Follow the bug

Our first prototype “Follow the bug” solution employs fit-for-purpose proxy indicators on antimicrobial resistance (AMR) based on already existing data generated by the health system and shareable across information systems to provide accessible insights for health care providers, administrators, and funders to optimize the performance of primary and secondary health system. Every day, thousands of people around the country are submitted to samples’ collection in local laboratories to help physicians clinical decisions. However, individual results are frequently negative and intrinsically late (days or weeks before a positive result). Therefore all individuals receiving unspecific antibiotic for any infectious disease will be affected by the problem. Also every single doctor prescribe unspecific antibiotic, will be equally affected, increasing health public costs. 

“Follow the bug” was designed to be easy-to-interpret georeferenced and temporal periodical reports containing relevant AMR proxy indicators for primary and secondary health care providers, administrators, and funders. These reports will be generated periodically from existing primary health bigdata, analyzed by artificial intelligence (AI) and geographical information systems (GIS), and aimed at producing useful indicators which are as close as possible to their real time and place of occurrence.

Our first challenge is focused on AMR because it poses a major threat to human health around the world.  The World Health Organization (WHO) has included AMR as one of the top ten threats to global health in 2019 and developed a global action plan. Several studies have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for different infectious agents and all over the globe.

On the one hand, health systems are complex in nature and face different challenges which are spatially and temporally dependent. On the other, despite the existence of large amounts of health data, it is challenging to obtain and interpret them effectively at the local level and on real time. Thus, time and geography will impact AMR occurrence, as well as other health problems, faced by primary and secondary health systems. And primary health data are generated on a routine daily basis by private and public laboratories, which are the focus of our solution.

We understand that easy-to-interpret georeferenced and temporal reports on any health indicator as close as possible to the real occurrence time and place is paramount to the correct use of resources on the most basic local level. The “Follow the bug” solution empowers the health system to provide a rational framework for prescription, dispensing, and ultimately use of antimicrobials, with potential impact on AMR globally. 

 “Follow the bug” solution will focus on proxy indicators to relevant to the most prevalent community infections: 1) Escherichia coli from urine samples resistant to ciprofloxacin and/or to any cephalosporins =  proxy of ESBL, a common resistance spreading from hospital to communities, and on urinary tract infections, which are extremely common in women of any age and on age extremes of any gender; 2) Streptococcus pneumoniae resistance to penicillin and ceftriaxone in specific samples = proxy to guide empiric treatment in respiratory tract infections, which are among the most common in adults and infants; 3) Staphylococcus aureus  resistant to methicillin (a beta-lactam antimicrobial) and susceptible to other agents (clindamycin, and others) = proxy of an extremely serious and potentially prevalent condition, defined as community-acquired Methicillin Resistant S. aureus (or CA- MRSA). These initial “Follow the bug” proxies are devoted to guiding the primary and secondary health systems on antimicrobial use decisions on the most common infections in any community environment.  

Surveillance systems to monitor trends in antimicrobial resistance (AMR) are evidently important. However, most existing systems face long term funding issues and are focused on specific AMR conditions. In addition, their results are usually restricted to certain periods and geographic areas and their conclusions, although published in scientific journals, are not close enough to the real occurrence time. We understand that the main challenge of any surveillance system is to produce relevant, wide information in a timely manner for the local health agent. Therefore, our proposal is to implement a model that allows the creation of surrogate indicators on priority circulating pathogens/AMRs based on the data sources generated by routine microbiology clinical laboratories.

Thus, this project proposes the development of a cost-effective surveillance and information system on AMR to be used on the local level and close to their real occurrence. Thus, with adequate information, the municipalities or other health administrative regions, health agents and administrators would be able to improve their action plans, by having easy access to information on their local resistance patterns, directly impacting the work of health care providers and the lives of patients.

The group experience is demonstrated by the various publications with the use of large health databanks on different indicators used as proxies to identify areas or regions (mostly within São Paulo state, Brazil) with the highest probability of antomicrobial resistance (AMR) on common urinary infections, antimicrobial consumption, neonatal mortality death, congenital syphilis, and on various AMR conditions. Also, our group’s solid and long-term partnerships with INPE (Instituto Nacional de Pesquisas Espaciais) and ITA (Instituto Tecnológico de Aeronautica) will contribute to the success of our prototype. 

Group publications

1. Camargo EC, Kiffer CR, Pignatari AC, Shimakura SE, Ribeiro PJ Jr, Monteiro AM. Proposal on the use of data on retained antimicrobial prescriptions: the EUREQA experience [A proposal for using data from antimicrobial prescriptions: the EUREQA experience]. Cad Saude Publica. 2012 May;28(5):985-90. Portuguese. doi: 10.1590/s0102-311x2012000500017; 

2. Abboud CS, Monteiro J, França JI, Pignatari AC, De Souza EE, Camargo EC, Monteiro AM, Dos Santos RG, Kiffer CR A space-time model for carbapenemase-producing Klebsiella pneumoniae (KPC) cluster quantification in a high-complexity hospital Epidemiol Infect 2015 Sep;143(12):2648-52. doi: 10.1017/S0950268814003811; 

3. Testoni Costa-Nobre D, Kawakami MD, Areco KCN, Sanudo A, Balda RCX, Marinonio ASS, Miyoshi MH, Konstantyner T, Bandiera-Paiva P, Freitas RMV, Morais LCC, Teixeira MP, Waldvogel B , Almeida MFB, Guinsburg R, Kiffer CRV. Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: A structured spatial analytical approach. PLoS One.j 2021 Aug 17;16(8):e0255882. doi: 10.1371/journal.pone.0255882; 

4. Marinonio ASS, Costa-Nobre DT, Miyoshi MH, Balda RCX, Areco KCN, Konstantyner T, Kawakami MD, Sanudo A, Bandiera-Paiva P, de Freitas RMV, Morais LCC, La Porte Teixeira M, Waldvogel BC, de Almeida MFB, Guinsburg R, Kiffer CRV. Clusters of preterm live births and respiratory distress syndrome-associated neonatal deaths: spatial distribution and co-occurrence patterns. BMC Public Health. 2022 Jun 20;22(1):1226. doi: 10.1186/s12889-022-13629-4.

We believe in the immediate potential of our project to contribute to the challenge of generating more assertive and timely prescriptions for community-acquired infections, without increasing the workload for professionals and generating lower costs for health systems.

In the near future, we envision to develop other proxies serving our region, but potentially applicable to the nation and to the world and impacting the quality of health of millions of people. With the group's versatility and experience in the geolocation of health events, as well as the development of proxy indicators, we see our project with the potential to combat, in the short and medium term, the main public health problems faced by low- and middle-income countries, contributing to the sustainable development goals achievement. As examples, we envision solutions based on proxies indicators for guiding policies on: a) regional antimicrobial consumption (“Follow the drug”); b) public health cause-specific neonatal mortality (“Follow the babies”); c) vertically and sexually transmitted conditions (“Follow the syphilis”); d) Diabetes disease (“Follow the sugar”); e) cardiovascular disease (“Follow the heart”); and others which may be developed with specialized and partner groups or companies.

However, despite the experience with geolocation of health events and proxy indicators development, our results are shown in articles and scientific conferences, but they need to reach health services in a punctual, fast and timely manner. Therefore, in order to enable the development of this business model, with the stages of product dissemination, negotiation with partner health services (responsible for providing the data) and creation of information delivery flows and return of results, we need of help from MIT Solve in order to fund the project, including improvement in analytical and outreach power, with the data scientists, software development, infrastructure, and the dissemination and maintenance of the business.

Our solution is in agreement with MIT Solve mission to solve world challenges through technology, innovation and science. 

Nowadays, in the face of a community-acquired infections, the medical prescription of antibiotics is a challenge because it is based on the type of infection and patient's clinical status. The culture and antibiogram, which could point out the best antibiotics to treat each type of infection, involve costs to the health system and take days to be analyzed, limiting their utility in the daily routine of primary health care units. Thus, the development of a proxy to identify the most relevant antimicrobial resistance (AMR) on a local level will contribute to a more fast and accurate initial empiric antimicrobial prescription (primary health care provider), dispensing (primary and secondary municipal or county level pharmacy), and use (regional public policy), resulting in timely treatment and lower expenses to the system. We understand our solution is innovative because it uses already generated data, analyzing it with epidemiological tools to generate relevant information close to the real time and place of occurrence. 

For the next year, our aim is to use data on antimicrobial resistance to better assess the sensitivity profile of antimicrobials in local communities, for better antibiotic planning in places of greater risk and to guide health professionals for a more individualized treatment. For the next five years our aim is to expand our solution to other Brazilian regions and to create new health proxies indicators  for guiding policies on: a) regional antimicrobial consumption (“Follow the drug”); b) public health cause-specific neonatal mortality (“Follow the babies”); c) vertically and sexually transmitted conditions (“Follow the syphilis”); d) Diabetes disease (“Follow the sugar”); e) cardiovascular disease (“Follow the heart”); and others which may be developed with specialized and partner groups or companies.

Our first prototype reports are being developed and applied in the context of a current Ministry of Health project, based on five (5) units located in five (5) municipalities and currently sharing data by the BR-Glass system. Firstly, we plan to improve our reports’ objectiveness and interpretability by interacting with primary and secondary health care systems from at least one of these five municipalities. Then, we plan to share these data with at least 50% of the basic health units located within the specific municipality selected. The next step will be to follow those selected basic health units and verify historical antimicrobial prescription pattern and compare it with prospective prescription data over six (6) months to understand if “Follow the bug” reports impacted their prescribing patterns.    

Everyday, thousands to millions of health records and data are generated. However, these data are generally focused on the individuals and, thus, on providing answers only to single health care providers. In the case of our “Follow the bug” solution, as stated, laboratory results on cultures and antimicrobial resistance (AMR) have limited and rather late impact for health care providers prescription only, while collective epidemiological data relevant at the regional level could be generated by putting together all cultures and AMRs available for specific conditions (proxy indicators). At the present, these collective epidemiological data are usually not interpreted nor analyzed for the common use of the system (the same applies for all other “Follow the” solutions envisioned by our group). Thus, using routinely generated large data sets (that could come from routine laboratory, resource’s consumption, or other health records) and applying artificial intelligence (AI) and geographical information systems (GIS) methods for generating easy-to-interpret valuable epidemiological reports may have impact for the entire local health system (health care providers, administrators, and funders) to rationally plan their actions on different aspects of local health policies.

In public health, geographic information system (GIS) is used to connect spatial or spatiotemporal information with its attributes (population, environmental or health care information), to explore geographical variation and to generate proxy indicators for the health systems. Thus, spatial analyzes allow the identification of health conditions to guide care and resources allocation for a given region (health regions, municipalities, health units and addresses) at a moment or over time.

"Follow the bug" will use the location of health units responsible for lab sample collection as a proxy of the health event of interest, in our case, relevant antimicrobial resistance (AMR) proxies (results of the antibiograms for urine, blood and other samples collected during routine care) allowing to direct, for each type of infection, the appropriate and timely antimicrobial use.

GAIA has extensive experience in the development of these proxies and is currently participating in the development of the CDC-RFA-CK21-2104 project focused on community-acquired pathogens relevant to primary and secondary health systems. Our members have already participated in other projects, which aimed at providing relevant proxy measures local health conditions. Unveiling the AMR real-time scenario in any region will contribute to site-specific policy making, particularly on infection prevention and control programs and access to essential antibiotics. 

Public and private labs.

GAIA is a group composed of professors and postgraduate students (doctoral and postdoctoral researchers) in the health area at the Universidade Federal de São Paulo (UNIFESP). Our group is composed by men and women with a diversity of age groups and professions (medical doctor - Pediatrics and Infectious Disease, physiotherapist and biomedical scientists), with different background and work experiences (research, clinical, labs, private companies, public and private hospitals). We meet monthly to discuss the projects in progress and in this space, all the group members contribute with their opinions for decision-making regarding the topic addressed. Inclusion of data scientists, software developer and professionals who will work on the dissemination and maintenance of the product will contribute to the diversity of the group, which is very receptive for new members.

Create partnership with public and private laboratories to obtain data from cultures and antibiograms. The partnership with public laboratories will be carried out through the municipalities, which will have as a return the product of our business – information. With the private laboratories, in addition to the return of the business product, we intend to provide consultancy for a better use of the information. Participating laboratories will benefit from the project, as they will receive feedback with periodic warning reports for possible problems they may be facing within their communities and/or hospitals, in addition to knowing their local resistance profile. Also, they will benefit from brand exposure for stakeholders. 

Although we still don't know how to price our product, the structure that will be necessary to expand it throughout Brazil, the way of obtaining data from partner institutions and the counterpart that will be necessary to provide the information (in addition to returning the analyzed data – product that will add value to society), especially from private institutions, we have the proposal that private institutions pay for the proposed service and public institutions receive the feedback generated from proxy free of charge, making the project financially sustainable.

Our group has the experience and knowledge to create geolocated proxies in the most different areas of public health and all of them have been proven with the scientific publications mentioned throughout the project. With the opportunity offered by MIT Solve, in collaboration with The Bill and Melinda Gates Foundation, we will be able to implement the business and prove that the antibiotic prescription based on the offered proxy indicators has benefits and cost reductions for public health, which will drive the progressive capture of private and public customers, and other stakeholders, ensuring the business sustainability. It is our intention to develop different levels of partnerships with private companies (both private clinical laboratories as with pharmaceutical companies) by implementing signature at different levels for accessing the reports, to obtain recurrent funding for generating data for the health system. In this model, the agreements with the private companies will be made to subsidize the costs of the “Follow the bug” reports to the public laboratories and for the primary and secondary health care providers. In this sense, we understand that our proposal is in line with Environment, Sustainability and Governance (ESG) initiatives. As additional income sources, GAIA may provide consultancy to private companies (clinical laboratories and pharmaceutical companies) on other proxy indicators. Initial funding for the construction of this business model is necessary to initiate these partnerships, in addition to mentoring to assist in the construction of the business model.