Households: the missing level of analysis in multilevel epidemiological studies- the case for multiple membership models

Transcription

1 Households: the missing level of analysis in multilevel epidemiological studies- the case for multiple membership models Tarani Chandola* Paul Clarke* Dick Wiggins^ Mel Bartley* 10/6/2003- Draft version 1.1 Please do not quote without permission * Department of Epidemiology and Public Health University College London 1-19 Torrington Place London WC1E 6BT ^ Department of Sociology City University Northampton Square London EC1V 0HB All correspondence to Tarani Chandola Department of Epidemiology and Public Health University College London 1-19 Torrington Place London WC1E 6BT Tel: Fax:

2 Abstract A number of studies have shown evidence for area level (or contextual) effects on health using multilevel models. However, although it is unlikely for a person to live in the same area all throughout their life, there has been little analyses of the effects of residency in different areas on a person s health. Most longitudinal multilevel epidemiological studies tend to ignore the effect of changes in area characteristics (such as area residence) on individual characteristics (such as health) and analyse measures (of area residence and health) at only two points in time, at baseline and at the end of the study period. Recent advances in multilevel modelling have enabled the estimation of such effects of different area and individual characteristics through multiple membership models. Furthermore, most multilevel analyses of area effects on health tend to ignore the household as a level of analysis. It is possible that the clustering of the health of individuals within areas is due, in part, to the clustering of the health of individuals within households. This hypothesis has not been examined in the literature before and is important to establish before any conclusions can be drawn about the importance of contextual or area level factors on health. Data from nine waves of the British Household Panel Survey (BHPS) were examined. Two-level (individuals within households and individuals within areas) and three-level (individuals within households within areas) multiple membership models of SF-36 health scores at wave nine were analysed adjusting for age, gender and prior self-rated health status. The results suggest that health appears to cluster within households and that the apparent clustering of health within areas (local authority districts) disappears when the clustering of health within households is taken into account. Studies which suggest that contextual or area effects on individual health exist independently of individual characteristics need to be aware of the problem of ignoring households as a level in their multilevel analyses. It is possible that apparent area level effects on health are explained, in part, by the clustering of health within households. 2

3 Households: the missing level of analysis in multilevel epidemiological studies- the case for multiple membership models Introduction A number of studies have shown evidence for area (or contextual) effects on health using multilevel (or hierarchical) models (Haan et al. 1987, Gould and Jones 1996, Congdon 1995, Hart et al. 1997, Diez-Rouz et al.1997). The studies were based using baseline measures of individual characteristics and area membership to predict health up to 15 years in the future. It was then argued that even when compositional (or individual level) characteristics are taken into account, individuals living in the same neighbourhoods, wards, districts or regions share similar health (or in other words, people s health tends to cluster within the areas they live). These studies argue that people s health is partly a result of the areas they live in. Although an improvement on studies looking at contextual effects based only on cross-sectional data (Duncan et al. 1993, Chandola 2001), the studies cited above still have drawbacks. One drawback is the reliance on measures at only two points in time, at baseline and at the end of the study period. Given that an individual s area membership and health can change regularly over the study period, repeated measures of important health and area characteristics can be used to model the covariation between individual characteristics and health, and between area characteristics and health. There has been little analysis of such lagged effects on health of area and individual characteristics at more than one time point even though people seldom live in the same place throughout their life. This is partly because of a relative lack of suitable longitudinal data on area and individual characteristics, and also because models that account for changing area membership over time have not been widely available to quantitative researchers. Recent advances in multilevel modelling have now enabled such complex modelling (Browne et al. 2001). Multiple membership models allow for membership of different units at the higher (area) level to reflect, for instance, 3

4 geographical migration over the life-course. Such models potentially allow for much greater flexibility in investigating the effect of contextual (or area) level characteristics on health as they can reflect the cumulative effect of different areas, not just a single area, on health outcomes. Another problem with previous studies of contextual effects on health is that they have tended to ignore the aggregation of individuals into households. Although around 14% of the adult population in Britain live in single person households, most of the adult population lives in multi-person households. It would therefore be appropriate to examine the health of individuals living within households which are grouped within areas (a 3 level multilevel model). This approach is important given the assumptions underlying the studies examining area effects on health. A number of such studies have concluded there are significant contextual effects on individual level health as they find the variance (in health) associated with the area level is significant even after adjusting for a number of individual level characteristics. However, if the clustering of the health of individuals within areas is due to the clustering of unhealthy or healthy individuals within households, then the omission of household as a level from the analysis could lead to misleading conclusions about the importance of contextual (area) effects on individual health. Given the above, we look to consider the following research questions: 1. Is there evidence for clustering of health within areas even after taking into account changing health and area membership? 2. Is there evidence for clustering of health within households even after taking into changing health and household membership? 3. If the answer to both 1 and 2 is yes, does the clustering of health within households explain the clustering of health within areas? Methods The British Household Panel Survey (BHPS) is a longitudinal cohort survey of adult members of a nationally representative sample of British households (5511 households with adult members). The initial survey was conducted in 1991 and subsequent annual surveys for the cohort were added 4

5 to the original data. Further information on the methodology of the survey can be found in Taylor et al. [1999]. Outcome variable- The short form (SF) 36 questionnaire was administered to the BHPS respondents for the first time at wave 9. Eight (continuous) health domain scores can be derived from this questionnaire- physical functioning, role functioning, bodily pain, mental health, social functioning, energy/vitality, general health perception and change in health can be derived from this questionnaire. The eight SF-36 domain scores were combined into two component scores- the physical (PCS) and mental (MCS) health component scores. Explanatory variables- Age, sex, self-rated health status at waves 1 to 8, household membership at waves 1 to 8, area of residence at waves 1 to 8. Household leavers were defined as per BHPS criteria (if they were no longer a member of their household identified from the previous wave). Area of residence is identified at the local authority district level. A cumulative measure of poor self-rated health (from waves 1 to 8) was defined as the number of occasions (or waves) the respondent reported having fair to poor self-rated health. Analysis- Multilevel multiple membership models were used to examine the association between individual characteristics grouped within changing households or areas and health. The classification diagrams are shown in Figure 1. Figure 1(a) refers to a simple 2 level nested model of either individuals nested within households or areas. This type of model has been investigated in the literature on contextual effects on health. Figure 1(b) refers to a multiple membership model- so the same individual can belong to a number of households or areas. Figure 1(b) can be expressed in terms of the following equation where we have a classification household and the weight w (2) i,j is the weight assigned to the random effect of household j in the equation for individual i. 5

6 Figure 1(c) generalises Figure 1(b) to a three level multiple membership model. The weights for the multiple membership models were calculated based on the length of time in residence in a household (for the household weights) or in an area (for the area weights). Figure 1 Classification diagrams for (a) simple 2 level nested model (b) 2 level multiple membership model (c) 3 level multiple membership model Household Area Household Area Individual Individual Individual Individual (a) (b) Area Household Individual (c) 6

7 Results Table 1 shows the distribution of individuals on whom there is complete information from waves one to eight in terms of whether or not they had left their household from the previous wave and whether or not they had moved areas (local authority districts) since the previous wave. Between 1-3% of the BHPS respondents on whom there is information at every wave of the BHPS leave their households or local areas every year. Table 2 shows the results of a series of multilevel models with the SF-36 mental health (MCS) scores as the outcome variable with age, gender and health status as the explanatory variables. The first column presents results for two simple 2 level nested models of individuals grouped within households and individuals grouped within areas. In these models, individual, household and area characteristics at wave one predict individual SF-36 scores at wave nine. Area of residence at wave one accounts for 5% of the total variance in SF-36 mental health at wave 9. Similarly, household membership at wave one account for 21.4% of the total variance in SF-36 mental health at wave nine. The second column shows the results of the 2 level multiple membership models (an individual nested within several households and similarly an individual nested within several areas). The variance in SF-36 mental health at both the area and household levels continue remain significantly different zero implying that the there is clustering of mental health at the area and household levels (which may differ from waves one to nine). The third column shows the results of a simple 3 level model (an individual nested within their household at wave one which in turn is nested with the area of residence at wave one). Once again, there appears to be significant clustering of SF-36 mental health at both the area and household levels. However, when changes in area and household membership (between waves one and nine) are taken in account (shown in the fourth column), the variance at the area level drops to non-significance while the household level continues to explain around 21% of the variance in SF-36 mental health at wave nine. 7

8 Similar results to the SF-36 mental health measure are observed when using the SF-36 physical health (PCS) score. In Table 2, only the random part of the models are shown because they are most relevant to the substance of the argument, and for the sake of simplicity. The coefficients in the fixed part of the model (not shown) were significant and in the expected direction- older respondents and women had poorer health compared to younger respondents and men. Respondents who reported greater instances of poor self-rated health status (from waves one to eight) were more likely to have poorer health at wave nine. Discussion The results suggest that 1. health appears to cluster within households- members of the same household share similarities in SF-36 health scores 2. the apparent clustering of health within areas (local authority districts) disappears when the clustering of health within households (both current and previous households) is taken into account. There is increasing evidence that households may have an important effect on health. Multilevel modelling of individuals nested within households has shown that people living in households share similar self-rated health status even after adjusting for a number of individual characteristics (Chandola 2003). There are a number of plausible mechanisms which could result in the clustering of health within households. Infectious diseases may spread more easily between household members. A household member s characteristics (such as their employment or health status) may have an effect on the health of others in the same household. The negative effects on health of unemployment or illness of other household members are well known. Furthermore, the multiple membership models used in the analyses allow for previous household membership to have an effect on current health. This allows for a life-course approach to the determinants of health by allowing for past household (or area) membership (which could be in childhood) have an 8

9 effect on current health. So for example, an individual who grew up in a household of smokers may have had their health affected by their membership of such a household (even though they may currently live in a household without smokers). The accumulation of such household level exposures to unhealthy risk factors can be taken into account by multiple membership models that explicitly takes into account changes in household membership and their effect on health. Furthermore, it appears that the accumulation of such household exposures to unhealthy (or healthy) risk factors explains some of the similarities in health between individuals living in the same area. Previous studies on area differences in health may have underestimated or ignored the clustering of health at the smallest area unithouseholds. There are three main caveats to the conclusions of this paper. Local authority districts may not be the appropriate area level to measure neighbourhood effects on health. Secondly, the (significant) variance at the household level observed in the analysis may be accounted for by individual level characteristics not accounted for by the model. Thirdly, the analyses only examined respondents on whom there was complete information at all waves of the BHPS. Selective drop out from the study may have biased these results. The results from this study suggest that some caution must be exercised when drawing conclusions from multilevel analyses about area effects on health. Studies which suggest that contextual or area effects on individual health exist independently of individual characteristics need to be aware of the problem of ignoring households as a level in their multilevel analyses. It is possible that apparent area level effects on health are explained, in part, by household level effects on health, as suggested by the results in this paper. 9

10 Reference List Browne, W., H. Goldstein, and J. Rabash, "Multiple membership multiple classification (MMMC) models," Statistical Modelling 1: (2001). Chandola, T., "The fear of crime and area differences in health," Health and Place 7 (2): (2001). Chandola, T. et al., "Social inequalities in health by individual and household measures of social position in a cohort of healthy people," Journal of Epidemiology and Community Health 57: (2003). Congdon, P., "The impact of area context on long-term illness and premature mortality; an illustration of multilevel analysis," Reg.Stud. 29: (2003). Diez-Roux, A. V. et al., "Neighborhood environments and coronary heart disease: a multilevel analysis," American Journal of Epidemiology 146: (1997). Duncan, C., K. Jones, and G. Moon, "Do places matter: a multilevel analysis of regional variations in health related behaviour in Britain," Social Science and Medicine 37: (1993). Gould, M. I. and K. Jones, "Analyzing perceived limiting long-term illness using UK census microdata," Social Science and Medicine 42: (1996). Haan, M., G. A. Kaplan, and T. Camacho, "Poverty and health: prospective evidence from the Alameda County study," American Journal of Epidemiology 125: (1987). Hart, C., R. Ecob, and G. Davey Smith, "People, places and coronary heart disease risk factors: a multilevel analysis of the scottish heart health study archive," Soc Sci Med 45 (6): (1997). Taylor, MF et al. (1999). British Household Panel Survey User Manual Volume A: Introduction, Technical Report and Appendices. Colchester: University of Essex. 10

11 Table 1 Wave on wave distribution of household leavers and area migrants BHPS- waves one to eight, complete cases in all waves Whether left household from previous wave No Yes Total Whether left local area district from previous wave No Yes Total

12 Table 2 Variance estimates (and standard errors) of SF36 MCS score at wave 9 Adjusted for age, gender and health status at wave 1 only at waves 1 to 8 at wave 1 only at waves 1 to 8 Variance (standard error) at Simple MM* Simple MM* 2 level model 2 level model 3 level model 3 level model area level 3.36 (0.67) 2.21 (0.58) 2.14 (0.67) 1.11 (0.61) household level (1.50) (1.70) (1.56) (1.67) Intra-area correlation Intra-household correlation Variance estimates (and standard errors) of SF36 PCS score at wave 9 Adjusted for age, gender and health status at wave 1 only at waves 1 to 8 at wave 1 only at waves 1 to 8 Variance (standard error) at Simple MM* Simple MM* 2 level model 2 level model 3 level model 3 level model area level 1.51 (0.53) 1.18 (0.46) 1.13 (0.54) 0.82 (0.50) household level 5.20 (1.42) 6.48 (2.04) 4.02 (1.48) 4.79 (2.10) Intra-area correlation Intra-household correlation *MM- Multiple Membership 12