Female Leadership during COVID-19: The Effectiveness of Diverse Approaches towards Mitigation Management during a Pandemic

This paper tackles the question of how female leaders at national levels of government managed COVID-19 response and recovery from the first COVID-19 case in their respective countries through to 30 September 2021. The aim of this study was to determine which COVID-19 mitigations were effective in lowering the viral reproduction rate and number of new cases (per million) in each of the fourteen female presidents' countries-Bangladesh, Barbados, Belgium, Bolivia, Denmark, Estonia, Finland, Germany, Iceland, Lithuania, New Zealand, Norway, Serbia, and Taiwan. We first compared these countries by finding a mean case rate (29,420 per million), mean death rate (294 per million), and mean excess mortality rate (+1640 per million). We then analyzed the following mitigation measures per country: school closing, workplace closing, canceling public events, restrictions on gatherings, closing public transport, stay-at-home requirements, restrictions on internal movement, international travel controls, income support, debt/contract relief, fiscal measures, international support, public information campaigns, testing policy, contact tracing, emergency investment in healthcare, investment in vaccines, facial coverings, vaccination policy, and protection of the elderly. We utilized the random forest approach to examine the predictive significance of these variables, providing more interpretability. Subsequently, we then applied the Wilcoxon rank-sum statistical test to see the differences with and without mitigation in effect for the variables that were found to be significant by the random forest model. We observed that different mitigation strategies varied in their effectiveness. Notably, restrictions on internal movement and the closure of public transportation proved to be highly effective in reducing the spread of COVID-19. Embracing qualities such as community-based, empathetic, and personable leadership can foster greater trust among citizens, ensuring continued adherence to governmental policies like mask mandates and stay-at-home orders, ultimately enhancing long-term crisis management.

The Memphis Pandemic Health Informatics System (MEMPHI-SYS)-Creating a Metropolitan COVID-19 Data Registry Linked Directly to Community Testing to Enhance Population Health Surveillance

The current coronavirus disease (COVID-19) pandemic has placed unprecedented strain on underfunded public health resources in the Southeastern United States. The Memphis, TN, metropolitan region has lacked infrastructure for health data exchange.This manuscript describes a multidisciplinary initiative to create a community-focused COVID-19 data registry, the Memphis Pandemic Health Informatics System (MEMPHI-SYS). MEMPHI-SYS leverages test result data updated directly from community-based testing sites, as well as a full complement of public health data sets and knowledge-based informatics. It has been guided by relationships with community stakeholders and is managed alongside the largest publicly funded community-based COVID-19 testing response in the Mid-South. MEMPHI-SYS has supported interactive Web-based analytic resources and informs federally funded COVID-19 outreach directed toward neighborhoods most in need of pandemic support.MEMPHI-SYS provides an instructive case study of how to collaboratively establish the technical scaffolding and human relationships necessary for data-driven, health equity-focused pandemic surveillance, and policy interventions.

Independent Predictors for Hospitalization-Associated Radiotherapy Interruptions

Purpose

Radiation treatment interruption associated with unplanned hospitalization remains understudied. The intent of this study was to benchmark the frequency of hospitalization-associated radiation therapy interruptions (HARTI), characterize disease processes causing hospitalization during radiation, identify factors predictive for HARTI, and localize neighborhood environments associated with HARTI at our academic referral center.

Methods and Materials

This retrospective review of electronic health records provided descriptive statistics of HARTI event rates at our institutional practice. Uni- and multivariable logistic regression models were developed to identify significant factors predictive for HARTI. Causes of hospitalization were established from primary discharge diagnoses. HARTI rates were mapped according to patient residence addresses.

Results

Between January 1, 2015, and December 31, 2017, 197 HARTI events (5.3%) were captured across 3729 patients with 727 total missed treatments. The 3 most common causes of hospitalization were malnutrition/dehydration (n = 28; 17.7%), respiratory distress/infection (n = 24; 13.7%), and fever/sepsis (n = 17; 9.7%). Factors predictive for HARTI included African-American race (odds ratio [OR]: 1.48; 95% confidence interval [CI], 1.07-2.06; P = .018), Medicaid/uninsured status (OR: 2.05; 95% CI, 1.32-3.15; P = .0013), Medicare coverage (OR: 1.7; 95% CI, 1.21-2.39; P = .0022), lung (OR: 5.97; 95% CI, 3.22-11.44; P < .0001), and head and neck (OR: 5.6; 95% CI, 2.96-10.93; P < .0001) malignancies, and prescriptions >20 fractions (OR: 2.23; 95% CI, 1.51-3.34; P < .0001). HARTI events clustered among Medicaid/uninsured patients living in urban, low-income, majority African-American neighborhoods, and patients from middle-income suburban communities, independent of race and insurance status. Only the wealthiest residential areas demonstrated low HARTI rates.

Conclusions

HARTI disproportionately affected socioeconomically disadvantaged urban patients facing a high treatment burden in our catchment population. A complementary geospatial analysis also captured the risk experienced by middle-income suburban patients independent of race or insurance status. Confirmatory studies are warranted to provide scale and context to guide intervention strategies to equitably reduce HARTI events.

The Effects of Lifestyle on the Risk of Lyme Disease in the United States: Evaluation of Market Segmentation Systems in Prevention and Control Strategies

The aim of this study was to investigate lifestyles at risk of Lyme disease, and to geographically identify target populations/households at risk based on their lifestyle preferences. When coupled with geographically identified patient health information (e.g., incidence, diagnostics), lifestyle data provide a more solid base of information for directing public health objectives in minimizing the risk of Lyme disease and targeting populations with Lyme-disease-associated lifestyles. We used an ESRI Tapestry segmentation system that classifies U.S. neighborhoods into 67 unique segments based on their demographic and socioeconomic characteristics. These 67 segments are grouped within 14 larger "LifeModes" that have commonalities based on lifestyle and life stage. Our dataset contains variables denoting the dominant Tapestry segments within each U.S. county, along with annual Lyme disease incidence rates from 2000 through 2017, and the average incidence over these 18 years. K-means clustering was used to cluster counties based on yearly incidence rates for the years 2000-2017. We used analysis of variance (ANOVA) statistical testing to determine the association between Lyme disease incidence and LifeModes. We further determined that the LifeModes Affluent Estates, Upscale Avenues, GenXurban, and Cozy Country Living were associated with higher Lyme disease risk based on the results of analysis of means (ANOM) and Tukey's post hoc test, indicating that one of these LifeModes is the LifeMode with the greatest Lyme disease incidence rate. We further conducted trait analysis of the high-risk LifeModes to see which traits were related to higher Lyme disease incidence. Due to the extreme regional nature of Lyme disease incidence, we carried out our national-level analysis at the regional level. Significant differences were detected in incidence rates and LifeModes in individual regions. We mapped Lyme disease incidence with associated LifeModes in the Northeast, Southeast, Midcontinent, Rocky Mountain, and Southwest regions to reflect the location-dependent nature of the relationship between lifestyle and Lyme disease.

Lifestyle Effects on the Risk of Transmission of COVID-19 in the United States: Evaluation of Market Segmentation Systems

The aim of this study was to associate lifestyle characteristics with COVID-19 infection and mortality rates at the U.S. county level and sequentially map the impact of COVID-19 on different lifestyle segments. We used analysis of variance (ANOVA) statistical testing to determine whether there is any correlation between COVID-19 infection and mortality rates and lifestyles. We used ESRI Tapestry LifeModes data that are collected at the U.S. household level through geodemographic segmentation typically used for marketing purposes to identify consumers’ lifestyles and preferences. According to the ANOVA analysis, a significant association between COVID-19 deaths and LifeModes emerged on 1 April 2020 and was sustained until 30 June 2020. Analysis of means (ANOM) was also performed to determine which LifeModes have incidence rates that are significantly above/below the overall mean incidence rate. We sequentially mapped and graphically illustrated when and where each LifeMode had above/below average risk for COVID-19 infection/death on specific dates. A strong northwest-to-south and northeast-to-south gradient of COVID-19 incidence was identified, facilitating an empirical classification of the United States into several epidemic subregions based on household lifestyle characteristics. Our approach correlating lifestyle characteristics to COVID-19 infection and mortality rate at the U.S. county level provided unique insights into where and when COVID-19 impacted different households. The results suggest that prevention and control policies can be implemented to those specific households exhibiting spatial and temporal pattern of high risk.

Implementing an Urban Public Health Observatory for (Near) Real-Time Surveillance for the COVID-19 Pandemic

The COVID-19 pandemic is broadly undercutting global health and economies, while disproportionally impacting socially disadvantaged populations. An impactful pandemic surveillance solution must draw from multi-dimensional integration of social determinants of health (SDoH) to contextually inform traditional epidemiological factors. In this article, we describe an Urban Public Health Observatory (UPHO) model which we have put into action in a mid-sized U.S. metropolitan region to provide near real-time analysis and dashboarding of ongoing COVID-19 conditions. Our goal is to illuminate associations between SDoH factors and downstream pandemic health outcomes to inform specific policy decisions and public health planning.

Location as Destiny: Identifying Geospatial Disparities in Radiation Treatment Interruption by Neighborhood, Race, and Insurance

PURPOSE

Radiation therapy interruption (RTI) worsens cancer outcomes. Our purpose was to benchmark and map RTI across a region in the United States with known cancer outcome disparities.

METHODS AND MATERIALS

All radiation therapy (RT) treatments at our academic center were cataloged. Major RTI was defined as ≥5 unplanned RT appointment cancellations. Univariate and multivariable logistic and linear regression analyses identified associated factors. Major RTI was mapped by patient residence. A 2-sided P value <.0001 was considered statistically significant.

RESULTS

Between 2015 and 2017, a total of 3754 patients received RT, of whom 3744 were eligible for analysis: 962 patients (25.8%) had ≥2 RT interruptions and 337 patients (9%) had major RTI. Disparities in major RTI were seen across Medicaid versus commercial/Medicare insurance (22.5% vs 7.2%; P < .0001), low versus high predicted income (13.0% vs 5.9%; P < .0001), Black versus White race (12.0% vs 6.6%; P < .0001), and urban versus suburban treatment location (12.0% vs 6.3%; P < .0001). On multivariable analysis, increased odds of major RTI were seen for Medicaid patients (odds ratio [OR], 3.35; 95% confidence interval [CI], 2.25-5.00; P < .0001) versus those with commercial/Medicare insurance and for head and neck (OR, 3.74; 95% CI, 2.56-5.46; P < .0001), gynecologic (OR, 3.28; 95% CI, 2.09-5.15; P < .0001), and lung cancers (OR, 3.12; 95% CI, 1.96-4.97; P < .0001) compared with breast cancer. Major RTI was mapped to urban, majority Black, low-income neighborhoods and to rural, majority White, low-income regions.

CONCLUSIONS

Radiation treatment interruption disproportionately affects financially and socially vulnerable patient populations and maps to high-poverty neighborhoods. Geospatial mapping affords an opportunity to correlate RT access on a neighborhood level to inform potential intervention strategies.

Abstract A124: Neighborhood, race and insurance predict for hospital admission during radiation therapy

Background: Hospital admission during radiotherapy (ADRT) is associated with increased cost, interrupted treatment, and inferior outcomes. The purpose of this study was to benchmark patient ADRT rates, define socioeconomic predictors for ADRT, and geographically map ADRT rates on the neighborhood level across a large Mid-Southern catchment region served by a single academic cancer referral center. Methods: Demographic, clinical and treatment information were collected for all patients treated with radiation therapy (RT) at our center from January 1, 2015 to December 31, 2017. Occurrence of ADRT included inpatient and emergency room admissions. ADRT was categorized as causing “minor interruption” if ADRT was associated with postponement in 1-4 RT treatments. “Major interruption” was defined as postponement in 5 or more treatments. Patients with Medicaid or no insurance were categorized as “At-Risk”. Patient predicted income (PPI) was modeled using 2017 US Census data for annual household income by patient residence census tract, categorized into low (&lt;$34k), middle, and high (&gt;$67k) thirds. ADRT rates were compared across variables, analyzed using Pearson’s Chi square testing, and geomapped by patient residence at the neighborhood (census tract) level. Results: 3,729 patients were included. 2,032 (54.5%) were Caucasian, 1,577 African American (42.3%), and 120 (3.2%) other. Insurance status was defined as Commercial, Medicare, or At Risk in 1,794 (48.1%), 1,503 (40.3%), and 432 (11.6%) patients. The mean PPI was $49,951 (range $10,871-$177,857). A total of 83,306 fractions (median 24, IQR 11-30) were delivered with 7,107 (8.5%) total interruptions. 727 interruptions (mean 0.19, range 0-21) were due to ADRT in 197 patients (5.3%). Minor interruption rates were significantly elevated in At Risk patients v. those with Commercial or Medicare insurance (7.4% v 3.5% p=&lt;0.0001; OR 2.21 [95%CI 1.47-3.31]), African American v. Caucasian patients (5.1% v 3.1% p=0.002; OR 1.66 [95%CI 1.19-2.32]), and low PPI v. high PPI patients (5.2% v 2.5% p=&lt;0.0005; OR 2.17 [95%CI 1.39-3.39]). Major interruption rates were similar across all groups: At Risk v. Commercial or Medicare insurance (1.6% v 1.2% p=0.591; OR 1.25 [95%CI 0.55-2.79]), African American v. Caucasian ([1.3% v 1.4% p=0.74; OR 0.91 [95%CI 0.51-1.61]), and low PPI v. high PPI (1.4% v 1.3% p=0.93; OR 1.03 [95%CI 0.52-2.05]). Elevated minor interruption rates were geographically associated with low income, predominately African American neighborhoods across our treatment region. Conclusion: At our high-volume academic radiotherapy practice, hospital admission during RT correlated significantly with uninsured or Medicaid coverage status, African American race, and low predicted income and mapped to low income neighborhoods, suggesting limited care access for these populations. Major hotspot locations have been identified, setting the stage for targeted studies to close gaps in RT quality.

Citation Format: Daniel V Wakefield, Matthew Carnell, Bo Jiang, Austin Dove, Wesley Garner, Drucilla Edmonston, Adam Hubler, Esra Ozdenerol, Ryan Hanson, Maria Pisu, David L Schwartz, David L Schwarts. Neighborhood, race and insurance predict for hospital admission during radiation therapy [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr A124.

Abstract A098: Socioeconomic and geographic predictors for radiotherapy quality disparities in the Mid-Southern U.S

Background: Approximately half of cancer patients are prescribed radiotherapy (RT) during their care. RT noncompliance (NC) is a well-studied quality measure associated with inferior survival. Cancer outcomes in the Mid-South (West Tennessee, Mississippi Delta, Eastern Arkansas) rank well below other U.S. regions. The purpose of this study is to benchmark RT NC rates, define socioeconomic predictors of RT NC, and geographically map NC across a large Mid-Southern catchment region served by a single academic cancer referral center.

Methods: Demographic, clinical, and treatment information were collected for all patients treated with RT at the University of Tennessee West Cancer Center from January 1, 2015 to December 31, 2017. RT NC was defined as delay in 2 or more treatments. “Critically high” RT NC was defined as delay in 5 or more treatments. Patients with Medicaid or no insurance were categorized as “At Risk.” Patient predicted income (PPI) was modeled using 2016 US Census data for patient residence zip code and grouped by thirds into three (low, middle, and high) income categories. RT NC was compared by insurance type, race, and PPI, analyzed for significance using Pearson's Chi square testing, and geomapped by patient residence zip code and urban and rural status.

Results: A total of 3,754 patients were included, of whom 1,981 (52.8%) were Caucasian, 1,582 African American (42.2%), and 217 (5.7%) other. Insurance status was defined as Commercial, Medicare, or At Risk in 1,803 (48%), 1,508 (40%), and 443 (12%) patients, respectively. Overall, RT NC was seen in 962 patients (25.6%) and critically high RT NC was seen in 422 patients (11.2%). At-risk patients experienced more RT NC (36.2% v 25.1% p=&lt;0.001) and more than double the critically high RT NC compared to patients with commercial insurance (20.3% v 9.6% p=&lt;0.001). African American patients in our population experienced higher rates of RT NC and critically high RT NC compared to Caucasian patients ([30.5% v 22.0% p=&lt;0.001] and [14.9% v 8.8% p=&lt;0.001]). Compared to patients with high PPI (&gt;67k), patients with low PPI (&lt;$34k) experienced higher RT NC (20.2% v 31.7% p=&lt;0.001) and critically high RT NC (7.3% v 15.7% p=&lt;0.001). High RT NC rates were geographically associated with patients living in rural zip codes, with critically high RT NC rates in urban zip codes with low PPI.

Conclusion: In our high-volume academic radiotherapy practice, RT noncompliance correlates significantly with uninsured or Medicaid coverage status, African American race, and low predicted income. Noncompliance disproportionately impacts rural patients and inner-urban patients with low predicted income. Further studies are needed to understand causative mechanisms requiring intervention to help close gaps in radiotherapy quality.

Citation Format: Daniel V. Wakefield, Matthew Carnell, Austin P.H. Dove, Elena Delavega, Esra Ozdenerol, Maria Pisu, Michelle Y. Martin, David L. Schwartz. Socioeconomic and geographic predictors for radiotherapy quality disparities in the Mid-Southern U.S. [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr A098.

GIS and Remote Sensing Use in the Exploration of Lyme Disease Epidemiology

Given the relatively recent recognition of Lyme disease (LD) by CDC in 1990 as a nationally notifiable infectious condition, the rise of reported human cases every year argues for a better understanding of its geographic scope. The aim of this inquiry was to explore research conducted on spatiotemporal patterns of Lyme disease in order to identify strategies for implementing vector and reservoir-targeted interventions. The focus of this review is on the use of GIS-based methods to study populations of the reservoir hosts, vectors and humans in addition to the spatiotemporal interactions between these populations. New GIS-based studies are monitoring occurrence at the macro-level, and helping pinpoint areas of occurrence at the micro-level, where spread within populations of reservoir hosts, clusters of infected ticks and tick to human transmission may be better understood.

Predictors of functional limitation trajectories after injury in a nationally representative U.S. older adult population

PURPOSE

Studies examining postinjury functional status have demonstrated that individuals with severe injuries often do not return to baseline levels of physical functioning. We sought to investigate the impact injuries have on changes in physical functioning across the life course of older adults. The study's objectives were to (1) identify trajectories of long-term functional limitations after injury in the older adult population to better characterize the recovery process and (2) predict which individuals are most at risk for poor functional trajectories after injury.

METHODS

A retrospective cohort study was conducted using six waves of data from the Health and Retirement Study, which surveys Americans older than 50 years every two years. A group-based trajectory model was used to identify trajectories of functional limitations in injured participants. Using multivariate regression, we identified significant predictors of each trajectory.

RESULTS

Five distinct trajectories were identified: Trajectory 1--consistently low functional limitations scores (18.9%), Trajectory 2--increase in functional limitations after injury followed by a gradual, incomplete recovery (46.3%), Trajectory 3--increase in functional limitations followed by further decline in functioning (10.5%), Trajectory 4--increase in functional limitations after injury followed by a gradual, complete recovery (13.4%), and Trajectory 5--consistently high functional limitations scores (10.8%). Gender, multiple health conditions, and insurance status predicted trajectory membership.

CONCLUSIONS

Functional limitations after injury follow distinct trajectories that can be predicted by baseline individual characteristics.

Exploring childhood lead exposure through GIS: a review of the recent literature

Childhood exposure to lead remains a critical health control problem in the US. Integration of Geographic Information Systems (GIS) into childhood lead exposure studies significantly enhanced identifying lead hazards in the environment and determining at risk children. Research indicates that the toxic threshold for lead exposure was updated three times in the last four decades: 60 to 30 micrograms per deciliter (µg/dL) in 1975, 25 µg/dL in 1985, and 10 µb/dL in 1991. These changes revealed the extent of lead poisoning. By 2012 it was evident that no safe blood lead threshold for the adverse effects of lead on children had been identified and the Center for Disease Control (CDC) currently uses a reference value of 5 µg/dL. Review of the recent literature on GIS-based studies suggests that numerous environmental risk factors might be critical for lead exposure. New GIS-based studies are used in surveillance data management, risk analysis, lead exposure visualization, and community intervention strategies where geographically-targeted, specific intervention measures are taken.

Pathogenic role of environmental toxins in immune-mediated liver diseases

Immune-mediated liver diseases contribute significantly to morbidity and mortality due to liver failure and the need for liver transplantation. The pathogenesis of the immune-mediated chronic liver diseases, primary sclerosing cholangitis, autoimmune hepatitis, and primary biliary cirrhosis, is poorly understood. Genetic susceptibility factors may play a role, but increasing attention is being given to the association between environmental factors and these diseases. The existence of such a relationship is supported by epidemiologic surveys, animal models, and geographic clustering analyses. Unearthing the cause of this association may provide insight into the pathogenesis of immune-mediated chronic liver diseases and autoimmunity.

Locating suitable habitats for West Nile Virus-infected mosquitoes through association of environmental characteristics with infected mosquito locations: a case study in Shelby County, Tennessee

Background

Since its first detection in 2001, West Nile Virus (WNV) poses a significant health risk for residents of Shelby County in Tennessee. This situation forced public health officials to adopt efficient methods for monitoring disease spread and predicting future outbreaks. Analyses that use environmental variables to find suitable habitats for WNV-infected mosquitoes have the potential to support these efforts. Using the Mahalanobis Distance statistic, we identified areas of Shelby County that are ecologically most suitable for sustaining WNV, based on similarity of environmental characteristics to areas where WNV was found. The environmental characteristics in this study were based on Geographic Information Systems (GIS) data, such as elevation, slope, land use, vegetation density, temperature, and precipitation.

Results

Our analyses produced maps of likely habitats of WNV-infected mosquitoes for each week of August 2004, revealing the areas that are ecologically most suitable for sustaining WNV within the core of the Memphis urban area. By comparing neighbourhood social characteristics to the environmental factors that contribute to WNV infection, potential social drivers of WNV transmission were revealed in Shelby County. Results show that human population characteristics and housing conditions such as a high percentage of black population, low income, high rental occupation, old structures, and vacant housing are associated with the focal area of WNV identified for each week of the study period.

Conclusion

We demonstrated that use of the Mahalanobis Distance statistic as a similarity index to assess environmental characteristics is a potential raster-based approach to identify areas ecologically most suitable for sustaining the virus. This approach was also useful to monitor changes over time for likely locations of infected mosquito habitats. This technique is very helpful for authorities when making decisions related to an integrated mosquito management plan and targeted health education outreach.

Assessing the impact of the local environment on birth outcomes: a case for HLM

Hierarchical linear Models (HLM) is a useful way to analyze the relationships between community level environmental data, individual risk factors, and birth outcomes. With HLM we can determine the effects of potentially remediable environmental conditions (e.g., air pollution) after controlling for individual characteristics such as health factors and socioeconomic factors. Methodological limitations of ecological studies of birth outcomes and a detailed analysis of the varying models that predict birth weight will be discussed. Ambient concentrations of criterion air pollutants (e.g., lead and sulfur dioxide) demonstrated a sizeable negative effect on birth weight; while the economic characteristics of the mother's residential census tract (ex. poverty level) also negatively influenced birth weight.

Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters

Background

The purpose of this study is to examine the spatial and population (e.g., socio-economic) characteristics of low birthweight using two different cluster estimation techniques. We compared the results of Kulldorff's Spatial Scan Statistic with the results of Rushton's Spatial filtering technique across increasing sizes of spatial filters (circle). We were able to demonstrate that varying approaches exist to explore spatial variation in patterns of low birth weight.

Results

Spatial filtering results did not show any particular area that was not statistically significant based on SaTScan. The high rates, which remain as the filter size increases to 0.4, 0.5 to 0.6 miles, respectively, indicate that these differences are less likely due to chance. The maternal characteristics of births within clusters differed considerably between the two methods. Progressively larger Spatial filters removed local spatial variability, which eventually produced an approximate uniform pattern of low birth weight.

Conclusion

SaTScan and Spatial filtering cluster estimation methods produced noticeably different results from the same individual level birth data. SaTScan clusters are likely to differ from Spatial filtering clusters in terms of population characteristics and geographic area within clusters. Using the two methods in conjunction could provide more detail about the population and spatial features contained with each type of cluster.