Swine producers are encouraged to practice antibiotic stewardship by reducing their use of antibiotics belonging to classes of medical importance to humans. We conducted a scoping review of non-antibiotic approaches in the form of products or management practices that might prevent or control disease and thus reduce the need for antibiotics in nursery pigs. Our objectives were to systematically describe the research on this broad topic for the North American context, identify specific topics that could feasibly support systematic reviews, and identify knowledge gaps. A search of multiple databases identified 11,316 articles and proceedings for relevance screening. From these, 441 eligible clinical trials and observational studies were charted. The majority were clinical trials (94%). Study results from EU countries were mostly communicated through journal articles, whereas study results from the USA were mostly communicated through conference proceedings. Interventions and health outcomes were diverse. The two most frequent intervention categories were feed additives and piglet vaccines. The three most frequent outcomes reported were diarrhea, mortality, and indices of vaccine immunity. There were 13 specific topics comprising various feed additives and vaccines that might feasibly support systematic reviews. There were relatively few studies in which interventions were compared with antibiotic comparison groups and relatively few studies evaluating management practices. Antimicrobial resistance (AMR) poses a serious threat to advances in modern human medicine, livestock health and production, and animal welfare , 2. TherWorldwide, nations are heeding the call for reduced antibiotic use in food production through regulation and industry guidelines. For example, in the USA, the US Food and Drug Administration Center for Veterinary Medicine promotes prudent use of livestock antibiotics belonging to classes of importance to humans by requiring they be administered with veterinary oversight and be limited to the purpose of assuring animal health. The use of medically important antibiotics in healthy animals for growth promotion was prohibited by 2017 in the USA . In CanaScoping review methodology is used to systematically map the literature with regard to the extent, range, and nature of existing research of a particular topic area , 14. ScoThe objectives of this scoping review were three-fold: (i) to examine and describe the volume, range, and nature of research on non-antibiotic approaches for disease prevention and control in commercial nursery pig production; (ii) to identify specific topics where available research literature may support systematic reviews; (iii) to identify knowledge gaps in the primary literature on the effectiveness of various non-antibiotic approaches. Summarizing the literature regarding intervention effectiveness was not an objective for this scoping review.https://atrium.lib.uoguelph.ca/xmlui/handle/10214/12929.This scoping review followed the framework for scoping reviews as outlined by Arksey and O'Malley  using thOur review question was as follows: What are the volume and nature of the primary research literature published between 2000 and 2018 that evaluated non-antibiotic interventions  to prevent or control bacterial and viral illnesses in nursery pig production in North America and regions or countries with similar production conditions? Viral illnesses were included because we presumed that preventing viral infections may reduce secondary bacterial illnesses.As the volume of literature on the broad topic of non-antibiotic approaches for nursery pig health was potentially very extensive, we sought expert opinion to refine our research question and help to inform our search strategy and study eligibility criteria. More specifically, the stakeholder engagement served six objectives: (i) to identify the countries or regions with similar commercial swine production practices to those of North America, (ii) to select the swine production stage(s) for which non-antibiotic approaches could most effectively reduce total AMU in swine production, (iii) to solicit opinions on the importance of various antimicrobial types to antimicrobial stewardship in the swine industry , (iv) to identify the health and production outcomes of greatest importance to swine producers, (v) to identify the most important non-antibiotic interventions in the form of specific products  or specific management practices ; and (vi) to identity any additional outcomes or interventions that we had not included on our initial lists. We also asked the stakeholders to recommend other experts who should be consulted.The expert opinion was gathered through an anonymous online survey . The respondents selected and ranked options from a list for a particular question and/or responded in an open-ended format. Expert stakeholders who were consulted included representatives within provincial or national Canadian swine industry associations, representatives in provincial agriculture departments, and Canadian scientists engaged in swine agriculture and/or swine veterinary research. We assumed that the responses from stakeholders of Canadian institutions would be representative of experts from the USA and other countries with commercial swine production. Stakeholders' most common responses and suggested additional responses were used to inform the parameters of the search, eligibility criteria, and data items. Ethics approval for this survey was not required, as the results were used strictly to inform the review process and not reported as a finding of the review.a priori selected outcomes of interest. These included health outcomes of bacterial or viral infections significant to swine health in North America, treatment costs, and measures of performance. Studies evaluating toxicities or parasitic infections were excluded. Non-English information sources were excluded. Quasi-experimental intervention studies designed as “before period” vs. “after period” of the intervention application were excluded.Eligible information sources were from North America, EU countries, Australia, and New Zealand, available since 2000, as these sources of evidence were most likely to reflect current commercial swine production systems most similar to those of North America. Eligible publication types included English-language journal articles, conference proceedings, theses, and technical reports. In addition to electronic databases, proceedings were sourced from the American Association of Swine Veterinarians (AASV) Annual Meeting and Pre-conference seminar from 2000 to 2018 and the International Pig Veterinary Society Congress biannual meetings from 2000 to 2016. We included research reports that reported a challenge trial, a clinical trial , or observational study of a modifiable intervention, and we reported a health or production outcome in nursery pigs. For purposes of this review, “interventions” also included modifiable risk factors. Consistent with our stakeholder engagement results, interventions related to breed or genetic improvement were not included. Our stakeholder engagement also informed our To aid in the development and validation of the search, we checked that 25 known relevant citations are included. The primary reviewer (LW) performed the database search from March 27, 2018, to April 19, 2018, using multiple databases hosted by the data platforms of ProQuest, Web of Science, and PubMed . Though The database search was filtered by language (English), date of publication (published between 2000 and date of search in 2018), and by location filters for eligible countries if available. Additional filters were applied as were allowable within the data platform. These included source and document type , subject or research areas, and Medical Subject Headings (MeSH) or qualifiers. The search terms were limited to the title or abstract in the PubMed and Web of Science platforms or “anywhere except full text” for the ProQuest platform databases. Management of the identified citations was as follows: first, they were imported into the reference manager software EndNote ; second, they underwent exact match deduplication in EndNote; and third, they were imported into the systematic review software DistillerSR  where they underwent further deduplication based on close matches.The database search strategy included a string of the population term groupings , one of two intervention term groupings, and an outcome term grouping  with each grouping connected by the Boolean operator “AND” . There wThe online proceedings identified as being potentially relevant based on subject headings and titles were entered onto a Microsoft Excel (2010) spreadsheet for tracking of further screening decisions. Relevant full-text proceedings were entered into the DistillerSR database. Proceedings that were duplicates of journal articles were removed.Our review team consisted of veterinary epidemiologists, one of whom acted as the primary reviewer, a topic expert in swine research (TOS), and two trained MSc epidemiologists who acted as second reviewers. Pre-testing of the relevance screening form was conducted on 100 citations based on title and abstracts. Pre-testing of the data charting was conducted on 25 full-text articles. Using forms created in DistillerSR, two independent reviewers screened and charted the data. Any disagreements were resolved by consensus or a third reviewer. After pre-testing, the level 1 relevance screening form did not change.Staphylococcus aureus). At level 4, the articles that only reported a performance outcome without any health outcomes of interest were excluded from data charting. Health outcomes of interest were defined a priori and included clinical outcomes , surrogate health outcomes such as shedding of clinically important pathogens , and measures of specific and non-specific immunity to vaccines or bacterins  was conducted by both the primary reviewer and a second reviewer working independently using a form in DistillerSR .We charted data for the publication type and for the following study characteristics: study design, study location and year of publication or conference year, study size, and farm settings of the study population . In addition, we charted data for the following: production stage(s) of animals receiving the intervention, purpose of intervention as disease prevention, and/or treatment, and/or performance; specific intervention evaluated; comparison group(s); health outcomes of interest reported; other outcomes reported; and study size and the hierarchical level at which the outcome was measured  . Data wevia injection or oral bolus were charted as a “medication,” whereas the same intervention given to groups of animals via feed was charted as a “feed additive.” There were two data charting options related to measures of immunity as an outcome, one specifically for vaccine immunity and another for non-vaccine immunity.In this review, interventions such as non-antibiotic medication, vitamin, mineral, or antibody given directly to individual animals Data charted regarding the purpose(s) of the intervention were based on information in the title or objective statement. Prevention was selected if the herd or group of pigs showed no clinical or subclinical evidence of disease or infection, whereas treatment was selected if the pigs as individuals or groups in part or whole showed evidence of infection, or were known to be exposed. Although we charted data for disease prevention and/or treatment, in the results, we reported disease control for those studies that described an intervention given for both purposes, treatment and prevention . Definitions for disease prevention, treatment, control, and pig performance were based on those provided by the United States Government Accountability Office (GAO)  and the Comparison groups that were not clearly stated as “no treatment” or “conventional practice” were charted as comparison groups that received a “different form or level” of the intervention or exposure. Studies could be charted with multiple types of comparison groups . Zinc oxide (ZnO) was charted as an intervention when it was the study intervention of interest and charted as a comparison group when another non-antibiotic intervention of interest was compared with zinc treatment.To meet our second objective of identifying specific intervention topics that could be combined for systematic reviews, we considered only clinical trials with clinically important outcomes. These included mortality; non-diarrheal, non-respiratory non-specific morbidity, or other morbidities such as lameness; diarrhea or fecal score; respiratory disease; and treatment for illness or antibiotic use. Thus, not all health outcomes of interest were considered as clinically important outcomes. The criteria for selection of topic areas as potentially extensive enough for systematic reviews were arbitrarily set at a minimum of 10 clinical trials reporting a similar intervention in the same population  and one or more clinically important outcomes, though not necessarily the same clinically important outcome among all clinical trials for the specific topic area.Data charted in DistillerSR were entered into a database in Stata 15.1 . These data were summarized descriptively and presented in the form of tables and figures in accordance with our stated data charting scheme. So as to emphasize the research with the highest evidentiary value, we present detailed results for articles that reported clinical trials and observational studies . For theA total of 73 experts were invited to respond to the survey, of which 33 responded (45%). We incorporated the stakeholder input into our search strategy and data charting items. There were no suggested publications that were not identified through our search.There were 11,316 unique citations screened for eligibility: 6,644 were from the database search and 4,672 were from the gray literature proceedings of the AASV Swine Information Library sources . Two pron = 414, 94%). The remainder were observational studies . The observational studies were conducted exclusively using animals living in commercial farm settings, whereas the clinical trials were conducted using animals living in experimental farm settings  and in commercial farm settings . The farm settings were unclear for 26 (6%) clinical trials, of which 20 were reported in proceedings.The majority of eligible studies were clinical trials  followed by the USA . There were 17 studies conducted in Australia or New Zealand. The five EU countries with the greatest numbers of studies were Spain, Denmark, Poland, Germany, and France. The five specific states of the USA with the greatest numbers of studies were Minnesota, Iowa, North Carolina, Illinois, and Nebraska. Among the 27 observational studies, 19 were conducted in EU countries and five were conducted in Canada. Among the 414 clinical trials, 265 were conducted in EU countries or the UK, 108 were conducted in the USA, and 25 were conducted in Canada.The majority of the studies were conducted in EU countries or the UK (n = 441) was composed of published articles  and proceedings . The 284 studies reported from EU countries and the UK were mostly communicated through published articles , whereas the 110 studies reported from the USA were mostly communicated through proceedings .The body of eligible studies  of the intervention as disease prevention, disease control, and/or performance varied according to the farm settings among the 414 clinical trials and 27 observational studies. There were no studies that evaluated an intervention for the clearly stated purpose of treatment of individual sick pigs.n = 134) were conducted primarily on commercial farms , whereas when the purpose of the intervention as prevention (n = 250), studies were conducted on both commercial farms  and research farms . Among the 414 clinical trials, 238 (57%) evaluated an intervention for the purpose of prevention of which the majority were an evaluation of vaccines in piglets and/or dams . Although all included studies (n = 441) reported a health outcome of interest in nursery pigs, many clinical trials  and some observational studies  also reported performance as a purpose of the intervention.Studies where the purpose of the intervention was for disease control (n = 553), interventions were most commonly applied to nursery pigs , followed by suckling piglets , and dams . Some studies reported the application of the intervention to all three populations (n = 27).All eligible studies measured a health outcome in nursery pigs; however, interventions were applied to one or more specific populations based on production stage comprising dams, suckling piglets, or nursery pigs. Some studies reported the application of the intervention to more than one population. Among the total populations described in the eligible studies (n = 179) and nursery or suckling piglet vaccination (n = 160) (n = 3) and producer education (n = 4). Details of all specific interventions for clinical trials are presented in n = 19) and vaccinations of dams (n = 14).We charted data for 11 different categories of interventions or risk factors . Among tn = 160) . Togethen = 328, 49%), followed by a no treatment control group . The clinical trials also described comparison groups that received placebo , antibiotics , ZnO , or a combination product containing an antibiotic and ZnO . Note that some studies compared a non-antibiotic intervention group with a ZnO comparison group, whereas other studies compared a ZnO treatment group with a no treatment control group, or antibiotic or other treatment comparison group.Among the clinical trials and the observational studies, there were 672 comparison groups described. The most frequently reported comparison group was “different form or level” of the intervention or exposure , mortality , and vaccine immunity . Immunity to vaccines included measures of specific immunity (n = 140)  and in five studies also included non-specific immunity. Reporting of measures of immunity to a vaccine, with or without reporting other outcomes, was common among vaccine clinical trials (n = 118). A total of 43 (36%) vaccine clinical trials only reported measures of immunity without reporting any clinically important outcomes. These trials were conducted on research farms (n = 18), commercial farms (n = 20), and farm settings in which it was unclear (n = 5). Other outcomes reported included treatment for illness or antibiotic use . Antibiotic treatments were typically for diarrhea when specified. The metric used for treatment for illness or antibiotic use varied . Less commonly reported outcomes included non-diarrheal, non-respiratory, non-specific morbidity  or other morbidities such as lameness  and pathogen shedding . The remainder of the reported health outcomes of interest included non-vaccine immunity , which comprised studies that measured specific immunity (n = 21), non-specific immunity (n = 10), or both (n = 1). Presence of pathological lesions   and clinical respiratory disease  also were reported , feed efficiency , and feed intake .Among the 441 included clinical trials and observational studies, all of which reported a health outcome of interest, the most commonly reported additional outcomes included performance outcomes such as growth  , vaccination of piglets [e.g., porcine circovirus 2 (PCV2), porcine reproductive and respiratory syndrome virus (PRRSV), and ., PCV2) .The selection of knowledge gaps identified discretionary items based on the authors' opinions. We found that there were relatively few studies in which a non-antibiotic intervention was compared directly with an antibiotic comparison group. Among the vaccine clinical trials, approximately one third failed to report a clinically important outcome.This scoping review of non-antibiotic approaches for disease prevention or control that may reduce the need for antibiotics in nursery pigs relevant to the North American context identified a large body of literature with considerable breadth and depth. Since most of the studies described in this ScR were conducted in the EU or the UK, we may have not captured a body of knowledge on this broad topic for the North American context. The breadth of this literature was reflected in the diversity of interventions or risk factors evaluated, whereas the depth of this literature was reflected in the number of specific topic areas with similar studies that might feasibly support systematic reviews.Clinical and policy decisions are generally regarded as best guided by the interpretation of findings of multiple studies evaluating the same research question rather than the findings of a single study, which is a random event from a distribution of possible results . A summaWe chose to focus our data charting on controlled clinical trials. We did not include quasi-experimental trial design , as this design does not provide an equal evidentiary value to clinical trials . Though This scoping review identified a wide variety of vaccines and feed additives, which together comprised the majority of interventions evaluated in clinical trials. There was a dearth of clinical trials that evaluated management interventions such as biosecurity and infection control, feed or nutrient restrictions, housing, and weaning. This may reflect the difficulty of evaluating these types of interventions in clinical trial settings and/or the difficulty in sourcing funding for trials of these interventions . BiosecuDespite the approximately equal number of studies conducted on research farms vs. commercial farms, there was a strong predominance of research with the purpose of preventing disease on research farms, whereas research with the purpose of disease control was predominately conducted on commercial farms. Variables that may impact the outcome of a trial, such as prior disease-free status, could potentially be better controlled on research farms than commercial farms, whereas the field conditions of commercial farms, such as an existing disease problem, provides a better setting to truly test the effectiveness of an intervention under “natural commercial” conditions . HoweverAmong the studies identified through this scoping review, approximately half reported a comparison group that was a different form or level of the intervention itself. Traditional systematic reviews and meta-analyses are not just based on studies with similar interventions, populations, and outcomes but also on similar comparison groups. In the absence of sufficient studies with similar comparison groups, combining studies with the same outcome through network meta-analysis may prove useful. Network meta-analysis allows comparisons of interventions that may not have been directly compared in head-to-head trials by mathematically evaluating both direct and indirect comparison evidence of multiple interventions and comparisons . If we hApproximately one third of vaccine clinical trials did not report a clinically important outcome, though they did report measures of immunity. The lack of reporting clinically important outcomes when they could have been measured reduces our opportunity to build a body of evidence best suited for clinical decision making. Clinically important outcomes as determined by guidelines, clinicians, patients, or the researcher provide the best evidence for inclusion in systematic reviews, whereas indirect outcomes such as measures of immunity provide a lower quality of evidence –33. To eBeyond describing the body of literature pertaining to non-antibiotic approaches that may reduce the need for antibiotics for disease prevention or control in nursery pigs, an additional objective of this review was to identify specific topic areas where there may be sufficient literature to support systematic reviews. We identified 13 specific topic areas with a minimum of 10 clinical trials that may feasibly support systematic reviews. These topic areas were composed of various vaccines and feed additives. Although we listed the ZnO as an intervention for which there may be sufficient material to support an SR, we do not recommend knowledge synthesis for this intervention given that concerns regarding AMR co-selection with the use of ZnO in swine . Though In determining the specific topic areas with sufficient similar studies to support systematic reviews, we used an arbitrary number of 10 similar clinical trials with some commonality of the intervention and population. Technically, a minimum of two studies are all that is needed for combination in a meta-analysis if those studies are similar enough to combine in a meaningful way . HoweverThere were potential limitations that may have impacted the comprehensiveness of this scoping review. First, we may have missed some articles if we did not include all possible terms for each of the many non-antibiotic interventions included in the search. Systematic reviews for specific interventions should maximize comprehensiveness by including all possible terms. Second, we may have overlooked some interventions or outcomes if they did not appear in the title or abstract. Third, we accessed bibliographic sources available through the University of Guelph data platforms and two conference proceedings available through the AASV library. We may have missed additional published articles available through other databases, and unpublished studies generated by companies testing products or proceedings from other conferences. Fourth, our search using the CAB Direct platform was unsuccessful due to technical difficulties. Without the additional studies identified through that platform, our search may not have been as comprehensive as we had intended. The coverage provided by CAB Abstracts was found to be excellent in a comparison of nine databases for veterinary journals , so it iIn addition to limitations to comprehensiveness, this review may have two additional limitations. First, we may have misclassified the purpose of the intervention for disease control by including in this category pig herds or groups that also had a known exposure to an infection and not solely groups that contained clinically ill or infected pigs as defined by AVMA and GAO reports. Finally, we did not assess inherent biases of included studies such as lack of appropriate randomization of clinical trials, lack of concealment or blinding, loss to follow-up, or selective outcome reporting. Any systematic reviews of non-antibiotic approaches that may reduce the need for antibiotic prevention or control in nursery pig production should include a risk of bias assessment .The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.LW coordinated the project, acted as the primary reviewer for relevance screening and data charting, analyzed data, interpreted the results, and wrote the manuscript drafts. JS oversaw the work, piloted the relevance screening and data charting forms, assisted with the interpretation of results, reviewed manuscript drafts, and approved the final manuscript. AO'C and SM provided guidance for interpretation of the results, commented on the manuscript drafts, and approved the final manuscript. TO'S provided guidance for the stakeholder survey and interpretation of the results, commented on the manuscript drafts, and approved the final manuscript. MR and KC conducted relevance screening and data charting as a second reviewer, commented on manuscript drafts, and approved the final manuscript version. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Systems epidemiology approaches may lead to a better understanding of the complex and dynamic multi-level constellation of contributors to cancer risk and outcomes and help target interventions. This grant portfolio analysis aimed to describe the National Institutes of Health (NIH) and the National Cancer Institute (NCI) investments in systems epidemiology and to identify gaps in the cancer systems epidemiology portfolio.The analysis examined grants funded (2013–2018) through seven NIH systems science Funding Opportunity Announcements (FOAs) as well as cancer-specific systems epidemiology grants funded by NCI during that same time. Study characteristics were extracted from the grant abstracts and specific aims and coded.Of the 137 grants awarded under the NIH FOAs, 52 (38%) included systems epidemiology. Only five (4%) were focused on cancer systems epidemiology. The NCI-wide search (N = 453 grants) identified 35 grants (8%) that included cancer systems epidemiology in their specific aims. Most of these grants examined epidemiology and surveillance-based questions (60%); fewer addressed clinical care or clinical trials (37%). Fifty-four percent looked at multiple scales within the individual , 49% looked beyond the individual , and few (9%) included both. Across all grants examined, the systems epidemiology grants primarily focused on discovery or prediction, rather than on impacts of intervention or policy.The most notable finding was that grants focused on cancer versus other diseases reflected a small percentage of the portfolio, highlighting the need to encourage more cancer systems epidemiology research. Opportunities include encouraging more multiscale research and continuing the support for broad examination of domains in these studies. Finally, the nascent discipline of systems epidemiology could benefit from the creation of standard terminology and definitions to guide future progress. Cancers are very complex phenotypes and, though many risk factors have been identified and studied through traditional epidemiological research, much of their etiology remains unknown. This is due in part to the relatively siloed focus of many studies on a few risk factors within specific domains . Studies are often designed without multilevel approaches, often focused on simple risk factor associations . More reA systems approach in science highlights the interconnections and feedback loops between multiple component causes of a disease and the importance of considering how these components interact dynamically over time and at multiple levels of analysis , 7, 8. SThe National Institutes of Health (NIH) has a long history of supporting research using system science approaches. Current high profile NIH programs in this area include: the National Institute of Biomedical Imaging and Bioengineering (NIBIB) Interagency Modeling and Analysis Group (IMAG) , the NatThe Division of Cancer Control and Population Sciences at NCI focuses on the research to decrease cancer incidence, morbidity, and mortality by supporting research across the cancer continuum. This portfolio analysis was conducted to determine the extent of NIH-supported research in systems epidemiology, examine the integration of systems epidemiology in cancer control research, and identify gaps in the cancer systems epidemiology portfolio. To accomplish this, we conducted a two-phase grant portfolio analysis. In phase I we sought to characterize the research grants funded under NIH systems science and computational modeling FOAs and to determine the number of systems epidemiology-focused grants. In phase II we aimed to identify and evaluate any additional cancer-focused, systems epidemiology grants in the NCI portfolio.reporter.nih.gov) following instructions provided  were selected that focused on systems science or computational methods. Funded grants from these FOAs were examined for the five years prior to the portfolio analysis start date to both limit the scope and capture the recent emergence of new technologies, omics, and informatic approaches , 17. Theprovided .We characterized the funded grants solicited from these FOAs based on their abstracts and specific aims. Reviewers extracted detailed information from the grants using the terms and definitions found in At the beginning of extraction, all reviewers  reviewed and extracted nine percent of the 137 grants (N = 12) to ensure consistency and similar interpretation of the data extraction template and term definitions by all reviewers. The remaining grants (N = 125) were divided into batches of 10–15 and double-extracted, whereby one reviewer extracted the information and a second reviewed and confirmed the extractions, to minimize extraction errors and reduce reviewer bias. The reviewers paired for extractions were rotated to maintain consistency in extractions across groups. Any disagreements were resolved through discussion amongst reviewer pairs, and any remaining discrepancies resolved via group consensus.Unlike phase I, where the focus was describing grants funded from the NIH FOAs, the focus in phase II was to identify and describe grants identified as “cancer systems-epidemiology,” regardless of what funding announcement was used. The decision to narrow phase II’s focus to cancer and epidemiology was based on our interest in an analysis to inform the NCI-specific cancer epidemiology research portfolio. In January 2019, the NIH grants database containing information on NCI extramural research projects was searched using NCI’s Portfolio Management Application (PMA) system to search IMPAC II to identify cancer-focused, systems epidemiology grants funded between fiscal years 2013–2018. The search strategy included performing a text search of the titles, abstracts, and specific aims of funded grants using variations of the following search terms: agent-based model; biomolecular model; computational model; machine learning; multilevel; multiscale model; network analysis; network-based; simulation model; systems-based; systems dynamics; systems epidemiology; systems model; and systems science. The project summaries of these grants may be viewed in NIH RePORTER using methods provided . Any dupThe first section of the results describe the grants identified in phase I and the phase II cancer-specific grants are described in the second section. Characteristics of the grants analyzed are described in detail in Tables The Systems Science and Computational Modeling FOAs were supported across several institutes at NIH and awarded 137 grants in fiscal years 2013–2018. Of these, only 84 (61%) and 57 (42%) utilized a multiscale approach within and/or beyond the person, respectively . DynamisOf the 137 grants awarded under these FOAs, 52 (38%) were identified as focused on systems epidemiology, of which five (10%) were categorized as cancer systems epidemiology grants . CompareOur database search for phase II initially yielded 453 grants. After review of the grant aims for inclusion of epidemiology and cancer, 145 grants were reviewed by paired reviewers, of which 35 grants were identified and coded as including cancer systems epidemiology . This reSystems approaches may lead to a better understanding of the complex and dynamic multi-level constellation of contributors to both cancer risk and outcomes and may more precisely inform and target interventions . The goaOverall, one of the most notable findings of the present analysis was that systems science grants focused specifically on cancer compared to the overall cancer portfolio was relatively small. Characteristically speaking, the majority of the overall NIH systems science portfolio over this time period was represented by: within person approaches, biology and genomics measurement domains, basic biology or epidemiology and surveillance research content areas, and with outcomes focused on mechanistic insights for the purposes of discovery or prediction  over time is needed to support cancer systems epidemiology research. Several additional challenges to conducting systems epidemiology included transdisciplinary research, data sharing, and training needs, as described in detail elsewhere .Notably, the number funded from NCI increased annually. Recent advances may have contributed to this growth, including the ability to link datasets; the growing availability of big data and usage of both real and simulated data; and more sophisticated machine learning systems for predictive modeling , 24. BotOur definition of systems epidemiology focused on disaggregating exposures by context via defined domains and did not explicitly include a requirement for research to cross multiple scales. However, complex diseases are influenced by factors on multiple scales and, thus, their integration into models can improve research from discovery and prediction through intervention and policy , 19. In It was encouraging to see that multiple domains (> two domains) were included in many systems epidemiology grants in phase II. We noticed that individual and group/community exposure domains  were commonly included and often together. Genomics and clinical domains were also often grouped together . There remains an opportunity for a broader inclusion of domains in systems epidemiology research. As previously mentioned, the linking of datasets and availability of electronic health records and genomics data may lead to more diversity and cross-domain work in future grant submissions.One overarching challenge of this portfolio analysis that applies to the current systems epidemiology field was the lack of universal nomenclature. There is a need for the widespread use of standard terminology and definitions–or “branding”–in this relatively nascent field to promote systems epidemiology research. Part of the challenge of succinctly defining systems epidemiology is that it is a research approach rather than a single method . Though The main limitation of this study is that we only extracted data from the grant abstracts and specific aims; full grants were not reviewed. If systems epidemiology was not clearly represented in projects’ abstracts and specific aims, it was not included. Therefore, our results may not reflect the totality of NIH funded grants in systems epidemiology or cancer systems epidemiology, respectively. Though we accounted for this limitation by being broad-minded in the interpretation of our definitions, this may conversely overestimate the extent of work being done in the portfolio. For example, an assumption was made that if a grant mentioned more than one time point in a model, dynamism was present. Similarly, if more than one domain was mentioned within an aim, we assumed the domains were integrated.A wide breath of opportunities for future research involving cancer systems epidemiology were identified by this portfolio review, including encouraging more multiscale research and continuing the support for broad examination of domains in these studies. Innovations in data science, medical informatics, electronic health records, mobile and wearable technologies, and new methods to link and analyze big data are creating a potentially ideal environment for the advancement of systems epidemiology as a complementary approach to traditional epidemiology. Keys to success will be the ability to integrate complex, multiscale data from a wide range of sources, and the fostering of interdisciplinary collaborations that will allow for the integration of expertise from different disciplines. The voluminous information available now for epidemiological inquiry in cancer can come from diverse data sources and will benefit from systems approaches to accurately model this complexity. Finally, the creation of standard terminology and definitions could prove instrumental in guiding future funding opportunities and supporting dialogue among the growing community of systems epidemiology scientists.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.


To further understandings of household food insecurity in First Nations communities in Canada and its relationship with obesity.Analysis of a cross-sectional dataset from the First Nations Food, Nutrition and Environment Study representative of First Nations communities south of the 60th parallel. Multivariate logistic regression was used to assess associations between food insecurity and sociodemographic factors, as well as the odds of obesity among food-insecure households adjusting for sociodemographic characteristics.Western and Central Canada.First Nations peoples aged ≥19 years.Forty-six percent of First Nations households experienced food insecurity. Food insecurity was highest for respondents who received social assistance; had ≤10 years of education; were female; had children in the household; were 19–30 years old; resided in Alberta; and had no year-round road access into the community. Rates of obesity were highest for respondents residing in marginally food-insecure households . In gender-specific analyses, the odds of obesity were highest among marginally food-insecure households in comparison with food-secure households, for both female (OR 1·57) and male (OR 1·57) respondents, adjusting for sociodemographic variables. For males only, those in severely food-insecure (compared with food-secure) households had lower odds of obesity after adjusting for confounding (OR 0·56).The interrelated challenges of food insecurity and obesity in First Nations communities emphasise the need for Indigenous-led, culturally appropriate and food sovereign approaches to food security and nutrition in support of holistic wellness and prevention of chronic disease. As a result, Indigenous peoples have been disproportionally affected by food insecurity – a challenge of uncertain or insufficient access to quality, healthy and personally and culturally appropriate/accepted food that persists in the Canadian context–8.The pronounced health challenges faced by Indigenous peoples worldwide are deeply rooted in legacies of colonisation that have generated loss of land and erosion of cultural and social identity. The 1 673 785 individuals who self-identify as Indigenous in the 2016 Canada Census are a young and growing population, representing three distinct and diverse cultural peoples: First Nations (58·4 %), Inuit (3·9 %) and Métis (35·1 %). In 2016, among the 76·2 % of First Nations peoples with registered Indian status,,10.In Canada, displacement from traditional territories, forced cultural assimilation, involuntary community relocation to reserves and environmental degradation have threatened Indigenous food systems and the resilience of communities to sustain a traditional diet and lifestyle–14. Alongside increased burden of chronic disease that has been observed such as type 2 diabetes, cancer and cardiovascular disease,8,15–20, particular concern has been drawn to the increasing prevalence of obesity as a modifiable risk factor for diet-related chronic diseases that are reported to be higher among Indigenous peoples–14,21–23. Understanding nutrition transition within the context of the dual and interrelated structural challenges of food insecurity and obesity confronting Indigenous peoples in Canada can support the adoption of culturally appropriate healthy nutritional policies, programmes and tools to be identified and informed by Indigenous peoples to reduce this nutritional burden and prevent chronic diseases,21,23–26.As widespread food insecurity among Indigenous households has accompanied transition away from traditional food sources, a nutritional burden has been placed on Indigenous peoples globally and perpetuated by the residential school system, they have experienced a rapid transition in diet and lifestyle, characterised by decreased physical activity and a shift towards consumption of energy-dense market-based food,17,29,30. This nutrition transition has been identified to play a key role in the rising rates of obesity-related chronic diseases in these populations,17,30–32, with this trend observed to be driven by a range of processes – first and foremost colonial policies and practices that enforced physical displacement from these lands, disrupting access to environments that enable engagement in traditional food activities,3,27,28; a changing food environment from the introduction of various food sources and imported goods; and increased engagement in market economy activity, leaving less time to engage in traditional food practices.Over the past century, as First Nations communities have been subject to severe marginalising pressures including, but not limited to, the loss of traditional land, relationships, language and culture caused by colonisation. The CCHS, however, excludes First Nations households living on reserve, and therefore this prevalence of food insecurity is considered to underestimate the levels among all First Nations households. In line with this, results from the First Nations Regional Health Survey Phase III, which includes Indigenous peoples living on-reserve and in northern communities, indicate 50·8 % of First Nations adults report their households as food-insecure. While the nine food security questions in the First Nations Regional Health Survey are similar to the eighteen-item Household Food Security Survey Module in the CCHS, more than one individual within the same household could respond to the survey, and this may therefore impact the estimated prevalence of food insecurity. A deepened understanding of food insecurity in First Nations communities and opportunities to address risks of chronic diseases, including its implications for holistic health and wellness, is therefore needed,13,24–26.In conjunction with this ongoing nutrition transition, the 2012 Canadian Community Health Survey (CCHS) observed disproportionately high rates of food insecurity among First Nations households (20 %) compared with non-Indigenous households in Canada (8 %), alongside attention to the importance of also considering ‘cultural food sovereignty’. Reduced nutrient intake and poor dietary quality related to food insecurity have contributed to obesity with implications for risks of chronic diseases,18,36,37.Food insecurity has been associated with the inadequacy of food supplies in households, resulting in reduced quality or desirability of food consumed, including a low healthy eating index score tied to a decreased consumption of fruit and vegetables, dairy products and grains, as well as among First Nations individuals living off-reserve (31 %) in comparison with the total population in Canada (17 %). The relationship between food insecurity and excess weight is not well understood. However, growing interest in food security as a determinant of health is expanding the emergence of literature on the positive association between food insecurity and obesity–44. This relationship has been linked to eating patterns characterised by a high consumption of low-cost, energy-dense and nutrient poor foods,42,44–46. This represents a dual interrelated concern whereby obesity is a challenge tied to an overabundance of energy-dense food, while the lack of affordable and accessible nutritious foods is an issue contributing to food insecurity. With First Nations communities observed to suffer disproportionally from both the burden of food security and of obesity, understanding the patterns of food insecurity and ties with obesity, therefore, merits attention in order to identify culturally relevant strategies to address this dual public health challenge.The prevalence rates of obesity have also been found to be higher among First Nations people living on-reserve and in northern communities (34·8 %) as indicated in the First Nations Regional Health Survey 2008/2010–42,44–46. To our knowledge, there has been no study that has investigated the association between food insecurity and obesity within an Indigenous context in Canada, in particular on-reserve First Nations communities. Further, as data from the CCHS excludes First Nations living on-reserve, diet-related health concerns associated with unique food security challenges faced by First Nations communities heighten the need for this nutritional and public health challenge to be assessed. This would support a better understanding of its differential effects within this population while not diminishing focus on the need to address the driving forces underlying food and nutrition challenges.While a few studies have examined the association between food insecurity and obesity, existing research has yielded conflicting findings across women and men as well as across levels of food insecurity measured – and has primarily examined non-Indigenous populations in Canada–51. The objectives of this article are to contribute to understandings of food insecurity regarding influences on its presence in First Nations households, as well as how this is associated with obesity. Given the challenges with understanding culturally appropriate indicators of food security, which includes consideration of access to traditional foods through practices such as hunting, gathering, fishing and trading, we wish to stimulate considerations about nutrition transition and the value of Indigenous-led approaches to health-promoting nutritional policies, programmes and tools to reduce the burden of food insecurity, obesity and chronic diseases.To deepen understandings of patterns of food insecurity and diet-related health concerns, our study examines the demographic and socioeconomic factors associated with household food insecurity in First Nations communities in four provinces of Canada, with attention to the relationship between household food insecurity and obesity. In the current study, the level of food insecurity reported from the 2008–2013 First Nations Food, Nutrition and Environment Study (FNFNES) was defined as a household’s inability to access food, including due to financial constraints–51. FNFNES was developed in response to growing concerns by First Nations communities regarding the presence of environmental contaminants in traditional food systems, and the need to document and address food-related health challenges across communities. This research was guided by the Tri-Council Policy Statement: Ethical Conduct for Research, including Chapter 9 of the Tri-Council Policy Statement, ‘Research Involving the First Nations, Inuit and Métis People of Canada’. In addition, the First Nations principles of Ownership, Control, Access and Possession (OCAPTM) guided this undertaking to ensure ethical health research and appropriate engagement with First Nations communities. Engagement with First Nations leadership at the national, regional and community levels informed the FNFNES project design, including the survey questionnaire, study objectives, methodology and implementation, with funding support from the First Nations and Inuit Health Branch, Indigenous Services Canada. Ethical approval was obtained from the Ethics Research Board at Health Canada, the University of Northern British Columbia, the Université de Montréal, University of Ottawa and the University of British Columbia.Data analyses were conducted using inputs from the FNFNES, a research initiative with a cross-sectional study design, regionally representative of First Nations communities south of the 60th parallel in Canada. FNFNES was supported by a resolution passed by the Assembly of First Nations in 2007 and was a collaborative partnership between the Assembly of First Nations, Indigenous Services Canada, the University of Northern British Columbia, the Université de Montréal and the University of OttawaUsing an ecological zone framework, a systematic multistage random sampling method was employed to select participants from communities from the provinces of British Columbia, Alberta, Manitoba and Ontario in Canada. This sampling strategy aimed to establish equal representation of communities from the Assembly of First Nations regions (in this case provinces) and ensure the likelihood of highly populated communities to be sampled. Within each community, questionnaire data were collected from a single adult  in each selected household. Within the selected households with more than one adult, an individual who self-identified as a First Nations person living on-reserve with the next birthday was invited to participate in the study.Between 2008 and 2013 of the FNFNES’s ten-year project timeline (2008–2018), 5355 households from fifty-eight communities were randomly selected across British Columbia, Alberta, Manitoba and Ontario. Of the 5355 households sampled, a total of 3847 adults participated in household interviews, representing a participation rate of 74·5 %. Households were excluded from the survey if the respondent did not meet the inclusion criteria, was unable to participate due to health reasons  and if homes were vacant. and collects information on general health status, sociodemographic characteristics and measured and self-reported height and weight. If accepted to be measured, weights were recorded using a Seca 803 digital scale with the participants lightly clothed. Height was measured using a measuring tape on shoeless participants on an even surface. BMI was calculated as weight/height (kg/m2) for 691 participants with both measured and self-reported height and weight, 922 participants with measured height and weight only, and 1749 participants with self-reported measures only. To account for a potential response bias among self-reported cases of height and weight, adjustments were made through the addition of estimated bias determined using a paired t test to estimate differences in means in BMI between measured and self-reported values. Adjustments were applied to self-reported BMI in Ontario and Alberta, but were not necessary for British Columbia and Manitoba. Participants were considered obese if their BMI was ≥30·0.Data utilised in the current study were collected from the social, health and lifestyle questionnaire and food security questionnaire administered to participating households by a trained community member. The social, health and lifestyle questionnaire in the FNFNES incorporates questions from the CCHS and previous work with Indigenous populations in Canada for comparative purposes and has been used in national surveys in Canada, such as the CCHS. The food security questionnaire comprises eighteen questions to measure self-reports of household food insecurity due to financial constraints within a 12-month period prior to being surveyed. Ten of the eighteen questions measure adult experiences of food insecurity, and the remainder measure food insecurity among households with children under the age of 18. From the survey, the degree of household food insecurity was determined from a numerical scale based on the number of affirmed responses . Household food security status was identified by categorising the severity of food insecurity from the food security numerical scale,56. In the current study, household food security status was described using the following categories: (i) food security (no affirmed response); (ii) marginal food insecurity ; (iii) moderate food insecurity ; (iv) severe food insecurity .The food security questionnaire used in the FNFNES is the income-related Household Food Security Survey Module. Household Food Security Survey Module, developed by the US Department of Agriculture, is internationally recognised as a measure of household food insecurityPrevalence estimates of household food security status were derived for marginal, moderate and severe food insecurity, as well as total household food (in)security. Prevalence estimates of obesity were also determined for female and male respondents living in food-secure households, as well as households experiencing marginal, moderate and severe food insecurity.v. food-insecure . Sociodemographic predictors identified in a review of the literature  sociodemographic predictors of household food insecurity, and (ii) the relationship between household food insecurity and obesity in First Nations communities in British Columbia, Manitoba, Alberta and Ontario. In the first model, sociodemographic predictors of household food insecurity were examined through multivariate logistic regression modelling using the backwards-stepwise selection method. The outcome variable of interest, household food insecurity, was dichotomised as food-secure re Table  were entIn the second model, the relationship between household food insecurity and obesity was examined with bivariate and multivariate logistic regression to assess the odds of obesity for individuals living in food-insecure households. The outcome variable, BMI, was dichotomised to assess the presence or absence of obesity in First Nations households by food insecurity status. The variable, household food insecurity status, was the main effect of interest and was classified into marginal food insecurity, moderate food insecurity, severe food insecurity and food security. Bivariate analyses were carried out to assess differences in the association between obesity and the primary explanatory variable, household food insecurity, with each sociodemographic variable of interest. Statistical effect sizes for household food insecurity were estimated for female and male respondents separately using the three levels of food insecurity. In the multivariate model, the odds of obesity among food-insecure households was determined adjusting for the variables age group, region, main source of income and years of education Table . UnadjusTable The prevalence of food insecurity varied across demographic and socioeconomic characteristics of the household Table . In mult2. The average BMI for female respondents living in moderate food-insecure households was 30·4 and 29·3 kg/m2 for females residing in severely food-insecure households. For male respondents residing in marginally food-insecure households, the average BMI was 30·2 kg/m2, while male respondents living in moderate and severe food-insecure households had an average BMI of 28·9 and 28·2 kg/m2, respectively.The prevalence rates of obesity were also high among the 3364 participants with calculated BMI in the sample, with minor differences reported between food-secure and food-insecure households. For both female and male respondents, the prevalence rates of obesity were highest in marginally food-insecure households and lowest for severe food-insecure households Table . Female In both bivariate and multivariate logistic regression gender-specific analyses, the odds of obesity were highest among marginally food-insecure households in comparison with food-secure households. Table The FNFNES provided the opportunity to examine the patterns of food insecurity, as well as its associations with obesity in First Nations communities, south of the 60th parallel, in the Canadian provinces of British Columbia, Alberta, Manitoba and Ontario. As one of the first published examinations of this dual interrelated challenge among First Nations households, our study contributes to the scholarship on nutrition transition and emerging literature focusing on food insecurity and obesity as priority areas for action.. Differences in the levels of food insecurity observed confirm how the omission of First Nations living on-reserve in the CCHS could underestimate the degree of food insecurity being experienced across Canada, as well as draw attention to the extreme injustice presented by the disproportionately higher rates for First Nations living on-reserve. This has implications for identifying Indigenous-led health promotion and prevention strategies and ensuring that approaches taken are culturally safe and reflective of Indigenous priorities.A very high prevalence of food insecurity (46·4 %) was observed among First Nations households surveyed in First Nations communities of Canada. This reveals a particular challenge to ensuring food security compared with the considerably lower 2012 CCHS levels of food insecurity among the general Canadian population that have attracted considerable concern –61.The predictors of food insecurity that are associated with highest odds of food insecurity within First Nations households – households within communities without year-round road access, respondents who are female, receiving social assistance, presence of children in the household, years of education ≤10, younger age (< 50) and residing in British Columbia or Alberta – highlight points for interventions to be considered. These sociodemographic characteristics were found to be significantly associated with higher odds of household food insecurity, consistent with studies examining the determinants of food insecurity in Indigenous households in other Canadian settings. Although we did not directly probe these drivers, the differences we observed among the predictors of First Nations household food insecurity can be best understood within the context of delocolonisation of food systems and the underlying stresses to traditional, historically sustaining Indigenous food systems. These stressors to Indigenous food systems are related to land dispossession, forced cultural assimilation through residential schools, and community relocation, which have been examined in other published work in Canada,27,28. For many Indigenous communities, the natural environment as a context for accessing food, water and livelihood represents an essential pathway to food security,26. Through gathering, harvesting and sharing of local plant or animal resources, the holistic health and wellness of Indigenous peoples has long been sustained by their food systems and deep relations with the environment,26.The interrelated challenge of food insecurity and obesity observed in our study has its roots in land dispossession and the ongoing impacts of colonisation that have undermined Indigenous peoples’ connection to the land and its resources,62. Studies have also found that women compromise their own food security by altering their eating patterns to ensure other members within the household are food-secure–65. For example, when food is limited in the household, women have been described to skip meals or delay eating in order to prevent hunger among children in the household,56. While the high prevalence of food insecurity among female respondents may point towards efforts taken by them to prevent food insecurity among children, findings consistently show that households with children experience greater food insecurity compared with households without children,63.As experiences of food insecurity differ between women and men, we recognise our inability to identify specific details, such as individual’s marital status or whether female respondents were responsible for food preparation, as a potential limitation of our study’s questionnaire design. Further, gender inequalities faced by Indigenous women as a consequence of reduced access to resources, such as land, education and agricultural inputs, have been previously exposed as structural determinants of food insecurity,67. In the FNFNES survey, from which the current study has drawn major inputs, precise income level was not measured as an indicator of low socioeconomic status, although household crowding, receiving social support and education level were considered. Food insecurity in households with children is most prevalent among families led by a single parent and households depending on social assistance as a source of income,68. The presence of more family members has placed an increased burden on household resources with implications on food insecurity,69. With regard to households with children, food-insecure households had, on average, more individuals living within the household compared with those that were food-secure. Further, food-insecure households had a greater number of individuals under the age of 15 (ineligible to participate in the labour market) who may have limited capacity to contribute to their household’s overall income. An association between food insecurity and income levels also indicates that households receiving social assistance or dependent on employment insurance/workers’ compensation are at a higher risk as these sources of income may not be sufficient to ensure food security,70.In Canada, low income levels have been identified as a primary barrier to healthy eating or obtaining food that meets individual’s preferences and needs–50,71 and differences in provincial social assistance policies and programmes. Additional factors may be linked to the number of participating communities located in rural, remote or isolated regions within each province, as households residing in communities accessible by winter road or plane only had higher rates of food insecurity compared with households in communities with year-round road access.While the current study was not designed to conduct a provincial comparative analysis of food insecurity, households within the province of British Columbia and Alberta were at higher odds of food insecurity compared with those in the province of Ontario. Provincial variations observed in food insecurity are likely to be tied to differences in the average cost of food in those provincesWe notably examined the dual interrelated challenges of food insecurity and obesity in the current study. While a non-linear association observed between household food insecurity and obesity derives from a cross-sectional design and, therefore, does not infer causality, the public health challenge that this exposes to exist calls for identifying culturally relevant approaches to reduce chronic disease risk factors among First Nations communities.–43.In our study, higher odds of obesity were found for both female and male respondents living in marginally food-insecure households compared with food-secure households. In comparison to women living in food-secure households, women living in marginally food-insecure households had higher odds of obesity, an association similar to that observed among men. However, the odds of obesity for men living in severe food-insecure households were lower in comparison to men living in food-secure households. While an association between living in severe food-insecure households and obesity was observed among women, this was not statistically significant. These results are consistent with findings of higher levels of overweight or obesity at low or intermediate levels of food insecurity and lower BMI observed at severe levels of food insecurity compared with food-secure households in non-Indigenous populations,33,70,73. This observed transition is characterised by an involuntary decrease in the use of traditional foods over time across Indigenous populations and particularly among younger individuals,31. With an extensive documentation of a steep growth in the prevalence of obesity over time, nutrition transition and links to chronic diseases are indicative of a forced transformation in diet and lifestyle observed in a context where options have been influenced by processes favouring reliance on processed foods–33,74–76.A rapid change in diet and lifestyle, characterised by a forced shift from traditional food use towards consumption of energy-dense market-based foods, has placed a high burden of chronic diseases among Indigenous peoples globally,44. Energy-dense, nutrient-poor food is often made available at low cost and is usually consumed by households experiencing financial constraints. For this reason, risks of obesity from diets composed of affordable, energy-dense foods are a concern among food-insecure households, particularly in households with ‘marginal food insecurity’ whose food choice is limited due to a lack of money. In a study examining community perspectives on the relationship between food insecurity and obesity, First Nations and Métis parents perceived low income as an underlying factor of food insecurity, and reliance on energy-dense food as the most affordable coping option. Similarly, changes in dietary practices observed among Inuit communities, especially those receiving income support, have also revealed their vulnerability to purchasing processed foods.Consistent with prior observations of similar associations linking higher obesity with lower levels of food security, these studies often point to the reduced quality of purchased food when concerns about food access are present.Contrastingly, when households experience compromises in the quantity of food or reduced food intake due to severely food insecure levels, differential impacts on individual body weight may be observed. The lower odds of obesity observed in the current study among severe food-insecure households may be tied to a decrease in energy intake due to underconsumption and disrupted eating patterns, contributing to weight loss over time. Decreased consumption and reduced energy intake have been shown to lower obesity at higher levels of food insecurity. Such approaches could strengthen local food systems and reduce the burden of food insecurity and obesity that is highly prevalent in the First Nations communities.Given the persistence of structural determinants driving the association of obesity with food insecurity, it is critical that solutions be explored not in individualised behavioural approaches often characterised as ‘shaming’ but rather on modifications to the circumstances undermining food security itself – and in a culturally appropriate mannerThe results of the current study substantiate the need for further investigations considering dietary intake and food supply when examining the association between food insecurity and obesity within First Nations communities. Until further work is undertaken, the current results should be interpreted with caution. However, given the health inequities already faced by this population, the potential consequences of both food insecurity and obesity on individual’s health and community wellness may risk furthering health challenges. The high and disproportionate burden of food insecurity and obesity among First Nations communities, therefore, represents priority areas for action. A consideration for food sovereign approaches in identifying interventions to strengthen local food systems can present opportunities to improve equitable access to healthy, affordable food with attention given to cultural appropriateness. This is necessary as efforts to mitigate food insecurity without concern for nutritional quality and access to culturally relevant food may limit the impact of prevention and health-promoting strategies aimed at addressing food insecurity and obesity.. Engagement with First Nations, Inuit and Métis communities, the government and key stakeholders working in the areas of food, nutrition and chronic diseases could inform the development of sustainable and multifaceted approaches in support of Indigenous-led action on food insecurity.Strategies targeted at improving the availability and access to affordable, culturally appropriate, healthy foods are essential for promoting sustainable and community-driven actions to ensure food security among First Nations households in remote or isolated communities. Indigenous-led food sovereign approaches aimed at protecting traditional food practices and improving access to culturally appropriate, healthy and affordable foods can present opportunities to ensure long-term food security against ongoing colonial pressuresThe current study contributes to improving the understanding of factors underlying food insecurity and obesity in First Nations communities, thus facilitating the needed discussion on the value of Indigenous-led food sovereign approaches to enhance food security, including access to culturally appropriate, healthy and affordable food. There are, however, limitations to the current study. While the data utilised from FNFNES are regionally representative of First Nations communities in Canada, the cross-sectional design of the study presents limitations with regard to drawing inferences on a cause-and-effect relationship of the association between food insecurity and obesity. Additional socioeconomic and demographic characteristics related to the prevalence of household food insecurity were not measured by the FNFNES survey, such as the precise income level, marital status, family medical history and disordered eating patterns. Future studies led and informed by Indigenous partners and communities that assess community-based interventions to improve food security and nutrition are needed to identify culturally appropriate responses.


Antimicrobial consumption (AMC) surveillance at global and national levels is necessary to inform relevant interventions and policies. This study analyzed central warehouse antimicrobial supplies to health facilities providing inpatient care in Uganda. We collected data on antimicrobials supplied by National Medical Stores (NMS) and Joint Medical Stores (JMS) to 442 health facilities from 2017 to 2019. Data were analyzed using the World Health Organization methodology for AMC surveillance. Total quantity of antimicrobials in defined daily dose (DDD) were determined, classified into Access, Watch, Reserve (AWaRe) and AMC density was calculated. There was an increase in total DDDs distributed by NMS in 2019 by 4,166,572 DDD. In 2019, Amoxicillin (27%), Cotrimoxazole (20%), and Metronidazole (12%) were the most supplied antimicrobials by NMS while Doxycycline (10%), Amoxicillin (19%), and Metronidazole (10%) were the most supplied by JMS. The majority of antimicrobials supplied by NMS (81%) and JMS (66%) were from the Access category. Increasing antimicrobial consumption density (DDD per 100 patient days) was observed from national referral to lower-level health facilities. Except for NMS in 2019, total antimicrobials supplied by NMS and JMS remained the same from 2017 to 2019. This serves as a baseline for future assessments and monitoring of stewardship interventions. Antimicrobial resistance (AMR) is a major global health threat with predicted impact of social, economic, and environmental dimensions . Common Staphylococcus Aureus and extended-spectrum beta-lactamase (ESBL)-producers was reported [AMR is rife in Uganda as shown by the 2015 situational analysis where high resistance to commonly used antibiotics, including cotrimoxazole, ampicillin, and ceftriaxone, was documented [reported . To addrreported . In linereported .Antimicrobial consumption (AMC) as a driver for the emergence and spread of AMR is a well-established connection ,10. EffoCurrently, there is no systematically collated information on the quantities and nature of antimicrobials consumed at both national and sub-national levels in Uganda. Additionally, there is no standardized system for monitoring AMC and use and thus, no clear basis to evaluate ongoing AMS interventions nor to recommend and target new interventions. The health commodity supply chain in Uganda consists of both the government-owned procurement, warehousing and distribution under the National Medical Stores (NMS), the private-not-for-profit (PNFP) mechanism under the Joint Medical Stores (JMS), and other private sector pharmaceutical firms . NMS and JMS together account for a substantial amount of antimicrobials supplied to health facilities in Uganda. Additionally, they reasonably represent the health commodity supply chain that mirrors Uganda’s mixed health system where public and privately-funded healthcare co-exist . TherefoThis was a descriptive cross-sectional study of aggregated data of antimicrobial products supplied by central warehouses; NMS and JMS to 442 hospitals and Level IV health centers in Uganda, from 2017 to 2019. The number of facilities from each level of care included in the study are shown in Uganda is a low-income country in East Africa, with an estimated population of 41.6 million people . It is sThe health services are delivered through a tiered system, with health center, Level II as the lowest rung of the formal health system. The higher level health facilities progressively provide a broader scope of health services and serve a higher number of people through to the national referral hospital level, as shown in Uganda’s health commodity supply chain system mirrors the existing mixed health system with both public and private providers. NMS and JMS are two of the pinch-point warehouses that handle supply chain functions for a substantial proportion of health commodities supplied in Uganda. For instance, for the financial year of 2019/2020, the turnover of NMS on essential medicines and health supplies (EMHS) was USD 97.5 million (UGX 363 billion) and that of JMS was USD 24.7 million (UGX 92 billion) ,18,19. TThe study included antimicrobials supplied by NMS and JMS to health facilities from the national referral hospital level down to Level IV health centers. Only health facilities that provide inpatient care were included because the current study analyzed antimicrobial exposure per health facility activity unit expressed in terms of number of patient days and admissions for standardization. Health centers, Level III and II, were excluded from the study as they only provide outpatient care services. We adopted the WHO global antimicrobial resistance surveillance systems (GLASS) guidelines for AMC surveillance in hospitals , that usThe data collection included all antimicrobials classified by the ATC classification system under the ATC groups J01 which are antibacterials for systemic use, including oral, parenteral, and inhalation formulations and P01AB, which includes Nitroimidazole derivatives for protozoal diseases. Antibiotics used for local therapy, for example, topical creams, and eye and ear drops were excluded. Health facility activity was measured by number of patient days and number of admissions. These were obtained from the National Health Management Information System (HMIS) based on the District Health Information Software 2 (DHIS2) platform.Microsoft Excel (MS Excel) software of antimicrobials supplied to study health facilities from 2017 to 2019 was obtained from the NMS and JMS. These were extracted from their internal electronic databases by the staff of the warehouses. The variables extracted included year of provision or supply, name and level of the facility, the warehouse unique product code, product name, generic name, unit of measure, route of administration, pack size, and number of packs supplied and received. The raw dataset in Data were collected, cleaned, checked for completeness, and sorted by a five-man team of trained enumerators from November 2019 to June 2020. The assigned DDD per antimicrobial product and the WHO AWaRe class were then added as separate columns to the dataset. DDD per pack of each antimicrobial product was calculated. MS Excel (version 2010) and additional variables derived to match the variables in the WHO methodology for antimicrobial consumption in hospitals and then imported into IBM SPSS Statistics 23 for analysis. First, we calculated the total quantity of each antimicrobial product supplied by NMS and JMS per year in grams or unit doses for combined products. Second, the total amounts of each antimicrobial was converted to DDD equivalent by dividing the total amount of a product in grams consumed in a given period by the WHO-assigned DDD value, , followed by parenteral route (41%), and only one product for rectal route. From JMS, 86% were oral formulations, and 14% were parenteral. Ceftriaxone was the most supplied parenteral, accounting for 48% of parenteral antimicrobials from NMS and 43% from JMS. This was followed by gentamicin  and metronidazole .Overall, amoxicillin was the most supplied antimicrobial from NMS, accounting for 39%, 38%, and 26% in 2017, 2018, 2019, respectively, and from JMS 16%, 18%, and 19%, respectively .By level of care, NMS supplied the most quantity of antimicrobials to Level IV health centers, followed by general hospitals, regional referral hospitals in 2017, 2018, and 2019, respectively. National referral hospitals consumed the least quantity across the three study years, as shown in The quantities of antimicrobials supplied by NMS and JMS to study health facilities were analyzed by the WHO AWaRe categorization as presented in When analyzed by level of care, for facilities supplied by NMS, national referral hospitals consumed 49% access, 47% watch, and 3% reserve antimicrobials. At regional referral level, 61% were consumed access, 37% watch, and 2% reserve antimicrobials. General hospitals consumed majorly access antimicrobials (79%), 20% watch, and only 0.1% reserve. Most antimicrobials at Level IV health center were access (80%), followed by watch at 20%, and no reserve antimicrobials.The antimicrobial consumption density was calculated only for health facilities receiving medicines from NMS as the data were readily available in the National Health Monitoring Information System (HMIS). The denominators used were patient days and admissions per year. There is a gradual increase in average DDD per 100 patient days and average DDD per 100 admissions at each level of care from 2017 to 2019. Additionally, the antimicrobial density in each year of study increased from the higher level of care at national referral hospitals to lower levels of care with the health centers, level 4 recording the highest average DDD/100 patient days and highest average DDD/100 admissions as shown in When analyzed by pharmacological subgroup for consumption in inpatient care facilities, penicillin and the beta-lactam antibacterials were most used with 41%, 42%, and 36% consumption in 2017, 2018, and 2019, respectively. The other commonly consumed pharmacological groups from NMS were tetracyclines, quinolones, sulphonamides, and trimethoprim . From JMThis study is one of the first of its kind in Uganda that analyzes the total quantities of antimicrobials supplied by NMS and JMS to health facilities in DDDs and by AWaRE categorization and calculates AMC density as antimicrobial exposure per hospital inpatient activity units. There was an increase in total DDDs of antimicrobials supplied by NMS in 2019, whereas the total antimicrobial DDDs supplied by JMS did not change over the study years. Additionally, NMS supplied eight-fold as much antimicrobials as JMS, the reason for the difference lies in the higher annual turnover of NMS as compared to JMS because NMS receives more funding from government  than JMS (USD 24.7 million) ,18,19. AThe majority of antimicrobials supplied to the public health facilities were from the access category of the WHO AWaRe classification, followed by watch antimicrobials, and a small proportion of reserve antimicrobials. Only higher-level facilities, at national referral and regional referral level received reserve antimicrobials from NMS. Level IV health centers consumed the most quantities of antimicrobials supplied by NMS, whereas hospitals consumed the most quantity of antimicrobials supplied by the JMS. Antimicrobial consumption density at health facilities showed a gradual increase from 2017 to 2019 and an even bigger increase as the level of care of the health facilities lowered. Analysis by pharmacological subclass showed that penicillins accounted for the largest proportion of DDD consumed. There was no significant difference in the mean DDD per 100 patient days both within the different levels of care and across the different years. The WHO report on surveillance of antimicrobial consumption 2016–2018 has reported antimicrobial exposure using DDD per 1000 population . HospitaAmoxicillin was the topmost distributed antimicrobial by both warehouses in all three years of the study. As amoxicillin is the first line recommended drug for the majority of simple infections in Uganda, such as bacterial pneumonia, dental infections, and ear infections, this could explain the high distribution. This use may also indicate overuse as other studies in Uganda have shown that antibiotics are prescribed for non-bacterial infections such as fevers attributed to malaria and upper respiratory tract infections of viral origin . Similarly, ceftriaxone represented more than half of all injectable antimicrobials used in the three years . This isAnalysis by pharmacological subclass showed that beta-lactam penicillins accounted for largest DDD consumed, which is a similar finding as reported in the four African countries of Burundi, Burkina Faso, Tanzania, and Cote d’Ivoire in the WHO 2018 report . Level IV health centers consumed the most antimicrobials by volumes supplied by NMS, while hospitals consumed the most antibiotics supplied by the private-not-for-profit warehouses. This could be explained by the fact that while Level IV health centers provide secondary care, they are numerous compared to the higher-level facilities and, therefore, their pooled consumption may appear high. The antimicrobial consumption shows, when analyzing DDD/100 admissions and DDD/100 patients, a decreasing trend from HC IV, hospital, regional referral, and a small amount at national referral level. This demonstrates more antimicrobial exposure in terms of numbers at the lower levels, which could explain that the lower levels treat more community-acquired infections. In contrast, regional and national referral hospitals cater to more complicated illnesses that span beyond just infectious diseases to complex conditions such as non-communicable illnesses including heart diseases, diabetes, kidney diseases among others. With the AWaRe analysis, Access antibiotics accounted for two-thirds of the total DDDs consumed, which is in line with the WHO guidance, whereby Access antibiotics should constitute at least 60% of total consumption . Lower-lEschericia coli to cotrimoxazole, amoxicillin and clavulanate (70–88%), and moderate resistance to fluoroquinolones and ceftriaxone (3–11%) [Staphylococcus aureas was also found to be multi-drug resistant to first line antibiotics [One of the reasons for conducting antimicrobial consumptions studies is to enable comparison with reports from national laboratory surveillance of AMR, and to draw relationships between sensitivity and resistance profiles of microorganisms and burden of antibiotics use. In Uganda, two studies conducted at tertiary hospitals and one in communities from urban and rural districts found high resistance of  (3–11%) ,28,29. Cibiotics  such as ibiotics , but effThis study has several implications on policy and practice of AMR consumption in Uganda. This study represents a baseline information on the antimicrobial consumption in health facilities in Uganda for three years, and will be useful for future studies to monitor annual trends in consumption. This will help to evaluate the effectiveness of stewardship programs within the health facilities. As Uganda does not have an established system for AMC surveillance yet, our study methodology can be used in national efforts to create AMC surveillance systems and to study AMC at national level and within the private-for-profit sector. There were some limitations to this study. First, the study only assessed national warehouses; the private-for-profit sector distributors’ sales data were not analyzed, as this information is not readily available due to the distributors’ hesitancy and concerns about revealing their sales data. This may have caused the antimicrobial consumption to be underestimated in this report. However, analysis of antimicrobial imports and domestically manufactured products would provide a more complete picture of antimicrobial exposure of the Ugandan population at national level as it incorporates the contribution of private sector. Second, the study did not consider facilities that provide primary care on an outpatient basis and community consumption as the methodology to measure density only provided for facilities with inpatient care. Third, the methodology also provided aggregate consumption data as a proxy for use but does not necessarily reflect the health facilities’ prescribing practices. Lastly, there were some antibiotic combinations that did not have ATC codes assigned to them, such as flucloxacillin and amoxicillin combination, and therefore were left out of the analysis, which could have led to underestimation of the total DDDs consumed. Our study showed that there could be a strong link between antimicrobial consumption and standard treatment guidelines, which can be explored as a venue to promote appropriate antimicrobials use. The study also revealed high use of ceftriaxone, which needs to be protected as a Watch antimicrobial through proactive stewardship activities. This study also provided baseline data for future studies on trends in the pattern of antimicrobial use in Uganda. Finally, the study methodology can be used to compare consumption across health facilities within the same and varying levels of care and in contexts similar to Uganda, and help to set up a standardized system for continuous monitoring of antibiotic consumption, and also contribute to the global WHO/GLASS surveillance system for AMC data.