Indices of neighborhood disadvantage and individual cancer control behaviors among African American adults: An Article Critique

Indices of neighborhood disadvantage and individual cancer control behaviors among African American adults by Fuemmeler and colleagues (2025): Methodological Challenges, ACS Data Uncertainty, and Racial Taxonomy Issues

Introduction

By Grok under the supervision of Dr. Christopher Williams

Indices of neighborhood disadvantage and individual cancer control behaviors among African American adults by Fuemmeler et al. (2025), published in JNCI Cancer Spectrum, examine the associations between neighborhood deprivation and cancer control behaviors among African American adults in the Religion and Health in African Americans (RHIAA) study. Using Bayesian index models and American Community Survey (ACS) 5-year estimates (2006–2010), the authors construct a Neighborhood Deprivation Index (NDI) to assess its impact on four prevention and four screening behaviors. While the study aims to address cancer disparities, it faces significant methodological challenges, including ACS data uncertainty, flawed racial taxonomy, and insufficient handling of confounding. This critique integrates Spielman et al.’s (2014) analysis of ACS uncertainty, Dr. Williams’ methodological critique of a related RHIAA study, Williams’ Critical Race Framework (CRF) challenging race in research, and my perspective as an AI model trained to evaluate data-driven studies. It also addresses the Dr. Williams concerns about the study’s perpetuation of an African American monolith, lack of practical significance, and inadequate statistical modeling.

Summary

Fuemmeler et al. (2025) analyze data from 2,222 African American adults in the RHIAA study, recruited between 2008 and 2010 via probability-based sampling across the US South, Midwest, and Mid-Atlantic. The NDI, comprising 12 census tract-level ACS variables (e.g., median household income, percentage of Black residents, unemployment rate), is used to estimate associations with cancer control behaviors: binge drinking, smoking, physical activity, fruit/vegetable consumption, colonoscopy, mammogram, Papanicolaou test, and PSA test. Bayesian index models adjust for individual-level covariates (age, gender, education) and quantify NDI component importance. Key findings include significant associations between higher NDI scores and increased likelihood of binge drinking (OR = 1.13), smoking (OR = 1.07), and reduced likelihood of colonoscopy, Papanicolaou test, and PSA test. Median household income and education are primary drivers for most outcomes.

Critique

ACS Data Uncertainty (Spielman et al., 2014)

Spielman et al. (2014) highlight the ACS’s large margins of error at the census tract level, driven by its smaller sample size (3.54 million housing units annually vs. 280 completed surveys per tract in the 2000 Census long form), resulting in margins of error 75% larger than the long form. Variables like median household income, central to Fuemmeler et al’s NDI, can have coefficients of variation (CVs) exceeding 40% in diverse or extreme-income tracts, rendering estimates unreliable. The ACS’s Master Address File (MAF) also suffers from undercoverage (6.4%) and overcoverage (10.2%), potentially biasing geocoded data, especially in African American neighborhoods. Fuemmeler et al. (2025) use 5-year estimates to increase sample size, but this dilutes temporal precision, misaligning with the 2008–2010 RHIAA data collection period. The study’s failure to address these margins of error undermines the NDI’s reliability, as high CVs could distort odds ratios, particularly for income-driven outcomes like screening behaviors.

Methodological Flaws (Dr. Williams’ Critique)

Dr. Williams’ critique of a prior RHIAA study using ACS-based NDI methods applies directly to Fuemmeler et al. (2025). Williams argues that ignoring ACS margins of error produces “highly attenuated” estimates, rendering findings potentially spurious. Fuemmeler et al. (2025) do not discuss ACS uncertainty, assuming tract-level estimates are precise. This is critical for Bayesian index models, which depend on accurate inputs to estimate component weights. Williams also critiques the use of decile quantization, noting it assumes uniform effects across deciles, oversimplifying complex relationships. Fuemmeler et al. (2025) employ this approach (e.g., Figure S2), potentially masking non-linear deprivation effects. The study’s sampling frame, managed by a professional firm with unclear methodologies, raises concerns about representativeness, given the overrepresentation of Southern participants and women.

Racial Taxonomy Issues (Williams’ Critical Race Framework)

Williams’ Critical Race Framework (CRF), discussed in prior conversations, challenges the sloppy use of race in research, arguing it lacks reliability and validity as a variable. The CRF critiques race as an anachronistic construct, often used without clear definitions, leading to measurement error, confounding, and threats to internal validity. Fuemmeler et al. (2025) perpetuate this issue by treating African American identity as a monolithic category, assuming a uniform shared experience across diverse regions (South, Midwest, Mid-Atlantic). The study’s reliance on self-identified African American participants, without clarifying survey options (e.g., single vs. multiple race selections, or distinctions like Jamaican, Nigerian, or US-born), risks flattening cultural and historical diversity into an outdated racial construct. This aligns with the Dr. Williams’ concern about perpetuating a “false notion of an African American monolith,” undermining the study’s conceptual framework.

The inclusion of the percentage of Black residents in the NDI to capture “social and economic disadvantage resulting from anti-Black racism” is problematic. As Williams’ CRF notes, such variables risk ecological fallacy, conflating racial composition with structural inequities. This approach assumes homogeneity in African American experiences, ignoring variations in systemic racism’s impact (e.g., apartheid-like conditions in Washington, DC’s Wards 7 and 8 vs. less segregated areas). A multilevel analysis, as suggested by Williams, could better capture structural racism’s effects across geographic and social contexts, improving causal inferences.

Statistical Modeling and Confounding

Dr. Williams raises valid concerns about the study’s assumption of conditional independence and inadequate handling of confounding. Fuemmeler et al. (2025) adjust for individual-level covariates (age, gender, education) but only briefly acknowledge residual confounding from unmeasured factors like structural racism. The effects of segregated neighborhoods, as seen in hyper-deprived areas like Wards 7 and 8, likely violate conditional independence, as African American experiences vary significantly across such contexts. This heterogeneity attenuates mean odds ratios, as the user notes, because the model does not account for correlated exposures or spatial dependencies. A hierarchical model, incorporating random effects for census tracts or regions, would better address these issues by modeling nested data structures and capturing area-level variations in deprivation and racism.

The Bayesian index model’s strengths—quantifying component importance—are undermined by its reliance on potentially noisy ACS data and simplistic priors. Williams’ CRF critiques vague priors in such models, which Fuemmeler et al. (2025) use, risking biased estimates. Dr. Williams’ suggestion of a hierarchical model aligns with Williams’ call for multilevel approaches to disentangle individual and structural effects, enhancing statistical power and causal clarity.

Spurious Data Findings

The model’s extensive transformations—decile quantization, arbitrary outcome thresholds, vague priors, uniform weighting, linear assumptions, and unspecified covariate preprocessing—introduce noise at every stage, from data input to model output. This noise can obscure the true NDI effect, reduce generalizability, and complicate interpretation. The cumulative effect is a risk of overfitting to dataset-specific variability rather than capturing robust, meaningful associations. By adopting more robust preprocessing, informative priors, flexible modeling, and thorough validation, the researchers could reduce noise and improve the reliability of their findings.

Practical Significance and Policy Implications

Dr. Williams questions the study’s practical significance, noting unclear guidance for policymakers on intervention targets, populations, and scope. Fuemmeler et al. (2025) identify associations (e.g., higher NDI linked to lower screening rates) but offer little actionable insight into where and how to intervene. The study’s broad regional scope (South, Midwest, Mid-Atlantic) lacks a unifying framework beyond the problematic African American monolith, making it difficult to tailor interventions to specific contexts. For example, addressing binge drinking in high-deprivation Southern neighborhoods may require different strategies than in Mid-Atlantic urban centers, yet the study does not disaggregate findings by region or deprivation severity. This limits its utility for managing resources “judiciously,” as Dr. Williams emphasizes.

My Perspective

As an AI model trained in statistical and geospatial analysis, I find Fuemmeler et al. (2025) ambitious but flawed. The Bayesian index model’s nuanced weighting of NDI components (e.g., income for screening, unemployment for smoking) is a strength, but its failure to address ACS uncertainty, as Spielman et al. (2014) warn, compromises validity. Sensitivity analyses, such as Monte Carlo simulations, could quantify the impact of ACS margins of error on NDI estimates, providing a probabilistic range for odds ratios. The exclusion of New England, Pacific, and Mountain divisions due to small sample sizes biases findings toward Southern contexts, where deprivation patterns differ, limiting generalizability.

The study’s racial taxonomy, critiqued by Williams’ CRF, is a significant weakness. By assuming a monolithic African American identity, it ignores diverse experiences shaped by migration, culture, and local racism. Alternative variables, such as historical redlining scores or healthcare access, could better capture structural inequities without relying on racial composition. Dr. Williams' call for a hierarchical model is apt; it would account for spatial and social heterogeneity, improving causal inferences over the current model’s simplistic adjustments. Finally, self-reported outcomes, unvalidated by medical records, introduce recall bias, particularly for “ever” screening questions.

Strengths

Fuemmeler et al. (2025) evaluate a wide range of cancer control behaviors, offering a comprehensive view of prevention and screening. The Bayesian index model enhances interpretability by identifying key NDI drivers (e.g., education for PSA testing). The focus on African American adults addresses a critical health disparities gap, and findings align with prior research on neighborhood poverty and screening uptake (e.g., Vutien et al., 2019). Supplemental analyses comparing Bayesian models to equal-weighted indices add robustness, though they do not address core issues like ACS uncertainty or racial taxonomy.

Proposed Improvements

1. Address ACS Uncertainty: Incorporate ACS margins of error via sensitivity analyses or Monte Carlo simulations, reporting CVs for key NDI variables to ensure transparency.

2. Refine Racial Taxonomy: Clarify survey race options and allow multiple or culturally specific categories (e.g., Jamaican, US-born). Replace the percentage of Black residents in the NDI with structural variables like redlining indices or healthcare proximity, as Williams’ CRF suggests.

3. Adopt Hierarchical Modeling: Implement a hierarchical model with random effects for census tracts or regions to account for spatial dependencies and confounding from structural racism, addressing Dr. Williams' concern about conditional independence.

4. Enhance Practical Significance: Disaggregate findings by region and deprivation level to guide targeted interventions. Specify policy entry points (e.g., screening access in high-deprivation urban areas) for manageable implementation.

5. Improve Sampling Transparency: Detail the professional sampling firm’s methodology and justify regional skew. Oversample underrepresented regions for national representativeness.

6. Validate Outcomes: Use time-bound screening questions or medical record validation to reduce recall bias.

7. Refine Bayesian Models: Use informative priors based on prior deprivation research, avoiding vague priors, and test alternative quantization strategies to capture non-linear effects.

Conclusion

Fuemmeler et al. (2025) contribute to understanding neighborhood deprivation’s role in cancer control behaviors but are hindered by ACS data uncertainty, flawed racial taxonomy, and inadequate statistical modeling. Spielman et al. (2014) highlight ACS limitations, while Williams’ methodological critique and CRF expose the study’s perpetuation of an African American monolith and simplistic assumptions. Dr. Williams' concerns about practical significance, regional variability, and confounding underscore these issues. Hierarchical models, refined racial constructs, and sensitivity analyses could enhance validity and utility. By addressing these flaws, future research can better inform equitable, targeted interventions for cancer disparities among diverse African American communities.

Williams' Analysis

Methodological Issues

American Community Survey (ACS)

Final Conclusion: NDI Odds Ratios and Weights Cannot be Reliably Interpreted - Because the authors did not account for, much less acknowledge, the considerable error associated with ACS data at the Census-tract-level, Figure 1 and Figure 2 cannot be reliably interpreted.

1. ACS Uncertainty - The authors used neighborhood-level variables from the American Community Survey dataset, 5-year estimates (2005-2010). "Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall." (census.gov) "The margin of error is the measure of the magnitude of sampling error." (Spielman and colleagues, 2014)

2. Margin of Error - "The margins of error on ACS census tract-level data are on average 75 percent larger than those of the corresponding 2000 long-form estimate. The practical implications of this increase is that data are sometimes so imprecise that they are difficult to use." (Spielman and colleagues, 2014)

3. Margin of Error - The practical implications of this increase are that users often face data like those in Table 1, which shows the ACS median income estimates for African American households for a contiguous group of census tracts in Denver, Colorado. Income estimates range from around $21,000 to $60,000 (American Factfinder website accessed 7/15/2013). Without taking account of the margin of error, it would seem that Tract 41.06 had the highest income, however, when one accounts for the margin of error, the situation is much less clear – Tract 41.06 may be either the wealthiest or the poorest tract in the group. (Spielman and colleagues, 2014)

4. Margin of Error - The authors did not consider the margin of error of ACS variables in analysis, which is considerable at the Census tract level.

5. Attenuated Index - The Neighborhood-Level Deprivation Index is artificial because the values are highly attenuated. 

6. Attenuated Index - Since the NDI is attenuated, then the data analysis has threats to internal validity. The weights are unlikely to account for significant error.

7. Poor Quality Data at Census Track Level - "(T)he ACS margins of error are so large that for many variables at the census tract and block group scales the estimates fail to meet even the loosest standards of data quality." (Spielman and colleagues, 2014)

The 12 ACS variables that comprised Neighborhood-Level Deprivation Index

• "We constructed neighborhood deprivation indices using census tract-level data and estimated their associations with outcomes using Bayesian index models, adjusting for individual-level covariates." (Fuemmeler et al, 2025)

• median household income, percentage of households with public assistance income, percentage of Black residents, percentage of renter-occupied housing units, percentage of residents without some college education, percentage of residents unemployed, percentage of single-parent households with children under 18 years of age, median house value, percentage of households with an income to poverty ratio below 1% crowded households, percentage of vacant housing units, and median rent as a percentage of income

Religion and Health in African Americans (RHIAA) study

• No construct - The authors rely on a single measure of race as the sampling frame ("African American"). "If the contact expressed interest, a brief eligibility screener was administered to determine whether they self-identified as African American and age 21 or older. Interested and eligible contacts heard an informed consent script and provided verbal assent. Eligible individuals were African Americans, age 21 and older, who had no cancer history" (Holt et al, 2014).

• No construct - The authors do not know and cannot verify who and why chose African American. The authors have no additional analysis with respect to respondents' racial identity.

• No considerable of multiracial identity - The authors do not discuss any consideration of multiracial identities, including those for whom African American may be one aspect of their identity. 

• Black Monolith - In every aspect of the data collection and analysis, the authors assume a Black or African American monolith. "Our sample consisted of 3117 participants who spoke English, self-identified as African American, were 21 years of age or older, had no history of cancer, and had provided their residential address" (Holt et al, 2014).

• Use of Professional Sampling Firm - Little is given about the precise sampling methodology and firm. It suggests that the firm relied on the race provided in publicly available data, such as driver's license information. It is also unclear if the firm using any selection process based on the photograph in these datasets. "Using probability-based methods, a professional sampling firm generated a call list of households from all 50 United States, drawing from publicly available data such as motor vehicle records" (Holt et al, 2014).

• Use of Professional Sampling Firm - RHIAA researchers have not published their telephone script, instrument, or detailed methodologies. The firm name is not given.

• Probabilistic Sample - The authors rely on what they consider to be national probabilistic sampling. However, without a related construct or theoretical framing, this does not mean much scientifically. The authors do not who is checking the African American box and why? Is African American people with brown skin? Is African American people with some or much African ancestry? Is African American also Nigerian, Jamaican, Brazilian, etc.? Is African American someone who strongly identifies with the people and culture? Does it exclude multiracial identities?

Bayesian index models

Final Conclusion: Spurious Data Findings - "The model’s extensive transformations—decile quantization, arbitrary outcome thresholds, vague priors, uniform weighting, linear assumptions, and unspecified covariate preprocessing—introduce noise at every stage, from data input to model output. This noise can obscure the true NDI effect, reduce generalizability, and complicate interpretation. The cumulative effect is a risk of overfitting to dataset-specific variability rather than capturing robust, meaningful associations. By adopting more robust preprocessing, informative priors, flexible modeling, and thorough validation, the researchers could reduce noise and improve the reliability of their findings" (Grok, 2025).

• Data Distribution - The authors did not address the distribution of the data to ensure appropriateness for quantization. "If the distribution of is skewed or has heavy tails, deciles may unevenly represent the data, with some deciles containing very few observations or extreme values lumped together."

• Quantization and ACS Data - The drawbacks in the ACS data are considerable, "(T)he ACS margins of error are so large that for many variables at the census tract and block group scales the estimates fail to meet even the loosest standards of data quality." (Spielman and colleagues, 2014). While there are advantages of quantization (e.g., mitigate influence of outliers, standardization into a common scale, and reduce impact o noisy data), it is not clear whether quantization holds up given the challenges with the ACS data. The authors needed to assess shifts in deciles based on ACS margin of error for each variable.

• Lack of Practical Significance - "Dividing data into deciles imposes artificial boundaries that may not reflect meaningful or natural thresholds in the data" (Grok, 2025) or in the real world.

• Assumption of Quantization - This method assumes that the effect of the outcome is uniform within each decile and that the difference between deciles is equivalent, "decile 1 and decile 2 is equivalent to the difference between decile 9 and decile 10 in terms of their impact on the log-odds. This may not be true if the underlying relationship is nonlinear or if certain parts of the distribution have more predictive power."

• Oversimplification - "If the relationship between 𝑞 and the outcome is complex (e.g., nonlinear or threshold-based), deciles may oversimplify the true effect, leading to model misspecification."

• Improper Prior - "The same prior (odds ratio range of 0.2 to 5) is applied to all adjustment covariates 𝜃, regardless of their nature (e.g., age, income, or health status). Some covariates may have well-established effect sizes in the literature (e.g., age strongly predicts health outcomes), and using a vague prior ignores this knowledge, potentially leading to less precise or biased estimates."

• Improper Prior - A flat prior over the entire real line is improper (i.e., it does not integrate to a finite value), which can lead to improper posterior distributions if the data does not provide sufficient information. In logistic regression, an improper prior on the intercept can cause issues if the outcome 𝑌𝑖 is highly imbalanced (e.g., very few participants with 𝑌𝑖 =1).

• Equal Weights - "The Dirichlet(1) prior assumes all index components are equally important a priori, which can introduce weighting noise if some components are irrelevant or less predictive. The uniform prior does not encourage sparsity, so noisy or irrelevant components may receive non-zero weights."