Browsing by Author "Lulla, Vijay O."
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Item Association of State Social and Environmental Factors With Rates of Self-injury Mortality and Suicide in the United States(AMA, 2022-02) Rockett, Ian R. H.; Jia, Haomiao; Ali, Bina; Banerjee, Aniruddha; Connery, Hilary S.; Nolte, Kurt B.; Miller, Ted; White, Franklin M. M.; DiGregorio, Bernard D.; Larkin, G. Luke; Stack, Steven; Kõlves, Kairi; McHugh, R. Kathryn; Lulla, Vijay O.; Cossman, Jeralynn; De Leo, Diego; Hendricks, Brian; Nestadt, Paul S.; Berry, James H.; D’Onofrio, Gail; Caine, Eric D.; Geography, School of Liberal ArtsImportance Self-injury mortality (SIM) combines suicides and the preponderance of drug misuse–related overdose fatalities. Identifying social and environmental factors associated with SIM and suicide may inform etiologic understanding and intervention design. Objective To identify factors associated with interstate SIM and suicide rate variation and to assess potential for differential suicide misclassification. Design, Setting, and Participants This cross-sectional study used a partial panel time series with underlying cause-of-death data from 50 US states and the District of Columbia for 1999-2000, 2007-2008, 2013-2014 and 2018-2019. Applying data from the Centers for Disease Control and Prevention, SIM includes all suicides and the preponderance of unintentional and undetermined drug intoxication deaths, reflecting self-harm behaviors. Data were analyzed from February to June 2021. Exposures Exposures included inequity, isolation, demographic characteristics, injury mechanism, health care access, and medicolegal death investigation system type. Main Outcomes and Measures The main outcome, SIM, was assessed using unstandardized regression coefficients of interstate variation associations, identified by the least absolute shrinkage and selection operator; ratios of crude SIM to suicide rates per 100 000 population were assessed for potential differential suicide misclassification. Results A total of 101 325 SIMs were identified, including 74 506 (73.5%) among males and 26 819 (26.5%) among females. SIM to suicide rate ratios trended upwards, with an accelerating increase in overdose fatalities classified as unintentional or undetermined (SIM to suicide rate ratio, 1999-2000: 1.39; 95% CI, 1.38-1.41; 2018-2019: 2.12; 95% CI, 2.11-2.14). Eight states recorded a SIM to suicide rate ratio less than 1.50 in 2018-2019 vs 39 states in 1999-2000. Northeastern states concentrated in the highest category (range, 2.10-6.00); only the West remained unrepresented. Least absolute shrinkage and selection operator identified 8 factors associated with the SIM rate in 2018-2019: centralized medical examiner system (β = 4.362), labor underutilization rate (β = 0.728), manufacturing employment (β = −0.056), homelessness rate (β = −0.125), percentage nonreligious (β = 0.041), non-Hispanic White race and ethnicity (β = 0.087), prescribed opioids for 30 days or more (β = 0.117), and percentage without health insurance (β = −0.013) and 5 factors associated with the suicide rate: percentage male (β = 1.046), military veteran (β = 0.747), rural (β = 0.031), firearm ownership (β = 0.030), and pain reliever misuse (β = 1.131). Conclusions and Relevance These findings suggest that SIM rates were associated with modifiable, upstream factors. Although embedded in SIM, suicide unexpectedly deviated in proposed social and environmental determinants. Heterogeneity in medicolegal death investigation processes and data assurance needs further characterization, with the goal of providing the highest-quality reports for developing and tracking public health policies and practices.Item Augmenting Indiana's groundwater level monitoring network: optimal siting of additional wells to address spatial and categorical sampling gaps(2014-11-21) Sperl, Benjamin J.; Banerjee, Aniruddha; Lulla, Vijay O.; Bein, Frederick L. (Frederick Louis), 1943-Groundwater monitoring networks are subject to change by budgetary actions and stakeholder initiatives that result in wells being abandoned or added. A strategy for network design is presented that addresses the latter situation. It was developed in response to consensus in the state of Indiana that additional monitoring wells are needed to effectively characterize water availability in aquifer systems throughout the state. The strategic methodology has two primary objectives that guide decision making for new installations: (1) purposive sampling of a diversity of environmental variables having relevance to groundwater recharge, and (2) spatial optimization by means of maximizing geographic distances that separate monitoring wells. Design objectives are integrated in a discrete facility location model known as the p-median problem, and solved to optimality using a mathematical programming package.Item Comparison between high-resolution aerial imagery and lidar data classification of canopy and grass in the NESCO neighborhood, Indianapolis, Indiana(2014) Ye, Nan; Johnson, Daniel P. (Daniel Patrick), 1971-; Bein, Frederick L. (Frederick Louis), 1943-; Lulla, Vijay O.Urban forestry is a very important element of urban structures that can improve the environment and life quality within the urban areas. Having an accurate classification of urban forests and grass areas would help improve focused urban tree planting and urban heat wave mitigation efforts. This research project will compare the use of high – resolution aerial imagery and LiDAR data when used to classify canopy and grass areas. The high – resolution image, with 1 – meter resolution, was captured by The National Agriculture Imagery Program (NAIP) on 6/6/2012. Its coordinate system is the North American Datum of 1983 (NAD83). The LiDAR data, with 1.0 – meter average post spacing, was captured by Indiana Statewide Imagery and LiDAR Program from 03/13/2011 to 04/30/2012.The study area is called the Near East Side Community Organization (NESCO) neighborhood. It is located on the east side of downtown Indianapolis, Indiana. Its boundaries are: 65 interstate, East Massachusetts Avenue, East 21st Street, North Emerson Avenue, and the rail road tracks on the south of the East Washington Street. This research will also perform the accuracy assessment based on the results of classifications using high – resolution aerial imagery and LiDAR data in order to determine and explain which method is more accurate to classify urban canopy and grass areas.Item Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments(MDPI, 2023-01) Heintzelman, Asrah; Filippelli, Gabriel M.; Moreno-Madriñan, Max J.; Wilson, Jeffrey S.; Wang, Lixin; Druschel, Gregory K.; Lulla, Vijay O.; Geography, School of Liberal ArtsThe negative health impacts of air pollution are well documented. Not as well-documented, however, is how particulate matter varies at the hyper-local scale, and the role that proximal sources play in influencing neighborhood-scale patterns. We examined PM2.5 variations in one airshed within Indianapolis (Indianapolis, IN, USA) by utilizing data from 25 active PurpleAir (PA) sensors involving citizen scientists who hosted all but one unit (the control), as well as one EPA monitor. PA sensors report live measurements of PM2.5 on a crowd sourced map. After calibrating the data utilizing relative humidity and testing it against a mobile air-quality unit and an EPA monitor, we analyzed PM2.5 with meteorological data, tree canopy coverage, land use, and various census variables. Greater proximal tree canopy coverage was related to lower PM2.5 concentrations, which translates to greater health benefits. A 1% increase in tree canopy at the census tract level, a boundary delineated by the US Census Bureau, results in a ~0.12 µg/m3 decrease in PM2.5, and a 1% increase in “heavy industry” results in a 0.07 µg/m3 increase in PM2.5 concentrations. Although the overall results from these 25 sites are within the annual ranges established by the EPA, they reveal substantial variations that reinforce the value of hyper-local sensing technologies as a powerful surveillance tool.Item Fatal self-injury in the United States, 1999–2018: Unmasking a national mental health crisis(Elsevier, 2021) Rockett, Ian R.H.; Caine, Eric D.; Banerjee, Aniruddha; Ali, Bina; Miller, Ted; Connery, Hilary S.; Lulla, Vijay O.; Nolte, Kurt B.; Larkin, G. Luke; Stack, Steven; Hendricks, Brian; McHugh, R. Kathryn; White, Franklin M.M.; Greenfield, Shelly F.; Bohnert, Amy S.B.; Cossman, Jeralynn S.; D'Onofrio, Gail; Nelson, Lewis S.; Nestadt, Paul S.; Berry, James H.; Jia, Haomiao; Geography, School of Liberal ArtsBackground Suicides by any method, plus ‘nonsuicide’ fatalities from drug self-intoxication (estimated from selected forensically undetermined and ‘accidental’ deaths), together represent self-injury mortality (SIM)—fatalities due to mental disorders or distress. SIM is especially important to examine given frequent undercounting of suicides amongst drug overdose deaths. We report suicide and SIM trends in the United States of America (US) during 1999–2018, portray interstate rate trends, and examine spatiotemporal (spacetime) diffusion or spread of the drug self-intoxication component of SIM, with attention to potential for differential suicide misclassification. Methods For this state-based, cross-sectional, panel time series, we used de-identified manner and underlying cause-of-death data for the 50 states and District of Columbia (DC) from CDC's Wide-ranging Online Data for Epidemiologic Research. Procedures comprised joinpoint regression to describe national trends; Spearman's rank-order correlation coefficient to assess interstate SIM and suicide rate congruence; and spacetime hierarchical modelling of the ‘nonsuicide’ SIM component. Findings The national annual average percentage change over the observation period in the SIM rate was 4.3% (95% CI: 3.3%, 5.4%; p<0.001) versus 1.8% (95% CI: 1.6%, 2.0%; p<0.001) for the suicide rate. By 2017/2018, all states except Nebraska (19.9) posted a SIM rate of at least 21.0 deaths per 100,000 population—the floor of the rate range for the top 5 ranking states in 1999/2000. The rank-order correlation coefficient for SIM and suicide rates was 0.82 (p<0.001) in 1999/2000 versus 0.34 (p = 0.02) by 2017/2018. Seven states in the West posted a ≥ 5.0% reduction in their standardised mortality ratios of ‘nonsuicide’ drug fatalities, relative to the national ratio, and 6 states from the other 3 major regions a >6.0% increase (p<0.05). Interpretation Depiction of rising SIM trends across states and major regions unmasks a burgeoning national mental health crisis. Geographic variation is plausibly a partial product of local heterogeneity in toxic drug availability and the quality of medicolegal death investigations. Like COVID-19, the nation will only be able to prevent SIM by responding with collective, comprehensive, systemic approaches. Injury surveillance and prevention, mental health, and societal well-being are poorly served by the continuing segregation of substance use disorders from other mental disorders in clinical medicine and public health practice.Item A Geographical Comparison of the Relationship Between Aerosol Optical Depth and Fine Particulate Matter in Indiana(2015-05) Douglas, April D.; Johnson, Daniel P.; Lulla, Vijay O.; Bein, Frederick L.This study looked at the time period of June through mid-October, 2013, based on the results of earlier studies that the strongest correlation between the PM2.5 and AOD data sets occurs during the summer and fall. Terra satellite data was used in this study due to availability of images for the geographic area of the state of Indiana during the time period of the study. PM2.5 measurements from 12 IDEM continuous monitoring sites, which were collected at noon local time, were compared with MODIS AOD data. Despite the limitations of useful data and smaller data sets, this study shows encouraging results, and illustrates that there is a relationship between remotely sensed MODIS AOD data and fine particulate matter (PM2.5) data collected from ground sensors within the geographic region of the state of Indiana. It is believed that this topic should be studied further and expanded upon.Item Integrating GIS in a Statewide Medical Education Administrative System(2019-04) Davis, Ashley Michelle; Wilson, Jeffrey S.; Kochhar, Komal; Lulla, Vijay O.Geographic technologies can be used to visualize and analyze data patterns that may go unnoticed from other approaches. The purpose of this project was to provide examples of how GIS and cartographic methods are being used to help facilitate communication and inform management processes for a complex statewide medical education system administered by the Indiana University School of Medicine, the largest medical school in the United States. The IU School of Medicine has nine regional campuses located around the state in addition to numerous partnering hospitals where medical students are trained. We illustrate geographic examples of various stages of medical student education from admissions, through campus assignments and clinical rotations, to residency training. These geographic processes are being used to inform reaccreditation processes as well as assisting administration with recruitment/retention strategies, statewide planning, and analysis in a complex medical education system.Item Medical Imaging Centers in Central Indiana: Optimal Location Allocation Analyses(2016-01) Seger, Mandi J.; Banerjee, Aniruddha; Wilson, Jeffrey S.; Lulla, Vijay O.; Wiehe, Sarah ElizabethWhile optimization techniques have been studied since 300 B.C. when Euclid first considered the minimal distance between a point and a line, it wasn’t until 1966 that location optimization was first applied to a problem in healthcare. Location optimization techniques are capable of increasing efficiency and equity in the placement of many types of services, including those within the healthcare industry, thus enhancing quality of life. Medical imaging is a healthcare service which helps to determine medical diagnoses in acute and preventive care settings. It provides physicians with information guiding treatment and returning a patient back to optimal health. In this study, a retrospective analysis of the locations of current medical imaging centers in central Indiana is performed, and alternate placement as determined using optimization techniques is considered and compared. This study focuses on reducing the drive time experienced by the population within the study area to their nearest imaging facility. Location optimization models such as the P-Median model, the Maximum Covering model, and Clustering and Partitioning are often used in the field of operations research to solve location problems, but are lesser known within the discipline of Geographic Information Science. This study was intended to demonstrate the capabilities of these powerful algorithms and to increase understanding of how they may be applied to problems within healthcare. While the P-Median model is effective at reducing the overall drive time for a given network set, individuals within the network may experience lengthy drive times. The results further indicate that while the Maximum Covering model is more equitable than the P-Median model, it produces large sets of assigned individuals overwhelming the capacity of one imaging center. Finally, the Clustering and Partitioning method is effective at limiting the number of individuals assigned to a given imaging center, but it does not provide information regarding average drive time for those individuals. In the end, it is determined that a capacitated Maximal Covering model would be the preferred method for solving this particular location problem.Item Predicting locations for urban tree planting(2014) King, Steven M.; Johnson, Daniel P. (Daniel Patrick), 1971-; Bein, Frederick L. (Frederick Louis), 1943-; Lulla, Vijay O.The purpose of this study was to locate the most suitable blocks to plant trees within Indianapolis, Indiana’s Near Eastside Community (NESCO). LiDAR data were utilized, with 1.0 meter average post spacing, captured by the Indiana Statewide Imagery and LiDAR Program from March 13, 2011 to April 30, 2012, to conduct a covertype classification and identify blocks that have low canopies, high impervious surfaces and high surface temperatures. Tree plantings in these blocks can help mitigate the effects of the urban heat island effect. Using 2010 U.S. Census demographic data and the principal component analysis, block groups with high social vulnerability were determined, and tree plantings in these locations could help reduce mortality from extreme heat events. This study also determined high and low priority plantable space in order to emphasize plantable spaces with the potential to shade buildings; this can reduce cooling costs and the urban heat island, and it can maximize the potential of each planted tree.Item Predicting Water Quality By Relating Secchi Disk Transparency Depths To Landsat 8(2015-08) Hancock, Miranda J.; Lulla, Vijay O.; Johnson, Daniel P. (Daniel Patrick), 1971-; Bein, Frederick L. (Frederick Louis), 1943-Monitoring lake quality remotely offers an economically feasible approach as opposed to in-situ field data collection. Researchers have demonstrated that lake clarity can be successfully monitored through the analysis of remote sensing. Evaluating satellite imagery, as a means of water quality detection, offers a practical way to assess lake clarity across large areas, enabling researchers to conduct comparisons on a large spatial scale. Landsat data offers free access to frequent and recurring satellite images. This allows researchers the ability to make temporal comparisons regarding lake water quality. Lake water quality is related to turbidity which is associated with clarity. Lake clarity is a strong indicator of lake health and overall water quality. The possibility of detecting and monitoring lake clarity using Landsat8 mean brightness values is discussed in this report. Lake clarity is analyzed in three different reservoirs for this study; Brookeville, Geist, and Eagle Creek. In-situ measurements obtained from Brookeville Reservoir were used to calibrate reflectance from Landsat 8’s Operational Land Imager (OLI) satellite. Results indicated a correlation between turbidity and brightness values, which are highly correlated in algal dominated lakes.