The Target Study: A Conceptual Model and Framework for Measuring Disparity in Healthcare Outcomes Using Secondary Data

Core Concepts

This paper proposes a novel conceptual model, the "target study," to measure healthcare disparities using secondary data, addressing challenges like non-random sample selection and providing a framework for emulation using various statistical techniques.

Abstract

• Bibliographic Information: Jackson, J. W., Hsu, Y., Greer, R. C., Boonyasai, R. T., & Howe, C. J. (Year). The Target Study: A Conceptual Model and Framework for Measuring Disparity. Journal Name, Volume Number(Issue Number), Page numbers.

• Research Objective: To develop a robust conceptual model and practical framework for measuring healthcare disparities, particularly when using secondary data not originally collected for research.

• Methodology: The authors propose a "target study" model based on a two-stage sampling strategy to address non-random sample selection and balance allowable covariates. They discuss different target study designs to address generalizability, transportability, and collider stratification bias. The framework also includes data structures and estimators like G-computation and weighting methods for emulating the target study.

• Key Findings: The target study model provides a clear conceptual framework for measuring disparity by:

  • Defining a specific target population and enrollment window.
  • Accounting for calendar time and differential enrollment timing.
  • Addressing non-random sample selection through various sampling designs.
  • Balancing allowable covariates to isolate disparity not attributable to those factors.
  • Providing flexibility in choosing a standard population for comparison.
  • Offering guidance on statistical analysis and interpretation of disparity measures.

• Main Conclusions: The target study model offers a rigorous and practical approach to measure healthcare disparities using secondary data, enhancing the validity and interpretability of disparity research. The authors provide R code for implementing the proposed estimators, making the framework accessible to researchers.

• Significance: This research contributes significantly to the field of health equity research by providing a much-needed conceptual model and practical framework for measuring disparities. The target study model can guide researchers in designing and analyzing studies using secondary data, leading to more accurate and meaningful disparity measures.

• Limitations and Future Research: The authors acknowledge that the target study model relies on certain assumptions, such as innocuous sampling and overlap assumptions, which may not always hold in practice. Future research could explore the sensitivity of the model to violations of these assumptions and develop methods to address them. Further investigation into the ethical implications of different standard population choices is also warranted.

Stats

Quotes

Key Insights Distilled From

The Target Study: A Conceptual Model and Framework for Measuring Disparity

by John W. Jack... at arxiv.org 11-04-2024

https://arxiv.org/pdf/2207.00530.pdf

Deeper Inquiries

How can the target study model be adapted to measure disparities in other fields beyond healthcare, such as education, criminal justice, or housing?

The target study model, with its emphasis on defining a meaningful target population, identifying allowable and non-allowable covariates, and addressing sample selection bias, provides a robust framework adaptable to various fields beyond healthcare. Here's how it can be applied:
1. Education:

Target Population: Students within a specific school district, grade level, or educational program (e.g., special education).
Outcome (Y): Standardized test scores, graduation rates, college enrollment, disciplinary actions.
Social Group (R): Race/ethnicity, socioeconomic status, gender, disability status.
Allowable Covariates (A): Prior academic performance, student motivation (if measurable and not considered a product of systemic disadvantage).
Non-Allowable Covariates (N): School resources, teacher quality, neighborhood characteristics.
Adaptation: The target study could compare educational outcomes across social groups while controlling for allowable factors. By examining the remaining disparity, researchers can highlight the impact of non-allowable covariates like school resources, potentially revealing inequitable resource allocation.
2. Criminal Justice:

Target Population: Individuals arrested for a specific type of offense within a jurisdiction.
Outcome (Y): Sentencing length, pre-trial detention status, recidivism rates.
Social Group (R): Race/ethnicity, socioeconomic status, gender.
Allowable Covariates (A): Criminal history (if not considered a product of systemic bias), severity of the offense.
Non-Allowable Covariates (N): Neighborhood crime rates, access to legal representation.
Adaptation: The target study can assess disparities in sentencing or pre-trial detention by comparing outcomes across social groups while adjusting for allowable factors. This can uncover potential biases in the justice system related to non-allowable covariates.
3. Housing:

Target Population: Applicants for housing assistance or mortgage loans within a specific geographic area.
Outcome (Y): Approval rates for housing assistance or loans, housing quality, eviction rates.
Social Group (R): Race/ethnicity, socioeconomic status, family structure.
Allowable Covariates (A): Credit score (if not considered a product of systemic disadvantage), income-to-rent ratio.
Non-Allowable Covariates (N): Neighborhood racial composition, proximity to amenities.
Adaptation: The target study can investigate disparities in housing access and quality by comparing outcomes across social groups. By adjusting for allowable factors and examining the remaining disparity, researchers can identify potential discriminatory practices related to non-allowable covariates.
Key Considerations for Adaptation:

Context-Specific Definitions: Clearly define the target population, outcomes, social groups, and allowable/non-allowable covariates relevant to the specific field.
Ethical Considerations: Carefully consider the ethical implications of defining certain covariates as allowable or non-allowable, ensuring that the analysis does not perpetuate existing biases.
Data Availability and Quality: Ensure access to comprehensive and reliable data on the chosen variables for the target population.
By carefully adapting the target study model to different fields, researchers and policymakers can gain a nuanced understanding of disparities, identify contributing factors, and develop targeted interventions to promote equity.

Could focusing solely on achieving statistical balance on "allowable" covariates inadvertently mask systemic factors contributing to disparities?

Yes, focusing solely on achieving statistical balance on "allowable" covariates, while seemingly objective, can inadvertently mask systemic factors contributing to disparities. This is because the very act of classifying certain covariates as "allowable" can be influenced by existing societal biases and power imbalances.
Here's how this masking effect can occur:

Ignoring Historical and Structural Factors: Systemic racism, sexism, and other forms of discrimination often create unequal playing fields, leading to disparities in "allowable" covariates themselves. For example, if historical discriminatory lending practices have resulted in lower credit scores for a particular racial group, simply adjusting for credit scores without acknowledging the root cause of the disparity can mask the impact of systemic racism.

Reinforcing Existing Power Structures: Defining certain factors as "allowable" can implicitly legitimize the status quo and reinforce existing power structures. For instance, in education, if access to high-quality pre-school is considered "non-allowable" and not factored into the analysis, it can perpetuate the disadvantage experienced by students from low-income backgrounds who may have limited access to such resources.

Overlooking Intersectional Identities: Focusing solely on a single "allowable" covariate might overlook the complex interplay of multiple identities. For example, adjusting for socioeconomic status alone might mask disparities experienced by Black women who face unique challenges at the intersection of race and gender.

To mitigate the risk of masking systemic factors:

Critical Examination of "Allowables": Engage in a thorough and critical examination of the social and historical context surrounding the chosen "allowable" covariates. Consider whether these factors are truly neutral or if they are themselves products of systemic disadvantage.
Incorporate Qualitative Data: Complement quantitative analyses with qualitative data collection methods like interviews and focus groups. This can provide valuable insights into the lived experiences of marginalized groups and uncover hidden biases.
Focus on Root Causes: Go beyond simply achieving statistical balance and delve into the root causes of disparities. Investigate the historical and structural factors that have led to the unequal distribution of resources and opportunities.
Engage with Affected Communities: Involve members of marginalized communities in the research process, from defining the research questions to interpreting the findings. Their perspectives are crucial in identifying and addressing systemic biases.
By acknowledging the limitations of solely focusing on "allowable" covariates and adopting a more holistic and critical approach, researchers can contribute to a more accurate and just understanding of disparities.

What role can technology and data-driven approaches play in not only measuring but also actively reducing healthcare disparities in real-world settings?

Technology and data-driven approaches have the potential to revolutionize how we address healthcare disparities, moving beyond measurement to actively promote equity in real-world settings. Here's how:
1. Enhanced Data Collection and Analysis:

Electronic Health Records (EHRs): EHRs can capture comprehensive patient data, including demographics, socioeconomic indicators, medical history, and treatment outcomes. This data can be analyzed to identify disparities in care access, utilization, and quality across different populations.
Wearable Technology and Remote Monitoring: Wearable devices and remote monitoring tools can collect real-time data on patients' health behaviors, medication adherence, and physiological indicators. This data can help identify disparities in health outcomes and tailor interventions to address specific needs.
Artificial Intelligence (AI) and Machine Learning (ML): AI and ML algorithms can analyze vast datasets to identify patterns and predict risks, enabling early detection of disparities and personalized interventions. For example, AI can be used to identify patients at high risk of developing chronic conditions and connect them with appropriate preventive care.
2. Targeted Interventions and Resource Allocation:

Precision Public Health: Data-driven approaches can be used to tailor public health interventions to specific communities and populations disproportionately affected by health disparities. This includes targeted health education campaigns, community outreach programs, and culturally sensitive interventions.
Resource Optimization: By analyzing data on healthcare utilization patterns and needs, technology can help optimize resource allocation to ensure equitable access to care. This includes identifying areas with limited access to healthcare providers and developing telehealth solutions to bridge the gap.
Social Determinants of Health (SDOH) Screening and Referrals: EHRs can be used to screen patients for SDOH, such as food insecurity, housing instability, and transportation barriers. This information can trigger automated referrals to community resources and social services, addressing the root causes of health disparities.
3. Empowering Patients and Communities:

Patient Portals and Mobile Health (mHealth) Apps: Patient portals and mHealth apps can empower patients with access to their health information, educational resources, and communication tools to connect with healthcare providers. This can be particularly beneficial for marginalized communities with limited access to traditional healthcare services.
Community Health Workers (CHWs) and Technology: Equipping CHWs with mobile devices and data-driven tools can enhance their ability to connect with underserved communities, provide culturally appropriate education, and facilitate access to care.
4. Addressing Algorithmic Bias:

Ethical AI Development: It's crucial to ensure that AI algorithms used in healthcare are developed and deployed ethically, addressing potential biases that could exacerbate existing disparities. This includes using diverse datasets for training algorithms and implementing fairness constraints in algorithm design.
Transparency and Accountability: Transparency in how algorithms are developed and used, along with mechanisms for accountability, are essential to build trust and ensure equitable outcomes.
Challenges and Considerations:

Data Privacy and Security: Protecting patient privacy and ensuring data security are paramount when using technology and data-driven approaches in healthcare.
Digital Divide: Addressing the digital divide and ensuring equitable access to technology and internet connectivity are crucial to prevent further marginalization.
Community Engagement: Engaging with communities and addressing their concerns about data privacy, algorithmic bias, and the potential unintended consequences of technology are essential for successful implementation.
By harnessing the power of technology and data-driven approaches responsibly and ethically, we can move towards a more equitable healthcare system that effectively addresses disparities and improves health outcomes for all.