BACKGROUND:Air pollution, especially particle pollution, is increasingly recognized as a potential perioperative risk factor, yet modeling environmental exposures in surgical cohorts remains methodologically underdeveloped. We demonstrate a Bayesian hierarchical framework to quantify probabilistic associations between preoperative fine particulate matter (PM2.5) exposure and postoperative complications, highlighting its interpretability and flexibility for clinical environmental epidemiology. METHODS:We conducted a single center, retrospective cohort study using data from 49,615 surgical patients in Utah who underwent elective surgical procedures from 2016 to 2018. Patients' addresses were geocoded and linked to daily Census-tract level PM2.5 estimates. The exposure variable was defined as the maximum PM2.5 concentrations in the 7 days prior to surgery. The binary outcome was a composite of postoperative complications: pneumonia, surgical site infection, urinary tract infection, sepsis, stroke, myocardial infarction, or thromboembolic event. A hierarchical Bayesians regression model with weakly informative priors was used adjusting for age, sex, season, neighborhood disadvantage, and the Elixhauser index of comorbidities with census tract as a group (random) effect. We present posterior estimates with credible intervals, highlight model transparency and sensitivity, and discuss contrasts with standard frequentist methods. RESULTS:Postoperative complications were associated in a dose-dependent manner with higher concentrations of PM2.5 exposure. We found a relative increase of 8.2% in the odds of complications (OR = 1.082) for every 10.ug/m3 increase in the highest single-day 24-h PM2.5 exposure during the 7 days prior to surgery. For an increase in PM2.5 from 1 to 30 ug/m3, the odds of complication rose to over 27% (95% CI: 4%-55%). The results were robust across prior choices and model specifications. We report full posterior distributions and highlight advantages of Bayesian modeling for uncertainty quantification and clinical interpretability. CONCLUSIONS:This case study demonstrates the application of hierarchical Bayesian modeling to quantify the probabilistic associations between preoperative PM2.5 exposure and postoperative complications, highlighting transparent risk estimation and uncertainty characterization that may inform the design of future multicenter perioperative environmental studies. EDITORIAL COMMENT:Using Bayesian statistical analysis, the authors demonstrate a dose-dependent risk for postoperative complications in patients exposed to air polluted with fine particulate matter with a size of less than 2.5 μm.

Background:Social determinants of health continue to drive persistent disparities in perioperative care. Our team has previously demonstrated racial and socioeconomic disparities in perioperative processes, notably in the administration of antiemetic prophylaxis, in several large perioperative registries. Given how neighborhoods are socially segregated in the United States, we examined geospatial clustering of perioperative antiemetic disparities. Objective:The study aimed to determine whether disparities in perioperative antiemetic prophylaxis exhibit geographic clustering based on neighborhood-level disadvantage and whether patients from disadvantaged communities are more likely to be undertreated after adjusting for individual postoperative nausea and vomiting risk. Methods:We conducted a retrospective cohort study of anesthetic records from the University of Utah Hospital involving 19,477 patients who met the inclusion criteria. We geocoded patient home addresses and combined them with the census block group-level neighborhood disadvantage, a composite index from the National Neighborhood Data Archive. We stratified our patients by antiemetic risk score and calculated the number of antiemetic interventions. We used Poisson spatial scan statistics, implemented in SaTScan (Information Management Services, Inc), to detect geographic clusters of undertreatment. Results:We identified 1 significant cluster (P<.001) of undertreated perioperative antiemetic prophylaxis cases. The relative risk of the whole cluster was 1.44, implying that patients within the cluster were 1.44 times more likely to receive fewer antiemetics after controlling for antiemetic risk. Patients from more disadvantaged neighborhoods were more likely to receive below-median antiemetic prophylaxis after controlling for risk. Conclusions:To our knowledge, this is the first geospatial cluster analysis of perioperative process disparities; we leveraged innovative geostatistical methods and identified a spatially defined, geographic cluster of patients whose home address census-tract level neighborhood deprivation index predicted disparities in risk-adjusted antiemetic prophylaxis.

Background:While exposure to fine particulate matter air pollution (PM 2.5 ) is known to cause adverse health effects, its impact on postoperative outcomes in US adults remains understudied. Perioperative exposure to PM 2.5 may induce inflammation that interacts insidiously with the surgical stress response, leading to higher postoperative complications. Methods:We conducted a single center, retrospective cohort study using data from 49,615 surgical patients living along Utah's Wasatch Front and who underwent elective surgical procedures at a single academic medical center from 2016-2018. Patients' addresses were geocoded and linked to daily Census-tract level PM 2.5 estimates. We hypothesized that elevated PM 2.5 concentrations in the week prior to surgery would be associated with an increase in a bundle of major postoperative complications. A hierarchical Bayesians regression model was fit adjusting for age, sex, season, neighborhood disadvantage, and the Elixhauser index of comorbidities. Results:Postoperative complications increased in a dose-dependent manner with higher concentrations of PM 2.5 exposure, with a relative increase of 8% in the odds of complications (OR=1.082) for every 10ug/m 3 increase in the highest single-day 24-hr PM 2.5 exposure during the 7 days prior to surgery. For a 30 fold increase in PM 2.5 (1 ug/m 3 to 30ug/m 3 ) the odds of complication rose to over 27% (95%CI: 4%-55%). The association persisted after controlling for comorbidities and confounders; our inferences were robust to modeling choices and sensitivity analysis. Conclusions:In this large Utah cohort, exposure to elevated PM 2.5 concentrations in the week before surgery was associated with a dose-dependent increase in postoperative complications, suggesting a potential impact of air pollution on surgical outcomes. These findings merit replication in larger datasets to identify populations at risk and define the interaction and impact of different pollutants. PM 2.5 exposure is a potential perioperative risk factor and, given the unmitigated air pollution in urban areas, a global health concern.

King Cholera and the first geospatial analysis (GA) by an anesthesiologist King Cholera had wreaked havoc on the health of Londoners for generations, but the outbreak on August 31, 1854 was extreme even by a society accustomed to mass death. Within 3 days, 127 people had died, and within a week, a majority of the population had fled the area. John Snow, considered a pioneer of anesthesia for administering Chloroform to Queen Victoria during the birth of Prince Leopold in 1853, was skeptical of the dominant miasma (airborne) theory of disease spread for Cholera.1 With the help of what would later become known as a Voronoi Diagram,2 Snow was able to visualize geospatial proximity, namely that patients who had consumed water from the Broad Street pump eventually contracted Cholera, while those who consumed water from different sources did not contract the disease. Presented with this map to show clusters of disease, the local authorities disabled the pump (Fig. 1).Figure 1: John Snow Map on mode of communication of cholera. This figure is taken from the map of the book “On the Mode of Communication of Cholera” by John Snow, published in 1854 C.F. Cheffins, Lith, Southampton. Use is in public domain. This is the first example in the literature of a dot map used to display density of cases in a geographic context and is a foundational map of medical geography and epidemiology.We leave it to historians to dispute whether or not the famous map of the outbreak was created until after the outbreak.3 Regardless, the reasoning and approach to the epidemic employed by Snow became foundational work for epidemiology and. GA Snow linked employment with certain companies to worsening of disease transmission, and by identifying geographic clusters of mortality, linked the Cholera outbreak to a polluted water source,4 an approach similar to the focus of this manuscript. The cross-disciplinary expertise, required for GA, common in the days before medical specialization and enabled by the relative paucity of development in various fields compared with today, can be reborn with the advent of Electronic Health Records (EHR) systems that make the tools of medical geography accessible to a wide variety of health care researchers and clinical subspecialties. As the story of John Snow demonstrates, the combination of GA and specialty-based medical knowledge (eg, Anesthesiology and Surgery), can serve as a method to derive insights into socioeconomic and environmental drivers of perioperative care and may lead to the design of appropriate interventions to ameliorate disparities in outcomes. Social context as key driver of health and health care disparities The focus of this manuscript is how context, location, and geography impact health and health care processes.5 Social determinants of health (SDOH) are the fundamental causes of disease, where primary causes manifest eventually as medical conditions. The central SDOH mediate the disease pathway all along; actionable mechanisms of disparity invite targeted countermeasures, once they are clearly identified and exposed. For example, food deserts, the absence of parks, and racial stress all contribute to obesity, a risk factor for obstructive sleep apnea. Night shifts with insufficient rest may lead to a car accident; language barriers and poor health literacy may hinder the provision of regional anesthesia; excessive opioid administration may trigger a respiratory arrest. In a second example, poor vision secondary to a cataract could be corrected with cataract surgery, but lack of social capital and health insurance prevent a surgical intervention. The poor vision compounds the social isolation of the patient, further enhancing cognitive decline and frailty. When a fall eventually leads to a hip fracture and hospital admission, the underlying fundamental causes of disease were poverty, lack of health insurance, social isolation, but the admission diagnosis code will be an unfortunate accident. In Table 1, a fictitious rural patient tells a harrowing story illustrating how geography, socioeconomic status, and ethnicity impact health care access and care, with the SDOHs of health tabulated as key drivers of health care processes and outcomes, with potential remedial action targeting concrete mechanisms. Our fictitious narrative of the tremendous hardships faced by rural, poor, underserved and minoritized populations is corroborated by quantitative and qualitative studies.6–8 An example of how SDOH impacts disease outcomes is in the treatment of melanoma—the skin cancer with the highest mortality in the United States. In fact, the largest Black-White disparities in cancer survival is in melanoma—an absolute survival difference of 22%.9 Retrospective database analysis have shown that older patients, non-White patients and those on Medicare/Medicaid are less likely to be offered a sentinel lymph node biopsy procedure as part of their surgical management.10,11 The sentinel lymph node biopsy is the most important predictor of prognosis for patients with melanoma and facilitates decision about adjuvant treatment as well as intensity of surveillance and therefore omitting it as part of treatment for patients in whom it is indicated is a great disservice. Patients who live in rural communities present with late-stage melanoma compared with their urban counterparts, face significant economic, financial and emotional hardships as care for complex cancers are usually hundreds of miles away from their work and local network of friends.6,12,13 The social and geographic circumstances we are born into, drive perioperative outcomes. Parental income and school district predicts scholastic achievement better than the teacher quality and is a better predictor of longevity than a person’s genetic code. Local pollution may trigger obstructive pulmonary disease.14 Occupational hazards can trigger acute and cause chronic conditions, contingent also on local mitigation strategies and safety precautions taken, which in turn depend on legal and social context. The recent US Supreme Court decision on Roe v Wade, with accompanying in state-level policy responses to the legality of abortion services, underlines the importance of legal and social context for health services offered and the circumstances under which care is offered or denied. Redlining, “cherry picking” and other racist and xenophobic policies illustrate that race and class both matter for access to quality care, and that context impacts the care we receive and the resulting outcomes.15–18 In summary, social circumstances are fundamental causes of some diseases.19 Social circumstances are distal causes of disease in contrast to the more proximal causes traditionally the focus of medical research and teaching. For example, myocardial infarct may be due to atherosclerosis, in turn the result of uncontrolled diabetes mellitus, hypertension, and hyperlipidemia, as the proximal causes. SDOH—the conditions we are born into, learn and live under, work and live in, and age in—are the distal and upstream drivers (through poverty, health literacy, insurance status, income, race/ethnicity) of the proximal causes (diabetes, obesity, hypertension)19 SDOH explains why some children have worse postsurgical outcomes after controlling for comorbidities.20 It also explains why some persons face barriers to access needed chronic pain services,21 the particular need of geriatric communities,22 or why some Black children receive less pain medication for acute appendicitis.23 SDOH facilitates postoperative recovery for some patients but fail to rescue others,24 who fall between the cracks of social networks and medical care. Table 1 - A fictitious narrative illustrating mechanisms of cancer disparities. Social determinant of health Mechanism Countermeasure Lack of transportation, high cost of travel, centralized care Reduced access to quality tertiary care Satellite clinics, public transport, decentralized health care Health literacy, social capital Delayed recognition of signs and symptoms Health curriculum, routine primary care physician visits Trust in health care system, culturally congruent care Compliance with primary prevention and best practices Community nurses, health system confidence building Poor working conditions, inflexible work hours Unable to attend to health and care needs Social policy and workers unions. family and sick leave of absence Poverty, lack of insurance, high health care cost Health care affordability Universal health insurance, living wages, social justice Unsafe working conditions Occupational exposure Occupational safety Environmental hazards Airborne exposure Environmental policy This fictitious narrative illustrates mechanism how social determinants of health (SDOH) can lead to disparities in rural cancer care, and what countermeasure might target such mechanism, [with SDOH in brackets in the text] 6–8:“Traveling back and forth for my cancer treatment is very challenging. [Lack of transportation, high cost of travel, centralized care] My name is Talamo. I live with my wife and two kids and work in one of the mines in Utah, [Unsafe working conditions, Environmental hazards] while my wife looks after the kids.Five years ago, I started having trouble breathing, and I didn’t think much of it until the dizziness began and the headaches began. [Health literacy] I needed to see a doctor, but I don’t get paid if I don’t work. [Inflexible work hours] My wife convinced me to go to a local clinic 40-minute drive away, [Centralized care] but it’s always hard taking off work and even worse getting there. [High cost of travel] Our area has no bus routes, and my car has been faulty for over a week now. [Lack of transportation], Thankfully, my buddy agreed to drive me to the clinic and back since he was off work. [High cost of travel]Three weeks was the earliest appointment time. [Social capital] While there, the doctor took my blood for tests and asked me several questions but couldn’t tell me what was wrong, or at least I did not understand what she was saying. [Health literacy] She spent very little time with me, no wonder she could not figure it out. [Culturally congruent care] I was given pills for the headache and told to get plenty of rest and drink lots of water. My grandmother died after she was seen in the same clinic. I decided not to take the pills. [Trust in healthcare system] The next day, I returned to work; I had no choice. [Inflexible work hours] But the headaches became more frequent and the dizziness almost unbearable. I could go to the ER an hour and a half away, but I can’t afford the bill. [Poverty] I have insurance, but I can’t afford the copay. [Lack of insurance, high healthcare cost] My wife’s grandfather works as a traditional healer in our small town. I saw him and he had a very long talk with me. He advised some life changes and a special tea, which made me feel somewhat better. [Culturally congruent care]Eventually, I went back to the same clinic six months later, and the doctor did a chest x-ray. He found a lump in my lungs but could not explain it precisely. This all did not make sense. [Health literacy] I was told to see a specialist a four hours’ drive from me, [Centralized care] but I don’t know what to do. [Lack of transportation, high cost of travel] I have a wife and kids to take care of. Time passed. My headache has worsened; some days, I could barely get out of bed, and now I can barely see from my right eye. [Health literacy] I woke up one morning coughing hard, struggling to breathe, and later found myself in an emergency room two hours away from our home [Centralized care] and a buddy of mine beside me. I had fainted. The doctor ran some tests and a CT scan of my chest. He also said a lump is growing on my chest, making it hard for me to breathe. He was also concerned about my other symptoms and thought this could be cancer that had spread to my brain, causing headaches, dizziness, and now a loss of vision in my right eye. I wept the entire night.” Dimensions of SDOH We propose to conceptualize SDOH in 3 dimensions, pertaining to (1) the identity (REAL for Race, Ethnicity, Affinity, and Language),25,26 (2) the social standing (SES) and (3) circumstances related to location and geography (GEO).17 Race and class both matter for health care outcomes, and so does geography.27 This article focuses on the third dimension, geography and location, but we need to consider interactions between REAL, SES, and GEO. The different dimension of SDOH (REAL, SES, GEO) can conspire insidiously to compound disparities in access to health, health care processes and outcomes: For example, poverty (SES) compounds barriers to access care in rural areas due to transportation cost.28,29 Provider racism (REAL) against a Black parturient may be accentuated by community health beliefs and health illiteracy (SES).30 Both examples also illustrate how SDOH at the individual person level (Black race, or poverty) may interact with family or community level SDOH (community health beliefs or absence of health services in rural areas).31 This conceptualization can be rendered in a polar diagram with 3 axis, REAL, SES, and GEO (Fig. 2); REAL stands for Race, Ethnicity, Affinity and Language, characteristics pertaining to identity; SES refers to socioeconomic characteristics like wealth, social or legal status, scholastic achievement, income; finally, geospatial pertains to geographic and location characteristics, for example the home, the neighborhood, the built environment, and other location characteristics. The individual is in center of the polar diagram. However, characteristics of an individual’s family, neighborhood, community, county, workplace, the state or nation they live in influence their health, access to care, health care processes and outcomes. These levels are depicted in progressively more peripheral perimeters from the individual. SDOH can be organized according to their axis and their proximity to the individual. For example, a person’s ethnicity is in the center of the polar diagram of SDOH on the REAL axis, and through xenophobia or racisms at the hand of clinicians can lead to barriers to optimal care. A food desert would be a characteristic of the built social environment of the neighborhood GEO axis and a bit further out on the polar diagram.17Figure 2: Polar dimensions of social determinants of health. We organize social determinants of health (SDOH) in this figure by spatial level and in 3 axis. SDOH concern (1) identity: REAL (Race, Ethnicity, Affinity, and Language), (2) socioeconomic status: SES (income, social capital, health literacy, etc.), and (2) the geographic domain: GEO (geographic factors, eg, food desert, availability of public transport, spatial accessibility of medical services). SDOH can act at different spatial levels, pertaining to the individual, their family, community, neighborhood, county, state, and nation. Individual mechanisms can be placed in a polar diagram to illustrate intersectionality and interaction between mechanisms leading to perioperative process and outcome disparities. Pollution is an example for a geographic SDOH acting at the state or community level. The impact of health literacy (SES factor) may span the personal, family and community level. Racism (REAL factor) may act at different spatial scales by different mechanisms: interpersonal racism drives disparities at the person-level, for example, when a clinician neglects a Black patient; structural racism may act at the state or community level, for example, through apartheid. Food desert or poor Public Transport are SDOH somewhere between the GEO and SES axis, acting more at the community than the personal level.The impact geospatial factors on perioperative access and process disparities Perioperative health care disparities can concern access to care, perioperative care processes or subsequent postoperative outcomes. Each approach (focus on access vs. process vs. outcome), has advantages and disadvantages. Below, we pick 2 examples of perioperative process disparities to illustrate the power of GA to explore actionable mechanism with a view to suggesting potential remedial action. The impact of US-census tract level SDOH on equitable antiemetic prophylaxis is the focus of the first case study,32 and the impact of state level policy on access to cataract surgery is illustrated in the second case study. SDOH impact the trajectory of cataract patients. The development of cataracts is driven by exposure to sun and professional exposure. Health literacy, social capital and connections facilitate the recognition of visual problems and a timely diagnosis, but these can be hindered by language barriers. Insurance status will drive access to ophthalmology services and the scheduling of cataract surgery. Lack of transport, social support, and poorly controlled comorbidities dues to lack of primary care can interfere with successful completion of cataract surgery and recovery. The goal is to understand processes leading to disparities with the same granular detail as any cancer pathophysiology. Such granular understanding of cause and effect, mechanism and pathways leading to perioperative disparity, would allow to target and test concrete countermeasures in a framework of continuous quality improvement as already practiced by other specialties.33,34 The added value of anesthesiologists as perioperative physicians would come from improving health equity through original investigations of mechanism of perioperative disparity embedded in continuous quality improvement efforts. GA and the boundaries of geography We begin by explaining GA as defined by the Environmental Systems Research Institute Inc. (ESRI): “The process of examining the locations, attributes, and relationships of features in spatial data through overlay and other analytical techniques in order to address a question or gain useful knowledge.” Spatial analysis extracts or creates new information from spatial data.35GA has been used in a variety of different fields, from urban planning to business development to social sciences and public health. What makes GA distinct from standard statistics is the use of spatial information within a relational database, where statistical analyses are dependent on the use of information that corresponds to a location in space, most usually corresponding to a precise latitude and longitude on planet Earth, and or to the relation of areas in space, notably polygons that represent specific areas such as a Census Tract. This point cannot be emphasized enough to the non-Geography audience. The use of any type of data with a “where” component requires special care and approaches to understand them properly and use them precisely in data analysis and interpretation. This is explained with reference to the case of the water crisis in Flint, Michigan, below. Geocoding and the perils of the Zip Code: The Water Crisis of Flint, Michigan Geocoding is a tool used extensively by Geospatial Information Systems (GIS) practitioners and is defined as the transformation of a textual address field into a latitude and longitude within a geographic information system36 that is a location on the earth’s surface. Geocoding can appear relatively straightforward, but it is important to emphasize that shortcuts in the process and a lack of understanding of GIS themselves can lead to classification bias. Typically, a GIS will use an address locator database to facilitate matching of textual addresses to known places on the earth’s surface. This gives varying levels of accuracy, which within the GIS are well described and tagged in the results of the geocoding. The use of Business Analyst extension from ESRI ArcGIS is one such toolset.37 The first attempt by the GIS is to match the address exactly based on street number, which then if that fails, it attempts to locate the address within a range of street numbers. If that is also not a match, most systems will then default to matching based on the street itself and attempt to find a median location in the street to which it can tag the address. Attempts are also made by the GIS to correct for common spelling errors, which are then given corresponding accuracy scores. Finally, if no matches are found through the above processes, a match based on Zip Code is attempted, with a location being assigned in the Zip Code centroid. Further attempts can also be made at the municipality level, though those are usually of limited utility depending on the intended application of the data. Special attention should be paid to choosing the right geographic projection for the data set, especially when concerning largely spread data sets (eg, multiple states/countries). In addition, matching to Zip Code (postal code) alone can lead to numerous problematic results. This is seen in one popular method of zip-code matching, which is called crosswalk Zip Code matching. This is a process by which a Zip Code from an address file is matched to a “Cross-walk” index file that has associations of Zip Codes with US Census Tracts. This process is problematic for several reasons, which are detailed further below. In summary, the greatest shortcoming is that Zip Codes can often occur across the boundaries of census tracts leading to misclassification bias in the results. Zip Codes have become mainstream within the popular health science media for describing the influence of geography on health of populations. The oft-cited catchphrase that “Zip Code is more important than genetic code”38 has seen a steady rise an adoption, and this has had the unfortunate side effect of leading many researchers without geography training to utilize Zip Codes as a unit of analysis for geocoding of patient data sets. It is the opinion of the authors that this method should only be used as a last resort; we will explain several examples of when this has become problematic. What are Zip Codes? Within the US, Zip Codes are designations of walking paths for delivery of mail created by the US Postal Service in the 1960s.39 During this time in the United States, discrimination by geography was commonplace and often codified, leading to phenomenon such as redlining40 where minorities were denied access to housing in predominately white neighborhoods, among other practices during the age of Jim Crow. The result is an inherent set of bias in the designation of Zip Codes and in the creation of their often-changing boundaries. Instead, Zip Code Tabulation Areas (ZCTAs) are utilized by many geographers to approximate the boundaries of Zip Codes. The issue with ZCTAs is that they also change periodically, as do Zip Codes with changes in populations and new housing developments, and so they can be an unreliable source of boundaries when comparing populations before and after a boundary change.41 The issues with Zip Codes stand in contrast to the design of US Census geographic units. Census Bureau designed their analysis units (Census Block, Census Tract, etc.) with geography in mind from the start and with a basis in the characteristics of the populations in the defined area. Contrast this with Zip Codes which are based on the convenience of mail sorting and delivery. In addition, the Census Bureau has standard processes to evaluate census tracts and other geographies on a regular basis, and utilized a well-grounded, transparent and scientific process.42 Census geographies are also much more amenable to associations based on location. That is, because they are designed to have “like within like,” if one is to geocode an address and it is found within a specific Census Tract, associating population characteristics from Census to individual characteristics for the purposes of a population health study (ie, associating social determinants data with an individual address) is statistically more valid than using Zip Codes. This incongruity is illustrated in Figure 3 below.Figure 3: Comparison of Zip Code and Census tract boundaries. This figure overlays Zip Code Tabulation Area (ZCTA) boundaries on top of Census Tract boundaries. Underlying this is Neighborhood Socioeconomic Disadvantage (NSD) which is detailed later in the text, showing red as most disadvantaged and blue as least disadvantaged. Note the different boundaries, as the ZCTA aggregates and, had that been used as the geography boundary, would have obscured heterogeneity that is evident in the Census Tracts. At the top of the figure, Zip Code 84103 is an example of this, with areas of high social disadvantage mixed together with areas of very low (blue) disadvantage within the same Zip Code.Bronx, a borough of New York City is home to the wealthiest and poorest who live only a few blocks apart. Often, Zip Codes can include both wealthy and disadvantaged neighborhoods within the same ZCTAs, while the US Census will attempt to prevent this type of grouping from occurring. The result of using ZCTA is a further potential for bias. The issue of Zip Codes is most painfully and obviously illustrated by the example of Flint, Michigan. Around 2015 Flint, Michigan began having water quality issues with its municipal tap water. Local physicians began noting elevated blood lead levels among area children, and reported this to the Michigan Department of Health and Human Services.43 The state examined the data and initially declared that there was no statistical association between the new service of Flint River water supply. There was a flaw in the data, however, as the state utilized area Zip Codes to examine the association of blood lead levels. Independent researchers recognized this and published their own study demonstrating that there was in fact an association.44 The independent research team recognized that the state had utilized Zip Codes, and upon examining a map of Flint and its water system the research team understood that the city water system was conterminous with the city boundaries. However, the Zip Codes crossed the City of Flint boundaries and included nearby towns. When the state examined blood lead levels of children by Zip Code, they included approximately 1/3 of addresses that were outside the boundaries of the Flint water system. This resulted in a massive misclassification bias within their data set, and lead them to concluding, erroneously, that there was no statistically significant elevation in blood lead levels. Fortunately, persistent community and medical community action, in collaboration with skilled geographers, were able to demonstrate that the new water system was poisoning the children in the community.43 The erosion of trust in the city and state officials has led residents to maintain a persistent level of suspicion with regards to anything said by local government officials,45 undermining the crucial trust in public health authorities, similar to other trauma experienced by minority communities, for example, the US Public Health Services (USPHS) Syphilis Study at Tuskegee. Zip Codes can have their role in GA, and they are sometimes the only information available to health research teams. However, as the above examples illustrate, they must be utilized with caution and a recognition of their limitations. Leveraging geospatial SDOH to improve perioperative health equity Continuous quality improvement framework Geospatial SDOH can be leveraged to inform public health policy, to improve equitable clinical practice processes for populations at risk, and for health systems science and health care disparity research. Obviously, in the framework of continuous quality improvement, these aforementioned activities are integrated in a process of collecting, analyzing & using data to improve the quality of health services for marginalized populations and ensure more equitable outcomes through process improvement for specific populations at risk, on an ongoing basis.46 Integration of SDOH and clinical data First, we detail how geospatial SDOH can be integrated into electronic health systems and perioperative health registries to leverage the neighborhood level information for health equity research and for equitable clinical care.47 Second, we illustrate with the 2 example use cases the power, promise, and pitfalls of GA for perioperative health systems science.48 Overview of geocoding of SDOH Geocoding SDOH from patient addresses follows this process: Starting from the patient home address at the time of service, we first perform geocoding in order to affix the textual address with a place on Earth corresponding to a specific latitude and longitude. This is then utilized in a GIS software package to match the latitude and longitude to a specific census geographic boundary.42 In terms of the National Neighborhood Data Archive (NaNDA),49 the boundary of choice is the US-Census Tract. The NaNDA database has a variety of socioeconomic (and geographic, such as park access) data sets, all coded at the US-Census Tract and ZCTA level. Matching takes place by the GIS examining each the boundaries of each census tract and determining which tract (polygon, in geographic speak) the geocode falls within. This is how the matching of NaNDA (or any geographic information) takes places within the GIS. As NaNDA data is encoded in Census Tracts (as well as ZCTA), any NaNDA data set can be matched to specific geocoded addresses. This data is then exported from the GIS as a flat file, associating the NaNDA data with each individual patient record and allowing for further statistical analysis. Likewise, data can remain in the GIS for further geostatistical analysis. Recall also, that any set of data that is geographic in nature, or has been coded into a GIS, can be associated with an individual geocode. Once this is complete, one can perform a GA to test hypotheses about social circumstances as key

Health Literacy and Social Determinants of Health are closely intertwined. Health disparities and inequities, overall health outcomes, understanding health information, and the ability to be fully informed about medical decisions can have long-term consequences. This connection is even more pronounced in rural and underserved urban areas where low health literacy is prevalent. This review seeks to identify correlations between health literacy and social determinants of health. It further proposes to indicate how key constituencies (healthcare organizations, healthcare providers, and patients) can further develop and disseminate health literacy initiatives to improve overall health and wellness. The research team used the Covidence database to screen, review, and extract peer-reviewed research articles for this study. The initial review identified 75 articles based on term harvesting and keyword searches considered relevant to the review. Selection, review, and characterization were performed by three reviewers on a team of four researchers. The articles’focuses are different regarding the observed impact on literacy concerning certain chronic health conditions, rural versus urban population centers, health education, ethnic and racial differentiation, and other variables. There are notable gaps in the current literature that relate to concrete methodologies to address these concerns.

Here we adapt and evaluate a full-face snorkel mask for use as personal protective equipment (PPE) for health care workers, who lack appropriate alternatives during the COVID-19 crisis in the spring of 2020. The design (referred to as Pneumask) consists of a custom snorkel-specific adapter that couples the snorkel-port of the mask to a rated filter (either a medical-grade ventilator inline filter or an industrial filter). This design has been tested for the sealing capability of the mask, filter performance, CO2 buildup and clinical usability. These tests found the Pneumask capable of forming a seal that exceeds the standards required for half-face respirators or N95 respirators. Filter testing indicates a range of options with varying performance depending on the quality of filter selected, but with typical filter performance exceeding or comparable to the N95 standard. CO2 buildup was found to be roughly equivalent to levels found in half-face elastomeric respirators in literature. Clinical usability tests indicate sufficient visibility and, while speaking is somewhat muffled, this can be addressed via amplification (Bluetooth voice relay to cell phone speakers through an app) in noisy environments. We present guidance on the assembly, usage (donning and doffing) and decontamination protocols. The benefit of the Pneumask as PPE is that it is reusable for longer periods than typical disposable N95 respirators, as the snorkel mask can withstand rigorous decontamination protocols (that are standard to regular elastomeric respirators). With the dire worldwide shortage of PPE for medical personnel, our conclusions on the performance and efficacy of Pneumask as an N95-alternative technology are cautiously optimistic.

What does the COVID-19 false-negative exposure problem mean in the context of a local anesthesia practice? We present a customizable online calculator designed to quantify and better understand individual and aggregate provider exposure risk.

Abstract Background The practice of clinical informatics (CI) is diverse and frequently tailored to individual skills and interests or to organizational/departmental needs. Prior studies have sought to define core content areas, educational milestones, and practice patterns within the clinical informatics subspecialty (CIS). Unfortunately, no single tool or framework currently exists that can succinctly define an informatician's role regardless of setting. The diversity of informatics practice makes it difficult to have one “pitch” that describes all possible domains, tasks, knowledge, and skills available to an informatician. Objective Using qualitative data from multiple informaticians, provide a succinct framework to describe and compartmentalize the various functions an informatician can contribute to the healthcare field. Methods We created an iterative focus group of five CIS fellows enrolled in different fellowship programs nationwide, one CIS program director, and an MD-PhD candidate in biomedical informatics. After much discussion, iteration, and consideration of career options within a young and burgeoning subspecialty, a dual-axis model was created that describes CIS practice in terms of settings (internal, external, and policy) and focus areas (knowledge, operations, and innovation). Results and Conclusion By combining both axes into a single “KOI pond” and then prioritizing sections by interest/resource investment, we are able to generate a unique snapshot for each informatician. These snapshots can be used (1) by informaticians to characterize their own practice succinctly as a pitch, (2) by CIS fellows who are considering career options, and (3) by those unfamiliar with CIS who want to learn more.

There are currently many tools available for capturing and defining the context of one's environment. Digital phenotyping, the use of technology and sensors to capture moment-to-moment behavior, has shown potential in quantifying the lived experience of mental illness and in the identification of individualized targets related to recovery. Environmental data suggests that greenspace may have a restorative capacity on mental health. In this paper, we explore the relationship of greenspace derived from geolocation with self-reported symptomatology from individuals with schizophrenia as well as healthy controls. Individuals with schizophrenia had less exposure to greenspace than controls, but their exposure demonstrated a dosage effect: high greenspace environments were associated with lower symptoms for anxiety (Cohen's d = -0.70), depression (d = -0.97), and psychosis (d = -0.94), whereas effect sizes for healthy controls were all negligible or small (d < 0.38). The notion that greenspace may have a more pronounced effect on individuals with mental illness presents both potential areas for recovery as well as implications for health care policy, especially in cities with a broad range of greenspace environments.

As the potential of smartphone apps and sensors for healthcare and clinical research continues to expand, there is a concomitant need for open, accessible, and scalable digital tools. While many current app platforms offer useful solutions for either clinicians or patients, fewer seek to serve both and support the therapeutic relationship between them. Thus, we aimed to create a novel smartphone platform at the intersection of patient demands for trust, control, and community and clinician demands for transparent, data driven, and translational tools. The resulting LAMP platform has evolved through numerous iterations and with much feedback from patients, designers, sociologists, advocates, clinicians, researchers, app developers, and philanthropists. As an open and free tool, the LAMP platform continues to evolve as reflected in its current diverse use cases across research and clinical care in psychiatry, neurology, anesthesia, and psychology. In this paper, we explore the motivation, features, current progress, and next steps to pair the platform for use in a new digital psychiatry clinic, to advance digital interventions for youth mental health, and to bridge gaps in available mental health care for underserved patient groups. The code for the LAMP platform is freely shared with this paper to encourage others to adapt and improve on our team’s efforts.

While it is clear that social determinants of health impact those with schizophrenia and often may impair access to treatment or support - to date accurate reporting of how patients interact with their environments has been challenging. Utilizing digital phenotyping via smartphone apps, it is now possible to study the correlations between patients; real time location and symptoms as well as quantity the environments patients interact with on a minute by minute time scale. Utilizing the open source Beiwe smartphone platform developed by Dr. Onnela at the Harvard School of Public Health and the open source LAMP smartphone platform developed by our group - we have to date captured smartphone data on location (GPS), sociability (anonymized call/text logs), physical activity (accelerometer), symptoms (phone surveys), and cognition (phone games) from 25 patents. Patients are asked to install these apps and then use them for 90 days. Using the ESRI ArcGIS 10.3 geospatial analysis platform, we are able to compare the locations of patients against a variety of U.S. census collected information including spaces that are considered ‘environmental justice areas’ where the government has raised concern about lack of access to public services, support, green spaces, and other factors. The amount of GPS data varies greatly by patient with a mean of 408,291 but standard deviation of 301,680. For results to date, the total time that patients with schizophrenia spent in environmental justice areas compared to non-environment justice area was 60% with a standard deviation with a standard deviation of 42.3%. With ongoing data collection, we expect the standard deviations will reduce. In examining psychotics symptoms that patients self-report to the smartphone apps, preliminary analysis suggests there is an association between environmental justice areas and more severe self-reported symptoms. Our results suggest that it is feasible to collect environmental and symptom data via smartphones in patients with schizophrenia. Our preliminary results that patients spend more time in environmental justice areas suggests that those with schizophrenia may have less access to spaces conducive to recovery. Further data collection and analysis from this work may offer quantification of the potential impact of the environment and produce the necessary data to lobby for specific change and improved services for those with schizophrenia.

Objectives To use publicly available, crowdsourced data to understand geospatial trends in discarded needles. Methods We completed multiple geospatial analyses of discarded needles reported through the Boston, Massachusetts, 311 service request system. Results Between May 2015 and August 2017, 4763 discarded needles were reported. The highest concentration of needles were reported in census block groups in the South End and Roxbury neighborhoods. Cumulatively, 78.3% of the needles were reported within 1 kilometer of methadone clinics, safe needle deposit sites, homeless shelters, or hospitals. Conclusions Publicly reported data can help identify hot spots of discarded needles and examine indicators of spatial association. In Boston, the number of discarded needles being reported is rising, with the highest density of needles found in 2 central neighborhoods with several outlying hot spots. Most needles were found near areas associated with social stress and substance use disorder. Public Health Implications. This analysis represents a novel way of leveraging publicly available information to target community responses to the opioid epidemic. Identifying hot spots of discarded needles may enable public health organizations to target future efforts to encourage safer needle disposal practices and reduce public injection drug use.