Assessing the daily natural history of asymptomatic Plasmodium infections in adults and older children in Katakwi, Uganda: a longitudinal cohort study.

BACKGROUND: Low-density asymptomatic Plasmodium infections are prevalent in endemic areas, but little is known about their natural history. The trajectories of these infections and their propensity to fluctuate to undetectable densities can affect detection in clinical trials and field studies. We aimed to classify the natural history of these infections in a high transmission area over 29 days. METHODS: In this longitudinal cohort study, we enrolled healthy, malaria-asymptomatic, afebrile, adults (age 18-59 years) and older children (age 8-17 years) in Katakwi District, Uganda, who were negative for Plasmodium infection on rapid diagnostic tests. Participants were instructed to self-collect one dried blood spot (DBS) per day for a maximum of 29 days. We excluded people if they were pregnant or taking antimalarials. During weekly clinic visits, staff collected a DBS and a 4 mL sample of venous blood. We analysed DBSs by Plasmodium 18S rRNA quantitative RT-PCR (qRT-PCR). We classified DBS by infection type as negative, P falciparum, non-P falciparum, or mixed. We plotted infection type over time for each participant and categorised trajectories as negative, new, cleared, chronic, or indeterminate infections. To estimate the effect of single timepoint sampling, we calculated the daily prevalence for each study day and estimated the number of infections that would have been detected in our population if sampling frequency was reduced. FINDINGS: Between April 9 and May 20, 2021, 3577 DBSs were collected by 128 (40 male adults, 60 female adults, 12 male children, and 16 female children) study participants. 2287 (64%) DBSs were categorised as negative, 751 (21%) as positive for P falciparum, 507 (14%) as positive for non-P falciparum, and 32 (1%) as mixed infections. Daily Plasmodium prevalence in the population ranged from 45·3% (95% CI 36·6-54·1) at baseline to 30·3% (21·9-38·6) on day 24. 37 (95%) of 39 P falciparum and 35 (85%) of 41 non-P falciparum infections would have been detected with every other day sampling, whereas, with weekly sampling, 35 (90%) P falciparum infections and 31 (76%) non-P falciparum infections would have been detected. INTERPRETATION: Parasite dynamics and species are highly variable among low-density asymptomatic Plasmodium infections. Sampling every other day or every 3 days detected a similar proportion of infections as daily sampling, whereas testing once per week or even less frequently could misclassify up to a third of the infections. Even using highly sensitive diagnostics, single timepoint testing might misclassify the true infection status of an individual. FUNDING: US National Institutes of Health and Bill and Melinda Gates Foundation.

A weather features dataset for prediction of short-term rainfall quantities in Uganda.

Weather data is of great importance to the development of weather prediction models. However, the availability and quality of this data remains a significant challenge for most researchers around the world. In Uganda, obtaining observational weather data is very challenging due to the sparse distribution of weather stations and inconsistent data records. This has created critical gaps in data availability to run and develop efficient weather prediction models. To bridge this gap, we obtained country-specific time series hourly observational weather data. The data period is from 2020 to 2022 of 11 weather stations distributed in the four regions of Uganda. The data was accessed from the Ogimet data repository using the "climate" R-package. The automated procedures in the R-programming language environment allowed us to download user-defined data at a time resolution from an hourly to an annual basis. However, the raw data acquired cannot be used to learn rainfall patterns because it includes duplicates and non-uniform data. Therefore, this article presents a prepared and cleaned dataset that can be used for the prediction of short-term rainfall quantities in Uganda.

A CRISPR/Cas9-based multicopy integration system for protein production in Aspergillus niger.

The filamentous fungus Aspergillus niger is well known for its high protein secretion capacity and a preferred host for homologous and heterologous protein production. To improve the protein production capacity of A. niger even further, a set of dedicated protein production strains was made containing up to 10 glucoamylase landing sites (GLSs) at predetermined sites in the genome. These GLSs replace genes encoding enzymes abundantly present or encoding unwanted functions. Each GLS contains the promotor and terminator region of the glucoamylase gene (glaA), one of the highest expressed genes in A. niger. Integrating multiple gene copies, often realized by random integration, is known to boost protein production yields. In our approach the GLSs allow for rapid targeted gene replacement using CRISPR/Cas9-mediated genome editing. By introducing the same or different unique DNA sequences (dubbed KORE sequences) in each GLS and designing Cas9-compatible single guide RNAs, one is able to select at which GLS integration of a target gene occurs. In this way a set of identical strains with different copy numbers of the gene of interest can be easily and rapidly made to compare protein production levels. As an illustration of its potential, we successfully used the expression platform to generate multicopy A. niger strains producing the Penicillium expansum PatE::6xHis protein catalysing the final step in patulin biosynthesis. The A. niger strain expressing 10 copies of the patE::6xHis expression cassette produced about 70 µg·mL-1 PatE protein in the culture medium with a purity just under 90%.

Rethinking detection of pre-existing and intervening Plasmodium infections in malaria clinical trials.

Pre-existing and intervening low-density Plasmodium infections complicate the conduct of malaria clinical trials. These infections confound infection detection endpoints, and their immunological effects may detract from intended vaccine-induced immune responses. Historically, these infections were often unrecognized since infrequent and often analytically insensitive parasitological testing was performed before and during trials. Molecular diagnostics now permits their detection, but investigators must weigh the cost, complexity, and personnel demands on the study and the laboratory when scheduling such tests. This paper discusses the effect of pre-existing and intervening, low-density Plasmodium infections on malaria vaccine trial endpoints and the current methods employed for their infection detection. We review detection techniques, that until recently, provided a dearth of cost-effective strategies for detecting low density infections. A recently deployed, field-tested, simple, and cost-effective molecular diagnostic strategy for detecting pre-existing and intervening Plasmodium infections from dried blood spots (DBS) in malaria-endemic settings is discussed to inform new clinical trial designs. Strategies that combine sensitive molecular diagnostic techniques with convenient DBS collections and cost-effective pooling strategies may enable more thorough and informative infection monitoring in upcoming malaria clinical trials and epidemiological studies.

Feasibility of community at-home dried blood spot collection combined with pooled reverse transcription PCR as a viable and convenient method for malaria epidemiology studies.

BACKGROUND: Many Plasmodium infections in endemic regions exist at densities below the limit of detection of standard diagnostic tools. These infections threaten control efforts and may impact vaccine and therapeutic drug studies. Simple, cost-effective methods are needed to study the natural history of asymptomatic submicroscopic parasitaemia. Self-collected dried blood spots (DBS) analysed using pooled and individual quantitative reverse transcription polymerase chain reaction (qRT-PCR) provide such a solution. Here, the feasibility and acceptability of daily at-home DBS collections for qRT-PCR was studied to better understand low-density infections. METHODS: Rapid diagnostic test (RDT)-negative individuals in Katakwi District, northeastern Uganda, were recruited between April and May 2021. Venous blood samples and clinic-collected DBS were taken at enrollment and at four weekly clinic visits. Participants were trained in DBS collection and asked to collect six DBS weekly between clinic visits. Opinions about the collection process were solicited using daily Diary Cards and a Likert scale survey at the final study visit. Venous blood and DBS were analysed by Plasmodium 18S rRNA qRT-PCR. The number of participants completing the study, total DBS collected, and opinions of the process were analysed to determine compliance and acceptability. The human internal control mRNA and Plasmodium 18S rRNA were evaluated for at-home vs. clinic-collected DBS and venous blood to assess quality and accuracy of at-home collected samples. RESULTS: One-hundred two adults and 29 children were enrolled, and 95 and 26 completed the study, respectively. Three individuals withdrew due to pain or inconvenience of procedures. Overall, 96% of participants collected ≥ 16 of 24 at-home DBS, and 87% of DBS contained ≥ 40 µL of blood. The procedure was well tolerated and viewed favourably by participants. At-home collected DBS were acceptable for qRT-PCR and showed less than a one qRT-PCR cycle threshold shift in the human control mRNA compared to clinic-collected DBS. Correlation between Plasmodium falciparum 18S rRNA from paired whole blood and DBS was high (R = 0.93). CONCLUSIONS: At-home DBS collection is a feasible, acceptable, and robust method to obtain blood to evaluate the natural history of low-density Plasmodium infections by qRT-PCR.

Comparison of treatment with insulin degludec and glargine U100 in patients with type 1 diabetes prone to nocturnal severe hypoglycaemia: The HypoDeg randomized, controlled, open-label, crossover trial.

AIM: To investigate whether the long-acting insulin analogue insulin degludec compared with insulin glargine U100 reduces the risk of nocturnal symptomatic hypoglycaemia in patients with type 1 diabetes (T1D). METHODS: Adults with T1D and at least one episode of nocturnal severe hypoglycaemia during the last 2 years were included in a 2-year prospective, randomized, open, multicentre, crossover trial. A total of 149 patients were randomized 1:1 to basal-bolus therapy with insulin degludec and insulin aspart or insulin glargine U100 and insulin aspart. Each treatment period lasted 1 year and consisted of 3 months of run-in or crossover followed by 9 months of maintenance. The primary endpoint was the number of blindly adjudicated nocturnal symptomatic hypoglycaemic episodes. Secondary endpoints included the occurrence of severe hypoglycaemia. We analysed all endpoints by intention-to-treat. RESULTS: Treatment with insulin degludec resulted in a 28% (95% CI: 9%-43%; P = .02) relative rate reduction (RRR) of nocturnal symptomatic hypoglycaemia at level 1 (≤3.9 mmol/L), a 37% (95% CI: 16%-53%; P = .002) RRR at level 2 (≤3.0 mmol/L), and a 35% (95% CI: 1%-58%; P = .04) RRR in all-day severe hypoglycaemia compared with insulin glargine U100. CONCLUSIONS: Patients with T1D prone to nocturnal severe hypoglycaemia have lower rates of nocturnal symptomatic hypoglycaemia and all-day severe hypoglycaemia with insulin degludec compared with insulin glargine U100.

Particulate matter exposure predicts residence in high-risk areas for community acquired pneumonia among hospitalized children.

Particulate matter exposure is a risk factor for lower respiratory tract infection in children. Here, we investigated the geospatial patterns of community-acquired pneumonia and the impact of PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) on geospatial variability of pneumonia in children. We performed a retrospective analysis of prospectively collected population-based surveillance study data of community-acquired pneumonia hospitalizations among children <18 years residing in the Memphis metropolitan area, who were enrolled in the Centers for Disease Control and Prevention sponsored Etiology of Pneumonia in the Community (EPIC) study from January 2010 to June 2012. The outcome measure, residence in high- and low-risk areas for community-acquired pneumonia, was determined by calculating pneumonia incidence rates and performing cluster analysis to identify areas with higher/lower than expected rates of community-acquired pneumonia for the population at risk. High PM2.5 was defined as exposure to PM2.5 concentrations greater than the mean value (>10.75 µg/m3), and low PM2.5 is defined as exposure to PM2.5 concentrations less than or equal to the mean value (≤10.75 µg/m3). We also assessed the effects of age, sex, race/ethnicity, history of wheezing, insurance type, tobacco smoke exposure, bacterial etiology, and viral etiology of infection. Of 810 (96.1%) subjects with radiographic community-acquired pneumonia, who resided in the Memphis metropolitan area and had addresses which were successfully geocoded (Supplementary Figure F2), 220 (27.2%) patients were identified to be from high- (n = 126) or low-risk (n = 94) community-acquired pneumonia areas. Community-acquired pneumonia in Memphis metropolitan area had a non-homogenous geospatial pattern. PM2.5 was associated with residence in high-risk areas for community-acquired pneumonia. In addition, children with private insurance and bacterial, as opposed to viral, etiology of infection had a decreased risk of residence in a high-risk area for community-acquired pneumonia. The results from this paper suggest that environmental exposures as well as social risk factors are associated with childhood pneumonia.

Effects of childhood exposure to PM2.5 in a Memphis pediatric asthma cohort.

The effects of childhood exposure to ambient air pollution and their influences on healthcare utilization and respiratory outcomes in Memphis pediatric asthma cohort are still unknown. This study seeks to (1) investigate individual-level associations between asthma and exposure measures in high asthma rate and low asthma rate areas and (2) determine factors that influence asthma at first year of a child's life, first 2 years, first 5 years, and during their childhood. Datasets include physician-diagnosed asthma patients, on-road and individual PM2.5 emissions, and high-resolution spatiotemporal PM2.5 estimates. Spatial analytical and logistic regression models were used to analyze the effects of childhood exposure on outcomes. Increased risk was associated with African American (AA) (odds ratio (OR) = 3.09, 95% confidence interval (CI) 2.80-3.41), aged < 5 years old (OR = 1.31, 95% 1.17-1.47), public insurance (OR = 2.80, 95% CI 2.60-3.01), a 2.5-km radius from on-road emission sources (OR = 3.06, 95% CI 2.84-3.30), and a 400-m radius from individual PM2.5 sources (OR = 1.33, 95% CI 1.25-1.41) among the cohort with residence in high asthma rate areas compared to low asthma rates areas. A significant interaction was observed between race and insurance with the odds of AA being approximately five times (OR = 4.68, 95% CI 2.23-9.85), public insurance being about three times (OR = 2.65, 95% CI 1.68-4.17), and children in their first 5 years of life have more hospital visits than other age groups. Findings from this study can guide efforts to minimize emissions, manage risk, and design interventions to reduce disease burden.

A Scalable Field Study Protocol and Rationale for Passive Ambient Air Sampling: A Spatial Phytosampling for Leaf Data Collection.

Stable, bioreactive, radicals known as environmentally persistent free radicals (EPFRs) have been found to exist on the surface of airborne PM2.5. These EPFRs have been found to form during many combustion processes, are present in vehicular exhaust, and persist in the environment for weeks and biological systems for up to 12 h. To measure EPFRs in PM samples, high volume samplers are required and measurements are less representative of community exposure; therefore, we developed a novel spatial phytosampling methodology to study the spatial patterns of EPFR concentrations using plants. Leaf samples for laboratory PM analysis were collected from 188 randomly drawn sampling sites within a 500-m buffer zone of pollution sources across a sampling grid measuring 32.9 × 28.4 km in Memphis, Tennessee. PM was isolated from the intact leaves and size fractionated, and EPFRs on PM quantified by electron paramagnetic resonance spectroscopy. The radical concentration was found to positively correlate with the EPFR g-value, thus indicating cumulative content of oxygen centered radicals in PM with higher EPFR load. Our spatial phytosampling approach reveals spatial variations and potential "hotspots" risk due to EPFR exposure across Memphis and provides valuable insights for identifying exposure and demographic differences for health studies.

Spatiotemporal patterns of childhood asthma hospitalization and utilization in Memphis Metropolitan Area from 2005 to 2015.

OBJECTIVE: To identify the key risk factors and explain the spatiotemporal patterns of childhood asthma in the Memphis metropolitan area (MMA) over an 11-year period (2005-2015). We hypothesize that in the MMA region this burden is more prevalent among urban children living south, downtown, and north of Memphis than in other areas. METHODS: We used a large-scale longitudinal electronic health record database from an integrated healthcare system, Geographic information systems (GIS), and statistical and space-time models to study the spatiotemporal distributions of childhood asthma at census tract level. RESULTS: We found statistically significant spatiotemporal clusters of childhood asthma in the south, west, and north of Memphis city after adjusting for key covariates. The results further show a significant increase in temporal gradient in frequency of emergency department (ED) visits and inpatient hospitalizations from 2009 to 2013, and an upward trajectory from 4 per 1,000 children in 2005 to 16 per 1,000 children in 2015. The multivariate logistic regression identified age, race, insurance, admit source, encounter type, and frequency of visits as significant risk factors for childhood asthma (p < 0.05). We observed a greater asthma burden and healthcare utilization for African American (AA) patients living in a high-risk area than those living in a low-risk area in comparison to the white patients: AA vs. white [odds ratio (OR) = 3.03, 95% confidence interval (CI): 2.75-3.34]; and Hispanic vs. white (OR = 1.62, 95% CI: 1.21-2.17). CONCLUSIONS: These findings provide a strong basis for developing geographically tailored population health strategies at the neighborhood level for young children with chronic respiratory conditions.

Routine functional assessment for hip fracture patients.

Background and purpose - Pre-fracture functional level has been shown to be a consistent predictor of rehabilitation outcomes in older hip fracture patients. We validated 4 overall pre-fracture functional level assessment instruments in patients aged 65 or more, used the prediction of outcome at 4 months post-fracture, and assessed cutoff values for decision making in treatment and rehabilitation. Patients and methods - 165 consecutive patients with acute primary hip fracture were prospectively included in the study. Pre-fracture Barthel-20, Barthel-100, cumulated ambulation score, and new mobility score were scored immediately after admission. Outcome defined as mortality, residential status, and independent walking ability was assessed at 4 months. Results - 3 of the assessment instruments, namely Barthel-20, Barthel-100, and new mobility score, correlated with outcome at 4 months post-fracture and were valid predictors. Thresholds were estimated. We found no evidence that Barthel-100, with its finer granularity, performs better than Barthel-20 as a predictor. Interpretation - Our findings indicate that pre-fracture scores of Barthel-20 and new mobility score have predictive ability, and further investigation of usage for guidance of clinical and rehabilitation decisions concerning hip fracture patients is warranted.

Geographic Variation in Mortality Among Children and Adolescents Diagnosed With Cancer in Tennessee. Does Race Matter?

Cancer is one of the leading causes of death among children in the United States. Previous research has examined geographic variation in cancer incidence and survival, but the geographic variation in mortality among children and adolescents is not as well understood. The purpose of this study was to investigate geographic variation by race in mortality among children and adolescents diagnosed with cancer in Tennessee. Using an innovative combination of spatial and nonspatial analysis techniques with data from the 2004-2011 Tennessee Cancer Registry, pediatric deaths were mapped and the effect of race on the proximity to rural areas and clusters of mortality were explored with multivariate regressions. The findings revealed that African American children and adolescents in Tennessee were more likely than their counterparts of other races to reside in rural areas with close proximity to mortality clusters of children and adolescents with a cancer. Findings have clinical implications for pediatric oncology nurses regarding the delivery of supportive care at end of life for rural African American children and adolescents.

Using an External Exposome Framework to Examine Pregnancy-Related Morbidities and Mortalities: Implications for Health Disparities Research.

OBJECTIVE: We have conducted a study to assess the role of environment on the burden of maternal morbidities and mortalities among women using an external exposome approach for the purpose of developing targeted public health interventions to decrease disparities. METHODS: We identified counties in the 48 contiguous USA where observed low birthweight (LBW) rates were higher than expected during a five-year study period. The identification was conducted using a retrospective space-time analysis scan for statistically significant clusters with high or low rates by a Discrete Poisson Model. RESULTS: We observed statistically significant associations of LBW rate with a set of predictive variables. However, in one of the two spatiotemporal models we discovered LBW to be associated with five predictive variables (teen birth rate, adult obesity, uninsured adults, physically unhealthy days, and percent of adults who smoke) in two counties situated in Alabama after adjusting for location changes. Counties with higher than expected LBW rates were similarly associated with two environmental variables (ozone and fine particulate matter). CONCLUSIONS: The county-level predictive measures of LBW offer new insights into spatiotemporal patterns relative to key contributory factors. An external framework provides a promising place-based approach for identifying "hotspots" with implications for designing targeted interventions and control measures to reduce and eliminate health disparities.

Exploration of preterm birth rates using the public health exposome database and computational analysis methods.

Recent advances in informatics technology has made it possible to integrate, manipulate, and analyze variables from a wide range of scientific disciplines allowing for the examination of complex social problems such as health disparities. This study used 589 county-level variables to identify and compare geographical variation of high and low preterm birth rates. Data were collected from a number of publically available sources, bringing together natality outcomes with attributes of the natural, built, social, and policy environments. Singleton early premature county birth rate, in counties with population size over 100,000 persons provided the dependent variable. Graph theoretical techniques were used to identify a wide range of predictor variables from various domains, including black proportion, obesity and diabetes, sexually transmitted infection rates, mother's age, income, marriage rates, pollution and temperature among others. Dense subgraphs (paracliques) representing groups of highly correlated variables were resolved into latent factors, which were then used to build a regression model explaining prematurity (R-squared = 76.7%). Two lists of counties with large positive and large negative residuals, indicating unusual prematurity rates given their circumstances, may serve as a starting point for ways to intervene and reduce health disparities for preterm births.

Scalable combinatorial tools for health disparities research.

Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual's genetic endowment, particularly to environmental exposures experienced across his or her life course. New analytical approaches are clearly required as investigators turn to complicated systems theory and ecological, place-based and life-history perspectives in order to understand more clearly the relationships between social determinants, environmental exposures and health disparities. While traditional data analysis techniques remain foundational to health disparities research, they are easily overwhelmed by the ever-increasing size and heterogeneity of available data needed to illuminate latent gene x environment interactions. This has prompted the adaptation and application of scalable combinatorial methods, many from genome science research, to the study of population health. Most of these powerful tools are algorithmically sophisticated, highly automated and mathematically abstract. Their utility motivates the main theme of this paper, which is to describe real applications of innovative transdisciplinary models and analyses in an effort to help move the research community closer toward identifying the causal mechanisms and associated environmental contexts underlying health disparities. The public health exposome is used as a contemporary focus for addressing the complex nature of this subject.

Association of naturally acquired IgG antibodies against Plasmodium falciparum serine repeat antigen-5 with reduced placental parasitemia and normal birth weight in pregnant Ugandan women: a pilot study.

Plasmodium falciparum infection during pregnancy contributes substantially to malaria burden in both mothers and offspring. Analysis of naturally acquired immune responses that confer protection against parasitemia and clinical disease is important to guide vaccine evaluation as well as identify immune correlates. Unfortunately, few studies have addressed the relationship between immune responses to malaria vaccine candidate antigens and protection against adverse effects on pregnant women and newborn birth weight. This study examines the relationship of maternal antibody responses to serine repeat antigen-5 (SE36) and merozoite surface protein-1 (MSP119 and MSP142) with placental parasitemia and birth weight. In a peri-urban setting in Uganda, pregnant women without placental parasites have high median ODs for antibodies against SE36 (P<0.001). Naturally acquired anti-SE36 IgG was most prevalent in women without placental parasitemia (P<0.001). Furthermore, pregnant women with significantly high levels of anti-SE36 IgG delivered babies with normal birth weights (P<0.001). That antibody to SE36 was associated with both a reduced risk of placental parasitemia and resulting normal birth weight in newborns suggests some protective role. In contrast, although antibody to MSP142 was also associated with reduced placental parasitemia and immune responses to both MSP119 and MSP142 may be of importance, there was no association between anti-MSP119 antibodies and infant birth weight outcomes. This study highlights the need for conducting further studies to investigate the association of antibodies against SE36 and outcomes of malaria infection in pregnant women.

Predictive and epidemiologic modeling of the spatial risk of human onchocerciasis using biophysical factors: a case study of Ghana and Burundi.

Although recent efforts taken have substantially contained human onchocerciasis in many African countries, published reports indicate a recrudescence of the disease. To understand this problem, biophysical factors that favor the establishment of human onchocerciasis in Ghana and Burundi-countries identified as threat locations of recrudescence for neighboring countries-were analyzed. Data pertaining to the prevalence of human onchocerciasis in both countries was obtained from published sources. Findings in this study suggest that there was a gradient in prevalence of onchocerciasis in geographic locations near the water streams. The predictive models suggest that rainfall, humidity, and elevation were statistically significant for Burundi data while in Ghana, only the effect of elevation was highly significant (p<0.0001). In 2010, the estimated at-risk population was 4,817,280 people (19.75% of the total population) and 522,773 people (6.23% of the total population) in Ghana and Burundi, respectively. Findings can help in the effective design of preventive control measures.

The new and computationally efficient MIL-SOM algorithm: potential benefits for visualization and analysis of a large-scale high-dimensional clinically acquired geographic data.

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM.

A New-Fangled FES-k-Means Clustering Algorithm for Disease Discovery and Visual Analytics.

The central purpose of this study is to further evaluate the quality of the performance of a new algorithm. The study provides additional evidence on this algorithm that was designed to increase the overall efficiency of the original k-means clustering technique-the Fast, Efficient, and Scalable k-means algorithm (FES-k-means). The FES-k-means algorithm uses a hybrid approach that comprises the k-d tree data structure that enhances the nearest neighbor query, the original k-means algorithm, and an adaptation rate proposed by Mashor. This algorithm was tested using two real datasets and one synthetic dataset. It was employed twice on all three datasets: once on data trained by the innovative MIL-SOM method and then on the actual untrained data in order to evaluate its competence. This two-step approach of data training prior to clustering provides a solid foundation for knowledge discovery and data mining, otherwise unclaimed by clustering methods alone. The benefits of this method are that it produces clusters similar to the original k-means method at a much faster rate as shown by runtime comparison data; and it provides efficient analysis of large geospatial data with implications for disease mechanism discovery. From a disease mechanism discovery perspective, it is hypothesized that the linear-like pattern of elevated blood lead levels discovered in the city of Chicago may be spatially linked to the city's water service lines.