Disparities In Health Insurance Coverage Research Articles

Medicare Eligibility and Changes in Coverage, Access to Care, and Health by Sexual Orientation and Gender Identity

Medicare provides nearly universal insurance coverage at age 65 years. However, how Medicare eligibility affects disparities in health insurance coverage, access to care, and health status among individuals by sexual orientation and gender identity is poorly understood. To assess the association of Medicare eligibility with disparities in health insurance coverage, access to care, and self-reported health status among individuals by sexual orientation and by gender identity. This cross-sectional study used the age discontinuity for Medicare eligibility at age 65 years to isolate the association of Medicare with health insurance coverage, access to care, and self-reported health status, by their sexual orientation and by their gender identity. Data were collected from the Behavioral Risk Factor Surveillance System for respondents from 51 to 79 years old from 2014 to 2021. Data analysis was performed from September 2022 to April 2023. Medicare eligibility at age 65 years. Proportions of respondents with health insurance coverage, usual source of care, cost barriers to care, influenza vaccination, and self-reported health status. The study population included 927 952 individuals (mean [SD] age, 64.4 [7.7] years; 524 972 [56.6%] females and 402 670 [43.4%] males), of whom 28 077 (3.03%) identified as a sexual minority-lesbian, gay, bisexual, or another sexual minority identity (LGB+) and 3286 (0.35%) as transgender or gender diverse. Respondents who identified as heterosexual had greater improvements at age 65 years in insurance coverage (4.2 percentage points [pp]; 95% CI, 4.0-4.4 pp) than those who identified as LGB+ (3.6 pp; 95% CI, 2.3-4.8 pp), except when the analysis was limited to a subsample of married respondents. For access to care, improvements in usual source of care, cost barriers to care, and influenza vaccination were larger at age 65 years for heterosexual respondents compared with LGB+ respondents, although confidence intervals were overlapping and less precise for LGB+ individuals. For self-reported health status, the analyses found larger improvements at age 65 years for LGB+ respondents compared with heterosexual respondents. There was considerable heterogeneity by state in disparities by sexual orientation among individuals who were nearly eligible for Medicare (close to 65 years old), with the US South and Central states demonstrating the highest disparities. Among the top-10 highest-disparities states, Medicare eligibility was associated with greater increases in coverage (6.7 pp vs 5.0 pp) and access to a usual source of care (1.4 pp vs 0.6 pp) for LGB+ respondents compared with heterosexual respondents. The findings of this cross-sectional study indicate that Medicare eligibility was not associated with consistently greater improvements in health insurance coverage and access to care among LGBTQI+ individuals compared with heterosexual and/or cisgender individuals. However, among sexual minority individuals, Medicare may be associated with closing gaps in self-reported health status, and among states with the highest disparities, it may improve health insurance coverage, access to care, and self-reported health status.

Health Disparities Among Children in Immigrant Households: New York City 2009 and 2017.

We tested the hypothesis that children in New York City (NYC) with an immigrant parent were more likely to lack health insurance and report poorer parent-rated health compared to those of US-born parents in this serial, cross-sectional study using existing data from 2009 to 2017 among children age < 12 years in two NYC health surveys. Main outcomes were parent-reported responses for (1) child insurance coverage and (2) child general health status. In multivariable logistic regression models, we estimated likelihood of outcomes for children of immigrants compared to those of US-born parents, adjusting for child, parent, and household characteristics. We included 2,637 children in 2009 and 7,042 in 2017 in NYC. In 2017, children of immigrant parents were more likely to experience uninsurance than children of US-born parents [adjusted odds ratio (AOR) 2.36 (95% CI: 1.05-5.31)]. Compared to children of US-born parents, children with an immigrant parent were more likely to have a gap in insurance coverage in both 2009 (AOR 1.88; 95% CI: 1.08-3.27) and 2017 (AOR 1.60; 95% CI: 1.06-2.41). Although more children of immigrants had poor/fair health than those of US-born parents in both years, differences were not statistically significant after adjusting for covariates. Our findings among a sample of children eligible for health insurance suggest policies intended to expand child health care access did not equitably reach children of immigrants despite their eligibility for health insurance. Tailored interventions for children of immigrants are needed to mitigate disparities in health insurance coverage.

Social and demographic determinants of health insurance status in India: Evidence from a nationally representative cross-sectional survey.

Out-of-pocket medical expenses are a crucial metric for assessing how well the healthcare system is working toward obtaining universal health coverage in any country. In India, out-of-pocket expenses for health are relatively high compared to other developed countries due to a lack of alternative finance arrangements. The disparity in out-of-pocket medical expenses largely depends upon the public health expenditure, government policies, and level of health insurance coverage. The study used a logit regression model to examine the association of the status of health insurance with socio and demographic variables using National Sample Survey 2018 data. The objective of the study is to analyze the impact of demographic variables on the status of health insurance in India. This research found that education and occupation have a significant impact on the status of health insurance, among other demographic factors. These findings underscore the importance of targeted policies and interventions aimed at improving access to health insurance among specific demographic groups. Addressing disparities in health insurance coverage based on educational and occupational factors is essential for achieving equitable healthcare access and improved health outcomes in the country. Increasing awareness of health insurance reduces out-of-pocket medical expenses and subsequently brings down economic poverty.

The impact of COVID-19 on healthcare coverage and access in racial and ethnic minority populations in the United States.

This study described spatiotemporal changes in health insurance coverage, healthcare access, and reasons for non-insurance among racial/ethnic minority populations in the United States during the COVID-19 pandemic using four national survey datasets. Getis-Ord Gi* statistic and scan statistics were used to analyze geospatial clusters of health insurance coverage by race/ethnicity. Logistic regression was used to estimate odds of reporting inability to access healthcare across two pandemic time periods by race/ethnicity. Racial/ethnic differences in insurance were observed from 2010 through 2019, with the lowest rates being among Hispanic/Latino, African American, American Indian/Alaska Native, and Native Hawaiian/Pacific Islander populations. Pre-pandemic insurance coverage rates were geographically clustered. The percentage of adults citing change in employment status as the reason for non-insurance increased by about 7% after the start of the pandemic, with a small decrease observed among African American adults. Almost half of adults reported reduced healthcare access in June 2020, with 38.7% attributing reduced access to the pandemic; however, by May 2021, the percent of respondents reporting reduced access for any reason and due to the pandemic fell to 26.9% and 12.7%, respectively. In general, racial/ethnic disparities in health insurance coverage and healthcare access worsened during the pandemic. Although coverage and access improved over time, pre-COVID disparities persisted with African American and Hispanic/Latino populations being the most affected by insurance loss and reduced healthcare access. Cost, unemployment, and eligibility drove non-insurance before and during the pandemic.

The effects of the American Rescue Plan Act on racial equity in health insurance coverage.

To evaluate the effects on racial disparities in health insurance coverage from the changes in the Premium Tax Credit (PTC) implemented in March 2021 as part of the American Rescue Plan Act (ARPA). We use nationally representative individual-level data from the Household Pulse Survey (HPS), which provides demographic, economic, and health insurance information for United States residents during the period April 2020-August 2022. While the PTC changes applied to all states, the 14 states that did not expand Medicaid received substantially more benefits than the expansion states since they had more uninsured individuals eligible for the PTC than the expansion states. In our analysis, the treatment (control) group includes all Medicaid nonexpansion (expansion) states. We use a difference-in-difference regression analysis to estimate the increase in the probability of insurance coverage after the expansion of the PTC. Furthermore, we conduct sensitivity and heterogeneity analyses. We focus on survey respondents ages 18-64. The expanded PTC increased the probability of an individual having coverage through the Health Insurance Exchange (HIX) in a nonexpansion state by 0.95 (95% CI: 0.6136, 1.2900), 1.75 (95% CI: 1.1795, 2.3291), and 1.75 (95% CI: 1.1815, 2.3269) percentage points among White, Black, and Hispanic respondents, respectively. It also increased overall health insurance coverage among all groups. The expanded PTC boosted HIX and overall health insurance coverage and reduced racial disparities.

Legal status and health disparities: An examination of health insurance coverage among the foreign-born

This paper employs a statistical matching procedure to impute the legal status of foreign-born adults in US Census surveys in order to estimate migration status disparities in health insurance coverage.

Effects of Medicaid Automatic Enrollment on Disparities in Insurance Coverage and Caregiver Burden for Children with Special Health Care Needs.

We analyze how Medicaid automatic enrollment policies for children with special health care needs (CSHCN) who are enrolled in Supplemental Security Income (SSI) reduce disparities in health insurance coverage and caregiving burden. Using the 2009-2010 National Survey of Children with Special Health Care Needs, we implement a difference-in-differences regression model comparing insurance enrollment rates between CSHCN receiving SSI and CSHCN not receiving SSI, in states with and without automatic enrollment policies. We find that Medicaid automatic enrollment has a meaningful impact on insurance enrollment for low-income CSHCN who participate in SSI and can be an effective method for mitigating disparities in insurance coverage (reducing uninsurance by 38%). Medicaid automatic enrollment also reduces caregiver burden among socioeconomically disadvantaged families with CSHCN. The effects of these policies are largest families who might be on the margin of eligibility or who face high administrative burden.

Racial and Ethnic Disparities in Health Insurance Coverage in the USA: Findings from the 2018 National Health Interview Survey.

Under the broad context of health care reforms, I explored racial and ethnic disparities in health insurance coverage as well as potential mechanisms that might contribute to these disparities for an extensive list of groups. The study used the 2018 National Health Interview Survey to analyze the effects of race/ethnicity on uninsurance, with adjustment for other predisposing characteristics (age, gender, marital status, and immigration status), enabling resources (education, income, language proficiency, and region), and need (self-reported health status). Results from the study documented an average uninsured rate of 13%, with substantial variations across different racial and ethnic groups, ranging from over 30% among Native Americans and Mexicans to less than 10% among whites andAsian subgroups. Enabling resources significantly reduced uninsurance among African Americans, Puerto Ricans, and Cubans compared to whites. On the other hand, these confounding factors only partially contributed to uninsurance among Native Americans, Mexicans, Mexican Americans, Dominicans, and other Central and South Americans. This study revealed substantial racial/ethnic disparities in uninsured rates, with Native Americans and a few Hispanic subgroups (Mexicans, Mexican Americans, and Dominicans) having higher and Asian Indians having lower rates than whites in uninsurance. Pathways leading to coverage varied by race/ethnicity. The main findings yielded empirical, theoretical, and policy implications that contributed to health care disparity research.

Effects of medicaid expansion on poverty disparities in health insurance coverage

BackgroundMore than 30 states have either expanded Medicaid or are actively considering expansion. The coverage gains from this policy are well documented, however, the impacts of its increasing coverage on poverty disparity are unclear at the national level.MethodAmerican Community Survey (2012–2018) was used to examine the effects of Medicaid expansion on poverty disparity in insurance coverage for nonelderly adults in the United States. Differences-in-differences-in-differences design was used to analyze trends in uninsured rates by poverty levels: (1) < 138 %, (2) 138–400 % and (3) > 400 % federal poverty level (FPL).ResultsCompared with uninsured rates in 2012, uninsured rates in 2018 decreased by 10.75 %, 6.42 %, and 1.11 % for < 138 %, 138–400 %, and > 400 % FPL, respectively. From 2012 to 2018, > 400 % FPL group continuously had the lowest uninsured rate and < 138 % FPL group had the highest uninsured rate. Compared with ≥ 138 % FPL groups, there was a 2.54 % reduction in uninsured risk after Medicaid expansion among < 138 % FPL group in Medicaid expansion states versus control states. After eliminating the impact of the ACA market exchange premium subsidy, 3.18 % decrease was estimated.ConclusionPoverty disparity in uninsured rates improved with Medicaid expansion. However, < 138 % FPL population are still at a higher risk for being uninsured.

The Other US Border: Health Insurance Coverage Among Latino Immigrants In Puerto Rico.

Puerto Rico is a US territory and a popular destination for Latino immigrants in the Caribbean. Even with few language and cultural barriers, however, many Latino immigrants in Puerto Rico are uninsured. Using data from the 2014-19 Puerto Rico Community Survey, we examined inequities in health insurance coverage for non-Puerto Rican Latinos ages 18-64 living in Puerto Rico according to citizenship status and Latino subgroup (Dominican, Cuban, Mexican, and other Latino). After controlling for potential confounders, we found that noncitizen Dominicans had a significantly lower probability of having any health insurance (57.2percent) and having any private insurance (31.5percent). Regardless of similarities in culture and language, Latino immigrants on the island, particularly Dominicans, experience major health insurance coverage inequities. Considering that Puerto Rico's immigration system is regulated by US federal statute, both federal and local policy makers should acknowledge and focus on reducing these immigrant disparities in health insurance coverage.

The convergence of racial and income disparities in health insurance coverage in the United States

ObjectiveThis study applied the vulnerability framework and examined the combined effect of race and income on health insurance coverage in the US.Data sourceThe household component of the US Medical Expenditure Panel Survey (MEPS-HC) of 2017 was used for the study.Study designLogistic regression models were used to estimate the associations between insurance coverage status and vulnerability measure, comparing insured with uninsured or insured for part of the year, insured for part of the year only, and uninsured only, respectively.Data collection/extraction methodsWe constructed a vulnerability measure that reflects the convergence of predisposing (race/ethnicity), enabling (income), and need (self-perceived health status) attributes of risk.Principal findingsWhile income was a significant predictor of health insurance coverage (a difference of 6.1–7.2% between high- and low-income Americans), race/ethnicity was independently associated with lack of insurance. The combined effect of income and race on insurance coverage was devastating as low-income minorities with bad health had 68% less odds of being insured than high-income Whites with good health.ConclusionResults of the study could assist policymakers in targeting limited resources on subpopulations likely most in need of assistance for insurance coverage. Policymakers should target insurance coverage for the most vulnerable subpopulation, i.e., those who have low income and poor health as well as are racial/ethnic minorities.

Regional and socioeconomic predictors of perceived ability to access coronavirus testing in the United States: results from a nationwide online COVID-19 survey

PurposeAccess to COVID-19 testing remained a salient issue during the early months of the pandemic, therefore this study aimed to identify 1) regional and 2) socioeconomic predictors of perceived ability to access Coronavirus testing. MethodsAn online survey using social media-based advertising was conducted among U.S. adults in April 2020. Participants were asked whether they thought they could acquire a COVID-19 test, along with basic demographic, socioeconomic and geographic information. ResultsA total of 6,378 participants provided data on perceived access to COVID-19 testing. In adjusted analyses, we found higher income and possession of health insurance to be associated with perceived ability to access Coronavirus testing. Geographically, perceived access was highest (68%) in East South Central division and lowest (39%) in West North Central. Disparities in health insurance coverage did not directly correspond to disparities in perceived access to COVID-19 testing. ConclusionsSex, geographic location, income, and insurance status were associated with perceived access to COVID-19 testing; interventions aimed at improving either access or awareness of measures taken to improve access are warranted. These findings from the pandemic's early months shed light on the importance of disaggregating perceived and true access to screening during such crises.

Children’s Coverage Vulnerabilities With Loss of a Parent’s Employer-Sponsored Insurance

* Abbreviations: ACA — : Affordable Care Act COVID-19 — : coronavirus disease ESI — : employer-sponsored insurance FPL — : federal poverty level In addition to the devastating health effects of the coronavirus disease (COVID-19) pandemic, the United States is experiencing the highest rates of unemployment since the Great Depression.1 One important and overlooked consequence of unemployment is the loss of employer-sponsored insurance (ESI) coverage for children whose parents lose their jobs. It is estimated 27 million US adults will become uninsured because of unemployment and lose ESI during the pandemic.2 During the pandemic, >4 out of 10 parents with children <19 years old reported they or a family member lost a job, work hours, or work-related income, which is slightly more than for childless adults.3 Two out of 5 adults in newly unemployed households reported having obtained their health insurance coverage from that employer. More than half of US children are covered by ESI,4 including 49% of children with preexisting conditions who have special health care needs.5 For lower-income and minority families, this loss of ESI during the COVID-19 pandemic will likely intensify already existing health disparities for children. Unemployment during the pandemic has differentially affected low-income and minority populations in the US. Children of low-wage workers are at risk for resulting coverage loss, including the 25% of children from families with incomes <138% of the federal poverty level (FPL) … Address correspondence to Lois K. Lee, MD, MPH, Division of Emergency Medicine, 300 Longwood Ave, Boston Children’s Hospital, Boston, MA 02115. E-mail: lois.lee{at}childrens.harvard.edu

Access denied: The relationship between patient insurance status and access to high-volume hospitals.

Underinsured patients face significant barriers in accessing high-quality care. Evidence of whether access to high-volume surgical care is mediated by disparities in health insurance coverage remains wanting. The authors used the National Cancer Data Base to identify all adult patients who had a confirmed diagnosis of breast, prostate, lung, or colorectal cancer during 2004 through 2016. The odds of receiving surgical care at a high-volume hospital were estimated according to the type of insurance using multivariable logistic regression analyses for each malignancy. Then, the interactions between study period and insurance status were assessed. In total, 1,279,738 patients were included in the study. Of these, patients with breast cancer who were insured by Medicare (odds ratio [OR], 0.75; P < .001), Medicaid (OR, 0.55; P < .001), or uninsured (OR, 0.50; P < .001); patients with prostate cancer who were insured by Medicare (OR, 0.87; P = .003), Medicaid (OR, 0.58; P = .001), or uninsured (OR, 0.36; P < .001); and patients with lung cancer who were insured by Medicare (OR, 0.84; P = .020), Medicaid (OR, 0.74; P = .001), or uninsured (OR, 0.48; P < .001) were less likely to receive surgical care at high-volume hospitals compared with patients who had private insurance. For patients with colorectal cancer, the effect of insurance differed by study period, and improved since 2011. For those on Medicaid, the odds of receiving care at a high-volume hospital were 0.51 during 2004 through 2007 and 0.99 during 2014 through 2016 (P for interaction = .001); for uninsured patients, the odds were 0.45 during 2004 through 2007 and 1.19 during 2014 through 2016 (P for interaction < .001) compared with patients who had private insurance. Uninsured, Medicare-insured, and Medicaid-insured patients are less likely to receive surgical care at high-volume hospitals. For uninsured and Medicaid-insured patients with colorectal cancer, the odds of receiving care at high-volume hospitals have improved since implementation of the Patient Protection and Affordable Care Act of 2010.

LGBTQ cultural competence for pharmacists

LGBTQ cultural competence for pharmacists

Changes in Health Insurance Coverage Over Time by Immigration Status Among US Older Adults, 1992-2016

Disparities in health insurance coverage by immigration status are well documented; however, there are few data comparing long-term changes in insurance coverage between immigrant and nonimmigrant adults as they age into older adulthood. To compare longitudinal changes in insurance coverage over 24 years of follow-up between recent immigrant, early immigrant, and nonimmigrant adults in the US. This population-based cohort study used data from the nationally representative Health and Retirement Study. Data were collected biennially from 1992 to 2016. The population included community-dwelling US adults born between 1931 and 1941 and aged 51 to 61 years at baseline. Statistical analysis was performed from February 3, 2017, to January 10, 2020. Participants were categorized as nonimmigrants (born in the US), early immigrants (immigrated to the US before the age of 18 years), and recent immigrants (immigrated to the US from the age of 18 years onward). Self-reported data on public, employer, long-term care, and other private insurance were used to define any insurance coverage. Longitudinal changes in insurance coverage were examined over time by immigration status using generalized estimating equations accounting for inverse probability of attrition weights. The association between immigration status and continuous insurance coverage was also evaluated. A total of 9691 participants were included (mean [SD] age, 56.0 [3.2] years; 5111 [52.6%] female). Nonimmigrants composed 90% (n = 8649) of the cohort; early immigrants, 2% (n = 201); and recent immigrants, 8% (n = 841). Insurance coverage increased from 68%, 83%, and 86% of recent immigrant, early immigrant, and nonimmigrant older adults, respectively, in 1992 to 97%, 100%, and 99% in 2016. After accounting for selective attrition, recent immigrants were 15% less likely than nonimmigrants to have any insurance at baseline (risk ratio, 0.85; 95% CI, 0.82-0.88), driven by lower rates of private insurance. However, disparities in insurance decreased incrementally over time and were eliminated, such that insurance coverage rates were similar between groups as participants attained Medicare age eligibility. Furthermore, recent immigrants were less likely than nonimmigrants to be continuously insured (risk ratio, 0.89; 95% CI, 0.85-0.94). Among community-dwelling adults who were not age eligible for Medicare, recent immigrants had lower rates of health insurance, but this disparity was eliminated over the 24-year follow-up period because of uptake of public insurance among all participants. Future studies should evaluate policies and health care reforms aimed at reducing disparities among vulnerable populations such as recent immigrants who are not age eligible for Medicare.

The ACA's Impact On Racial And Ethnic Disparities In Health Insurance Coverage And Access To Care.

Large disparities in health insurance coverage and access to health services have long persisted in the US health care system. We considered how the insurance coverage expansions of the Affordable Care Act have affected disparities related to race and ethnicity. In the years since the law went into effect, insurance coverage has increased significantly for all racial/ethnic groups. Because coverage increased more for non-Hispanic blacks and Hispanics than for non-Hispanic whites, disparities in coverage have decreased. Despite these improvements, a large number of adults remain uninsured, and the uninsurance rate among blacks and Hispanics is substantially higher than the rate among whites.

The Affordable Care Act Medicaid Expansion Positively Impacted Community Health Centers and Their Patients.

Community health centers (CHCs) provide primary care for underserved children and adults. The Patient Protection and Affordable Care Act (ACA) aimed to strengthen the CHC network by increasing federal funds and expanding Medicaid eligibility. The ACA also aimed to boost preventive and mental health services and to reduce health and healthcare disparities. Here, we summarize our results to-date as experts in investigating the impact of ACA Medicaid expansion on CHCs and the patients they serve. We found the ACA Medicaid expansion increased access to care and preventive services, primarily in Medicaid expansion states. Rates of physical and mental health conditions rose substantially from pre- to post-ACA in expansion states, suggesting underdiagnosis pre-ACA. Disparities in health insurance coverage by race/ethnicity decreased at CHCs, yet some remain. These findings indicate that the ACA Medicaid expansion significantly helped CHCs and patients. Insurance expansion buoyed CHCs' financial viability by increasing reimbursement. Therefore, the ACA Medicaid expansion enhanced the health of underserved patients and repeal would jeopardize these advances for CHCs and their patients.

Disparities in Health Insurance Coverage and Access to Care by English Language Proficiency in the USA, 2006-2016.

In the USA, people with limited English proficiency (LEP) disproportionately experience gaps in health insurance coverage and access to care. The Patient Protection and Affordable Care Act (ACA) of 2010 included reforms that could improve these outcomes. To describe changes in insurance coverage and access to health care by English language proficiency over 2006-2016. We used regression models to estimate changes in coverage and access after 2010 for adults with high vs. limited English proficiency, adjusting for socio-economic status, demographic characteristics, and health care needs. We used difference-in-differences models to assess adjusted changes in disparities by English proficiency after 2010. Supplemental analyses used nearest-neighbor propensity score matching to balance the characteristics of respondents. Respondents aged 18-64 in the Medical Expenditure Panel Survey over 2006-2016, with high (n = 174,214) or limited (n = 16,484) English language proficiency. Insurance coverage was a binary variable indicating any health insurance coverage during the past 12months. Access to care was measured using binary variables indicating whether the respondent had a usual source of care and received necessary medical, dental, and preventive care. Gains in health insurance coverage after 2010 were significant for adults with high English proficiency (1.7 percentage points, p < 0.001) and adults with limited English proficiency (4.6 percentage points, p = 0.007); gains did not significantly vary by English proficiency. Adults with LEP showed larger improvements than adults with high English proficiency in having a usual source of care (5 percentage points, p = 0.007) and receiving needed medical care and dental care (1.4 percentage points, p = 0.013, and 2.8 percentage points, p = 0.009, respectively). Findings remained similar when matching was used to balance the measured characteristics of respondents with high vs. limited English proficiency. Disparities in health care access by English proficiency narrowed after 2010, the year of passage of the ACA.

Extending cluster-based ensemble learning through synthetic population generation for modeling disparities in health insurance coverage across Missouri

In a previous study, Mueller et al. (ISPRS Int J Geo-Inf 8(1):13, 2019), presented a machine learning ensemble algorithm using K-means clustering as a preprocessing technique to increase predictive modeling performance. As a follow-on research effort, this study seeks to test the previously introduced algorithm’s stability and sensitivity, as well as present an innovative method for the extraction of localized and state-level variable importance information from the original dataset, using a nontraditional method known as synthetic population generation. Through iterative synthetic population generation with similar underlying statistical properties to the original dataset and exploration of the distribution of health insurance coverage across the state of Missouri, we identified variables that contributed to decisions for clustering, variables that contributed most significantly to modeling health insurance distribution status throughout the state, and variables that were most influential in optimizing model performance, having the greatest impact on change-in-mean-squared-error (MSE) measurements. Results suggest that cluster-based preprocessing approaches for machine learning algorithms can result in significantly increased performance, and also demonstrate how synthetic populations can be used for performance measurement to identify and test the extent to which variable statistical properties within a dataset can vary without resulting in significant performance loss.