMBASE, AJOL, Web of Science, PubMed, DOAJ and JSTOR from inception to 10 November 2023, for studies published in English reporting on approaches of mentorship in health research in African countries. We also searched grey literature repositories, institutional websites, and reference lists of included studies for additional literature. Two independent reviewers conducted screening of titles and abstracts of identified studies, full-text screening, assessment of methodological quality, and data extraction. We assessed study quality against the Mixed Methods Appraisal Tool (MMAT). We resolved any disagreements through discussion and consensus. We employed a narrative approach to synthesize the findings. We retrieved 1799 articles and after screening, included 21 studies in the review. The reviewers identified 20 mentorship programs for health researchers (N=1198) in 12 African countries mostly focusing on early career researchers and junior faculty members.  A few included mid-career and senior researchers. We categorized the programs under three key mentoring approaches: international collaborative programs, regional and in-country collaborations, and specialized capacity-building initiatives. Our review highlighted the following successes and benefits of health research mentorship programs: the establishment of collaborations and partnerships, development of research programs and capacities, improvement of individual skills and confidence, increased publications, and successful grant applications. The gaps identified were limited funding, lack of a mentorship culture, negative attitudes towards research careers, and lack of prioritization of research mentorship. Our review highlights a diverse landscape of health research mentorship aspects predominantly targeting early career researchers and heavily driven by the North.  There is a need for locally driven mentorship initiatives in Africa to strengthen mentorship in order to advance health research in the region.

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This work received funding from the African Research Excellent Fund (AREF) through the Research and Related Capacity Strengthening (RRCS) unit at the African Population and Health Research Center. The funder had no role in the design, execution, synthesis, interpretation or decision to publish this manuscript.

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Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil

Leonardo hoinaski, robson will, camilo bastos ribeiro.

Developing air quality management systems to control the impacts of air pollution requires reliable data. However, current initiatives do not provide datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Here, we introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database of air quality and its drivers in Brazil. BRAIN encompasses hourly datasets of meteorology, emissions, and air quality. The emissions dataset includes vehicular emissions derived from the Brazilian Vehicular Emissions Inventory Software (BRAVES), industrial emissions produced with local data from the Brazilian environmental agencies, biomass burning emissions from FINN – Fire INventory from the National Center for Atmospheric Research (NCAR), and biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) ( https://doi.org/10.57760/sciencedb.09858 , Hoinaski et al., 2023a; https://doi.org/10.57760/sciencedb.09886 , Hoinaski et al., 2023b). The meteorology dataset has been derived from the Weather Research and Forecasting Model (WRF) ( https://doi.org/10.57760/sciencedb.09857 , Hoinaski and Will, 2023a; https://doi.org/10.57760/sciencedb.09885 , Hoinaski and Will, 2023c). The air quality dataset contains the surface concentration of 216 air pollutants produced from coupling meteorological and emissions datasets with the Community Multiscale Air Quality Modeling System (CMAQ) ( https://doi.org/10.57760/sciencedb.09859 , Hoinaski and Will, 2023b; https://doi.org/10.57760/sciencedb.09884 , Hoinaski and Will, 2023d). We provide gridded data in two domains, one covering the Brazilian territory with 20×20  km spatial resolution and another covering southern Brazil with 4×4  km spatial resolution. This paper describes how the datasets were produced, their limitations, and their spatiotemporal features. To evaluate the quality of the database, we compare the air quality dataset with 244 air quality monitoring stations, providing the model's performance for each pollutant measured by the monitoring stations. We present a sample of the spatial variability of emissions, meteorology, and air quality in Brazil from 2019, revealing the hotspots of emissions and air pollution issues. By making BRAIN publicly available, we aim to provide the required data for developing air quality policies on municipal and state scales, especially for under-developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights into and opportunities for air pollution research in Brazil.​​​​​​​

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Hoinaski, L., Will, R., and Ribeiro, C. B.: Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil, Earth Syst. Sci. Data, 16, 2385–2405, https://doi.org/10.5194/essd-16-2385-2024, 2024.

It is consensus that air pollution threatens public health (OECD, 2024), economic progress (OECD, 2016), and climate (US EPA, 2023a). The negative outcomes associated with air pollution are not uniform within populations, and the impacts may be greater for more susceptible and exposed individuals (Makri and Stilianakis, 2008). Due to their social vulnerability and increasing emissions, developing countries urgently require reliable databases to provide information for designing air quality management systems to control air pollution (Sant'Anna et al., 2021).

Brazil has continental dimensions, is the seventh most populous country in the world, and has the 12th largest gross domestic product (IBGE, 2024). The combination of poorly planned development and the huge socioeconomic discrepancy has led to air quality impacts in Brazil. Air-pollution-related problems are not only restricted to great Brazilian cities and industrialized areas. Vehicular fleets and fuel consumption have also increased in small municipalities (CEIC, 2021; MME, 2023), posing a challenge to controlling vehicular emissions. In preserved and rural areas, large fire emissions have occurred due to illegal deforestation and poor soil management (Escobar, 2019; Rajão et al., 2020).

Following the practices of developed countries, Brazilian air quality policies have been enforced through legislative laws, using air quality standards as key components. However, the air quality management system remains incomplete in Brazil, with policies falling short of effectiveness due to a lack of air quality monitoring data across much of the country, primarily limited to well-developed areas (Sant'Anna et al., 2021). Moreover, Brazilian environmental agencies have not provided enough data and guidance to permit progress. Air quality consultants are still struggling to find mandatory inputs to understand and predict air quality for regulatory purposes. Efforts toward the permanent improvement of high-spatiotemporal-resolution emissions inventories and of meteorological and air quality data are needed.

An effective air quality management system must provide data to determine what emission reductions are needed to achieve the air quality standards (US EPA, 2023b). It requires air quality monitoring, a robust and detailed emissions inventory, reliable meteorological datasets, and methodologies to adapt the state-of-the-art air quality models to Brazil's reality. Moreover, it is crucial to undertake ongoing evaluation and to fully understand the air quality problem to design and implement the programs for pollution control. Currently, available initiatives including reanalysis and satellite products are still not providing datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Global reanalyses such as the Copernicus Atmospheric Monitoring Service (CAMS) (Inness et al., 2019) and the second version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) (GMAO, 2015a, b) can provide estimates of air pollutants by combining chemical transport models (CTMs) with satellite and ground-based observations and physical information, assimilating data to constrain the results. However, the currently available reanalysis products do not provide data with high spatial resolution (up to 0.75°  ×  0.75° and 0.5°  ×  0.625°) and could be biased toward representing local and regional air quality (Arfan Ali et al., 2022). Moreover, they provide data for only a small list of air pollutants. Satellite-based products such as Sentinel-5P TROPOMI (Veefkind et al., 2012) and the Moderate-Resolution Imaging Spectroradiometer (MODIS) (Levy et al., 2015; Platnick et al., 2015) are still challenging due to their low temporal resolution, data gaps due to cloud coverage, and uncertainties (Shin et al., 2020). Besides, satellites rely on total tropospheric column measurements, which do not represent surface concentrations (Shin et al., 2019).

In this article, we present the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database to elaborate upon air quality management systems in Brazil. BRAIN combines local inventories, consolidated datasets, and the usage of internationally recommended models to provide hourly emissions and meteorological and air quality data covering the entire country.

Figure 1 Spatial distribution of CO emissions from (a) vehicles, (b) industries, (c) biomass burning, and (d) biogenic sources, as provided by BRAIN.

BRAIN contains three types of hourly datasets: emissions, meteorology, and air quality. The emissions inventories include vehicular, industrial, biogenic, and biomass burning emissions. We provide meteorological data derived from the Weather Research and Forecasting (WRF) model. Coupling emissions, WRF, and the Community Multiscale Air Quality Modeling System (CMAQ) version 5.3.2, we provide air quality gridded data. All datasets are available on two spatial resolutions: the largest (Fig. S1 in the Supplement – d01) covers the entire country, while the smallest covers southern Brazil (Fig. S1 – d02). The BRAIN datasets in d01 are freely available at https://doi.org/10.57760/sciencedb.09858 (Hoinaski et al., 2023a), https://doi.org/10.57760/sciencedb.09857 (Hoinaski and Will, 2023a), and https://doi.org/10.57760/sciencedb.09859 (Hoinaski and Will, 2023b). The BRAIN datasets in d02 are available at https://doi.org/10.57760/sciencedb.09886 (Hoinaski et al., 2023b), https://doi.org/10.57760/sciencedb.09885 (Hoinaski and Will, 2023c), and https://doi.org/10.57760/sciencedb.09884 (Hoinaski and Will, 2023d). The Federal University of Santa Catarina (UFSC) institutional repository, https://brain.ens.ufsc.br/ (last access: 8 May 2024​​​​​​​), and the web platform, https://hoinaski.prof.ufsc.br/BRAIN/ (last access: 8 May 2024​​​​​​​), have served the BRAIN database since 2019. We envision making available more detailed datasets for other Brazilian regions, especially in the southeast, where the anthropogenic emission effects are more prominent. Future versions will also provide more detailed modeling outputs to properly cover medium- and small-sized cities.

BRAIN is intended to fill the gaps in those cases where adequately representative monitoring data to characterize the air quality are not available. BRAIN would be useful in providing background concentrations for a good procedure for licensing new sources of air pollution.

2.1  Emissions inventory

The BRAIN emissions inventory allows the spatiotemporal analysis of vehicular, industrial, biomass burning, and biogenic emissions in Brazil. The present version of this database does not account for other South American countries' emissions, apart from biomass burning and biogenic sources. We envision implementing other sources and a more detailed emissions inventory from other South American countries in a future version. Figure 1 presents a sample of the inventory, showing the annual carbon monoxide (CO) emissions by source. Section S2 in the Supplement (Table S1) summarizes the species in each emission source inventory. More information on each emissions dataset can be found in Sect. 2.1.1 to 2.1.5.

We observed higher vehicular emission rates of CO in urban areas with large population and vehicle fleet densities, mainly in the south and southeast (Fig. 1a). High industrial emission rates have been detected in the Brazilian regions, with large stationary sources such as refining units, thermoelectric power plants, and cement and paper industries (Fig. 1b) (Kawashima et al., 2020). In general, the north shows a higher concentration of biogenic and fire emissions. While the hotspots of biogenic emissions are predominately in the extreme north, the hotspots of fire emissions turn up in the midwestern, northern, and southern regions, as well as on the Brazilian western border (Fig. 1c–d).

2.1.1  Vehicular emissions

BRAIN uses the multispecies and high-spatiotemporal-resolution database of vehicular emissions from the Brazilian Vehicular Emission Inventory Software (BRAVES) (Hoinaski et al., 2022; Vasques and Hoinaski, 2021). The BRAVES database disaggregates municipality-aggregated emissions using the road density approach and temporal disaggregation based on vehicular flow profiles. SPECIATE 5.1 (Eyth et al., 2020) from the United States Environmental Protection Agency (USEPA, https://www.epa.gov/air-emissions-modeling/speciate , last access: 8 May 2024) speciates conventional pollutants in 41 species. BRAVES considers local studies (Nogueira et al., 2015) and data from Companhia Ambiental do Estado de São Paulo (CETESB) ( https://cetesb.sp.gov.br/veicular/relatorios-e-publicacoes/ , last access: 8 May 2024) to speciate acetaldehydes, formaldehyde, ethanol, and aldehydes in order to account for biofuel particularities in Brazil.

In BRAVES, vehicular activity is defined by fuel consumption in each municipality using data provided by the Brazilian National Agency for Oil, Natural Gas and Biofuel (ANP) ( https://www.gov.br/anp/pt-br/centrais-de-conteudo/dados-abertos/vendas-de-derivados-de-petroleo-e-biocombustiveis , last access: 8 May 2024). A fraction of fuel consumed by road transportation is based on data from the National Energy Balance (BEN) ( https://www.epe.gov.br/pt/publicacoes-dados-abertos/publicacoes/balanco-energetico-nacional-ben , last access: 8 May 2024), and MMA (2014). BRAVES calculates the weighted emission factor (EF) to address the effect of the fleet composition in terms of category, model year, and fuel utilization.

Vasques and Hoinaski (2021) compared BRAVES with different vehicular emission inventories from a local to national scale. On a national scale, vehicular emission rates from BRAVES underestimate the Emission Database for Global Atmospheric Research (EDGAR) and are slightly higher for CO (14 %) and non-methane volatile organic compounds (NMVOCs) (9 %) compared with the national inventory from Ministério do Meio Ambiente (MMA). The differences between estimates from BRAVES and from well-developed state inventories vary from −1  % to 35 % in São Paulo and from −2  % to 52 % in Minas Gerais. In addition, a relatively small bias between BRAVES and the Vehicular Emission Inventory (VEIN) was observed in São Paulo and Vale do Paraiba (Vasques and Hoinaski, 2021).

2.1.2  Industrial emissions

We derived the industrial emissions inventory by combining data from the state environmental agencies of Espírito Santo, Minas Gerais, and Santa Catarina. The emission rates of point sources from Espírito Santo and Minas Gerais are publicly provided by Instituto de Meio Ambiente e Recursos Hídricos do Espírito Santo (IEMA-ES) ( https://iema.es.gov.br/qualidadedoar/inventariodefontes , last access: 8 May 2024) and Fundação Estadual de Meio Ambiente (FEAM) ( http://www.feam.br/qualidade-do-ar/emissao-de-fontes-fixas , last access: 8 May 2024). Data from IEMA-ES contain emissions from the metropolitan region of Vitória from 2015, compiling measurements from regulatory procedures and emissions estimates. We did not convert the emissions inventory to the current modeling year since the data are not continuously updated. Therefore, we assumed that all emissions from these sources occurred in 2019.

In Santa Catarina, industrial emission data have been provided by Instituto de Meio Ambiente (IMA) ( https://www.ima.sc.gov.br/index.php , last access: 8 May 2024). These data are collected in the licensing process of potentially polluting industries. The base year of emission rates varies according to the availability. Summary information about the industrial sector types, the number of industries, and the respective emission rates in Santa Catarina can be found in Hoinaski et al. (2020) and at https://github.com/leohoinaski/IND_Inventory/blob/main/Inputs/BR_Ind.xlsx (last access: 8 May 2024​​​​​​​). Emissions from large stationary sources (refining units, thermoelectric power plants, cement, and paper industries) provided by Kawashima et al. (2020) have been included when not encountered in the environmental agencies' inventories.

We chemically speciated the industrial emission rates by adopting the following steps: (i) grouping each point source using the same categories as in the Emission Database for Global Atmospheric Research (EDGAR) (Crippa et al., 2018) and the Intergovernmental Panel on Climate Change (IPCC) industrial segments, (ii) selecting compatible profiles in SPECIATE 5.1 for each group (Eyth et al., 2020), (iii) averaging the speciation factor by group and pollutant, and (iv) applying the speciation factor for the targeted pollutant (PM, NO x , VOCs). The SPECIATE 5.1 profiles used in this work are listed at https://github.com/leohoinaski/IND_Inventory/tree/main/IndustrialSpeciation (last access: 8 May 2024​​​​​​​). The speciation factors by industrial group and pollutant are available at https://github.com/leohoinaski/IND_Inventory/blob/main/IndustrialSpeciation/IND_speciation.csv (last access: 8 May 2024​​​​​​​).

We also vertically allocate the industrial emissions according to the plume's effective height, estimated by the sum of the geometric height and the superelevation of the plume. The plume superelevation was estimated by the Briggs method (Briggs, 1975, 1969). The initial vertical distribution of the plume has been estimated by disaggregating the emissions using a Gaussian approach, as proposed in the Sparse Matrix Operator Kernel Emissions (SMOKE) model (Bieser et al., 2011; Gordon et al., 2018; Guevara et al., 2014). Python code to estimate the plume's effective height and the initial vertical disaggregation of industrial emissions is available at https://doi.org/10.5281/zenodo.11167115 (Hoinaski, 2024a​​​​​​​).

Figure 2 Annual average of meteorological variables in 2019, simulated by the WRF with 20  ×  20 km resolution. (a) Atmospheric pressure, (b) planetary boundary layer height, (c) specific humidity, (d) annual accumulated precipitation, (e) temperature, (f) wind intensity and direction. All variables are annual averages except for precipitation, which represents the annual accumulated total.

2.1.3  Biomass burning emissions

The Fire INventory from NCAR (FINN) version 1.5 (Wiedinmyer et al., 2011) provides data on biomass burning emissions in BRAIN. FINN outputs contain daily emissions of trace gas and particle emissions from wildfires, agricultural fires, and prescribed burnings and do not include biofuel use and trash burning. Datasets have a 1 km spatial resolution and are available at https://www.acom.ucar.edu/Data/fire/ (last access: 8 May 2024​​​​​​​).

Since CMAQ requires hourly emissions, a Python code ( https://github.com/barronh/finn2cmaq , last access: 8 May 2024) temporally disaggregates daily emissions into hourly emissions. The same code vertically splits the fire emissions to consider the plume rise effect and represents the vertical distribution (Henderson, 2022), converting text files into hourly 3D netCDF files.

Pereira et al. (2016) suggest that fire emissions estimated by FINN are strongly related to deforestation in many Brazilian regions. FINN estimates have a high correlation with both the Brazilian Biomass Burning Emission Model (3BEM) (0.86) and the Global Fire Assimilation System (GFAS) (0.84). The emissions estimated from FINN are commonly overestimated in comparison to other biomass burning emission inventories. An overestimation also occurs when FINN is used in air quality models and compared with observations. However, the use of FINN as input in air quality models can capture the temporal variability of pollutants emitted by biomass burning (Vongruang et al., 2017).

We have implemented the FINN v1.5 in this first version of BRAIN. However, FINN version 2.5 (Wiedinmyer et al., 2023) will be included in our emissions inventory in future work; this version uses an updated algorithm for determining fire size based on MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite instruments. We also provide data from 2020 with the same modeling grid upgraded to FINN v2.5.

Figure 3 Spatial distribution of air pollutant concentration (a, c, e) and number of violations of air quality standards (b, d, f) for NO 2 (a–b) , O 3 (c–d) , and PM 10 (e–f) .

2.1.4  Biogenic emissions

We derived the biogenic emissions using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 3.2 (Guenther et al., 2012; Silva et al., 2020). MEGAN is based on the leaf area index and plant functional groups. The model estimates emissions of gases and aerosols for different meteorological conditions and land cover types (Guenther et al., 2012). The leaf-level temperature and photosynthetically active radiation, as well as the vegetative stress conditions implemented in MEGAN, provide more physically realistic parameterizations for biosphere–atmosphere interactions (Silva et al., 2020). Input datasets, emission factor processors, and emission estimation modules are available at https://bai.ess.uci.edu/megan/data-and-code (last access: 8 May 2024​​​​​​​). Data from WRF and the Meteorology-Chemistry Interface Processor (MCIP) have been used in MEGAN simulations.

MEGAN is commonly adopted to estimate emissions from biogenic fluxes, which constitute an important input for air quality modeling in many regions worldwide (Hogrefe et al., 2011; Kitagawa et al., 2022; Kota et al., 2015). Although MEGAN overestimates nighttime biogenic fluxes, the modeled emissions are correlated with measurements in the Amazon during both wet and dry seasons. The model is capable of capturing relatively well the seasonal variability of important organic pollutants in tropical forests (Sindelarova et al., 2014).

2.1.5  Sea spray aerosol emissions

Sea spray aerosol (SSA) is an important source of particles in the atmosphere. Due to its properties, SSA influences gas–particle partitioning in coastal environments (Gantt et al., 2015). SSA has been implemented in CMAQ as an inline source and requires the input of an ocean mask file (OCEAN) to identify the fractional coverage in each model grid cell allocated to the open ocean (OPEN) or surf zone (SURF). CMAQ uses this coverage information to calculate sea spray emission fluxes from the model's grid cells (US EPA, 2022). Detailed information on the mechanism of sea spray aerosol emissions and its implementation in CMAQ can be found in Gantt et al. (2015).

We provide a Python code ( https://github.com/leohoinaski/CMAQrunner/blob/master/hoinaskiSURFZONEv2.py , last access: 8 May 2024) to reproduce the OCEAN time-independent Input/Output Applications Programming Interface (I/O API) ( https://www.cmascenter.org/ioapi/ , last access: 8 May 2024) file so that it is ready to use in CMAQ. This code uses a shoreline Environmental Systems Research Institute (ESRI) shapefile from the National Oceanic and Atmospheric Administration (NOAA), available at https://www.ngdc.noaa.gov/mgg/shorelines/ (last access: 8 May 2024​​​​​​​).

Figure 4 Spearman rank, bias, root mean squared error (RMSE), and mean absolute error (MAE) of O 3 dataset of BRAIN vs. observed values. Box plots of statistical metric by Brazilian state (considering only states with monitoring stations with representative data in 2019).

2.2  Meteorology

The WRF model has been used in this work to produce inputs for CMAQ and for meteorology characterization in Brazil. We provide hourly simulations in netCDF files. WRF has been set up to reproduce 36 h simulations, where the initial 12 h have been dedicated to model stabilization; these are excluded from the analysis. Thirty-three vertical levels have been employed, spaced at 50 hPa intervals. The parameterizations used in this work are described in Sect. S3. The remaining vertical levels followed a hybrid modeling scheme, accounting for terrain in the lower layers and gradually minimizing its influence at the higher levels. Details of WRF outputs can be found in Sect. S4 (Table S2).

The Global Forecast System (GFS) from the National Center for Atmospheric Research (NCAR) provided inputs with a spatial resolution of 0.25°  ×  0.25° and a temporal resolution of 6 h for the WRF simulations (Skamarock et al., 2008). Land use data and classification parameters are from the United States Geological Survey's (USGS) Moderate Resolution Imaging Spectroradiometer (MODIS).

Figure 5 Time series of O 3 and PM 10 modeled and measured at Limeira  (a, c, e) and CIPP  (b, d, f) monitoring stations.

Figure 6 Annual average concentration of CO from BRAIN with its original resolution (a) , from BRAIN regridded to MERRA-2 resolution (b) , and from MERRA-2 (c) and the difference between MERRA-2 and BRAIN (d) .

Table 1 BRAIN datasets freely available.

Download Print Version | Download XLSX

The Brazilian regions (north, northeast, midwest, southeast, and south) encompass three distinct climatic zones, namely the equatorial, tropical, and subtropical zones. The climatic diversity in Brazil is also shaped by topographical variations, landscape or vegetation, and the coastal areas. The temperature in Brazil follows a latitudinal pattern, increasing from south to north (Fig. 2e). The highest average temperatures are observed in the Amazon region, matching the historic data (Cavalcanti, 2016). The southern region exhibits the lowest average temperatures, which is also consistent with historical data (Cavalcanti, 2016).

The highest values of atmospheric pressure occurred in the northern region and in the extreme south of the country, and the lowest values were between the southeastern and southern regions (Fig. 2a). The planetary boundary layer height (PBLH) reaches the highest levels in the northeastern region and the lowest levels at the southern and southeastern coasts (Fig. 2b). The highest values of wind speed occurred in part of the northern and southern region. The Amazon region presented the lowest values of surface wind speed (Fig. 2f).

Humidity and precipitation exhibit similar patterns in the northern and northeastern regions (Fig. 2c, d) due to the trade winds that transport moisture from the tropical Atlantic (Mendonça and Danni-Oliveira, 2017). Except for the coast, the northeastern region is characterized by low humidity and drought during half of the year. The southern and southeastern regions have well-distributed rainfall throughout the year, as well as intermediate levels of humidity, except for the northern coast of the southern region, which has an elevated level of precipitation and humidity throughout the year.

The WRF model demonstrated the ability to reproduce diurnal and seasonal variability in winds in the Brazilian northeastern region (Souza et al., 2022a), although it underestimated the height of the planetary boundary layer (PBLH) by up to 20 %, as well as the temperature and humidity at 4 °C and 15 %, respectively. Pedruzzi et al. (2022) tested several model configurations, including an alternative land use scheme, and found a WRF tendency to overestimate temperature and humidity in the Brazilian southeastern region. Macedo et al. (2016) also evaluated the model's ability to predict extreme precipitation events. Although the WRF reasonably predicts the main meteorological aspects of the Brazilian southern region, the precipitation extremes were underestimated. A wind mapping study (Souza et al., 2022b) using WRF indicated that the average errors presented by the model in Brazil are minor, with an average bias of 2 m s −1 at 200 m in terms of wind intensity and errors at temperatures of 2 °C and humidity of approximately 10 %. Winds at lower levels tended to be overestimated, whereas PBLH was generally underestimated during the day.

Figure 7 Concentration of CO from BRAIN vs. MERRA-2 in Brazil (a) , northern Brazil (b) , northeastern Brazil (c) , midwestern Brazil (d) , southeastern Brazil (e) , and southern Brazil (f) .

2.3  Air quality

We coupled emissions inventories, WRF, and CMAQ to produce the BRAIN air quality dataset for Brazil. CMAQ version 5.3.2 was set up using the third version of the Carbon Bond 6 chemical mechanism (cb6r3_ae7_aq) (Yarwood et al., 2010; Emery et al., 2015) with AERO7 treatment of secondary organic aerosol for standard cloud chemistry (Appel et al., 2021). The other model configurations used in this work can be found in Sect. S5 and at https://github.com/leohoinaski/CMAQrunner (last access: 8 May 2024​​​​​​​). The pollutant list in CMAQ outputs, containing 216 species, can be found in Sect. S6 (Table S3).

The CMAQ standard profile of boundary conditions is used in the larger domain (d01), which provides the boundary conditions for the smaller one (d02). Further improvements to the database could include the boundary conditions derived from the GEOS-Chem model (Bey et al., 2001) ( https://geoschem.github.io/ , last access: 8 May 2024) or other better alternatives for the largest domain. The simulations have 24 h length and a time step interval of 1 h. The last hour of the previous simulation has been set up as the initial condition of the next one. We used the standard profile for the first hour of the first simulation (00:00:00 GMT on 1 January 2019). The figures with the spatial distribution and violations of criteria pollutants can be found in Sect. S7. Section S8 also presents the time series of criteria pollutants in Brazilian cities.

Using the BRAIN air quality dataset, we can observe the highest concentrations of NO 2 (Fig. 3a–b), O 3 (Fig. 3c–d), and PM 10 (Fig. 3e–f) in southeastern and southern Brazil. The concentration of O 3 violates the World Health Organization (WHO) air quality standards in multiple locations all over the country, while for NO 2 and PM 10 , this occurred mostly in southeastern and southern Brazil.

Figure 8 Annual average concentration of CO and NO 2 from BRAIN at its original resolution (a, c) and from Sentinel-5P TROPOMI spatially aligned to the BRAIN resolution (b, d) .

2.3.1  Models' performance

We sampled pixels around the monitoring station using a buffer of 0.5° to calculate the Spearman rank, bias, root mean squared error (RMSE), and mean absolute error (MAE) of the sampled pixels. We selected the highest Spearman rank of each pixel to demonstrate the model's performance in Figs. 4 and 5. Section S10 presents the box plots with overall statistical metrics for all stations. Section S11 presents statistical metrics by means of a monitoring station and pollutant, considering the pixel with the highest Spearman rank around each monitoring station. Section S12 presents the scatter plots comparing the BRAIN air quality dataset and the observations of each monitoring station. We used the simulations with domain d01 in the statistical analysis.

We observed the highest Spearman rank (0.72) in the state of São Paulo for O 3 concentration. Bias analysis revealed an underestimation in the São Paulo metropolitan area, while an overestimation occurred in Minas Gerais, Santa Catarina, Rio Grande do Sul, and the interior of São Paulo. In the northeast and in the state of Espírito Santo, bias is closer to zero. In Rio de Janeiro, the model over- and underestimated the observations. Regarding RMSE and MAE, the model performed better in coastal areas (maps in Fig. 4).

Comparing the states with air quality monitoring stations, the Spearman correlation of the O 3 dataset of BRAIN is higher in São Paulo, Minas Gerais, and Rio de Janeiro. However, these states also have a higher range of bias values, which could be negative and positive in São Paulo and Rio de Janeiro and are only positive in Minas Gerais (box plots in Fig. 4).

The heterogeneity in the stations' types and the insufficient spatial representativeness of observations in the Brazilian states must be considered while evaluating the model performance. According to the IEMA (2022), the strategic planning for the implementation of air quality monitoring stations, the financing and political efforts, and the technical characteristics (from installation to calibration and maintenance) vary significantly between Brazilian states. The lack of data quality assurance may compromise the credibility of the available air quality observations in Brazil.

BRAIN reproduced well the concentrations in moderately urbanized areas, such as Limeira and Piracicaba (Sect. S12). The database reached moderate performance in highly urbanized areas such as Copacabana and Rio de Janeiro (RJ) and at Marginal Tietê in the megacity of São Paulo (Sect. S12). Regarding the temporal profiles of O 3 and PM 10 , the seasonal and daily profiles are captured for both modeled pollutants, showing a suitable fit with the observations at the Limeira and Pecém Industrial and Port Complex (CIPP) air quality monitoring stations (Fig. 5). This reveals that the database can capture temporal patterns of air pollutant concentrations in urbanized and industrialized areas.

Figures with statistical metrics for other pollutants can be found in Sect. S13. Figures of modeled and observed time series for all monitoring stations can be found in Sect. S14.

Figure 9 Concentration of CO from BRAIN vs. Sentinel-5P TROPOMI in Brazil (a) , northern Brazil (b) , northeastern Brazil (c) , midwestern Brazil (d) , southeastern Brazil (e) , and southern Brazil (f) .

Overall, the average concentrations are well simulated by CMAQ in BRAIN, with fair to good correlations (up to ∼  0.7) between modeling and local measurements in São Paulo. Similar results have been reported by Albuquerque et al. (2018). Kitagawa et al. (2021) simulated PM 2.5 in a Brazilian coastal–urban area and showed that the CMAQ results commonly overestimated the observations, which agrees with the BRAIN air quality dataset. In another comparison between observations and CMAQ simulations (Kitagawa et al., 2022), the model overestimated the PM and NO 2 concentrations in the metropolitan region of Vitória (MRV) and underestimated O 3 . The authors suggest that the CMAQ simulations are suitable over the MRV, even though the model could not capture some local variabilities in air pollutant concentrations. It is already reported that the short-time abrupt variations are difficult to reproduce by means of air quality models (Albuquerque et al., 2018). The complex task of predicting air quality is associated with multiple error factors, including the lack of an emissions inventory, meteorology parameterizations, initial and boundary conditions, chemical mechanisms, numerical routines, etc. (Cheng et al., 2019; Albuquerque et al., 2018; Park et al., 2006; Pedruzzi et al., 2019).

Figure 10 Annual average and hourly time series of CO from BRAIN (a) , MERRA-2 (b) , and Sentinel-5P TROPOMI (daily averages) (c)  in Porto Velho, Brazil.

We analyzed the performances of 4×4  km simulations for CO, NO 2 , O 3 , and SO 2 , drawing a buffer of 0.5° degrees around monitoring station positions in southern Brazil. Our findings indicated higher Spearman values for the spatial resolution of 20×20  km for CO, O 3 , and SO 2 . Specifically, for O 3 , the best result at 20×20  km was 0.76, whereas the same point at 4×4  km resolution showed a correlation of 0.46. This pattern was also observed for CO, with the best result at 20×20  km being a Spearman value of 0.47 and 0.23 at the same point at 4×4  km resolution. The smallest differences in Spearman rank were observed for SO 2 (0.22: 20×20 , 0.19: 4×4 ). Even though improving the spatial resolution did not increase the correlation with measured data, we found the best results for bias, RMSE, and MAE for almost all pollutants at a 4×4  km resolution, except for CO. Please refer to Sect. S15 for the complete statistical analysis of 4×4  km simulations.

BRAIN captures seasonal patterns and the absolute magnitude of PM 2.5 in the northwest of the Amazonas state (near the Amazon Tall Tower Observatory – ATTO), as presented by Artaxo et al. (2013). This shows that our database can reproduce the concentrations in background areas (far from highly urbanized centers). Comparing BRAIN with observations at heavily biomass-burning-impacted sites in southwestern Amazonia (Porto Velho) (Artaxo et al., 2013) revealed that BRAIN can capture seasonal variations caused by wet and dry seasons, as well as the magnitude of average and peak concentrations. However, BRAIN PM 2.5 estimates are closer to the coarse mode of the time series rather than the fine mode shown in Artaxo et al. (2013). Even though BRAIN has captured the O 3 pattern observed by Artaxo et al. (2013), the estimates are around 2.7 times higher than the observations in the dry season and a factor of 2 higher for the wet season. It is worth mentioning that BRAIN uses 2019 data, while the study by Artaxo et al. (2013) consists of a sampling campaign from 2008 to 2012.

Figure 11 Scatter plot and daily time series of CO (a) , O 3 (b) , and NO 2 (c) from BRAIN and Sentinel-5P TROPOMI at T0a (GoAmazon reference). Values extracted using a buffer of 0.2° around the site.

Figure 12 Scatterplot and daily time series of CO, O 3 , and NO 2 from BRAIN and Sentinel-5P TROPOMI at T0t/TT34 (GoAmazon reference). Values extracted using a buffer of 0.2° around the site.

BRAIN has a similar spatial pattern compared with MERRA-2 (GMAO, 2015a b), capturing hotspots in higher populated areas located in the southeast, south, and midwest. In the Amazon region, BRAIN can also capture hotspots similarly to MERRA-2 (Fig. 6). BRAIN estimates for carbon monoxide are lower than those of MERRA-2, except in the southern region and in some urban centers in the southeast and midwest (Fig. 6). Carbon monoxide concentrations estimated by BRAIN are moderately correlated with MERRA-2, mainly in the south (0.57) and southeast (0.55), while in the midwest, north, and northeast, the correlation is weaker (Fig. 7). Compared with the consolidated MERRA-2 database, BRAIN has the advantage since it uses local and more refined information and provides data at a higher spatial resolution for multiple species. We provide a detailed comparison between the MERRA-2 and BRAIN datasets for PM 2.5 , SO 2 , O 3 , and CO in Sect. S16.

We also compare our database with Sentinel-5P TROPOMI (Veefkind et al., 2012) data to demonstrate BRAIN's ability to capture the spatiotemporal variability of air pollutants in unmonitored areas (Fig. 8). We spatially realign Sentinel-5P TROPOMI products to the BRAIN resolution ( 20×20  km) using data from the NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC) ( https://disc.gsfc.nasa.gov/ , last access: 8 May 2024). We merged all layers of the same day and interpolated them to match the BRAIN resolution. We computed the daily averages for both datasets. In this evaluation, we must consider the differences between the datasets since Sentinel-5P TROPOMI relies on tropospheric column measurements and BRAIN surface concentrations. BRAIN captured the hotspots of CO and NO 2 similarly to Sentinel-5P TROPOMI products, especially in southeastern Brazil. However, the hotspots of CO are dislocated towards the ocean in Sentinel-5P TROPOMI. NO 2 estimates from BRAIN present a higher number of hotspots. We emphasize that surface concentration data are more suitable than tropospheric column data in representing air quality. In this analysis, we removed negative values from Sentinel-5P TROPOMI products since they represent low-quality measurements (Eskes et al., 2022).

When we compared CO daily datasets from BRAIN and Sentinel-5P TROPOMI by Brazilian regions, we observed a moderate correlation in the north (0.41), midwest (0.32), and south (0.3). This analysis shows that BRAIN can reasonably detect temporal and spatial patterns of air pollutants. The complete comparison of CO and NO 2 from Sentinel-5P TROPOMI and BRAIN can be found in Sect. S17.

Figure 13 Scatter plot and daily time series of CO (a) , O 3 (b) , and NO 2 (c) from BRAIN and Sentinel-5P TROPOMI at T1 (GoAmazon reference). Values extracted using a buffer of 0.2° around the site.

We highlight that BRAIN, MERRA-2, and Sentinel-5P TROPOMI can capture similar temporal patterns of air pollutant concentrations in heavily biomass-burning-impacted sites such as Porto Velho in Rondônia (Fig. 10) and in urban areas such as São Paulo. We provide time series (minimum–maximum and average) of BRAIN, MERRA-2, and Sentinel-5P TROPOMI data spatially averaged within Brazilian capitals in Sect. S18. Section S19 contains time series (only average) of BRAIN data in Brazilian capitals.

To analyze BRAIN's performance in background regions (with low anthropogenic influence), we extracted data from two forested sites in the Brazilian northern region. As reference, we used the sampling sites of the GoAmazon experiment (Martin et al., 2016), named T0a (forested site situated 154.1 km from the Manaus urban area) and T0t/TT34 (a broken-canopy forested site situated 60.9 km from the city of Manaus). Sentinel-5P TROPOMI data spatially aligned to the BRAIN resolution were also extracted for comparison. A buffer of 0.2° around the sites selected the data of CO, O 3 , and NO 2 from both datasets. The results revealed that BRAIN captured the seasonal profile at T0a (Fig. 11), showing a moderate correlation with the tropospheric column measurements of Sentinel-5P TROPOMI, especially for CO and O 3 .

BRAIN estimates are slightly higher than the observed concentrations in background areas of CO, O 3 , and NO 2 in TT34 (Fig. 12) and T0a (Fig. 11). While O 3 concentrations simulated by BRAIN range around 18 ppb (average in 2019) at the TT34 site, observed concentrations in 2013 (Artaxo et al., 2013) were around 8.5 ppb  ±  1.9 ppb. In T0a, BRAIN simulated concentrations around 16 ppb, overestimating the observations (7 ppb  ±  2 ppb during the wet season from March to April 2013–2020) (Nascimento et al., 2022). Concerning CO, the concentrations simulated by BRAIN are slightly lower, ranging around 73 ppb (average) at TT34 compared to the 130 ppb observed during the GoAmazon experiment from 2010 to 2011 (Artaxo et al., 2013). We emphasize that the BRAIN and GoAmazon datasets are reported in different periods and, consequently, are influenced by different emission rates. For instance, fire emissions have changed significantly since 2011 in the Amazon (Copernicus, 2022; Naus et al., 2022).

We also analyzed BRAIN results in the Manaus urban area. We adopted the sampling site of the GoAmazon experiment (Martin et al., 2016) named T1 (INPA campus in Manaus). Compared with Sentinel-5P TROPOMI data, BRAIN reproduced fairly the temporal pattern of CO, O 3 , and NO 2 in the T1 site (Fig. 13). Abou Rafee et al. (2017) reported mean concentrations of 88.7 ppb for NO x and 382.6 pbb for CO in the Manaus urban area, while BRAIN reached 79 and 99 ppb (maximum of 383 ppb), revealing an underestimation in this area. Again, the sampling campaign presented by Abou Rafee et al. (2017) and the BRAIN simulations use different base years. Comparing BRAIN at T0a/TT34 (background sites) and T1 (urbanized), the database has reached consistent results, with lower concentration levels in preserved areas.

The inability to better predict the observations is mostly due to the quality of the emissions inventory. The lack of information on industrial emissions and their temporal variability is an important source of errors. Moreover, the vehicular emissions inventory also needs improvements to properly disaggregate the emissions in high-flow roads. Future versions of BRAIN could address these issues and incorporate other emission sources.

Emission data are available at https://doi.org/10.57760/sciencedb.09858 (Hoinaski et al., 2023a) and https://doi.org/10.57760/sciencedb.09886 (Hoinaski et al., 2023b). Meteorology data are available at https://doi.org/10.57760/sciencedb.09857 (Hoinaski and Will, 2023a) and https://doi.org/10.57760/sciencedb.09885 (Hoinaski and Will, 2023c). Air quality data are available at https://doi.org/10.57760/sciencedb.09859 (Hoinaski and Will, 2023b) and https://doi.org/10.57760/sciencedb.09884 (Hoinaski and Will, 2023d).

Code to generate the database, statistics, and figures is available at https://github.com/leohoinaski/CMAQrunner (last access: 8 May 2024) or https://doi.org/10.5281/zenodo.11166975 (Hoinaski, 2024b) and https://github.com/leohoinaski/IND_Inventory (last access: 8 May 2024) or https://doi.org/10.5281/zenodo.11167115 (Hoinaski, 2024a).

In this paper, we present BRAIN, the first comprehensive database for air quality management in Brazil. BRAIN provides emissions, meteorology, and air quality datasets for the entire country at a reliable spatiotemporal resolution. The BRAIN database covers a wide range of pollutant species (emissions and ambient concentrations) and atmospheric variables. So far, Brazil has lacked a comprehensive and easily accessible database for developing air quality management systems in urbanized and rural areas. This work contributes to overcoming this gap. BRAIN is a step forward toward a good procedure for licensing new sources of air pollution in Brazil.

Using a sample of BRAIN, we observed several violations of WHO air quality recommendations. The violations are not restricted to densely populated areas but also occur in rural ones. This reinforces the need for better air quality policies and a deep restructuring of the environmental agencies' procedures and data management in Brazil.

Compared with observations, the BRAIN air quality dataset has achieved good overall performance in predicting the criteria pollutants. However, there is plenty of room for improvement, mainly in relation to the quality of the emissions inventory. The lack of information on industrial emissions and their temporal variability is an important source of error. Moreover, the vehicular emissions inventory also needs improvements to properly disaggregate the emissions in high-flow roads. Improvements in boundary conditions and the inclusion of emission sources from other Latin American countries could also enhance the CMAQ performance. The influence of long-range transport will be addressed in a future version of the database by implementing boundary contributions from GEOSCHEM and other tools. Future versions of BRAIN could address these issues, incorporate other emission sources, and provide CMAQ outputs using different chemical mechanisms. We envision providing enough data to reproduce the historical pattern and future scenarios of air pollution in Brazil through a web platform to facilitate the access and usage of our database. We believe in an ongoing process that will improve the database.

The supplement related to this article is available online at:  https://doi.org/10.5194/essd-16-2385-2024-supplement .

LH designed the methodology and developed the software. LH, RW, and CBR processed the data curation, conducted the formal analysis, and created the figures. LH, RW, and CBR prepared the original draft and revised the paper. LH is the project administrator and laboratory supervisor.

The contact author has declared that none of the authors has any competing interests.

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

The authors would like to thank the Secretaria de Estado do Desenvolvimento Econômico Sustentável do governo de Santa Catarina. The authors are grateful for the doctoral scholarships provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES).

This research has been supported by the Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (grant no. 2018/TR/499; “Avaliação do impacto das emissões veiculares, queimadas, industriais e naturais na qualidade do ar em Santa Catarina”).

This paper was edited by Jing Wei and reviewed by two anonymous referees.

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• Introduction
• BRAIN database
• Data availability
• Code availability
• Author contributions
• Competing interests
• Acknowledgements
• Financial support
• Review statement

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• v.105(Suppl 2); Apr 2015

Defining and Assessing Quality Improvement Outcomes: A Framework for Public Health

A. W. McLees led the conceptualization and development of the framework described in the article and drafted and revised large sections of the article. S. Nawaz assisted with the development of the framework, analyzed data, and drafted sections of the article. C. Thomas provided conceptual input into the framework’s development and provided substantive review of and input into the drafting and revision of the article. A. Young contributed to revisions to the framework and provided substantive review of and input into the drafting and revision of the article. All authors approved the final version of the article.

We describe an evidence-based framework to define and assess the impact of quality improvement (QI) in public health. Developed to address programmatic and research-identified needs for articulating the value of public health QI in aggregate, this framework proposes a standardized set of measures to monitor and improve the efficiency and effectiveness of public health programs and operations.

We reviewed the scientific literature and analyzed QI initiatives implemented through the Centers for Disease Control and Prevention’s National Public Health Improvement Initiative to inform the selection of 5 efficiency and 8 effectiveness measures.

This framework provides a model for identifying the types of improvement outcomes targeted by public health QI efforts and a means to understand QI’s impact on the practice of public health.

At a time when taxpayer resources are scarce, government agencies are expected to deliver on broader missions while reducing operating costs. 1–6 As stewards of public funds, agencies must implement programs and deliver services as effectively and efficiently as possible on the basis of the best evidence available. Federal programs are required to engage in rigorous measurement and evaluation and use the findings to facilitate continuous improvement and understand the value of services and programs for improved accountability and decision making. 7 This approach relies on the adoption of valid measures that track progress toward goals, identify areas for improvement, and assess achievement of outcomes. 7,8

In the public health field, quality improvement (QI) is an increasingly recognized approach to maximizing the effectiveness of services while minimizing costs. As defined by Riley et al., public health QI “refers to a continuous and ongoing effort to achieve measurable improvements in the efficiency, effectiveness, performance, accountability, outcomes, and other indicators of quality in services or processes which achieve equity and improve the health of the community.” 9 (p6) To date, several initiatives have promoted the use of QI among public health agencies with the goals of reaching these outcomes and building the evidence base. Tools, such as the National Public Health Performance Standards, and initiatives, such as the Turning Point Performance Management Collaborative and the Robert Wood Johnson Foundation–supported Multi-State Learning Collaborative, represent some of the earliest efforts that encouraged health departments to adopt performance management and QI methods as a strategy to strengthen public health systems. 10–12

More recently, new initiatives aimed at integrating QI into the practice of public health have included the Robert Wood Johnson Foundation–funded Communities of Practice for Public Health Improvement, which serves as a forum for public health agencies to exchange best practices related to QI, 13 and the Centers for Disease Control and Prevention’s National Public Health Improvement Initiative (NPHII), through which 73 state, tribal, local, and territorial public health agencies are funded to achieve public health standards and adopt and institutionalize cross-cutting performance management and QI approaches to improve the accountability, efficiency, and effectiveness of their public health programs and services. 14,15 Most recently, the establishment of the Public Health Accreditation Board and its release of version 1.0—and subsequently version 1.5—standards and measures have driven public health agencies to integrate performance management into their daily practice. The Public Health Accreditation Board has further highlighted QI as an important aspect of the performance management system, 16 supporting the Turning Point initiative, which includes QI as a core component of its performance management framework as a demonstrated means to manage change and make improvements based on data. 11

As a result of these efforts, the body of evidence for public health QI is growing, with a focus on the extent to which public health agencies have adopted QI and the kinds of QI processes and tools implemented. 6,12,17–21 However, conceptualizing and assessing outcomes resulting from the implementation of public health QI has proven challenging, in large part because of the diversity of public health contexts 12,20 and the scarcity of evidence-based measurement methods. 5,22,23 Only recently have researchers and practitioners begun to describe or assess outcomes of public health QI in a way that has the potential to demonstrate the impact of this work on public health organizations and the public health system more broadly. 5,22,24,25 Recent studies have described the role of public health QI in addressing service and program processes as well as operational processes. 5,22 Another study highlighted that certain characteristics of QI initiatives correlate with an increased likelihood of attaining stated objectives, including clarity around select measurement parameters such as time frames, baselines, and targets. 24 Although progress has been made, these studies have acknowledged that the evidence base for what works in public health QI is still growing and standardized measures for improvement initiatives targeting operational or programmatic efficiency and effectiveness are lacking. To improve performance, public health practitioners and researchers need to clarify what we hope to achieve and continue to build the evidence base for what works. 5,22,25

In recognition of this need, the Public Health Services and Systems Research national research agenda has focused attention on the following QI research questions:

• What measures provide the most valid and reliable indicators of the implementation and impact of QI strategies in public health settings?
• What types of QI strategies have the largest effects on the effectiveness, efficiency, and outcomes of public health strategies delivered at local, state, and national levels? 26

To advance the science and practice of QI outcome measurement, we conceptualized a framework that proposes and defines a standardized way to assess public health QI outcomes related to efficiency and effectiveness. The primary purposes of this QI measurement framework are to (1) support public health agencies’ efforts to achieve demonstrable outcomes, (2) provide a means to aggregate the impact of individual QI initiatives, and (3) advance the science and practice of this emerging field.

We based our identification of specific outcomes and the development of a standardized measurement approach on an iterative process that used both theoretical and grounded approaches, including a review by Centers for Disease Control and Prevention evaluators (A. W. M., C. T., and A. Y.) and a contractor (S. N.) of both the existing literature and data collected by the Centers for Disease Control and Prevention on QI initiatives reported by NPHII awardees.

We identified peer-reviewed journal articles in PubMed by means of a title–abstract search. The search terms public health and quality improvement were applied together and in combination with each of these additional terms: outcomes, efficiency, effectiveness, and evaluation . After removing duplicates, we identified 147 articles. We conducted an additional PubMed search, applying the terms measurement and public health in the title–abstract field and quality improvement as a text word. This search resulted in 34 articles. Once all duplicates were removed, 170 articles remained. Evaluators reviewed abstracts for these 170 articles and removed those that focused on health care settings, accreditation, or laboratory services, resulting in 35 articles that directly addressed the topic of public health QI. We identified an additional 3 articles through a manual review of a table of contents (volume 16, issue 1, of the Journal of Public Health Management and Practice ), resulting in a final total of 38 articles.

Many studies documented the process of QI, including workforce development, 27,28 establishing a culture or environment conducive to QI, 12,29–31 integrating QI within a broader framework, 20,32,33 or describing QI implementation. 17–19,34–37 Other articles referred to QI outcomes without specifying them. 24,38 Many articles described various types of public health QI efficiency-related outcomes, including cost reductions 22,31,39–42 and time savings. 5,9,21,22,40,41,43 Effectiveness-related outcomes were also described, including increased reach of, or access to, programs and services 5,22,39,41,43,44 ; improved quality of data, 5,43,45 programs, or services 21,31,41,45 ; increased customer or client satisfaction 5,42,44 ; changes to organizational structure 31 ; increased preventive behaviors 5,22,41–44,46 ; and reduced disease incidence or prevalence. 22,43,45 The review also highlighted the need for a robust measurement system 23,31,33,38,47 to accompany the articulation of outcomes.

Additional inputs included reports by the Institute of Medicine that focused on performance measurement, public health, and health care quality, as well as the US Department of Health and Human Services’ national framework for public health quality. In Crossing the Quality Chasm, 48 the Institute of Medicine highlighted dimensions of improvement in the personal health care delivery system that are also relevant to public health, including a focus on quality, timeliness, and cost of administrative and clinical or service-delivery processes. Other Institute of Medicine reports 49,50 emphasized the importance of measurement and of maximizing the efficiency and effectiveness of public health services and strategies as a means to make progress toward population health outcomes. The US Department of Health and Human Services’s framework identified efficiency and effectiveness as critical public health system characteristics and core components of successful QI. 8,51 Both the Institute of Medicine reports and the Department of Health and Human Services framework provided conceptual guidance for the organization of the QI measurement framework, yet neither source provided specific guidance on operationalizing concepts in a manner that would facilitate measurement of discrete QI initiatives.

To ensure the framework’s relevance to current practice, we also conducted a grounded review of measures for QI initiatives reported by 74 NPHII awardees to the Centers for Disease Control and Prevention at the end of the 2nd program year. We conducted this review to (1) determine the extent to which awardees’ efforts aligned with outcomes found in the literature, (2) identify additional outcomes to consider for inclusion in the framework, and (3) identify potential measurement challenges. The review confirmed the relevance of efficiency-related outcomes such as cost and time savings, the importance of a focus on health outcomes, and the need for a series of outcomes associated with business processes or program or service delivery improvements, such as standardization and enhancements to services or systems, and a focus on reducing steps associated with various processes. The review also highlighted measurement challenges, including the lack of baseline values or consistent units of measurement.

An initial version of the QI measurement framework was used by 73 awardees during the 3rd year of the NPHII program (September 30, 2012–September 29, 2013) to test its relevance to and utility for their efforts. This testing resulted in a more grounded and refined measurement framework by revealing additional nuances to existing outcomes, and new outcomes, that were subsequently incorporated into the final version. For example, recognizing that several awardees engaged in QI efforts to support work by other public health system partners, we defined a new outcome to capture how broadly QI products or practices are disseminated.

Defining Public Health Quality Improvement Outcomes

The QI measurement framework ( Table 1 ) defines outcomes for 2 key constructs—efficiency and effectiveness—and provides standardized-measure language for each outcome. Specifically, 5 efficiency outcomes and 8 effectiveness outcomes were developed. We used 2 primary criteria in the selection and definition of these outcomes: (1) applicability to a wide variety of public health processes, programs, or services, and (2) relevance to public health agencies’ differing contexts and stages of familiarity with QI.

TABLE 1—

Efficiency and Effectiveness Outcomes in the Quality Improvement Measurement Framework

By definition, efficiency outcomes typically reflect reductions in the amount of resources required to implement activities resulting from a QI initiative. Efficiency outcomes included in the framework are time saved, reduced number of steps, revenue generated from billable services, costs saved, and costs avoided. Compared with the other efficiency outcomes, reduced number of steps is process focused but is the first step to realizing other efficiency gains and may be a more realistic outcome for agencies new to QI.

Three outcomes track efficiencies based on dollar amounts. Revenue generated captures increases in resources, particularly revenue, resulting from expansion of coverage or increases in productivity. For example, if a QI initiative results in timely and accurate billing for services or more productive service delivery, a public health agency might experience increases in revenue. The costs-saved outcome focuses on investments made by the public health agency in labor, resources, and overhead to achieve monetary returns. Finally, costs avoided captures future costs that are offset by current investments in efficiencies. These offsets might occur because of improved allocation of staff or current investments in automation.

Effectiveness outcomes include results associated with improved service or program delivery or improved implementation of organizational processes to achieve agency or program goals. The 8 effectiveness outcomes are increased customer or staff satisfaction; increased reach to a target population; dissemination of information, products, or evidence-based practices; quality enhancement of services or programs; quality enhancement of data systems; organizational design improvements; increased preventive behaviors; and decreased incidence or prevalence of disease. Each of these outcomes may be short or long term with respect to the time frame required to demonstrate improvements. They are intended to represent a range of potential improvements that are feasibly achieved by a broad array of public health organizations and within myriad different programs or service delivery settings.

The existing literature has emphasized the need to link QI initiatives to programmatic successes or increased equity in service delivery, such as increased reach to a target population, and health outcomes, such as increased preventive behaviors (or, alternatively captured in this outcome, reduced risk factors) and decreased incidence or prevalence of disease. 5,8,12 However, in the early stages of QI efforts, public health agencies may not yet be able to detect improvements in these outcomes. Therefore, we included a range of outcomes to highlight more immediate QI successes. Dissemination of information, products, or evidence-based practices tracks results of public health agencies’ efforts to share products with or provide other forms of technical assistance to their community or regional partners. Quality enhancement of services tracks standardization of services, adoption of evidence-based practices, and compliance with established policies with the goal of improved service delivery, and quality enhancement of data systems captures improvements in data systems’ accuracy, functionality, and standardization. Finally, improved effectiveness may result as public health organizations reorganize or adjust their service delivery models for more effective use of human resources. These changes are captured under organizational design improvements.

Within the framework, each outcome is defined independently for purposes of clarity and simplicity, recognizing that any given QI initiative may address multiple outcomes either within or across the constructs of efficiency and effectiveness. Also, public health agencies may identify other outcomes of interest. To increase the framework’s usability, the outcomes are accompanied by a series of steps to consider at the outset of any QI initiative. First, practitioners are asked to determine what they hope to achieve if their QI initiative is successful: increased efficiency, increased effectiveness, or both. On the basis of the response to this first question, practitioners can identify the specific outcome of interest. Any initiative may have a primary intended outcome as well as additional intended benefits or outcomes that should be considered. The framework provides a series of guiding questions for consideration when deciding on outcomes, notably, Is the outcome relevant? Does it reflect the intent of the initiative given the problem or opportunity being addressed? Is the outcome achievable given the available resources and the given time period? Is the outcome measurable? Are data sources available?

Quality Improvement Measurement Framework

The framework has been implemented in the field for 2 years. For the framework to be relevant and useful, it had to improve the consistency of measurement of efficiency and effectiveness outcomes while simultaneously acknowledging and respecting the diversity of public health agencies and their QI initiatives. To this end, the framework guides practitioners through an approach to developing measures that is both standardized and customizable to individual agency priorities.

Standardizing measurement of public health quality improvement outcomes.

Each QI initiative is unique to each jurisdiction’s needs and context. Therefore, the measurement approach uses a standard set of generic measures ( Table 1 ) that address each of the framework’s key outcomes associated with efficiency and effectiveness. This approach allows each organization to tailor the measures to the aims of its specific programmatic, service-oriented, or process-oriented QI initiative and facilitates a consistent approach to measurement despite the wide array of QI efforts.

To ensure common interpretation and application of this generic measurement language, the framework incorporates additional guidance regarding the calculation of these measures and considerations for other contextual information. Specifically, for each outcome and associated measure, the framework includes (1) a definition of the intended outcome and further clarification of the measure itself, including sample measures; (2) specific information about what should be considered when establishing baseline and target values for the measure and what should be captured after implementation of the QI initiative; (3) guidance on how to calculate the measurement specifications, such as the numerator and denominator, start and stop time, or criteria to consider for qualitative measures; and (4) when applicable, additional information that may provide context to the measure itself.

Given that some public health QI initiatives are more conducive to quantitative measurement than others, 5,24 the framework includes a combination of quantitative and qualitative measures, depending on the outcome. For example, measures for increased customer or staff satisfaction would be quantitative, specifically, the percentage of customers who were satisfied or extremely satisfied with a service. For the time-saved outcome, the measure would represent start and stop times to calculate the average time taken to complete a service or activity. An example of an outcome with a qualitative measure is quality enhancement of services. For this outcome, a baseline may describe gaps in effectiveness resulting from variability in services, and the postimplementation value would reflect gains achieved because of standardization or policy implementation.

Implementation of the framework.

After the first 1.5 years of implementation among 73 NPHII awardees, 97.3% of awardees (71 of 73) submitted measures for at least 1 QI initiative. This yielded 693 measures for 357 QI initiatives because several of the initiatives addressed more than 1 outcome and therefore resulted in development of more than 1 measure. A variety of data sources informed measures, including but not limited to process maps, customer satisfaction surveys, vital records, programmatic data, and electronic health records.

The most commonly addressed outcomes were quality enhancement of services (18.2%; n = 126), time saved (17.7%; n = 123), and increased customer or staff satisfaction (11.4%; n = 79). The outcomes least frequently addressed were revenue generated from billable services (0.7%; n = 5), costs avoided (0.9%; n = 6), and costs saved (1.2%; n = 8).

NPHII awardees reported both quantitative and qualitative measures. Of all measures, 83% (n = 575) tracked quantifiable improvements, and 16.7% (n = 116) tracked improvements qualitatively. The remaining measures (0.3%; n = 2) were somewhat ambiguous and difficult to categorize. Quality enhancement of services had the highest percentage of qualitative measures (5.6%; n = 39), followed by quality enhancement of systems (4.2%; n = 29). Examples of quantitative and qualitative measures are provided in Table 2 .

TABLE 2—

Examples of Quantitative and Qualitative Measures Using the Quality Improvement Measurement Framework

This standardized framework represents 1 approach to operationalizing and defining measures for QI efficiency and effectiveness outcomes that can be applied in a broad array of public health contexts. The framework identifies measures relevant to a range of public health programs, services, and operational processes. Although initially originated as a framework for specific QI initiatives, the close linkage between QI, especially at the organization level, and performance management 38 allows it to be useful within, or considered a part of, performance management efforts. For example, these outcomes and measures may be used to assess changes related to agency priorities or captured within an agency’s performance management system. Whether used specifically in the context of discrete QI projects or embedded in broader performance management efforts, this framework provides a unique balance between standardization and customization through a focus on outcomes without prescribing specific processes to achieve them and generic measures that can be tailored to agency- or program-specific initiatives and contexts.

According to the Public Health Services and Systems Research national research agenda and recent literature on the science of QI, the field of public health QI has grown in both visibility and attention, presenting opportunities for innovative approaches to practice and research. 5,22,24–26 To advance the science and practice of public health QI, the field needs more studies that use valid and reliable instruments and draw conclusions from representative samples. 52 The field can be advanced by establishing a standardized set of QI measures that can be used to support individual project aims as well as systemwide initiatives. This framework has the potential to advance the dialogue around these needs by (1) presenting a parsimonious measurement model for collecting data on QI efficiency and effectiveness outcomes at the program and agency levels; (2) testing the face validity of the framework through implementation in the field across a variety of state, local, tribal, and territorial health departments; and (3) identifying the types and frequency of QI approaches used to improve public health programs’ and services’ efficiency and effectiveness.

This framework is unique in its articulation of a standard set of outcomes and measures uniquely applicable to public health QI that are responsive to needs identified in the literature and reflect current public health practice. However, a review of resulting measures and other information on QI initiatives is critical to determine whether other core outcomes of public health QI need to be considered. Similarly, an analysis of timeframes required for achievement of various outcomes may help inform improvements to guide the application of various outcomes and expectations to achieve results. Additional analysis of data derived from implementation of the framework will further test the validity and reliability of the measurement constructs across varying QI initiatives, programs, and organizations, as well as build an understanding of how context affects its use. Further research can build on the measurement framework and explore how it may be used to understand the impact of QI across multiple contexts and over time. The framework is intended to be a living document that can expand as understanding of the science and practice of QI in public health progresses, ultimately contributing to the “so what” of public health QI.

Acknowledgments

We acknowledge Laura Hsu and Cassandra Frazier from the Division of Public Health Performance Improvement, Office for State, Tribal, Local, and Territorial Support, at the Centers for Disease Control and Prevention for their work on the validation and cleaning of the performance measures data presented in this article.

Human Participation Protection

No human participants were involved in this work. Institutional review board approval was not required.

ORIGINAL RESEARCH article

This article is part of the research topic.

Green Finance & Carbon Neutrality: Strategies and Policies for a Sustainable Future

Study on the Effect of Carbon Trading on the Carbon Emission Intensity of Enterprises——A Mechanism Test Based on ESG Performance Provisionally Accepted

• 1 College of Management, Sichuan Agricultural University, China
• 2 College of Management, Sichuan Agricultural University, China
• 3 Macquarie University, Australia

The final, formatted version of the article will be published soon.

Facing the double constraints of the "double carbon" target and high-quality economic development, carbon trading policy is an important tool for realizing the emission reduction commitment; based on the perspective of microenterprises, the specific mechanism and spatial effect of carbon trading policy still need to be evaluated. Taking China's carbon emissions trading pilot as a quasi-natural experiment, this paper empirically investigates the impact of carbon trading policy on the carbon emission intensity of pilot enterprises and its mechanism of action, and its impact on the carbon emission intensity of neighboring enterprises, based on the multi-temporal double-difference model, moderating effect model, and spatial Durbin model with the A-share-listed enterprises in the period of 2009-2019 as the samples. It is found that: (1) Carbon trading policy will reduce the carbon emission intensity of enterprises to different degrees, and there are significant differences under different ownership types, degrees of marketization and the level of digitization.(2) Under the influence of environmental uncertainty, ESG disclosure will weaken the effectiveness of carbon emission reduction in the pre-pilot stage of the policy; with the gradual improvement of the carbon trading policy and ESG disclosure mechanism, ESG ratings will positively regulate the inhibitory effect of the carbon trading policy on the carbon emission intensity of enterprises through multiple paths.(3) Carbon trading policy effectively reduces multiple negative spillovers through the demonstration effect and competition effect of neighboring enterprises, driving the carbon emission reduction behavior of non-pilot enterprise. The research in this paper enriches the research paradigm of carbon emission intensity influencing factors, provides reference suggestions for the government to improve its policies, and better contributes to the realization of the "dual-carbon" vision in China as soon as possible.

Keywords: Carbon trading policy, ESG, Corporate carbon abatement, Multi-temporal doubledifference, Moderating effect, Spatial spillovers Carbon trading policy, Spatial spillovers

Received: 25 Mar 2024; Accepted: 13 May 2024.

Copyright: © 2024 Zi, Qiang, Lei, Hu and Chang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Liu Y. Qiang, College of Management, Sichuan Agricultural University, Chengdu, Sichuan Province, China

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