Background: U.S. rural populations have been disproportionately affected by the syndemic of opioid-use disorder (OUD) and the associated increase in overdoses and risk of hepatitis C virus (HCV) and human immunodeficiency virus (HIV) transmission. Local health departments (LHDs) can play a critical role in the response to this syndemic. We utilized two geospatial approaches to identify areas of discordance between LHD service availability and disease burden to inform service prioritization in rural settings. Methods: We surveyed rural Illinois LHDs to assess their OUD-related services, and calculated county-level opioid overdose, HIV, and hepatitis C diagnosis rates. Bivariate choropleth maps were created to display LHD service provision relative to disease burden in rural Illinois counties. Results: Most rural LHDs provided limited OUD-related services, although many LHDs provided HIV and HCV testing. Bivariate mapping showed rural counties with limited OUD treatment and HIV services and with corresponding higher outcome/disease rates to be dispersed throughout Illinois. Additionally, rural counties with limited LHD-offered hepatitis C services and high hepatitis C diagnosis rates were geographically concentrated in southern Illinois. Conclusions: Bivariate mapping can enable geographic targeting of resources to address the opioid crisis and related infectious disease by identifying areas with low LHD services relative to high disease burden. The United States is in the midst of a syndemic of opioid-use disorder (OUD), overdose and injection drug-related hepatitis C virus (HCV) infection incidence, with increased risk of human immunodeficiency virus (HIV) among people who inject drugs (PWID) . Recent Both the Centers for Disease Control and Prevention (CDC) and the National Association of County and City Health Officials have provided guidance with recommendations for state and local health departments (LHDs) on how to address this syndemic. These include strengthening surveillance and the provision of prevention and intervention services related to OUD, hepatitis C and HIV ,6. Many Illinois is one of several U.S. states that experienced a statistically significant increase in drug overdose deaths from 2015 to 2016 (34%) and again from 2016 to 2017 (14.3%) . IllinoiWe conducted a needs assessment using data from rural Illinois. Our analysis utilized bivariate choropleth mapping, a geographic information system (GIS) approach that is easily implemented and replicable, to evaluate concurrently and display the disease burden and LHD provision of services. This approach can assist state and LHDs in identifying geographic areas with higher disease burden and limited service provision to prioritize public health services and resources and to address the requirements of federal funding and state-level planning .A survey of Illinois LHD administrators was conducted to assess both OUD and injection-related infectious disease services currently provided by LHDs. We also used disease surveillance data to describe the burden of opioid overdoses, HIV diagnoses, and hepatitis C diagnoses at the county level. We first performed descriptive analyses of access to services and disease burden individually, and then constructed bivariate choropleth maps to display the relationship between these measures. Counties were the units of analysis as they serve as proxies for policy boundaries and most LHDs in Illinois serve individual counties. The analysis was considered non-human subjects research and so was determined to be exempt from review by the University of Chicago Institutional Review Board.Study data were collected and managed using REDCap electronic data capture tools hosted at the Illinois Department of Public Health (IDPH) . The surThe survey was sent to each of the 97 Illinois LHD administrators in April 2018. Follow-up emails were sent to non-responding administrators two weeks following the initial email and again, two weeks after the first follow-up email. We used the IDPH Center for Rural Health criteria to identify rural counties; counties were defined as rural if they were not part of a metropolitan statistical area (MSA) or, if they were part of an MSA and had a population of less than 60,000 . AnalysiIndividual services offered in a county were counted according to the number of geographic locations at which the service was provided. We then grouped services into disease-specific service categories: OUD treatment services; HIV prevention and treatment services; and hepatitis C prevention and treatment services. We assessed the median, mean, and range of the number of services provided by LHDs for each disease-specific service grouping.We calculated crude county-level disease diagnosis rates for the most recent years of data available from IDPH. We used 2016–2017 death certificates and emergency department visit data to identify fatal and nonfatal opioid-related overdoses by county ,19. CounA crude service density measure was calculated for all services and for sub-categories  for each county. We also calculated EB service density rates in GeoDa using these data. This measure was calculated by dividing the number of relevant services within a county by the 2017 county-level population estimates from the U.S. Census Bureau  expressed per 100,000 persons [We then developed bivariate choropleth maps to show the relationship between provision of LHD services and disease burden by tertile of each variable of interest. The following bivariate choropleth maps were developed for both crude and EB rates: (1) opioid overdose rates and OUD treatment services; (2) HIV diagnosis rates and HIV prevention and treatment services; and (3) hepatitis C diagnosis rates and hepatitis C prevention and treatment services. This approach was chosen to display covariation between the two variables of interest  .There are 83 rural counties in Illinois; 53 rural LHDs completed the survey for a response rate of 63.9% . Most reLower provision of OUD treatment was primarily located in the western and southern rural counties as indicated by the crude rate map A, while Opioid overdose, HIV and hepatitis C diagnosis rates also varied geographically . CountieBivariate mapping showed areas of high opioid overdose rates and low LHD provision of OUD treatment resources in different parts of the state . This waWe found that most rural counties in Illinois provided few harm reduction services, PrEP clinics, MAT, and naloxone-related services. While counties with low provision of OUD treatment services and HIV services and corresponding high diagnosis rates were more generally dispersed, counties with low LHD provision of hepatitis C services and high rates of hepatitis C diagnosis were primarily located in the southern part of the state. Interestingly, the southernmost 16 counties in Illinois are part of the federally designated Delta Regional Authority, an area of 252 counties and parishes described as the most economically distressed area of the country with low access to health care services like primary care ,25. FurtOf note, counties of highest discordance between disease burden and LHD service availability were not consistent across all three disease categories, demonstrating that jurisdictions could prioritize specific interventions  to optimize public health impact and strategically utilize available resources. This is critical for rural LHDs, who often face the dual challenge of limited resources and personnel for planning and implementation, as well as the increased responsibility to provide a broad range of programs including direct health services for their communities . ServiceComparing results across both crude and adjusted data, empirical Bayes rates are helpful for identifying persistent trends and assessing the sensitivity of results. Empirical Bayes smoothing stabilizes rates by borrowing strengths from other spatial units of the sample, using global characteristics of the whole study region to define a prior distribution. The assessment of prevention and treatment service rates in particular was sensitive to small reported numbers per county and skewed distribution, making adjusted rates essential to defining true patterns. Adjusted rates additionally made regional trends explicit in bivariate choropleth maps, highlighting persistent areas of relative low access and high disease burden—especially in southern Illinois.GIS approaches have frequently been used for disease surveillance as well as health policy and planning purposes . SpecifiOthers have noted that simultaneously mapping health services and population need is an effective use of GIS for LHD priority setting and program planning; however, bivariate choropleth mapping has been an underutilized approach for mapping health services and disease burden . PreviouOur study has several limitations. First, we did not receive a response from all rural LHDs in the state, giving us an incomplete representation of the provision of services by rural LHDs. Second, we assessed solely services provided by LHDs, not those provided by 87 rural federally qualified health centers, 233 rural health clinics, and other primary care providers, hospitals, and non-profit organizations; therefore, OUD-related resources available to rural populations may be underestimated . For exaOur study’s strengths include the replicability of the survey development, dissemination, and analysis, particularly the crude rate analysis. Additionally, our framework allows for quick and accessible needs assessments that can accommodate limited resources. Our analysis is unique in its attempt to gather health department service availability at the local jurisdictional level, and to overlay this information with disease surveillance data. Furthermore, in addition to crude rates, we calculated EB estimated rates to help reduce problematic variances and outliers.Our analysis proposes a framework for states and LHDs to perform rapid needs assessments of OUD, overdose, and related HIV diagnosis and hepatitis C diagnosis rates using simple survey and GIS methods. Such approaches may help to prioritize resource allocation in rural areas with low levels of opioid-related services, and higher rates of opioid overdoses and related infectious diseases.


Heart disease, diabetes, cancer, arthritis, and other chronic diseases are the leading causes of death and disability and the leading drivers of health care costs in the United States . Health 2017 Epidemiology Capacity Assessment Report enumerated 304 CDEs in all 50 states and the District of Columbia  in a state or local public health agency. Field assignees assist states by providing epidemiologic consultation and leadership for surveillance systems; offering expertise in designing epidemiological studies, analyzing data, evaluating chronic disease prevention and health promotion programs, and disseminating findings; providing data and identifying priority populations for public health program planning; and mentoring and training entry-level and mid-level CDEs and other staff members in epidemiologic methods and data interpretation.To date, CDC’s State Chronic Disease Epidemiology Assignee Program has benefited 36 states and New York City during its 28-year history . Field aArizona American Indian Health Status Summary Report for Data Year 2015  identify champions for enhancing capacity, 2) continually review and update the essential roles of CDEs, 3) expand the skills and competencies of the current and future workforce, 4) develop and enhance partnerships to improve data sharing, 5) leverage and link existing data sources, 6) improve the availability of local data, 7) fill data gaps to better measure determinants of health and health disparities, and 8) make data more actionable. Strong commitment is vital to building and maintaining capacity-building efforts in chronic disease epidemiology and surveillance in state, territorial, local, and tribal public health agencies. Throughout these capacity-building efforts and across all chronic disease epidemiology and surveillance efforts, the default view must be through a health equity lens.


We provide details of 306 plant species used to treat and manage respiratory infections and related symptoms in South Africa. About a third of the documented species belong to four plant families, namely the Asteraceae (15.7%), Fabaceae (6.1%), Lamiaceae (5.6%) and Amaryllidaceae (4.6%). An overwhelming majority of documented species are used as medicine to treat tuberculosis (40.2%), cough (36.6%), fever (29.1%), chest complaints (28.8%) and cold (23.2%). The potentially bioactive phytochemical compounds and associated pharmacological properties of the documented plant species are also provided. This data demonstrated strong correlation between phytochemistry, pharmacological properties and medicinal uses of more than three quarters (80.1%) of the documented species used against respiratory infections and related symptoms. Data of this nature can be used to identify research gaps on ethnomedicinal uses, phytochemistry and pharmacological properties of plant species used as herbal medicines. Specifications tableValue of the data•The data provide medicinal plants that are a primary source of medicines used against respiratory infections and related symptoms in South Africa.•The data on medicinal plants used against respiratory infections and related symptoms can be used as a vehicle for ethnopharmacological basis for drug research, pharmaceutical and health products development.•The data can also be used as a source of information for preserving useful plants implicated as therapy for respiratory infections and related symptoms.•Data on plants used as medicine for respiratory infections and related symptom in South Africa scattered across literature compiled in one database, and this can be used as baseline data in future research focusing on ethnomedicinal uses, phytochemistry and pharmacological properties of plant species used as herbal medicines1Plant-based remedies for respiratory infections and related symptoms have been in use in South Africa for several centuries. Respiratory infections particularly asthma, pneumonia, rhinitis, sinusitis and tuberculosis constitute the major causes of mortality and morbidity in both developing and developed countries of the world 2Data presented in the database encompasses plant species that are used to treat and manage respiratory infections and related symptoms in South Africa. Also included in the database are the plant parts used, phytochemistry and pharmacological activities of the documented species. This database was compiled based on selected research articles. In selecting the research articles, several pre-determined criteria were taken into consideration. Firstly, the data was compiled from research articles that focused on medicinal plants used to treat and manage respiratory infections and related symptoms in South Africa. The identity of the utilized herbal medicines was considered to be very important, including details of utilized plant parts. The data was generated from research articles written in English and published between 1950 and 2017. The research articles that were published as abstract, letters and data that could not be extracted or overlapped with data from other articles were excluded.www.ipni.org), the Missouri Botanical Garden׳s Tropicos Nomenclatural database (www.tropicos.org) and the Royal Botanic Garden and Missouri Botanic Garden plant name database (www.theplantlist.org). Information on the phytochemistry and pharmacological properties of the documented medicinal plants was generated from the electronic search engines and other literature sources included papers published in international journals, reports from international, regional and national organizations, conference papers, books, theses, websites and other grey literature. A total of 437 articles matched the inclusion criteria and were included in the review (Literature search for information on plant species that are used to treat and manage respiratory infections and related symptoms in South Africa was carried out from January to December 2017. The information was obtained from the main online scientific sites including Science Direct, SciFinder, Pubmed, Google Scholar, Medline, and SCOPUS. Searches were also undertaken in the library, University of Fort Hare, University of Limpopo and the dissertation search engines like ProQuest, Open-thesis, OATD and EThOS. The species name, botanical families, plant authority, and synonyms were verified using books, journal articles and internet sources such as the International Plant Name Index (e review . The dat


As a recognised effective and economical agent for dental caries prevention, fluoride has been used in many different fluoridation schemes implemented across the world. Considering the narrow ‘dose-gap’ between the benefit of caries reduction and the risk of dental fluorosis, it is recommended that fluoride intake is monitored by measuring urinary fluoride excretion. The aim of this scoping review is to map the current literature/evidence on fluoride intake and excretion studies in relation to the study population, settings, type of study design, methodology, and analytical approach.Embase/Ovid, MEDLINE/Ovid, CINAHL/EBSCO, Scopus/Elsevier were searched for relevant articles until April 2018. Studies were included if they reported intake and excretion of fluoride in healthy humans of all age groups. Findings were explored using a narrative synthesis to summarise studies characteristics and outcome measures.Removal of duplicates from the originally 2295 identified records yielded 1093 studies of which 206 articles were included. Only 21.6% of the studies were conducted in children (<8-year-olds). Most studies (38.8%) used drinking water concentration as a proxy for fluoride intake, whereas only 11.7% measured fluoride intake from all sources. Of the 72 studies that measured dietary fluoride intake, only 10 reported the validity of the employed dietary assessment method. Only 14 studies validated the urine sample collection methods. No information on the validity of the employed analytical method was reported by the majority (64.6%) of studies. Only a small proportion (8.7%) of the included studies investigated the association between fluoride intake and excretion.The findings reveal much variability in terms of conducting the studies and reporting the findings, illustrating a high heterogeneity in data collection across settings and populations. Future studies should provide more detail on sampling technique, measurement protocols , and on clearly defining the relationship between intake and urinary excretion of fluoride. While dental caries is a largely preventable condition, it still remains an important global public health problem, affecting 60–90% of schoolchildren and the vast majority of adults . The Worthere is not enough evidence to use biomarkers of fluoride exposure such as urine and nails, despite the fact that they take into account the absorption rate” along with the suggestion of more research being needed to establish a reliable biomarker of fluoride exposure [As urine is the most important metabolic pathway for elimination of fluoride from the body, it has been considered as a useful biomarker for contemporary fluoride exposure at a population- but not individual- level . The relexposure .Many factors can modify fluoride metabolism and consequently alter the relationship between fluoride intake and excretion, including age , dose and form of fluoride as well as acid-base disturbances , renal impairment, physical activity, nutritional status, composition of diet, and genetics . NeverthFluoride intake and excretion studies also have the potential to reach erroneous conclusions if they use an unfocussed study design, invalid methods of data collection (particularly fluoride exposure data) or non-standardised analytical techniques. For example, a study in which the existing analytical techniques for fluoride analysis were examined amongst nine internationally recognised laboratories, showed inconsistencies in the use of these fluoride assay techniques . The latDirect assessment of total fluoride exposure in a population (particularly children) can be difficult and expensive. Measurement of fluoride levels in urine has been suggested as an alternative method for estimation of fluoride exposure in populations and consequently as a basis for decisions on the use of fluoride for caries prevention. However, a clearer perspective is needed to better understand the association between fluoride intake and urinary fluoride excretion and specific situations in which urinary fluoride excretion is strongly associated with intake. Therefore, the aim of this scoping review was to map the current literature/evidence on the association between fluoride intake and excretion. The specific objectives of this review were to map this association in relation to the study population, setting, type of study design and methodology, including the analytical approach used. The ultimate aim of this systematic scoping review is to review the observational data found in fluoride intake and excretion studies in humans to provide a basis for more targeted research questions to address any research gaps and inform the design of future interventions.10.17605/OSF.IO/9CQGE).This systematic scoping review was guided by the Joanna Briggs Institute for systematic scoping reviews –16; and A three-step approach was applied for the search –16. The Searches were limited to studies in English and were included if they; (a) were conducted with heathy participants of any age group, gender or ethnicity; (b) were carried out at nursery, schools, preschool, kindergarten, child care centres, hospitals or community settings; (c) estimated fluoride intake from any customary sources of fluoride including water, diets, dental products and supplements and; (d) estimated urinary fluoride concentration or excretion.Studies were excluded if they measured occupational fluoride exposure. Studies were also excluded if they were reviews, opinion papers, policy documents, case reports, editorials or letters.A two-stage screening process was used to assess the relevant studies. The first level of screening involved only the study title and abstract by two reviewers (IO and JS). Afterwards, the full texts of the included studies were examined independently by two reviewers: IO and one of the co-authors . Discrepancies were resolved by discussions and consensus or by consultation with a third reviewer (VZ or LA).A standardised data extraction tool was developed by the research team and included the following information: author(s); year; title; aim of study; study design; country; setting; number of participants; age; gender; intake and excretion data; methods of data collection, analytical procedures, and outcome(s). The developed data extraction tool was tested with 10% of the articles before implementation. Data extraction was then carried out by one reviewer (IO) and verified by another (PV). Due to the work undertaken being a scoping review, no quality appraisal was undertaken .The extracted data were originally recorded in Excel and then imported to SPSS (version 22). Descriptive analysis was used to report the studies by their characteristics and outcome measures:A. Studies’ characteristics:Year: which was divided into two groups according to the year of publication; “prior to-” and “at or after-” 1999. This was based on the year of release of the 1999 World Health Organisation (WHO) manual guidelines on monitoring fluoride excretion in community prevention programmes ;Country: which was divided into low income economy, lower middle income economy, upper middle income economy and higher income economy, according to the World Bank Group classification ;Setting: nursery/schools, hospitals, community or unclear;Age: 0–8 years, 9–16 years, 17 years and above, or a combination of age groups;Gender; male, female, or both;Study design ,semi-FFQ, household survey, observed food frequency, or not reported;Method of assessment of fluoride intake from dental products: sample collection–i.e. toothpaste applied/expectorate collected, toothbrushing questionnaire, or not reported;Fluoride excretion: urine, or urine and faeces;Assessment of fluoride in urine: urinary fluoride concentration, urinary fluoride excretion , or not specified.Analytical method: fluoride-ion selective electrode, gas chromatography, spectrophotometry, titration, ion-exchange chromatography, or not reported;Validity of data and methods: fluoride intake, fluoride excretion (urine collection) and fluoride analytical method, or not reported;Reporting of the investigation of any relationship between fluoride intake and fluoride excretion.Findings were then further explored using a narrative synthesis.The search of four databases yielded 2295 records : Ovid MeStudies originated from 43 countries across most continents, with the majority from India (n = 36), USA (n = 35) and China (n = 24) . Table 1Tables A considerable number of studies  included in the review did not report the water fluoride concentration of the area investigated . Most stA small proportion  of studies measured fluoride excretion from both urine and faeces , whereasLess than half 45%; n = 93) of the included studies reported the investigation of any relationship between fluoride intake and excretion. Of the studies 3 of the –100 thatThis systematic scoping review synthesised the evidence for the association between fluoride intake and fluoride excretion in healthy human participants including the characteristics of the studies and findings of the existing research.The review showed that number of published studies increased post 1999; which might be due to a publication by the World Health Organisation (WHO) on “Monitoring of renal fluoride excretion in community prevention programmes in oral health”  in 1999.The findings of this scoping review show an unbalanced geographical distribution of articles and heavy concentration of studies from higher-income-economy countries. Interestingly, many studies were conducted in Asia, particularly in China and India. This could be related to; i) an increase in budgets for research and development  and/or; Studies suggest that the first two years of life are the most important period for development of fluorosis in early-erupting teeth, whereas the first eight years of life appear to be the most important period for development of dental fluorosis in late-erupting permanent teeth . HoweverThe findings also reveal great variability in terms of study conduct and the reporting of findings, illustrating a high heterogeneity in data collection across settings and populations. One of the main sources of heterogeneity was the methods of assessment of fluoride intake (from diet and toothpaste ingestion). In particular, the methods of dietary data collection widely differed between the studies , emphasiThis scoping review also shows the heterogeneous nature of the methods used to collect urine samples . When spAny relationship between fluoride intake and urinary fluoride excretion was explored in only 45% of the studies. The association between fluoride concentration of water and fluoride excretion was reported in 28 studies, whereas only 18 studies (9%) reported the association between fluoride intake and fluoride excretion . ConsideThe main limitations of this review were that; i) the searches were limited to studies published in English which may lead to language bias and the omission of relevant reports published in other languages, ii) the “grey literature”, which could reduce the impact of publication bias, was not included (mainly due to the concern with the manageability of overwhelming volume of identified peer-reviewed articles) and; iii) being a scoping review, the quality of the included studies was not assessed and the conclusion was mainly based on the existence of studies rather than their quality.The diversity of the included studies in this review and their findings highlight the need for strong evidence (collected via reliable reproducible methods) on appropriateness of urinary fluoride as a suitable measure of fluoride exposure for different situations; e.g. different age groups and types of fluoride-based caries-prevention programmes across diverse communities with their different geographical locations and wide ranges of dietary and oral hygiene habits.Future research should focus on: i) development  of gold standard universal methods for assessing fluoride intake from diet and toothpaste ingestion according to age group and dietary/oral hygiene habits; ii) development  of alternative methods for 24-hour urine collection such as urinary fluoride: creatinine ratio and urinary fluoride: specific gravity ratio in spot urine samples and iii) assessment of the ability of spot urine sample(s) to predict fluoride intake, according to age group, sources of fluoride exposure including fluoridation scheme.Likewise, future studies should fully report detail on sampling technique , measurement protocols , and clearly defined outcomes. Studies should also include reporting on the investigation of the relationship between total fluoride intake and urinary excretion of fluoride. Furthermore, researchers should ensure that potential differences between their findings and other studies/situations are included in their reporting.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.


Urbanization modifies landscape structure in three major ways that impact avian diversity in remnant habitat: habitat amount is reduced and habitat configuration and matrix quality are altered. The relative effects of these three components of landscape structure are relatively well-studied in agricultural landscapes, but little is known about the relative effect of urban matrix quality. We addressed this gap by investigating the relative effects of forest amount, forest configuration, and matrix quality, indicated by degree of urbanization and agriculture amount, on the diversity of three guilds of forest birds using data from 13,763 point counts from Pennsylvania, USA. Forest amount had the largest independent effect on forest bird diversity, followed by matrix quality, then forest configuration. In particular, urbanization had strong negative effects on the relative abundance and species evenness of all forest birds and the relative abundance of forest generalist birds. To our knowledge, these are the first results of the effect of urban matrix quality on forest bird relative abundance and species evenness independent of forest amount and forest configuration. Our results imply that conservation practitioners in human-modified landscapes prioritize maximizing forest amount, then reducing the effects of disturbances originating in the matrix, and then preserving large, spatially-dispersed forest patches to most effectively conserve forest birds. The urbanization of landscapes creates a pattern of remnant habitat patches surrounded by a matrix of residential, commercial, and industrial land uses. Three major aspects of landscape structure have important effects on avian diversity in remnant habitat in urban landscapes: habitat amount, habitat configuration, and matrix quality.The projected doubling of developed land in the USA in the first quarter of this century3. For example, Smith et al.4 found that total habitat amount had strong and consistently positive effects on the presence of forest birds within human-altered landscapes. Habitat amount has been hypothesized to positively influence biodiversity by means of passive sampling, higher habitat diversity, and lower extinction rates due to larger population sizes or less frequent and intense disturbances5.Habitat amount is the total area of remnant habitat in a landscape. Declines in biodiversity due to decreasing habitat amount are well-documented across taxa and regions6 fragmentation per se). Habitat configuration has somewhat equivocal effects on biodiversity. Empirical studies show that the independent effects of configuration are generally weak and may be positive or negative7. For example, Villard et al.8 found that the number of forest fragments had a significant positive effect on Veery (Catharus fuscescens) occurrence, whereas fragment mean nearest-neighbor distance had a significant negative effect on Scarlet Tanager (Piranga olivacea) occurrence. Habitat configuration has been hypothesized to affect biodiversity through a number of different mechanisms: the reduced persistence of small, isolated populations in small patches, negative or positive edge effects, increased predator-prey system stability through the provision of prey refugia in small patches, the enhanced co-existence of competing species, the reduced probability of simultaneous extinction of all subpopulations, higher immigration rate as a function of increased edge, increased colonization success due to smaller distances among patches, and landscape complementation6.Habitat configuration is the spatial arrangement of habitat in a landscape, independent of habitat amount  than within suburban or exurban landscapes 11. Three mechanisms have been hypothesized to explain the effect of matrix quality on biodiversity12. First, matrix quality may be indicative of dispersal mortality, e.g., collisions with buildings are a major source of mortality for birds13. Second, matrix quality may encompass variation in the availability of resources that are supplemental to those in habitat patches, such as bird feeders14. Finally, variation in matrix quality may be associated with varying disturbance occurrence, intensity, and frequency in habitat patches, such as human trail use15.Matrix quality is the degree to which human activities in the matrix disturb natural processes9. Though there is broad support for habitat amount being a more important determinant of biodiversity than habitat configuration23, there is less consensus regarding the relative effect of matrix quality. Existing evidence points to matrix quality having an effect on biodiversity smaller than that of habitat amount but larger than that of habitat configuration28.The relative effects of these three aspects of landscape structure on biodiversity are of great interest because they can directly inform the effectiveness of management and planning for species conservation in human-altered landscapes23), only one has included an explicitly urban indicator of matrix quality in analyses, i.e., amount of developed land29. Matrix quality in other studies has been assessed in terms of the influence of agricultural land uses and/or roads25 or manipulated in small-field experiments31. Therefore, there is a significant need for research investigating the relative impacts of habitat amount, habitat configuration, and urban matrix quality.Although some of the empirical studies that have compared the relative effects of these aspects of landscape structure have been carried out in landscapes altered by urban development  = −0.06 ; species evenness: β (95% CI) = −0.04 ) Fig. , S2. TheConsidering the effects just described, forest amount ranked first in importance to forest bird diversity relative to other components of landscape structure, having the absolute largest meaningful effects on three diversity measures  and the absolute second largest meaningful effect on an additional measure (the species evenness of forest-area sensitive birds) Figs  and 3. T6 and (references therein20). Forest amount typically had strong positive effects on the diversities of forest-area sensitive and forest generalist birds, which may be explained by passive sampling or lower extinction rates. Matrix quality variables also exhibited strong effects, particularly the negative effects of urbanization on the diversity of all forest birds and the negative effect of agriculture amount on forest-area sensitive bird species evenness. These effects are likely a result of disturbances associated with urbanization and agricultural practices that reduce habitat quality for forest birds and increase dispersal mortality. In comparison, forest configuration was the least important component of landscape structure, particularly to forest-area sensitive birds. These species preferentially occur in landscapes with large amounts of forest where variation in the configuration of forest may be restricted.Forest amount was the most important determinant of patterns of forest bird diversity in our data, followed by matrix quality and forest configuration. This order of importance matches our prediction based on the findings of a large majority of studies on the species5. The Passive Sampling Hypothesis assumes that the individuals in a community are randomly distributed within habitat in a region. A larger sample or area of habitat should contain a larger sample of the community’s individuals and species. The Theory of Island Biogeography predicts an increase in species number with island size as a function of the lower extinction rates of larger populations on larger islands. Larger populations of a variety of species may explain the positive effect of forest amount on species evenness in our data. The Disturbance Hypothesis posits that the frequency and intensity of disturbances diminish on larger islands, thereby leading to lower extinction rates. A fourth explanation for the positive effect of habitat amount on species number is the Habitat Diversity Hypothesis that states that niche diversity increases with habitat area. We discount this hypothesis in the present context because we controlled for habitat diversity in our models by including measures of landscape heterogeneity that accounted for the diversity of land cover types, including forest types, in landscapes. Our results also included a negative effect of forest amount on the species evenness of forest generalist birds. Some forest generalist bird species, such as the Prairie Warbler (Setophaga discolor) and the Louisiana Waterthrush (Parkesia motacilla), may make use of habitat types in addition to forest, such as shrubland and wetland. For a given area of agriculture and degree of urbanization in a landscape, an increase in forest cover results in a decrease in the covers of other habitat types, potentially negatively impacting a subset of forest generalist bird species and leading to lower species evenness for the group.According to our results, landscapes with more forest cover in Pennsylvania should host more forest generalist bird species and a greater relative abundance and species evenness of forest-area sensitive birds. The Passive Sampling Hypothesis, the Theory of Island Biogeography, and the Disturbance Hypothesis predict just such outcomes33 and domestic cat (Felis catus) densities34, the four most common sources of mortality for birds in North America13. Urban development is also associated with increased air pollution, higher air temperatures, human intrusion into remnant habitat, introduced plant species occurrence, higher brood parasite density, greater noise, and lower caterpillar abundance39. These factors have been shown to negatively impact the presence of breeding territories, hatching success, chick survival, the number of fledglings per nesting attempt, and chick and fledgling weight, and positively affect nest predation rate44.Urbanization had negative effects on the relative abundance and species evenness of all forest birds and the relative abundance of forest generalist birds. We attribute these results to the cumulative effects of the variety of disturbances associated with urbanization that act to increase avian mortality and decrease reproductive productivity. Urbanization is accompanied by increases in buildings, roads, and power lines45 and exposure to herbicides and pesticides48. Forest-area sensitive birds may also be particularly vulnerable to dispersal mortality in an agricultural matrix. Species that evolved in landscapes with continuous habitat and little or no matrix exhibit weak boundary responses and tortuous movement, characteristics that make them more prone to dispersal mortality in the matrix following habitat loss compared to species that evolved in patchy habitat49. Forest generalist birds, on the other hand, clearly benefited from the additional resources provided by agriculture. For example, approximately a quarter of our forest generalist bird species are predators of insect pests in crop fields and pastures, e.g., Northern Parula (S. americana)50.Agriculture amount was just as likely to have negative as positive effects on forest bird diversity. Forest-area sensitive bird relative abundance and species richness declined with increasing agriculture amount in landscapes, whereas forest generalist bird relative abundance and species richness increased. Forest-area sensitive bird species were defined as requiring large amounts of forest in landscapes that, because of the positive association between forest amount and forest aggregation in our data, were likely more continuous. Thus, forest-area sensitive birds may also be considered as species adapted to interior forest conditions. As such, they would be particularly vulnerable to the negative edge effects associated with agriculture due to elevated nest parasitism and predation rates20 and the diversity of other taxa6 and (references therein31). The lesser importance of habitat configuration with respect to habitat amount has been explained by the Habitat Amount Hypothesis, which states that the major determinant of species richness at a sampling site is the amount of surrounding habitat, by means of passive sampling, and that habitat configuration is unimportant51. Our results support this hypothesis in that forest configuration variables were less important to forest generalist bird species richness than forest amount. Forest configuration variables were also of lesser importance than forest amount and matrix quality variables to forest-area sensitive bird relative abundance and species evenness, which may seem surprising given that this guild is associated with continuous forest (see above). We suggest that, in the landscapes in which forest-area sensitive bird species preferentially occur, i.e., those with large amounts of continuous forest, there is necessarily less variation in forest configuration, and, as a consequence, a reduced likelihood of large meaningful effects of our configuration variables. We did find, however, relatively large effects of forest configuration variables on the diversity of forest generalist birds. Forest generalist bird relative abundance was higher in landscapes with lower forest patch densities or higher clumpiness, or spatial aggregation, of forest cover. Species evenness of the guild increased with decreasing forest patch density or decreasing forest aggregation. These results suggest that forest generalist species populations are larger in larger forest patches  and more evenly so when forest is spatially dispersed. The dispersion of forest, manifested as increasing inter-patch distances and/or increasingly irregular and elongated patch shapes, increases the interdigitation of forest and other land covers, thereby facilitating landscape complementation for the subset of forest generalist species that make use of multiple habitat types.Habitat configuration was the least important component of landscape structure affecting forest bird diversity in our study. This result adds to the existing evidence that the configuration of habitat, independent of habitat amount or matrix quality, exerts the smallest relative influence on forest bird diversity4, whose inclusion in future analyses may result in different conclusions than those presented here. Thus, it may be difficult to predict the relative importance of habitat amount, habitat configuration, and matrix quality for any given measure of biodiversity and set of landscape structure indicators.The relative importance of components of landscape structure on biodiversity is likely to be contingent on the diversity measure and indicators of landscape structure used in any given study. In the present study, the order of importance of forest amount, matrix quality, and forest configuration differed among diversity measures, with only two measures, forest-area sensitive bird relative abundance and forest generalist bird species richness, exhibiting the same ordering as forest bird diversity overall. The order of importance of our landscape structure components of interest would also differ if we had used a limited subset of landscape structure variables. For example, matrix quality would have been the least important determinant of overall forest bird diversity if we had not included agriculture amount in analyses, whereas it would have been the most important if we had not included forest patch density. Similarly, there may be indicators of matrix quality for forest birds that we did not measure, such as road densityThe overall order of importance of forest amount, matrix quality, and forest configuration that we report implies that planners and managers prioritize maximizing the amount of forest, irrespective of its spatial configuration, to most effectively conserve forest birds. If this is not feasible, e.g., in multi-use landscapes with high development pressure, planners and managers should focus on minimizing the effects of disturbances originating in the matrix that negatively impact habitat quality in forest remnants and increase dispersal mortality. This could involve educational campaigns, incentives, and/or bylaws to keep cats indoors, make windows more visible to birds, and reduce pesticide and herbicide use, road traffic, impervious surface cover, introduced plant species cover, and recreation in forest remnants. Finally, if maintaining or increasing forest amount and/or limiting disturbances originating in the matrix are not possible, the focus should be on preserving large forest patches that are elongate or irregularly-shaped and/or isolated from one another. This sequential implementation of conservation measures based on the relative effects of components of landscape structure is important. It ensures that planners and managers are implementing the most effective conservation measure, relative to those evaluated, given the limits of time and funding and the demands of other, often competing goals, such as building housing.In conclusion, we report that forest amount had the largest independent effect on forest bird diversity, followed by matrix quality and forest configuration. Forest amount generally had positive effects on forest bird diversity. Urbanization had negative effects on the relative abundance and species evenness of all forest birds and the relative abundance of forest generalist birds. To our knowledge, these are the first results of the effect of urban matrix quality on forest bird relative abundance and species evenness independent of forest amount and forest configuration. Agriculture amount had negative effects on the diversity of forest-area sensitive birds and positive effects on the diversity of forest generalist birds. Finally, forest patch density generally had negative effects on forest bird diversity, whereas forest clumpiness had positive and negative effects. The order of importance of forest amount, matrix quality, and forest configuration to forest bird diversity implies that planners and managers with the goal of conserving forest birds in landscapes modified by urbanization and agriculture prioritize maximizing forest amount, then reducing the effects of disturbances originating in the matrix, and then preserving large, spatially-dispersed forest patches.2 and intersects three Bird Conservation Regions: the Appalachian Mountains, the Lower Great Lakes, and the Piedmont52. Mountains (maximum elevation = 979 m a.s.l.) are covered by northern hardwood forest, whereas agriculture and urban development dominate lower elevations (minimum elevation = 0 m a.s.l.). Over half of the state’s population of 12.7 million people is concentrated in the metropolitan areas of Philadelphia and Pittsburgh in the southeast and southwest corners of the state, respectively53. Developed land in Pennsylvania more than doubled between 1992 and 2005, primarily at the expense of open space and agricultural land surrounding urban areas53.Pennsylvania has an area of 119,283 km32. Between 2004 and 2009, 22 trained PBBA staff performed 33,763 unlimited-distance roadside point counts, resulting in the observation of 176 bird species. Approximately eight point counts were conducted in each of 4,937 ‘blocks’, each approximately 24 km2 in area, covering the state. Points were situated at random locations that were shifted to the nearest non-highway road, while maintaining a minimum 400 m distance between points54. Counts occurred between May 25 and July 4 each year in suitable weather conditions between 5 and 10 am, with each point being visited once over the atlas period for 6 minutes and 15 seconds.We used point count data from the Second Pennsylvania Breeding Bird Atlas (PBBA)4. This range of scales generally encompassed the median natal dispersal distances and territory or home range sizes of the non-raptor and non-waterfowl species observed during the PBBA at the count locations that we selected59, as well as the scales at which habitat amount, habitat configuration, and matrix quality have been found to affect forest bird diversity20. In addition, we considered a wide range (1.9 orders of magnitude) and high density  of scales in order to improve the likelihood that we identified the true scales of effect of our landscape variables of interest60. In our selection of point count locations, we omitted those that were ≤16 km from the state border to ensure that all selected points were surrounded by entire landscapes at all scales. These selection criteria resulted in 13,763 landscapes at each of 10 scales  to account for scale-dependent variability in the relative importance of habitat amount, habitat configuration, and matrix quality63 and temporally64. For example, the effects of forest configuration on the Wood Thrush (Hylocichla mustelina)65 and Scarlet Tanager have been shown to vary regionally67, which might explain why evidence for area-sensitivity can be equivocal for some species . Our empirical approach avoided such ambiguities by ensuring that guild definitions were accurate in the context of our study. We derived guilds by comparing the cumulative distribution of counts of each species, across all 33,763 count locations, to the cumulative distribution of forest amount in landscapes at each scale using Kolmogorov-Smirnov tests of no difference (see Supplementary Table We excluded species from the 176 detected at selected points that had the following characteristics: (1) hybrid species; (2) irregular breeder in the state; (3) raptor; (4) waterfowl; and (5) occurrence at fewer than 30 count locations. The resultant selection of 101 species was divided into three habitat association guilds using species’ empirical patterns of occurrence across landscape scales. Avian responses to forest configuration have been shown to vary both regionally70. The estimator performs well when most observations are relatively rare species, as is commonly the case for point counts69. We measured species evenness using Pielou’s evenness index71. Chao 1 and Pielou’s evenness index were calculated using the fossil72 and asbio73 packages, respectively, in R, version 3.3.174.We calculated nine measures of diversity at each count location: relative abundance, species richness, and species evenness for each of the forest-area sensitive, forest generalist, and all forest  bird guilds. We measured relative abundance as the sum of species counts. We estimated species richness using the Chao1 estimator. Chao1 is a non-parametric estimator of true species richness that is based on the number of rare species observed75) and FRAGSTATS, version 4.276. We quantified forest amount as the proportional area of landscapes covered by the Deciduous, Evergreen, and Mixed Forest classes. We measured forest configuration using two metrics: patch density and the clumpiness index76. We chose these metrics because patch density is one of the more common habitat configuration metrics in the literature, while the clumpiness index is supported as a measure of configuration with low correlation to habitat amount that retains differentiability among landscapes79. Increasing forest patch density indicates that forest cover in landscapes is divided into a greater number of patches, whereas decreasing forest clumpiness indicates that forest cover in landscapes is more spatially dispersed.We measured the amount of forest and its configuration in landscapes using the National Land Cover Database 2006  and the areas of Developed land covers (r ≥ 0.70). The second component was highly correlated with High Intensity Developed cover (r ≥ 0.75). We termed the first component simply ‘urbanization’ and the second component ‘high intensity urbanization’. We quantified variation in matrix quality due to agriculture as the proportional area of landscapes covered by the NLCD’s Cultivated Crop and Pasture/Hay land cover classes. We chose the amount of agriculture, rather than a more explicit measure of the intensity of agricultural practices, because, to our knowledge, the latter data do not exist for our study extent. Also, we chose to include both of the NLCD’s agricultural cover classes because they each represented activities that would impact matrix quality for forest birds, such as row cropping (encompassed by the Cultivated Crop class) and pasturing (encompassed by the Pasture class)83. Matrix quality variables were created using FRAGSTATS, version 4.276, the isectpolypoly tool in the Geospatial Modelling Environment81, and R, version 3.3.174.We used degree of urbanization and agriculture amount in landscapes as measures of matrix quality. For each landscape scale, degree of urbanization was derived using a principal component analysis (PCA) of six standardized variables: the proportional area of landscapes in each of the NLCD’s four Developed land covers , area-weighted average population density, and area-weighted average housing density. We measured the population density and housing density variables as the averages of 2010 US Census block-level population and housing densities in landscapesWe used general linear modeling to estimate the relative effects of forest amount, forest patch density, forest clumpiness index, urbanization, high intensity urbanization, and agriculture amount on each of the nine measures of forest bird diversity. Models also included variables that accounted for local habitat quality, landscape heterogeneity, and species detectability r = 0.90, assuming that major sources of variation in the response have been included in the model85. In our case, the absolute value of correlations between pairs of explanatory variables in the best models of measures of forest bird diversity averaged 0.30 (range = 0.00–0.93) and variance inflation factors averaged 4.40 (range = 1.03–32.97) ), local habitat quality, and species detectability. As such, an increase in forest amount in a landscape occurred at the detriment of open water, barren land, shrubland, herbaceous covers, and/or wetlands, and represented an increase in the amount of habitat available to forest birds. Similarly, a change in forest patch density or forest clumpiness in a landscape represented a change in forest configuration, with no change in the amounts and intensities of other land covers, including forest, landscape heterogeneity, including the amount of forest-developed edge, local habitat quality, or species detectability. An increase in urbanization, high intensity urbanization, or agriculture amount in a landscape represented a positive or negative change in matrix quality for forest birds, depending on whether developed covers, including open space and low-density development, and cultivated crops and pastureland replaced land covers, i.e., open water, barren land, shrubland, herbaceous covers, and/or wetlands, with higher or lower rates of dispersal mortality, fewer or more resources, and/or higher or lower levels of disturbance for forest birds.74 using the glm function in the stats package and the vif function in the car package86.In order to gain a general understanding of the relative importance of forest amount, forest configuration, and matrix quality to forest bird diversity overall, we counted the number of times the variable with the largest absolute effect representing each component of landscape structure placed first, second, or third in effect magnitude across the nine diversity measures. In doing so, we considered only meaningful effects, i.e., those with 95% confidence intervals that did not overlap 0, with the exception of meaningless effects when diversity measures were meaningfully affected by two landscape structure components. We ranked these meaningless effects third in relative importance. The landscape structure component that placed first most often across diversity measures was deemed the most important determinant of overall forest bird diversity, the component that placed second most often was considered the second most important determinant, and the component that placed third most often was considered the least important. All analyses were performed in R, version 3.3.1Supplementary Information


The moulding together of artificial intelligence (AI) and the geographic/geographic information systems (GIS) dimension creates GeoAI. There is an emerging role for GeoAI in health and healthcare, as location is an integral part of both population and individual health. This article provides an overview of GeoAI technologies , and their current and potential applications in several disciplines within public health, precision medicine, and Internet of Things-powered smart healthy cities. The potential challenges currently facing GeoAI research and applications in health and healthcare are also briefly discussed. Artificial intelligence (AI), such as methods in machine learning, has been increasingly used in health and healthcare, particularly with the rise of high-performance and cloud computing capabilities , 2. HealBroadly, health intelligence can be characterised as applications of AI to improve health at the population level and at the individual level. Population-level applications include those aimed at promoting public health such as through the disciplines of environmental health, epidemiology, genetics, social and behavioural sciences, and infectious diseases. In contrast, individual-level applications can be geared towards precision medicine, or disease management that considers individual variability in genetics, environment, and lifestyle , 8. RegaSpatial science offers tools and technologies that enable us to understand, analyse, and visualize real-world phenomena according to their locations . GeospatThe purpose of this editorial is to provide an overview of GeoAI technologies , and current and emerging/potential applications of GeoAI in several disciplines within public health and precision medicine, as well as IoT (Internet of Things)-powered smart healthy cities. Smart healthy cities gather, generate and consume large amounts of big health and environmental data. GeoAI can play a key role in making sense of these data through intelligent, location-based big data analytics.Geospatial data refers to data containing a geographic component that identifies locations  or indicates geographically referenced features and conditions, such as the population of a district, seasonal weather of a region, number of vehicles passing a highway intersection, and geo-tagged social media data . NationaMachine learning, deep learning and data mining are essential methods forming the foundation of GeoAI. In particular, machine learning includes AI methods and algorithms for computers to obtain knowledge by iteratively extracting and learning from patterns hidden in raw data . Deep leFor example, Situational Awareness Geospatially Enabled (SAGE) is a military application and a GeoAI tool developed by the US Army Engineer Research and Development Centre . The SAGIn the commercial sector, the combination of business data, operational data and geospatial data with analytics and mapping visualisations has been suggested to provide valuable insights across many industries . There aAs the primary provider of geospatial data since 1990s , the pubPublic health seeks to promote health and prevent disease at the population level, involving multiple specialised disciplines that aim to understand and/or intervene on different aspects of population health. Several examples of recent and emerging GeoAI applications in the disciplines of environmental health, epidemiology, genetics, social and behavioural sciences, and infectious diseases are provided below.In environmental health, GeoAI has been used to conduct accurate and highly resolved modelling of environmental exposures, including measuring exposures that have historically been difficult to capture . For exa2.5) levels in the US using multiple predictors including satellite-based aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observing System satellite [2.5 concentrations [2.5 exposures in China, accounting for spatial non-stationarity in associations between predictors and PM2.5 using spatial smoothing kernels [Machine learning has experienced an increased presence in air pollution exposure modelling, allowing for methodologic advantages such as modelling nonlinear associations and the integration of multiple spatial big data sources to improve predictive performance . For exaatellite . Random trations , 26. A g kernels .2, and black carbon at a 30 m spatial resolution [Beyond satellite remote sensing, mobile air pollution sensors is another novel source of spatial big data that has been used to improve air pollution exposure modelling . Urban asolution . Spatialsolution . Furthersolution . For exasolution . The UFPsolution . High-diIn epidemiology, GeoAI has been used to describe and analyse the spatial distribution of diseases and to study the effect of location-based factors on disease outcomes. For example, to facilitate hypothesis generation related to the aetiology of preterm births, machine learning (K-means clustering) was used to determine spatiotemporal patterns of gestational age at delivery for 145 million births in over 3000 US counties from 1971 to 2008 using National Centre for Health Statistics Natality Files . These e2.5 and ozone with ZIP Codes from Medicare big data to examine their associations with mortality risk in the US [Environmental health also has close ties with epidemiology, as measured and/or modelled environmental exposures can be used for exposure assessment in study populations as part of environmental epidemiological studies. For example, a recent study used CNNs on Google Maps Static images and Google Places (of interest) to extract natural and modified elements of the built environment  to study in relation to Census tract-level obesity prevalence in the US from the Centres for Disease Control and Prevention (CDC) 500 Cities project . This ren the US . These eFurthermore, future epidemiological research could harness emerging sources of spatial big data to examine research questions regarding disease aetiology, potentially providing new insights into novel risk factors. For example, personal sensing collects data using the sensors embedded in mobile phones as well as through wearables such as Fitbits . SpatialIn genetics, deep learning has been applied to studying fields such as functional genomics  . Gene-enIn social and behavioural sciences, GeoAI has been used to help identify social and behavioural determinants of health as well as to conduct interventions using locational information. Electronic health records (EHRs) are a valuable longitudinal population-based big data source. EHRs allow for the linkage of spatial data to geographic variables such as ZIP Codes as patient addresses are routinely checked and updated for billing purposes . For exaDating apps, such as Tinder, are a feature of modern dating that could be a novel data source to answer health-related research questions. There are over 4 million paying users on Tinder . Tinder GeoAI has been used in infectious disease research for modelling or prediction of disease occurrence and for disease surveillance. Deep learning recurrent neural networks (RNNs) were used for real-time influenza forecasting at regional and city spatial scales in the US using spatial big data on Google Flu Trends (weekly estimates for different cities) and climate  from the National Climatic Data Centre . AnotherMachine learning (support vector regression) was used for dengue fever forecasting in China using data on climate, weekly dengue fever cases, and Baidu search queries . Validat2.5, pollen, and meteorological variables using high temporal resolution environmental monitoring data and spatial interpolation methods . Participants, most of whom reported having either asthma or allergic rhinitis, created saved locations via the app and were sent notifications when these saved locations experienced elevated PM2.5 or pollen levels. AirRater users reported developing a better understanding of how environmental conditions affect their health, which sometimes prompted action such as their timely use of medications.The practice of medicine involves making decisions based on obtaining as much information about a patient’s health as possible . PrecisiAnother potential application of GeoAI for precision medicine is through geomedicine, a term that has been used to describe considering the importance of a patient’s place history in disease diagnosis and treatment . CliniciA notable research endeavour in precision medicine is the NIH All of Us Research Program, which aims to collect data from one million or more people living in the US to accelerate research to improve health outcomes, advance the development of new disease treatments, and contribute to evidence-based research to develop more precise preventive care and medical treatments . One areIoT-powered smart cities rely heavily on the usage of sensors that can be embedded into buildings, roads, vehicles, devices, plants, animals and human bodies turning these physical objects into digitally connected “things” . These IIn light of this discussion, smart city initiatives include inherent components for a healthier environment, referring to the concept of smart healthy cities that aim to improve the quality of city lives and enhance wellbeing of citizens . The GeoFor example, in Barcelona, a city-wide network of IoT sensors and connected devices , provides valuable real-time data on noise, air quality and other types of environmental pollutants as well as the flow of citizens and traffic conditions, covering diverse locations across the city . The comThe integration of health big data and IoT sensing data could maximise the utility of GeoAI in realising the vision of smart healthy cities. However, such integration may not be easily achievable in practice. For example, China has initiated over 700 smart city projects in more than 500 cities since 2012. This endeavour led to the emergence of hundreds of smart city apps and services on health and transportation . While mThe US NIH is expected to fund more research into mobile imaging, pervasive sensing, social media, and location tracking in the future . As massA methodological challenge is the lack of labelled training data for AI algorithms. In particular, supervised learning involves predicting the label or response of each data point using a set of labelled training examples . TraininThere is an emerging role for GeoAI in health and healthcare as location is an integral part of both population and individual health. Novel sources of spatial big data, including social media, satellite remote sensing, and personal sensing, are being (and could be) analysed to answer research questions in more nuanced ways as part of a variety of disciplines including environmental health, epidemiology, genetics, social and behavioural sciences, and infectious diseases. GeoAI has been used to model and capture the environment around us, linking locations in which we live, work, and spend our time to these exposures  to explore their potential role in influencing health outcomes. GeoAI has also led to research for hypothesis generation, conducting new data linkages, and predicting disease occurrence. There are currently numerous population-level GeoAI applications for public health and IoT-powered smart healthy cities, and there are emerging opportunities for integration of GeoAI and location-based information into precision medicine such as via mHealth for interventions. Future research can expand on current GeoAI applications, such as modelling location-based features that have not been previously captured at a high spatiotemporal resolution, or analytics for newly emerging spatial big data source, to unlock new areas of research and advance our understanding of human health.


Since 1979, multiple CDC Kenya programs have supported the development of diagnostic expertise and laboratory capacity in Kenya. In 2004, CDC’s Global Disease Detection (GDD) program within the Division of Global Health Protection in Kenya (DGHP-Kenya) initiated close collaboration with Kenya Medical Research Institute (KEMRI) and developed a laboratory partnership called the Diagnostic and Laboratory Systems Program (DLSP). DLSP built onto previous efforts by malaria, human immunodeficiency virus (HIV) and tuberculosis (TB) programs and supported the expansion of the diagnostic expertise and capacity in KEMRI and the Ministry of Health. First, DLSP developed laboratory capacity for surveillance of diarrheal, respiratory, zoonotic and febrile illnesses to understand the etiology burden of these common illnesses and support evidenced-based decisions on vaccine introductions and recommendations in Kenya. Second, we have evaluated and implemented new diagnostic technologies such as TaqMan Array Cards (TAC) to detect emerging or reemerging pathogens and have recently added a next generation sequencer (NGS). Third, DLSP provided rapid laboratory diagnostic support for outbreak investigation to Kenya and regional countries. Fourth, DLSP has been assisting the Kenya National Public Health laboratory-National Influenza Center and microbiology reference laboratory to obtain World Health Organization (WHO) certification and ISO15189 accreditation respectively. Fifth, we have supported biosafety and biosecurity curriculum development to help Kenyan laboratories safely and appropriately manage infectious pathogens. These achievements, highlight how in collaboration with existing CDC programs working on HIV, tuberculosis and malaria, the Global Health Security Agenda can have significantly improve public health in Kenya and the region. Moreover, Kenya provides an example as to how laboratory science can help countries detect and control of infectious disease outbreaks and other public health threats more rapidly, thus enhancing global health security. Since 1979, the US Centers for Disease Control and Prevention (CDC) has worked in collaboration with the Kenya Medical Research Institute (KEMRI), Ministry of Health (MoH) and other key government institutions as partners to conduct public health science of national, regional and global relevance. Initially this work focused on malaria, tuberculosis (TB) and human immunodeficiency virus (HIV) and these efforts established the groundwork of this laboratory collaboration in Kenya. Then in 2004, the Kenya CDC Global Disease Detection (GDD) program within the now-named Division of Global Health Protection (DGHP) was established along with 10 other sites globally following the international outbreak of Severe Acute Respiratory Syndrome (SARS), and subsequent expansion of Influenza A/H5N1 globally. Later in 2006, the CDC Kenya Influenza Program was established, to provide specific focus on influenza surveillance, epidemiology, control and prevention. As part of the collaboration between KEMRI and DGHP, the Diagnostics and Laboratory Systems Program (DLSP) was developed comprising KEMRI and DGHP-Kenya scientific laboratory technical staff, aimed to support the establishment of diagnostic laboratory capacity at KEMRI for surveillance, epidemiological studies, outbreak investigations, vaccine trials, and clinical research. In parallel, complementary laboratory programs in the CDC Divisions of Global HIV and TB (DGHT), operating with funds from the President’s Emergency Plan for AIDS Relief (PEPFAR), and the Division of Parasitic Diseases and Malaria (DPDM) support similar work targeting those specific diseases. In 2014, concurrent with the West Africa Ebola epidemic, the Global Health Security Agenda (GHSA) was initiated, providing a framework to expand DSLP laboratory capacity building efforts.DSLP currently has several objectives in Kenya. First, to support infectious disease surveillance for acute respiratory infections, diarrheal diseases, vector-borne, viral hemorrhagic fever and zoonotic diseases in order to detect pathogens, determine disease burden and the impact of intervention studies. Second, to support outbreak response in order to detect epidemic pathogens and allow for rapid implementation of pathogen-specific control measures. Third, for diagnostic development and evaluation to allow for advanced pathogen characterization and discovery. Fourth, the development of laboratory capacity for the Government of Kenya (GoK) and the East African region in alignment with the International Health Regulations (IHR) with the expectation that all countries have the capacity to rapidly detect and control infectious disease outbreaks and other public health threats at their source. Lastly is the implementation of biosafety and biosecurity capacity in Kenya. This manuscript outlines the achievements of DLSP over the years in achieving these core objectives.DLSP current activities take place in three KEMRI laboratories, one in Kisumu and two in Nairobi Fig. . The KEMLaboratory-based surveillance is critical in accurately assessing trends in the incidence and prevalence of a disease and for monitoring impact on morbidity or mortality of disease-or-pathogen targeted interventions. DSLP supports several ongoing surveillance platforms for enteric and respiratory disease, acute febrile illness (AFI) and jaundice. Disease burden and intervention effectiveness cannot be easily translated from one setting to another, especially when the epidemiology, circulating strains or co-morbidities and other ecologic considerations are dissimilar. To introduce and sustain prevention programs such as vaccination, policy-makers need evidence of disease burden and impact of the intervention among other factors. DLSP has contributed to national and global burden estimates for a range of pathogens, most notably enteric and respiratory pathogens –10 TheseBetween 2014 and 2017, DSLP provided laboratory support for two observational rotavirus vaccine studies: Rotavirus-Vaccine Impact on Diarrhea in Africa (R-VIDA), a multi-country study that includes Kenya, Mali and Gambia; and Rotavirus Immunization Program Evaluation in Kenya ((RIPEK), a multi-site study within Kenya) to determine the effectiveness of the rotavirus vaccine. Recent influenza studies in Kenya that were supported by DLSP assisted in describing influenza disease burden in the country which was crucial information used by the Kenyan National Immunization Technical Advisory Group (KENITAG) for the recommendation of influenza vaccination for children ages 6–23 months –15. In aBacillus anthracis, Brucella spp., spotted fever group rickettsioses, and typhus group rickettsioses from persons aged 15–64 years and determined a high seropositivity suggesting frequent exposure to these pathogens in the Kenyan population. These studies allowed a recent prioritization of zoonotic diseases of public health importance in Kenya [Other zoonotic studies that DLSP has supported include those on brucellosis in humans and animals and risk factors for brucellosis transmission such as ingestion of raw milk, exposure to goats, and handling of animal hides, as well as the risk of transmission of other enteric pathogens from animals to humans , 18. DLSin Kenya , 20., cholera, Salmonella Typhi, shigellosis), aflatoxicosis, and vector-borne diseases   confirmation of Ebola virus from suspected and probable cases of Ebola virus disease for clinical management. The mobile Ebola laboratory tested ~ 6000 specimens in support of the Ebola response . DLSP haThe diagnostic capacity in DLSP laboratories has been augmenting annually since 2004. To date DLSP, across its three participating laboratories, is capable of testing for 153 different pathogens by rRT-PCR, serological assays, microscopy, bacterial and viral culture, immunofluorescence and TAC.  [Candidatus Rickettsia asembonensis strain NMRCiiT [As part of the DLSP collaboration, DGHP has recently acquired next generation sequencing (NGS) technology in KEMRI laboratories. NGS technology has expanded beyond the 1970s Sanger technology and 1988 capillary electrophoresis based sequencing instruments allowing for high throughput to produce 1.8 terabases in a single run which is approximately 1000-fold increase from the first generation sequencing technology. NGS is also capable of identifying new and multiple pathogens from single specimens  as well d, skin) . The mai NMRCiiT . SequencSince 2014, DSLP has joined with other CDC laboratory programs to further build national laboratory capacities, and augment previously established laboratory strengthening efforts. We began by identifying three important gaps within the national system . First, NPHLS is the national reference laboratory for Kenya MoH, that serves to confirm or test specimens submitted from all other tier level county hospital laboratories within the network. Two important gaps within NPHLS was the capacity of both the microbiology and influenza laboratories. In July 2016, DGHP, with support from the East African Public Health Laboratory Network (EAPHL), the World Bank, the American Society for Microbiology and FIND, assisted in preparing the MoH-NPHLS Microbiology reference laboratory for ISO15189 accreditation. There were 8 key elements needed for accreditation: 1) developing Standard Operating Procedures for all microbiology tests used for clinical diagnosis, 2) laboratory training for all microbiology assays 3) training for microbiology equipment , 4) equipment preventive maintenance, 5) participation in External Quality Assessment panel for microbiology 6) internal quality control 7) quality improvement protocols and 8) process improvement and control protocols. To ensure a successful ISO 15189 accreditation, DGHP worked together with MoH on all non-conformities identified by WHO auditors. Similarly DGHP assisted and prepared KEMRI laboratories operating as DLSP to successfully obtain ISO 15189 accreditation for its Nairobi and Kisumu laboratories in 2014., MacConkey agar, chocolate agar and sheep blood agar plates) to facilitate microbiology testing at both NPHLS and tier 2–6 hospital laboratories throughout Kenya. DLSP, in collaboration with NPHLS, provided training for microbiology in the AMR pilot sites at Thika and Kitale as well as mentorship in order to systematically implement this capacity throughout Kenya. Within 3 years, the project will expand to include an additional 8 counties with improved capacity in microbiology techniques, AMR, specimen referral and shipping  where DLSP trained county staff in stockpile management, sample collection, sample packaging, shipment and specimen chain-of-custody to support microbiology laboratories in these regions. Establishing an effective system for AMR characterization and surveillance requires a properly functioning microbiology laboratory with inventory management, quality media production, biochemical testing, serotyping, drug susceptibility testing capacity, specimen referral system, and shipping. In preparation for AMR testing, DGHP with support of the Defense Threat Reduction Agency (DTRA) and DGHT, assisted in developing the KEMRI Production Unit for the manufacturing of prepared microbiology reagents  from a CDC/KEMRI program, to the GoK MoH-NPHLS. DGHP and the CDC Influenza Program provided state-of-the-art molecular equipment and training to MoH-NPHLS technical staff to successfully diagnose influenza viruses. The capacity for rRT-PCR used for influenza detection will also be leveraged for the detection of other viral respiratory pathogens of public health interest such as Respiratory Syncytial Virus, Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and other emerging or reemerging respiratory infections. This enhanced surveillance effort is part of an initiative called Detection and Response to Respiratory Events which will incorporate both viral and bacterial respiratory pathogens. As of 2016, NPHLS NIC has successfully assumed diagnostic testing responsibility for 2 of the 5 influenza sentinel surveillance sites in the country . Laboratory mapping of level 2–6 laboratories allowed for systematic determination of the most effective use of laboratory capacity in the country and specimen referral systems as well as identification of the existing gaps. In collaboration with NPHLS and the Association of Public Health Laboratories (APHL), we developed a protocol to map all level laboratories in Kenya. There were three phases of this project starting in 2015. In the first pilot phase, we developed the tools and procedures to collect accurate data pertaining to the capacity of 30 laboratories located in three counties  within the NPHLS laboratory network. This information served to guide the second phase of this project, which consisted of estimating the resources required for data collection in the 47 counties and the frequency of sampling to keep the information for each laboratory updated to allow for the referral network to be developed and maintain updated. In the third phase, we then mapped 1776 tier level 2–6 laboratories within 40 counties in Kenya. Mapping was delayed for the remaining 7 counties due to security issues. We determined the diagnostic capacity of these laboratories using a standardized questionnaire, and geomapped their location. The aim is to use this information to develop a referral and reporting system within the country to rapidly refer test results and specimen transport within the counties or to NPHLS for surveillance and disease outbreak investigations. The referral system was designed to refer specimens from lower to higher level by diagnostic capacity of the 12 Kenya designated priority diseases, 8 zoonotic diseases and 18 especially dangerous pathogens and contaminants. For example, if Machakos county was unable to diagnose a suspected cholera case at a level 3 laboratory, those mapping data would identify the closest level tier 5 county hospital laboratory with cholera culturing capacity for referral of the specimen. If within that county the diagnostic capacity did not exist, those mapping data would provide the closest neighboring county with cholera diagnostic capacity or the specimen would be referred to NPHLS.Laboratory Information Systems (LIS) ties these GHSA laboratory capacity building activities by supporting the real-time reporting of infectious diseases required by Integrated Disease Surveillance and Response (IDSR) and the IHR. Using LIS platforms (Labware and Basic Laboratory Information System (BLIS)) established by DGHT and our implementing partners ITECH, we assessed the expansion of LIS to report other pathogens in addition to HIV and TB in two counties in Kenya, across a total of 26 facilities (13 facilities per county). A questionnaire was administered to collect information from county and sub county surveillance officers as well as the facility focal persons for Information and Communication Technology, surveillance and laboratory. The goal of the project is to support eventual increased efficiency, sensitivity and timeliness of IDSR reporting through establishment of automated LIS. Although HIV and TB reporting was electronic, our assessment of the 26 selected facilities for other pathogens revealed that collation of surveillance data at the facilities was manual and paper-based while transmission of these data to sub county surveillance officers was through an unstructured SMS which is then fed to the web-based IDSR website. Currently, we are coordinating the strengthening of LIS in the two pilot sites for AMR, Thika and Kitale counties, which had a functional LIS supported by CDC DGHT and EAPHLS (World Bank) respectively, but only for HIV data. DGHP supported the creation of a microbiology module to allow for reporting microbiology surveillance laboratory data to a central repository at NPHLS. Future plans are to continue to expand the LIS to other facilities with proprietary systems and then progress to lower tiered facilities using BLIS, an open source software.Lastly, DLSP provides laboratory capacity by mentoring interns and sponsoring training courses in coordination with pathogen specific subject matter experts from CDC. Over the last decade DLSP trained technical staff from Kenya and neighboring countries in PCR, ELISA and microbiology diagnostic techniques to detect multiple pathogens Fig. . In addiKenya has a biosafety program developed by DGHT and MoH which established containment practices of those individuals working in the laboratory and principles to avoid unintentional exposure to pathogens or biologicals however this program did not include biosecurity which is the physical containment of a pathogen to protect both animals and humans from either intentional exposure to harmful biological agents . The bioKenya’s laboratory expertise and capacity has expanded since 2005 to include the detection of a wide range of pathogens that are threats to global public health. DGHP Kenya has supported KEMRI by establishing DLSP to support this effort and leveraged established platforms from all CDC programs in Kenya such as HIV, TB and malaria in strengthening national laboratory capacity especially at the county level. Moreover, DLSP has participated in over 50 research projects which have yielded over 180 publications. These studies have increased the diagnostic testing capacity of the CDC supported KEMRI and MoH laboratories and thereby contributed to a more rapid detection and control of public health threats thus enhanced global health security. Also, the expertise of DLSP has been harnessed through participation in disease outbreak investigations in Kenya and surrounding countries with measurable public health impact. As a result of these efforts, DGHP-Kenya has supported the establishment of > 100 assays for 153 pathogens for surveillance, disease outbreak investigations and research in Kenya, provided reference laboratory support for the investigations of 205 outbreaks from 2007 to 2016, and detected 2 pathogens not previously diagnosed in Kenya. DLSP support to the national-level laboratories has led to WHO accreditation for the national microbiology laboratory and by 2018 the Kenya NIC is expected to be accredited by the WHO.In recent years as part of GHSA, DLSP through NPHLS, has transferred laboratory capacity country-wide to various tier level laboratories in strategic locations informed by our GHSA-sponsored laboratory mapping project and through expansion of surveillance for AMR, respiratory disease and AFI, to ultimately provide a network of decentralized laboratories and high quality reference laboratories prepared to respond rapidly to and prevent nascent public health events and threats to Kenya and worldwide. This capacity was recently recognized by the newly established African Centers of Disease Control which has designated Kenya as a regional collaborating center, to support disease prevention and outbreak response throughout the region.