新英格兰医学杂志：超越多样性——新健康模式的时代

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Despite the ability to collect and analyze far richer health data than ever before, public health and medical experts have failed to use that information to develop new conceptual models for health. Although data from research inform clinical decision making, many possibilities suggested by health data are lost when we insist on fitting those data into our existing health constructs rather than building new constructs on their basis. The challenge is to ensure that the full range of what we know — from genomics to the social determinants of health for each person — is available, valued, and understood, which may necessitate the development of new models of health and illness. But though the accumulation of new evidence may warrant a paradigm shift, the human tendency is to hold on to our familiar conceptual models even when new data urge us to develop alternative ones.

Health data for Hispanic or Latinx people, who account for nearly one fifth of the U.S. population, provide a platform for reconceptualizing health and risk factors. Contrary to expectations, Hispanic people with many known health risk factors (low income, low educational levels, lack of health insurance, diabetes, and excess weight) live longer than non-Hispanic White people in the United States; have higher rates of diabetes but lower rates of cardiovascular disease; and even with low incomes and lower-quality prenatal care, have infant mortality rates only slightly higher than those among their non-Hispanic White counterparts. The health profile of Hispanic Americans does not adhere to the paradigm in which minority ethnic or poverty status determines poor health outcomes.

Instead of using Hispanic Americans’ health data to recalibrate or rethink existing models of health and risk factors, however, the U.S. health enterprise has typically diminished the importance of the data on longevity and good health outcomes. Some health experts began referring to these findings as “the Hispanic paradox”1 — the exception to the rule. As data from other countries in the Americas were revealed as supporting U.S. observations regarding longevity among Hispanic people, researchers responded not by developing a new model but by expanding the Hispanic paradox into “the Latin American paradox.”2 Other researchers developed various explanations for longevity in Hispanic populations: the “salmon bias,” according to which Hispanic Americans did not in fact live longer but rather, like salmon, returned to their location of birth to die and were thus excluded from U.S. mortality data3; the hypothesis that only relatively healthy people migrated from Latin American countries to the United States; or the possibility that death certificates were being filled out incorrectly. The first two explanations were rejected in 1999, and in 2010 researchers put the third to rest.4 Yet even now, some observers dismiss the accuracy of outcome data for Hispanic Americans, arguing that this population is too diverse to be analyzed as a single group — while overlooking the fact that non-Hispanic White Americans, Asian Americans, and African Americans are also ethnically diverse.

The opportunity that data for Hispanic populations presented for developing new models of health was ignored; analysts simply noted that the findings did not fit the prevailing conceptual framework for health, and there was no alternative model available to explain them. But unbiased analysis of data on the health of Hispanic and other communities can move us beyond existing conceptual frameworks and allow us to leverage modern science in elucidating mechanisms of health and disease. Achieving such progress will require several steps.

The first requirement is introspection and discernment. Data are neither collected nor analyzed in a vacuum. Though objectivity is assumed, what we measure is often affected by the prevailing culture. Cultural beliefs and values become lenses through which researchers and clinicians experience the world, and they often harden into biases that act as intellectual blinders. To look at information and data in a new way, researchers and clinicians need to acknowledge the influence of their own culture as well as their views of other cultures. Introspection is essential for identifying implicit beliefs and biases that become ingrained in research, models of care, and the artificial intelligence that increasingly both drives and undermines clinical decision making.

Second, we need to reconsider the value of decades-long trend lines. If we focus only on areas of health in which we have sufficient data to produce trend lines spanning decades, we systematically omit substantial portions of the population whose data were not collected, and we remain wedded to health models that fail to reflect the realities of the current U.S. population. Though the national model death certificate includes the decedent’s gender, race, and age, and has incorporated a Hispanic identifier since 1989, other sources of health information have included minimal Hispanic data (e.g., <6% in clinical trials, 3% in the Cancer Genome Atlas, and <1% in genomewide association studies). The health data we are collecting — through the U.S. Census Bureau, Centers for Disease Control and Prevention, and Amazon — from the 24 million Asian Americans, 47 million African Americans, 62 million Hispanic Americans, and many other communities (American Indians, Alaska Natives, Native Hawaiians, Pacific Islanders, and others) no doubt carry important implications for future health and health care.

Third, it is important to collect data on multiple factors and to engage experts from varied disciplines — public health, medicine, economics, behavioral science — in analyzing those data. Building research enterprises (e.g., the National Institutes of Health “All of Us” research program) that aggregate multiple sources of data from varied disciplines, as well as using information gathered by commercial entities (from insurers to Meta [formerly Facebook]), can be part of the solution. These strategies can lead to new partnerships for the public health community and the availability of more data from diverse sources to help elucidate individual patients’ health. Increased access to relevant data will allow for the aggregation (of data on everyone who identifies as Hispanic) and disaggregation (by specific ancestry) that can reveal the nuances of health risk factors in various racial, ethnic, or gender-based subgroups. Clustering diverse groups under a single category — “disadvantaged,” “minority,” “people of color,” “underrepresented minorities,” or “BIPOC” (Black, Indigenous, and people of color) — is not informative, since it homogenizes disparate health experiences. Moreover, other key aspects of human identity — such as gender, sexual orientation, religion, and disability status — also affect health. An appropriate model of health would include multiple factors in explaining, for example, why the leading causes of death among non-Hispanic Black Americans and non-Hispanic White Americans are diseases of the heart, whereas those among Asian Americans and Hispanic Americans are malignant neoplasms.5

Fundamentally, it is not adequate to collect and analyze data from diverse people and sources. We must be willing to step back and look critically at what we think we know, reflect on the adequacy of current models, and pursue alternative models. The health effects of toxic substances in the environment, one’s microbiome, and epigenetic factors can contribute to new paradigms. Researchers and clinicians who are open to more nuanced models that take into account multiple factors will be able to pursue an exciting new path.

Although many relevant fields are still in their infancy, in the future our understanding of health will be personalized and based on models that combine research findings in genomics and biology (the microbiome, immunology, and other areas) with comprehensive or integrative modeling built on public and private data sets. Without a fundamental shift in our conceptual models of health research and care, we will perpetuate the barriers we claim to want to dismantle and compromise the health of all communities.

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尽管能够收集和分析比以往任何时候都丰富的健康数据，但公共卫生和医学专家未能利用这些信息来开发新的健康概念模型。尽管来自研究的数据为临床决策提供信息，但当我们坚持将这些数据纳入我们现有的健康结构而不是在其基础上构建新结构时，健康数据提出的许多可能性就会丢失。挑战在于确保我们所知道的全部范围——从基因组学到每个人健康的社会决定因素——都可用、重视和理解，这可能需要开发新的健康和疾病模型。但是，尽管新证据的积累可能需要范式转变。

占美国人口近五分之一的西班牙裔或拉丁裔人的健康数据为重新定义健康和风险因素提供了一个平台。与预期相反，具有许多已知健康风险因素（低收入、低教育水平、缺乏健康保险、糖尿病和超重）的西班牙裔人比美国的非西班牙裔白人寿命更长；糖尿病发病率较高，但心血管疾病发病率较低；即使是低收入和低质量的产前护理，婴儿死亡率也仅略高于非西班牙裔白人。西班牙裔美国人的健康状况不符合少数族裔或贫困状况决定健康状况不佳的范式。

然而，这家美国健康企业并没有使用西班牙裔美国人的健康数据来重新校准或重新思考现有的健康和风险因素模型，而是通常降低了关于长寿和良好健康结果的数据的重要性。一些健康专家开始将这些发现称为“西班牙人悖论” 1——规则的例外。由于来自美洲其他国家的数据被揭示为支持美国对西班牙裔人长寿的观察，研究人员的回应不是开发新模型，而是将西班牙裔悖论扩展到“拉丁美洲悖论”。2其他研究人员对西班牙裔人群的长寿提出了各种解释：“鲑鱼偏见”，根据这种偏见，西班牙裔美国人实际上并没有活得更久，而是像鲑鱼一样，回到出生地死亡，因此被排除在美国死亡率数据之外3；假设只有相对健康的人从拉丁美洲国家迁移到美国；或者死亡证明填写错误的可能性。前两种解释在 1999 年被拒绝，2010 年研究人员搁置了第三种解释。4然而，即使是现在，一些观察家也否认西班牙裔美国人结果数据的准确性，认为这一人群过于多样化，无法作为一个群体进行分析——同时忽视了非西班牙裔美国白人、亚裔美国人和非裔美国人也是种族多样。

西班牙裔人口数据为开发新的健康模式带来的机会被忽视了；分析人员只是指出，这些发现不符合流行的健康概念框架，并且没有替代模型可以解释它们。但是，对西班牙裔和其他社区健康数据的公正分析可以让我们超越现有的概念框架，让我们能够利用现代科学来阐明健康和疾病的机制。实现这样的进步将需要几个步骤。

第一个要求是内省和洞察力。数据既不是在真空中收集也不是分析。尽管假设客观性，但我们测量的内容通常会受到流行文化的影响。文化信仰和价值观成为研究人员和临床医生体验世界的镜头，而且它们经常固化成偏见，成为智力上的盲点。为了以新的方式看待信息和数据，研究人员和临床医生需要承认他们自己的文化以及他们对其他文化的看法的影响。内省对于识别在研究、护理模型和越来越多地驱动和破坏临床决策制定的人工智能中根深蒂固的隐含信念和偏见至关重要。

其次，我们需要重新考虑长达数十年的趋势线的价值。如果我们只关注我们有足够数据来生成跨越数十年的趋势线的健康领域，我们就会系统地忽略数据未被收集的大部分人口，并且我们仍然固守无法反映现实的健康模型。目前美国人口。尽管国家标准死亡证明包括死者的性别、种族和年龄，并且自 1989 年以来已包含西班牙裔标识符，但其他健康信息来源包括最少的西班牙裔数据（例如，临床试验中 <6%，癌症中 3%基因组图谱，在全基因组关联研究中<1%）。我们正在收集的健康数据——通过美国人口普查局、疾病控制和预防中心、

第三，重要的是要收集有关多种因素的数据，并让来自不同学科——公共卫生、医学、经济学、行为科学——的专家参与分析这些数据。建立研究企业（例如，美国国立卫生研究院“我们所有人”研究计划），聚合来自不同学科的多个数据源，以及使用商业实体（从保险公司到 Meta [前 Facebook]）收集的信息，可以成为解决方案的一部分。这些策略可以为公共卫生界带来新的伙伴关系，并从不同来源获得更多数据，以帮助阐明个体患者的健康状况。增加对相关数据的访问将允许汇总（关于所有被认定为西班牙裔的数据）和分解（按特定血统），这可以揭示各种种族、民族或基于性别的亚组中健康风险因素的细微差别。将不同的群体归为一个类别——“弱势群体”、“少数族裔”、“有色人种”、“代表性不足的少数族裔”或“BIPOC”（黑人、土著和有色人种）——并不提供信息，因为它使不同的健康体验。此外，人类身份的其他关键方面——如性别、性取向、宗教和残疾状况——也会影响健康。一个适当的健康模型将包括多种因素来解释，例如，5

从根本上说，从不同的人和来源收集和分析数据是不够的。我们必须愿意退后一步，批判性地审视我们认为我们知道的东西，反思当前模型的充分性，并寻求替代模型。环境中的有毒物质、一个人的微生物组和表观遗传因素对健康的影响可以促成新的范式。对考虑到多种因素的更细致入微的模型持开放态度的研究人员和临床医生将能够寻求一条令人兴奋的新道路。

尽管许多相关领域仍处于起步阶段，但未来我们对健康的理解将是个性化的，并基于将基因组学和生物学（微生物组、免疫学和其他领域）的研究结果与基于公共的综合或综合模型相结合的模型和私有数据集。如果我们的健康研究和护理概念模型没有发生根本性转变，我们将继续存在我们声称想要拆除和损害所有社区健康的障碍。