Effect of Neighborhood Social Participation on Individual Use of Hormone Replacement Therapy and Antihypertensive Medication: A Multilevel Analysis

Juan Merlo1,2,, John W. Lynch3,4, Min Yang5, Martin Lindström1, Per Olof Östergren1, Niels Kristian Rasmusen6 and Lennart Råstam1

1 Department of Community Medicine, Malmö University Hospital, Lund University, Malmö, Sweden.
2 The NEPI Foundation, Malmö University Hospital, Lund University, Malmö, Sweden.
3 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI.
4 Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI.
5 Multilevel Models Project, Institute of Education, University of London, London, United Kingdom.
6 Danish National Institute of Public Health, Copenhagen, Denmark.

Received for publication September 24, 2001; accepted for publication November 15, 2002.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The authors investigated a possible contextual effect of neighborhood on individual use of hormone replacement therapy (HRT) and antihypertensive medication (AHM) and the impact of neighborhood social participation on individual use of these medications. They attempted to disentangle contextual from individual influences. Multilevel logistic regression modeling was used to analyze data on 15,456 women aged 45–73 years (first level) residing in 95 neighborhoods (second level) of the city of Malmö, Sweden (250,000 inhabitants) who participated in the Malmö Diet and Cancer Study (1991–1996). AHM use was studied among 7,558 participants with defined hypertension. Of the total variability in medication use in this population, only 1.7% (HRT) and 0.5% (AHM) was between neighborhoods. After adjustment for age, individual socioeconomic factors, individual low levels of social participation, and health and behavioral variables, no neighborhood effect on AHM use was found. However, women living in neighborhoods with low social participation were much less likely to use HRT (odds ratio = 0.36, 95% confidence interval: 0.21, 0.63), especially if they themselves experienced low social participation (synergy index, 1.53) or were immigrants (synergy index, 1.68). The Malmö neighborhoods were homogeneous with regard to HRT and especially AHM use. However, differences in neighborhood social participation affected HRT use independently of individual characteristics.

analysis of variance; antihypertensive agents; hormone replacement therapy; pharmacoepidemiology; social environment; social medicine

Abbreviations: Abbreviations: AHM, antihypertensive medication; ATC-97, 1997 version of the Anatomic Therapeutic Chemical classification system; CI, confidence interval; HRT, hormone replacement therapy; OR, odds ratio.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The role of pharmacologic agents from a population perspective is an increasingly important topic in public health (1, 2). A great deal of knowledge about the effects of pharmacologic agents has been obtained in randomized, double-blind, placebo-controlled clinical trials that test clinical or preventive efficacy. However, much less knowledge exists about how and why medications are actually used in the population (3) or how their use differs across socioeconomic groups (2, 4).

Medication use may be conditioned by factors other than strictly pharmacologic indications (2, 5), such as individual patients’ beliefs and socioeconomic resources and expectations (7, 8), that may be shaped by the living environment (6); these health-related indications may be determined differentially by the social environment (9). Persons living in the same area may be more similar to each other than to persons living in other areas because they share a number of social, economic, health care system, and lifestyle characteristics. This collective phenomenon may partly condition a common level of health and health care behavior over and above individual variation. Better comprehension of this collective phenomenon is necessary for understanding environmental effects on medication use. However, little empirical knowledge exists about the size of such collective effects or about the mechanisms underlying contextual effects on individual health (10, 11) and health care utilization such as medication use.

A relevant concept for understanding contextual effects on individual health is social participation. This concept has been used in the operationalization of social integration that was originally focused on the social network and social support of the person, and it was measured at the individual level (12). However, the group dynamics and contextual characteristics of social networks may also exert a collective effect on the health of the citizens in a community, and neighborhood social participation has been regarded as a key component of the concept of social capital (1315). Thus, social participation is a relevant concept for understanding both individual-level and environment (contextual)-level effects on the health of persons (16). In addition, social participation is also influenced by the environment in which a person is living (17).

Both individual and area social participation has been related to health and health care behaviors (15, 18). Better social networks have been hypothesized to have positive effects on health behaviors, possibly as a result of information exchange and the establishment of health-related group norms (15, 19). Higher levels of social participation and social networks in the neighborhood may help women in those neighborhoods use these conduits for information and norm setting regarding medication use, and these neighborhood effects may be related to medication use independently of individual social participation. However, to date, we know of no studies that have simultaneously accounted for individual and neighborhood levels of social participation (15).

In the present study, we aimed to identify and quantify a possible collective effect of neighborhood on individual use of hormone replacement therapy (HRT) and antihypertensive medication (AHM) in the city of Malmö, Sweden. Another goal was to evaluate the general impact of neighborhood social participation on use of these medications after adjusting for individual characteristics, especially individual social participation.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The Malmö Diet and Cancer Study
The Malmö Diet and Cancer Study is a prospective cohort study performed in the city of Malmö. The 17,388 women aged 45–73 years who participated in the study cohort represented 41 percent of all women born in 1923–1950 who were living in Malmö during the baseline period 1991–1996. Subjects were requested to participate as a result of mailed letters of invitation and advertisements placed in the local media and through collaboration with major employers in Malmö. In total, the letters of invitation provided 80 percent of the participants. Of these women, 89 percent (15,456/17,388) were included in the present study; 5.6 percent (975/17,388) were excluded because of a lack of information on drug use, 4.9 percent (844/17,388) because of incomplete information on other variables studied, and 0.4 percent (63/17,388) because they lived in neighborhoods with fewer than 20 participants. The Ethical Committee at the Medical Faculty of Lund University in Malmö approved the study proposal, and all of the participants gave signed informed consent. A detailed description of the design and aims of the cohort study is given elsewhere (20).

Baseline survey
The baseline examination took place during 1991–1996. A self-administered questionnaire and a 7-day personal diary were used to obtain information on relevant characteristics of the women, including use of medication. Each participant completed both information sources at home within the same 1–2-week period between the first and second consecutive baseline visits to the project office (20).

The City of Malmö
The city of Malmö in southern Sweden has a population of approximately 250,000 inhabitants, and it is administratively divided into 110 neighborhoods. A total of 95 neighborhoods from which there were more than 20 Malmö Diet and Cancer Study respondents were included (table ).


View this table:
[in this window]
[in a new window]
 
TABLE 1. Characteristics of the population of 15,456 women aged 45–73 years residing in 95 of the 110 neighborhoods of the city of Malmö, Sweden, by increasing quartile of neighborhood low social participation, 1991–1996*
 
Assessment of variables
Age was aggregated into four groups: 45–49, 50–59, 60–69, and 70–73 years, with the youngest considered the reference. Body mass index was computed as weight in kilograms divided by height in meters squared (kg/m2) and was divided into four groups by quartile (<22.6, 22.6–24.8, 24.9–27.8, and >27.8). Age and body mass index were categorized arbitrarily because the association between these continuous variables and medication use may not be linear.

Educational achievement was dichotomized depending on the number of years of education reported on the self-administered questionnaire. Low educational achievement consisted of 9 or fewer years compared with 10 or more years.

Self-rated health was assessed by using an ordinal scale from 1 ("worst possible") to 7 ("best possible"). Low self-rated health was arbitrarily defined as a value of <=4 on this scale. Participants also reported whether they were immigrants, were living alone, were on sick leave, or were unemployed. All women aged less than 65 years reporting that they were retired were considered to be receiving a disability pension.

All reported pharmacologic agents were classified according to the 1997 version of the Anatomic Therapeutic Chemical classification system (ATC-97) (21). Use of AHM (diuretics, beta-adrenergic blocking agents, calcium channel blockers, and angiotensin-converting enzyme inhibitors) was defined by using ATC-97 codes C02, C03, C07, C08, and C09. Female sexual hormone therapy (estrogens, progestogens, or their combination) was defined by using ATC-97 codes G03C, G03D, and G03F (22).

Systolic and diastolic (phase V) blood pressure was measured under standardized conditions on the subject’s right arm after 5 minutes of supine rest; a mercury manometer and a rubber cuff were used. High blood pressure was defined as either a systolic blood pressure of >=140 mmHg or a diastolic blood pressure of >=90 mmHg (23). Hypertension was defined as the presence of high blood pressure or use of AHM.

Smoking was classified as regular, occasional, stopped, or never having smoked. The group of current smokers included occasional smokers and former smokers who had stopped for less than 1 year.

Individual social participation was defined by involvement in 13 formal and informal groups (study circle/course at place of work, other study circle/course, union meeting, meeting of other organizations, theater/cinema, arts exhibition, church, sports event, letter to the editor of a newspaper/journal, demonstration, night club/entertainment, large gathering of relatives, private party) that the respondent may have participated in during the last year (24). Items were summed, and those subjects whose score was three or less (lowest quartile) were classified as having low social participation. Low neighborhood social participation was assessed by the proportion of persons in the neighborhood classified as having individual low social participation.

Statistical methods
We used two-level logistic regression models (25, 26). Persons (first level) were nested within neighborhoods (second level).

Fixed-effects analysis
We observed the association (the slopes of the regression) between individual medication use and individual variables as well as the contextual variable: neighborhood low social participation. Odds ratios and 95 percent confidence intervals were obtained from beta coefficients (standard errors) in the fixed part of the model.

We also analyzed the fixed-effects, cross-level interaction between neighborhood low social participation, defined as a neighborhood value below the median percentage, and selected individual variables. We calculated the synergy index (SI) according to Rothman (27), as follows: SI = [OR (AB) – 1]/[OR (ABn) + OR (AnB) – 2], where OR = odds ratio, A = exposed and An = nonexposed to neighborhood low social participation, and B = exposed and Bn = nonexposed to individual low social participation.

Random-effects analysis
We calculated the second-level variance (variation between neighborhoods) in the prevalence of medication use. Next, to determine whether medication use was more similar between women living in the same neighborhood than between women from different neighborhoods (i.e., whether high or low medication use clustered in certain neighborhoods), we calculated the intraclass correlation (ICC), which is the percentage of the total variance between the neighborhoods, as follows: ICC = [Vn]/[Vn + Vi] x 100, where Vn = neighborhood variance and Vi = individual variance.

For dichotomous variables, the intraclass correlation was calculated by following the formula of Snijders based on an underlying continuous variable with Vi = {pi}2/3 (25) and also by using the simulation method proposed by Goldstein et al. (28). A high intraclass correlation indicated high clustering of medication use in the neighborhoods and a strong neighborhood influence on individual medication use. A low intraclass correlation, on the other hand, expressed the existence of small geographic differences and a weak neighborhood influence on individual medication use.

To determine the proportion of neighborhood differences in the prevalence of medication use explained by the model, we calculated the percentage of second-level variance explained as [(V0 – V1)]/[V0] x 100, where V0 = second-level variance of the initial model and V1 = second-level variance of the adjusted model. Parameters were estimated by using the iterative generalized least-squares method. The Markov chain Monte Carlo method was also used to calculate the crude neighborhood variance in the empty model. Extra-binomial variation was explored systematically in all models, and we found no evidence for under- or overdispersion. The MLwiN software package, version 1.1 (29), was used to perform the analyses.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Population characteristics
The median percentage of women with low social participation across the Malmö neighborhoods was 29.3 percent (first–third quartiles, 22.9–36.7 percent). Table shows that as low neighborhood social participation increased from low (group 1) to high (group 4), the women in those neighborhoods had, on average, worse health, behavioral, and socioeconomic profiles. Figure 1 shows that the prevalence of HRT use decreased and the prevalence of AHM use increased as low social participation in neighborhoods increased. Table also shows intraclass correlations for these population characteristics and demonstrates the degree to which certain individual-level characteristics were similar within neighborhoods. We found a considerable clustering of women with certain socioeconomic characteristics such as living alone (20.3 percent of the total variation in women living alone was attributable to between-neighborhood differences). Table shows the percentage of women who responded affirmatively to the 13 items used to define low social participation.



View larger version (25K):
[in this window]
[in a new window]
 
FIGURE 1. Percentage of women using antihypertensive medication and hormone replacement therapy plotted against the percentage of women with low levels of social participation in 95 neighborhoods of the city of Malmö, Sweden, 1991–1996. The size of the circles is proportional to neighborhood population.

 

View this table:
[in this window]
[in a new window]
 
TABLE 2. Percentage of women answering affirmatively to the items used to construct the social participation score, by level of individual social participation (defined as a score of <=3), Malmö, Sweden, 1991–1996
 
Compositional and contextual effects
Table shows the results from the "fixed-effects" part of the multilevel model. HRT use was highest among younger women aged 50–59 years and among former smokers. HRT use decreased in a dose-response manner with increasing body mass index and was lower among women with hypertension and those who were immigrants, were receiving a disability pension, had low educational achievement, and whose own levels of social participation were low (OR = 0.69, 95 percent confidence interval (CI): 0.63, 0.76). Use of AHM increased with age and body mass index. The age-adjusted odds ratio of AHM use was higher for women with low self-rated health and for those who were on sick leave and were receiving a disability pension. Interestingly, low educational achievement and a lack of individual social participation did not affect AHM use. In other words, the fixed-effects part of the multilevel analysis showed that use of HRT and AHM was associated with rather opposite individual-level profiles, with AHM use being much less socioeconomically patterned. Table also shows that after adjustment for age, the contextual variable of neighborhood low social participation was strongly associated with lower HRT use (OR = 0.17, 95 percent CI: 0.10, 0.29) and higher AHM use (OR = 1.98, 95 percent CI: 1.17, 3.38).


View this table:
[in this window]
[in a new window]
 
TABLE 3. Fixed effects from separate models showing odds ratios and 95 percent confidence intervals of neighborhood and individual variables for use of hormone replacement therapy by 15,456 women aged 45–73 years and use of antihypertensive medication by 7,559 women age 45–73 years with defined hypertension residing in 95 of the 110 neighborhoods of the city of Malmö, Sweden, 1991–1996
 
Table provides summary results from "random-effects" multilevel models that examined the relative contribution of individual characteristics (compositional factors) and the contextual characteristic of living in a neighborhood in which social participation was low. The so-called empty model enabled us to examine how much of the variation in the outcomes was between neighborhoods (i.e., intraclass correlation) without considering any independent variable at all. There was actually very little clustering of either AHM or HRT use (intraclass correlations = 0.5 percent and 1.7 percent, respectively), suggesting much greater heterogeneity within than between neighborhoods. Table also shows how much of the neighborhood variation was explained by each of the individual-level variables and neighborhood social participation. After we accounted for the age composition of the neighborhoods, we found that living in a neighborhood with low social participation explained 50 percent of the between-neighborhood variation in HRT use and 29 percent of AHM use.


View this table:
[in this window]
[in a new window]
 
TABLE 4. Random-effects results from separate models* showing neighborhood variance and explained neighborhood variance of use of hormone replacement therapy by 15,456 women aged 45–73 years and use of antihypertensive medication by 7,559 women aged 45–73 years with defined hypertension residing in 95 of the 110 neighborhoods of the city of Malmö, Sweden, 1991–1996
 
Table examines the relative contribution of individual characteristics (compositional factors) and the contextual characteristic of living in a neighborhood with low social participation. Compared with the "empty model" (model 1), age and individual-level socioeconomic factors explained 50 percent of this variation in HRT (0.058 – 0.029/0.058 x 100) and 25 percent of AHM use (model 2). Adding health and behavioral factors to model 2 showed that 55 percent and 50 percent of the neighborhood variation in HRT and AHM use, respectively, were explained by individual-level factors (model 3).


View this table:
[in this window]
[in a new window]
 
TABLE 5. Neighborhood-level variance and explained variance for successively nested models showing use of hormone replacement therapy by 15,456 women aged 45–73 years and use of antihypertensive medication by 7,559 women aged 45–73 years with defined hypertension residing in 95 of the 110 neighborhoods of the city of Malmö, Sweden, 1991–1996
 
In this model (not shown in the tables), the fixed effects of both individual educational level (OR = 0.77, 95 percent CI: 0.70, 0.85) and individual low social participation (OR = 0.76, 95 percent CI: 0.69, 0.84) were importantly associated with HRT use but were unrelated to AHM use. For AHM use, the fixed effect of individual educational level was 0.99 (95 percent CI: 0.90, 1.10), and the effect of individual low social participation was 0.97 (95 percent CI: 0.87, 1.08).

Including neighborhood low social participation in model 4 increased the explained variance in between-neighborhood medication use by 5 percentage points to 60 percent for HRT and by 6 percentage points to 56 percent for AHM.

In model 4 (not shown in the tables), after control for individual-level socioeconomic, behavioral, and health factors, the odds ratios (i.e., fixed effects) associated with living in a neighborhood with low social participation were 0.36 (95 percent CI: 0.21, 0.63) for HRT use and 1.37 (95 percent CI: 0.78, 2.38) for AHM use. In this full model (fixed-effects analysis), HRT use remained associated with both individual low educational level (OR = 0.79, 95 percent CI: 0.72, 0.87) and individual low social participation (OR = 0.77, 95 percent CI: 0.70, 0.85). Use of AHM was not associated with individual low educational level (OR = 0.98, 95 percent CI: 0.88, 1.09) or individual low social participation (OR = 0.96, 95 percent CI: 0.86, 1.07).

Cross-level fixed-effects interactions
For use of HRT, evidence was found of cross-level interaction between neighborhood low social participation and being an immigrant (synergy index, 1.68) and having individual low social participation (synergy index, 1.53). In relation to AHM use, we found no evidence of cross-level interactions.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Our results showed that neighborhood-level social participation was importantly associated with HRT use but not with AHM use among women, after adjustment for individual-level socioeconomic, behavioral, and health factors. However, although neighborhood social participation was associated with HRT use, individual medication use was rather homogeneous all over the city, so that between-neighborhood differences in HRT use explained only a very small part of the total variability in medication use in this population of women. This type of information can be gained only from multilevel models that contain both fixed- and random-effect estimates (30). Of the total variability in medication use in this population of women, only 1.7 percent (HRT) and 0.5 percent (AHM) was between neighborhoods. The low intraclass correlation found in our study may reflect the fact that for medication use, the Malmö neighborhoods are rather similar to each other. That is, the low intraclass correlations suggest that the clustering of medication use in the 95 neighborhoods of Malmö is fairly close to what would be expected if 95 repeated random samples were chosen from the total population of the city. Even though these neighborhoods represent socially meaningful boundaries, it is possible that they are not the best proxy for defining the "true social boundaries" that may influence medication use. Although clustering of medication use was low, we also showed that clustering of other individual characteristics was much higher within these same neighborhoods. Table showed that in contrast to medication use, some social variables such as living alone were highly clustered (intraclass correlation = 20.3 percent).

The relatively small neighborhood differences in medication use were largely explained by the composition of the neighborhoods: 55 percent (HRT) and 50 percent (AHM). Over and above these individual factors, some of the between-neighborhood variation was, however, explained by the neighborhood social participation variable.

In spite of the low clustering of medication use in the Malmö neighborhoods, the data yielded enough variation to enable us to detect area-level associations (i.e., fixed effects). In low social participation neighborhoods, women were much more likely to use AHM (OR = 1.98, 95 percent CI: 1.17, 3.38) but much less likely to use HRT (OR = 0.28, 95 percent CI: 0.20, 0.40), and this contextual effect remained for both types of medication use after adjustment for individual characteristics (age, social participation, education, unemployment, disability pension, living alone, and being an immigrant). After further adjustment for individual health and behavioral characteristics (smoking habits, body mass index, low self-rated health, hypertension (for HRT use only), and sick leave), the observed contextual effect of neighborhood low social participation persisted for HRT use (OR = 0.36, 95 percent CI: 0.21, 0.63) but was not related to AHM use (OR = 1.37, 95 percent CI: 0.78, 2.38). Furthermore, cross-level interaction analyses showed that the effect of low levels of neighborhood social participation on HRT use was especially strong for women with low levels of individual social participation and among immigrants.

We emphasize that if aspects of the social environment influence health by operating as upstream determinants of individual characteristics (31), then control for many downstream individual factors may overadjust the true effects of the context (9). This possibility is even more salient if one poses the cross-level causal question in a life-course developmental framework, where the effects of various aspects of the environment are literally embodied over time (32, 33) so that what is assigned as an individual-level variable at one time point could equally be conceptualized as a characteristic of the past environments in which those persons grew up.

Pharmacologic treatment of high blood pressure is a widespread therapy that has been promoted strongly in many populations. In our study, it also seems reasonable that individual conditions related to higher cardiovascular morbidity were associated with AHM use. However, this use did not differ in immigrants, the unemployed, women with low educational achievement, and women with a lack of social participation, which may reflect the fact that once hypertension is established, the known socioeconomic differences in the onset of hypertension (34) are not followed by inequality in access to pharmacologic therapy (AHM), at least in Malmö, Sweden. Therefore, although the underlying disease burden is unequally distributed across socioeconomic groups, and disease determinants may be conditioned by the social environment, equality exists in access to and use of AHM. The same may not be true in other less-equitable health care environments.

A rather different pattern of associations was observed for HRT. Individual conditions related to higher morbidity were not associated with HRT use; if they were, the association indicated less HRT use (e.g., among those receiving a disability pension). Immigrants and the unemployed used HRT less often, and the same was true for women with less education and lower levels of social participation. Our results agree with previous observations indicating that women who use HRT are healthier and have a higher socioeconomic position (35). Therefore, reasons concerning maintenance of social roles, aspects of communication networks, women’s demands, and physicians’ attitudes related to socioeconomic position (36) could be involved in the higher HRT use among these women. Better social networks have been hypothesized to have positive effects on health behaviors, possibly through information exchange and the establishment of group norms (15). Thus, higher levels of social participation and social networks in the neighborhood may help women in those neighborhoods use these conduits for information and norm setting regarding HRT use. At the other extreme, women on a disability pension, immigrants, or those with low individual social participation may be the most deprived regarding HRT use when living in neighborhoods in which social participation is low.

It is also possible that the association observed here for a neighborhood effect of social participation on HRT use was the result of residual confounding by unobserved socioeconomic characteristics. Although we cannot rule out residual socioeconomic confounding, our study did adjust for more individual socioeconomic variables than any of the 25 studies of the effect of neighborhood socioeconomic context on health outcomes recently reviewed by Pickett and Pearl (37).

Limitations: selection bias, information bias, and confounding
Selection of geographic units should not have been a source of bias, since 95 of the 110 administrative geographic areas were included and only the least populated areas were omitted. Malmö neighborhoods are very coherent in terms of both ownership and types of buildings that make up the area. Each neighborhood has a name that people refer to and recognize. Therefore, this level may represent a rather natural delimitation of the social environment. On the other hand, the participation rate (median, first–third quartile) in the neighborhoods was low (42 percent, 32–50 percent). Hence, the cohort may not be representative of the whole population, which may have reduced the external validity of our results. However, participants could be regarded as fairly representative of the general population, at least in relation to the main sociodemographic variables studied (38).

Selective residential mobility (i.e., deprived women move to deprived neighborhoods) is part of the process that drives compositional neighborhood differences, but the end results of this compositional process were explicitly considered in our study—at least for a number of relevant variables. Information on drug use was self-reported, which seems to be a valid method of measuring current drug use (22). The reliability (test-retest stability) of the social participation variable, as assessed in a previous paper, was high (kappa = 0.77) (39). Table shows that scores of low social participation were not driven by just a few items, and similar items have been used in several other studies of social networks (40).

Conclusions
When neighborhood effects on individual health are studied, both measures of health variation (e.g., explained neighborhood variance, intraclass correlation) and traditional measures of association (e.g., odds ratios) produce relevant and complementary information (41). Our study indicated that neighborhoods in Malmö were homogeneous with regard to HRT and, especially, AHM use, but the same may not be true in other countries or contexts or for other outcomes. Sweden has a long history of equitable and broad-based social investments, and it is possible that these types of investments over time "even out" place-based differences in health-enhancing resources and thus place-based health differences. This finding may be much less true in countries such as the United States and the United Kingdom, where place-based economic and social segregation go hand-in-hand with disinvestments in a whole array of potentially health-enhancing resources.

We found that after considering individual social, economic, behavioral, and health characteristics of women, low levels of neighborhood social participation decreased individual HRT use but had little impact on AHM use. We also showed cross-level interactions between individual characteristics and neighborhood low social participation. Results suggested equity in access to AHM use, probably based on individual disease burden, but inequity in HRT use, based on both individual and neighborhood characteristics that may have to do with aspects of the communication networks among women living in more advantaged neighborhoods.

Our study empirically supports the idea that contextual factors related to aspects of the social environment in which people live do contribute to differences in individual behavior such as medication use. Our findings thus have relevance for the burgeoning literature on social capital and health (15, 42, 43). Our results clearly show the potential for aspects of the neighborhood social environment to affect health but that the strength and direction of these effects differ according to the outcome being studied. This finding suggests that future research may build a stronger evidence base by examining more specific links between social environment and outcomes that have clearly conceptualized cross-level mechanisms (44).


    ACKNOWLEDGMENTS
 
This study was supported by a General Research Compensation for University Clinics (ALF)–government grant to Dr. Merlo (Dnr M: E 39 390/98), the County of Skåne’s Pharmacological Council, the National Institute for Public Health, the Swedish Medical Research Council, and the Swedish Cancer Society.

The authors thank Prof. Göran Berglund for his support regarding the Malmö Diet and Cancer Study database and Nalini Ranjit for her invaluable comments on the manuscript.


    NOTES
 
Correspondence to Dr. Juan Merlo, Department of Community Medicine, Malmö University Hospital, S-205 02 Malmö, Sweden (e-mail: Juan.Merlo{at}smi.mas.lu.se). Back


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Hemminki E. The future of population strategies in prevention: drugs for all? Scand J Soc Med 1995;23:225–6.[ISI][Medline]
  2. Haaijer-Ruskamp FM, Hemminki E. The social aspects of drug use. WHO Reg Publ Eur Ser 1993;45:97–124.[Medline]
  3. Black N. Why we need observational studies to evaluate the effectiveness of health care. BMJ 1996;312:1215–18.[Free Full Text]
  4. Antonov K, Isacson D. Use of analgesics in Sweden—the importance of sociodemographic factors, physical fitness, health and health-related factors, and working conditions. Soc Sci Med 1996;42:1473–81.[CrossRef][ISI][Medline]
  5. Wing S. The role of medicine in the decline of hypertension-related mortality. Int J Health Serv 1984;14:649–66.[ISI][Medline]
  6. Groenewegen PP, Leufkens HG, Spreeuwenberg P, et al. Neighbourhood characteristics and use of benzodiazepines in the Netherlands. Soc Sci Med 1999;48:1701–11.[CrossRef][ISI][Medline]
  7. Jolleys JV, Olesen F. A comparative study of prescribing of hormone replacement therapy in USA and Europe. Maturitas 1996;23:47–53.[CrossRef][ISI][Medline]
  8. Imanaka Y, Araki S, Nobutomo K. Effects of patient health beliefs and satisfaction on compliance with medication regimens in ambulatory care at general hospitals. Nippon Eiseigaku Zasshi 1993;48:601–11.[Medline]
  9. Diez-Roux AV. Multilevel analysis in public health research. Annu Rev Public Health 2000;21:171–92.[CrossRef][ISI][Medline]
  10. Geronimus AT, Bound J. Use of census-based aggregate variables to proxy for socioeconomic group: evidence from national samples. Am J Epidemiol 1998;148:475–86.[Abstract]
  11. Emmons KM. Health behaviors in a social context. In: Berkman LF, Kawachi I, eds. Social epidemiology. New York, NY: Oxford University Press, 2000.
  12. Berkman LF, Syme SL. Social networks, host resistance, and mortality: a nine-year follow-up study of Alameda County residents. Am J Epidemiol 1979;109:186–204.[Abstract]
  13. Putnam RD. Making democracy work: civic traditions in modern Italy. Princeton, NJ: Princeton University Press, 1993.
  14. Woolcock M, Narayan D. Social capital: implications for development theory, research and policy. World Bank Research Observer 2000;15:225–49.[ISI]
  15. Kawachi I, Berkman L. Social cohesion, social capital, and health. In: Berkman L, Kawachi I, eds. Social epidemiology. New York, NY: Oxford University Press, 2000:174–90.
  16. Cullen M, Whiteford H. The interrelations of social capital with health and mental health. Discussion paper. Canberra, Australia: Commonwealth of Australia, 2001. (http://www.mentalhealth.gov.au/pdf/inter.pdf).
  17. Lindstrom M, Merlo J, Ostergren PO. Individual and neighbourhood determinants of social participation and social capital: a multilevel analysis of the city of Malmö, Sweden. Soc Sci Med 2002;54:1779–91.[CrossRef][ISI][Medline]
  18. Berkman LF, Glass T, Brissette I, et al. From social integration to health: Durkheim in the new millennium. Soc Sci Med 2000;51:843–57.[CrossRef][ISI][Medline]
  19. Rogers E. Diffusion of innovations. New York, NY: The Free Press, 1983.
  20. Berglund G, Elmstahl S, Janzon L, et al. The Malmö Diet and Cancer Study. Design and feasibility. J Intern Med 1993;233:45–51.[ISI][Medline]
  21. Capella D. Descriptive tools and analysis. WHO Reg Publ Eur Ser 1993;45:55–78.[Medline]
  22. Merlo J, Berglund G, Wirfält E, et al. Self-administered questionnaire compared with a personal diary for assessment of current use of hormone therapy: an analysis of 16,060 women. Am J Epidemiol. 2000;152:788–92.[Abstract/Free Full Text]
  23. Kaplan NM, Lieberman E. Clinical hypertension. Baltimore, MD: Williams & Wilkins, 1998.
  24. Living conditions, isolation and togetherness—an outlook on social participation 1976. Stockholm, Sweden: Statistics Sweden (The National Central Bureau of Statistics), 1980. (Report no. 18).
  25. Snijders TAB, Bosker RJ. Multilevel analysis—an introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage Publications, 1999.
  26. Leyland AH, Goldstein H. Multilevel modeling of health statistics. Chichester, United Kingdom: Wiley, 2001.
  27. Rothman KJ. Interaction between causes. In: Rothman KJ, ed. Modern epidemiology. Boston, MA: Little, Brown & Company, 1986:311–26.
  28. Goldstein H, Browne W, Rasbash J. Partitioning variation in multilevel models. (http://www.ioe.ac.uk/hgpersonal/Variance-partitioning.pdf.2002).
  29. Rasbash J, Browne W, Goldstein H, et al. A user’s guide to MLwiN. London, United Kingdom: Multilevels Models Project, Institute of Education, University of London, 2000.
  30. Merlo J, Ostergren PO, Hagberg O, et al. Diastolic blood pressure and area of residence: multilevel versus ecological analysis of social inequity. J Epidemiol Community Health 2001;55:791–8.[Abstract/Free Full Text]
  31. Kaplan GA. What is the role of the social environment in understanding inequalities in health? Ann N Y Acad Sci 1999;896:116–19.[Free Full Text]
  32. Davey Smith G, Gunnell D, Ben-Shlomo Y. Lifecourse approaches to socioeconomic differentials in cause-specific adult mortality. In: Leon D, Walt G, eds. Poverty, inequality and health. Oxford, United Kingdom: Oxford University Press, 2000.
  33. Krieger N. Theories for social epidemiology in the 21st century: an ecosocial perspective. Int J Epidemiol 2001;30:668–77.[Free Full Text]
  34. Kaplan GA, Keil JE. Socioeconomic factors and cardiovascular disease: a review of the literature. Circulation 1993;88:1973–98.[Abstract]
  35. Barrett-Connor E. Hormone replacement therapy. BMJ 1998;317:457–61.[Free Full Text]
  36. Lomranz J, Becker D, Eyal N, et al. Attitudes towards hormone replacement therapy among middle-aged women and men. Eur J Obstet Gynecol Reprod Biol 2000;93:199–203.[CrossRef][ISI][Medline]
  37. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health 2001;55:111–22.[Abstract/Free Full Text]
  38. Manjer J, Carlsson S, Elmstahl S, et al. The Malmö Diet and Cancer Study: representativity, cancer incidence and mortality in participants and non-participants. Eur J Cancer Prev 2001;10:489–99.[CrossRef][ISI][Medline]
  39. Hanson BS, Ostergren PO, Elmstahl S, et al. Reliability and validity assessments of measures of social networks, social support and control—results from the Malmö Shoulder and Neck Study. Scand J Soc Med 1997;25:249–57.[ISI][Medline]
  40. Berkman LF. Assessing social networks and social support in epidemiologic studies. Rev Epidemiol Sante Publique 1987;35:46–53.[ISI][Medline]
  41. Merlo J. Multilevel analytical approaches in social epidemiology: measures of health variation vs. traditional measures of association. J Epidemiol Community Health (in press).
  42. Lynch J, Due P, Muntaner C, et al. Social capital—is it a good investment strategy for public health? J Epidemiol Community Health 2000;54:404–8.[Free Full Text]
  43. Muntaner C, Lynch J, Smith GD. Social capital, disorganized communities, and the third way: understanding the retreat from structural inequalities in epidemiology and public health. Int J Health Serv 2001;31:213–37.[ISI][Medline]
  44. Blakely TA, Woodward AJ. Ecological effects in multi-level studies. J Epidemiol Community Health 2000;54:367–74.[Abstract/Free Full Text]