The study of group-level factors in epidemiology: Rethinking variables, study designs, and analytical approaches
The study of group-level factors in epidemiology: Rethinking variables, study designs, and analytical approaches
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DOI：
10.1093/epirev/mxh006
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发表时间：
2004-01-01
影响因子：
5.5
通讯作者：
Roux, AVD
中科院分区：
文献类型：
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作者：
Roux, AVD
过去几年流行病学中备受关注的一个关键概念是，并非所有疾病决定因素都可以被概念化为个人层面的属性，因此在研究健康状况不佳的原因时，需要考虑个人所属群体的特征。这导致流行病学家和公共卫生研究人员重新思考传统流行病学中支持的生态研究和生态变量的想法（1-6）。这种生态或群体层面变量的重新概念化已经表现出来，例如，最近对群体层面结构可能的健康影响的兴趣和辩论，如收入不平等（7，8），社会资本（9，10）和邻里特征（11-14）。在这种情况下，多层次模型的统计技术的出现被认为是特别有前途的，因为它能够在健康研究中结合群体水平和个人水平的预测因素（4，15-17）。个人以外的因素，被称为群体水平，生态，宏观水平，或人口水平的因素（1，3，5，18，19），对健康很重要的想法并不新鲜。两个著名的例子包括传染病中的群体免疫概念和罗斯对慢性病病例原因和发病率原因的区分。群体免疫力意味着一个人感染传染病的可能性部分取决于他或她所属人群的免疫力水平（20）。杰弗里·罗斯（Geoffrey Rose）在他的开创性论文《患病个体和患病人群》（Sick Individuals and Sick Populations）中讨论了一个相关概念：研究重点放在人群或群体中区分患病个体和健康个体的方法上，可能会遗漏重要的疾病决定因素。这是因为群体水平因素在群体中是不变的，因此，不能在仅限于群体内个体比较的研究中进行研究（21）。为了检测这些因素，研究人员需要比较不同人群（或群体）并调查人群水平（或群体水平）因素的研究。流行病学中对群体水平和个体水平因素的讨论有时被解释为暗示群体水平因素对理解群体间差异很重要，而个体水平因素对理解个体间差异很重要。然而，一个关键点是，多个水平上的因素对于理解一个水平内的变异性的原因可能很重要。例如，个人和群体两个层面的因素对于理解人口间疾病率差异的原因都很重要。同样，人口水平和个人水平的因素对于理解个人疾病的原因都很重要。例如，群体免疫是一种群体水平的属性，它不仅对于理解疾病发生率群体差异的原因很重要，而且对于理解个体感染疾病的可能性也很重要。群体水平或人口水平的因素，如食品的大规模生产，可能不仅对理解高血压发病率的差异，而且对理解个体高血压的原因都很重要。当然，只有个体水平的因素才能解释组内结果的个体间差异（18，21）。虽然流行病学中对群体水平或人群水平因素重要性的讨论由来已久，但对流行病学研究中群体效应的经验检验的兴趣相对较新。这种兴趣最近激发了许多方法论的讨论的使用和误用.
A key notion that has received much attention in epidemiology over the past few years has been that not all disease determinants can be conceptualized as individual-level attributes, hence the need to consider features of the groups to which individuals belong when studying the causes of ill health. This has led epidemiologists and public health researchers to rethink the ideas on ecologic studies and ecologic variables traditionally espoused in epidemiology (1–6). This reconceptualization of ecologic or group-level variables has been manifested, for example, in recent interest and debate on the possible health effects of group-level constructs, such as income inequality (7, 8), social capital (9, 10), and neighborhood characteristics (11–14). In this context, the advent of the statistical technique of multilevel models has been viewed as especially promising because of its ability to incorporate both group-level and individuallevel predictors in the study of health (4, 15–17). The idea that factors beyond individuals, referred to as group-level, ecologic, macro-level, or population-level factors (1, 3, 5, 18, 19), are important to health is not new. Two well-known examples include the concept of herd immunity in infectious diseases and Rose’s distinction between the causes of cases and the causes of incidence rates in chronic diseases. Herd immunity implies that a person’s likelihood of contracting an infectious disease depends in part on the level of immunity in the population to which he or she belongs (20). In his seminal paper,“Sick Individuals and Sick Populations,” Geoffrey Rose (18) discusses a related concept: the idea that studies that focus on what distinguishes sick individuals from healthy individuals within a population or group may miss important disease determinants. This is because population-level factors are invariant within a population and, hence, cannot be investigated in studies restricted to comparisons of individuals within a population (21). To detect these factors, researchers need studies that compare different populations (or groups) and investigate population-level (or group-level) factors. Discussions of group-level and individual-level factors in epidemiology are sometimes interpreted as implying that population-level factors are important in understanding between-population differences and that individual-level factors are important in understanding between-individual differences. A key point, however, is that factors at multiple levels may be important to understanding the causes of variability within a level. For example, both individual-level and group-level factors are important in understanding the causes of between-population differences in disease rates. Likewise, both population-level and individual-level factors are important in understanding the causes of disease in individuals. For example, herd immunity, a group-level property, is important in understanding not only the reasons for group differences in the incidence of disease but also an individual’s probability of contracting the disease. Group-level or population-level factors, such as the mass production of foods, may be important in understanding not only betweencountry differences in rates of hypertension but also the causes of hypertension in an individual. Of course, only individual-level factors will explain interindividual differences in outcomes within groups (18, 21). Although discussion of the importance of group-level or population-level factors has long been present in epidemiology, the interest in empirically testing for group effects in epidemiologic studies is relatively new. This interest has recently motivated many methodological discussions on the uses and misuses of …