CHAPTER 4. More Complex Study Designs
4.3 Ecologic and Multilevel Studies
incidence conducted in the 1950s and 1960s (Doll et al, 1966). These suggested hypotheses concerning the possible causes of the international patterns, which were investigated in more depth in further studies. In some instances these hypotheses were
consistent with biological knowledge at the time, but in other instances they were new and striking, and might not have been proposed, or investigated further, if the population level analyses had not been done.
Example 4.4
The International Study of Asthma and Allergies in Childhood (ISAAC) (Asher et al, 1995;
Pearce et al, 1993) was described in example 3.1. Figure 4.1 shows the findings for current wheeze (i.e. wheeze in the previous 12 months) and its association with
tuberculosis notification rates (von Mutius et al, 2000). It shows a negative association between tuberculosis rates and asthma prevalence. This is not compelling evidence in itself (because of the major shortcoming of ecologic analyses that
are described below), but it is generally consistent with the
“hygiene hypothesis”
that suggests that asthma prevalence is increasing in Western countries because of the loss of a protective effect from infections such as tuberculosis in early life.
A second reason that ecologic studies are experiencing a revival is that it is increasingly being recognised that some risk factors for disease genuinely
operate at the population level (Pearce, 2000). In some instances they may directly cause disease, but perhaps more commonly they may cause disease as effect modifiers or determinants of exposure to individual-level risk factors.
For example, being poor in a rich
country or neighbourhood may be worse than having the same income level in a poor country or neighbourhood, because of problems of social exclusion and lack of access to services and resources (Yen
et al, 1999). The failure to take account of the importance of population context, as an effect modifier and determinant of individual-level exposures could be termed the “individualistic fallacy”
(Diez-Rouz, 1998) in which the major population determinants of health are ignored and undue attention is focussed on individual characteristics. In this situation, the associations between these individual characteristics and health can be validly estimated, but their importance relative to other potential interventions, and the importance of the context of such interventions, may be ignored.
W Figure 4.1
heeze last 12 months (written questionnaire) vs tuberculosis notification rate for the period 1980-1982 in countries with valid
tuberculosis notification data
0 5 10 15 20 25 30 35 40
0 10 20 30 40 50 60 70 80
Tuberculosis notification rate per 100,000
Wheeze last 12 months %
Association of tuberculosis notification rates for the period 1980-1982 (in countries with valid tuberculosis notification data) and the prevalence of asthma symptoms in 13-14 year old children in the International Study of Asthma and Allergies in Childhood (ISAAC)
Source: von Mutius et al (2000)
Example 4.5
Wilkinson (1992) has analysed measures of income inequality and found them to be positively associated with national mortality rates in a number of Western countries. This is a true “ecologic
exposure” since the level of income inequality is a characteristic of a country, and not of an individual. If this
evidence is correct, this
is clearly of crucial importance since it implies that
‘development’ in itself may not automatically be good for health, and that the way in which the Gross National Product (GNP) is 'shared' may be as important as its absolute level. It should be noted, however, that this evidence has been disputed by other
researchers (e.g. Lynch et al, 2000; Pearce and Davey Smith, 2003) who have argued that the level of income
inequality in a country, or in a state, is a surrogate measure for other socioeconomic factors, including the provision of public education and health services, as well as social welfare services.
Ecologic Fallacies
While stressing the potential value of ecologic analyses, it is also important to recognise their limitations. In particular, ecologic studies are a very poor means of assessing the effects of individual exposures (e.g. diet or tobacco
smoking) since confounding (and effect modification) can occur at the individual level, the country (population) level, or both (Morgenstern, 1998). For example, almost any disease that is associated with affluence and westernisation has in the past been associated at the national level with sales of television sets, and nowadays is probably associated at the national level with rates of internet
usage. This does not mean that
watching television causes every type of disease, but rather than in many
instances the association between sales of television sets and disease at the national level is confounded by other exposures (at both the national and individual level). A hypothetical example is given in example 4.6. Another
problem is that individual level effects can confound ecologic estimates of population-level effects (Greenland, 2001).These problems of cross-level inference are avoided (or reduced) in multilevel analyses (see below).
Example 4.6
Table 4.2 shows the data for a hypothetical
ecological analysis. The numbers of cases and population numbers (and hence disease rates), as well as the percentage of the population exposed, are known for each country. Thus, the numbers of people exposed and non- exposed within each country are known, but it is not known how many cases were exposed and how many were not; thus it is not possible to estimate the rates in the exposed and non-exposed groups within each country. The country-level data indicate a negative association between exposure and disease at
the country level: if a regression is performed on the country-level data it indicates (comparing 100% exposure with 0%
exposure) a relative risk of 0.5. However, it is not known whether this association applies to individuals, since the data are not available.
Tables 4.3-4.5 give three different scenarios, each of which could generate the data in table 4.2. In table 4.3, there is no confounding at the country level (because the rate in the non- exposed is the same - 200 per 1,000 - in each country), although there could of course still be uncontrolled confounding at the individual level.
Thus, the ecologic analysis correctly
estimates the individual- level relative risk of 0.5.
In table 4.4, there is confounding at the country level (because the rate in the non- exposed differs by country) and there is in fact no association at the individual level. In table 4.5, there is effect modification at the country level, and the relative risk is positive, but of differing
magnitude, in all three countries. These three very different situations (a protective effect, no effect, a positive effect which is different in each country) all yield the same country-level data shown in table 4.2.
Table 4.2
Hypothetical example of an ecologic analysis
Country 1 Country 2 Country 3